different types of problem solving heuristic

Heuristics: Definition, Examples, And How They Work

Benjamin Frimodig

Science Expert

B.A., History and Science, Harvard University

Ben Frimodig is a 2021 graduate of Harvard College, where he studied the History of Science.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

On This Page:

Every day our brains must process and respond to thousands of problems, both large and small, at a moment’s notice. It might even be overwhelming to consider the sheer volume of complex problems we regularly face in need of a quick solution.

While one might wish there was time to methodically and thoughtfully evaluate the fine details of our everyday tasks, the cognitive demands of daily life often make such processing logistically impossible.

Therefore, the brain must develop reliable shortcuts to keep up with the stimulus-rich environments we inhabit. Psychologists refer to these efficient problem-solving techniques as heuristics.

Heuristics can be thought of as general cognitive frameworks humans rely on regularly to reach a solution quickly.

For example, if a student needs to decide what subject she will study at university, her intuition will likely be drawn toward the path that she envisions as most satisfying, practical, and interesting.

She may also think back on her strengths and weaknesses in secondary school or perhaps even write out a pros and cons list to facilitate her choice.

It’s important to note that these heuristics broadly apply to everyday problems, produce sound solutions, and helps simplify otherwise complicated mental tasks. These are the three defining features of a heuristic.

While the concept of heuristics dates back to Ancient Greece (the term is derived from the Greek word for “to discover”), most of the information known today on the subject comes from prominent twentieth-century social scientists.

Herbert Simon’s study of a notion he called “bounded rationality” focused on decision-making under restrictive cognitive conditions, such as limited time and information.

This concept of optimizing an inherently imperfect analysis frames the contemporary study of heuristics and leads many to credit Simon as a foundational figure in the field.

Kahneman’s Theory of Decision Making

The immense contributions of psychologist Daniel Kahneman to our understanding of cognitive problem-solving deserve special attention.

As context for his theory, Kahneman put forward the estimate that an individual makes around 35,000 decisions each day! To reach these resolutions, the mind relies on either “fast” or “slow” thinking.

The fast thinking pathway (system 1) operates mostly unconsciously and aims to reach reliable decisions with as minimal cognitive strain as possible.

While system 1 relies on broad observations and quick evaluative techniques (heuristics!), system 2 (slow thinking) requires conscious, continuous attention to carefully assess the details of a given problem and logically reach a solution.

Given the sheer volume of daily decisions, it’s no surprise that around 98% of problem-solving uses system 1.

Thus, it is crucial that the human mind develops a toolbox of effective, efficient heuristics to support this fast-thinking pathway.

Heuristics vs. Algorithms

Those who’ve studied the psychology of decision-making might notice similarities between heuristics and algorithms. However, remember that these are two distinct modes of cognition.

Heuristics are methods or strategies which often lead to problem solutions but are not guaranteed to succeed.

They can be distinguished from algorithms, which are methods or procedures that will always produce a solution sooner or later.

An algorithm is a step-by-step procedure that can be reliably used to solve a specific problem. While the concept of an algorithm is most commonly used in reference to technology and mathematics, our brains rely on algorithms every day to resolve issues (Kahneman, 2011).

The important thing to remember is that algorithms are a set of mental instructions unique to specific situations, while heuristics are general rules of thumb that can help the mind process and overcome various obstacles.

For example, if you are thoughtfully reading every line of this article, you are using an algorithm.

On the other hand, if you are quickly skimming each section for important information or perhaps focusing only on sections you don’t already understand, you are using a heuristic!

Why Heuristics Are Used

Heuristics usually occurs when one of five conditions is met (Pratkanis, 1989):

• When one is faced with too much information
• When the time to make a decision is limited
• When the decision to be made is unimportant
• When there is access to very little information to use in making the decision
• When an appropriate heuristic happens to come to mind at the same moment

When studying heuristics, keep in mind both the benefits and unavoidable drawbacks of their application. The ubiquity of these techniques in human society makes such weaknesses especially worthy of evaluation.

More specifically, in expediting decision-making processes, heuristics also predispose us to a number of cognitive biases .

A cognitive bias is an incorrect but pervasive judgment derived from an illogical pattern of cognition. In simple terms, a cognitive bias occurs when one internalizes a subjective perception as a reliable and objective truth.

Heuristics are reliable but imperfect; In the application of broad decision-making “shortcuts” to guide one’s response to specific situations, occasional errors are both inevitable and have the potential to catalyze persistent mistakes.

For example, consider the risks of faulty applications of the representative heuristic discussed above. While the technique encourages one to assign situations into broad categories based on superficial characteristics and one’s past experiences for the sake of cognitive expediency, such thinking is also the basis of stereotypes and discrimination.

In practice, these errors result in the disproportionate favoring of one group and/or the oppression of other groups within a given society.

Indeed, the most impactful research relating to heuristics often centers on the connection between them and systematic discrimination.

The tradeoff between thoughtful rationality and cognitive efficiency encompasses both the benefits and pitfalls of heuristics and represents a foundational concept in psychological research.

When learning about heuristics, keep in mind their relevance to all areas of human interaction. After all, the study of social psychology is intrinsically interdisciplinary.

Many of the most important studies on heuristics relate to flawed decision-making processes in high-stakes fields like law, medicine, and politics.

Researchers often draw on a distinct set of already established heuristics in their analysis. While dozens of unique heuristics have been observed, brief descriptions of those most central to the field are included below:

Availability Heuristic

The availability heuristic describes the tendency to make choices based on information that comes to mind readily.

For example, children of divorced parents are more likely to have pessimistic views towards marriage as adults.

Of important note, this heuristic can also involve assigning more importance to more recently learned information, largely due to the easier recall of such information.

Representativeness Heuristic

This technique allows one to quickly assign probabilities to and predict the outcome of new scenarios using psychological prototypes derived from past experiences.

For example, juries are less likely to convict individuals who are well-groomed and wearing formal attire (under the assumption that stylish, well-kempt individuals typically do not commit crimes).

This is one of the most studied heuristics by social psychologists for its relevance to the development of stereotypes.

Scarcity Heuristic

This method of decision-making is predicated on the perception of less abundant, rarer items as inherently more valuable than more abundant items.

We rely on the scarcity heuristic when we must make a fast selection with incomplete information. For example, a student deciding between two universities may be drawn toward the option with the lower acceptance rate, assuming that this exclusivity indicates a more desirable experience.

The concept of scarcity is central to behavioral economists’ study of consumer behavior (a field that evaluates economics through the lens of human psychology).

Trial and Error

This is the most basic and perhaps frequently cited heuristic. Trial and error can be used to solve a problem that possesses a discrete number of possible solutions and involves simply attempting each possible option until the correct solution is identified.

For example, if an individual was putting together a jigsaw puzzle, he or she would try multiple pieces until locating a proper fit.

This technique is commonly taught in introductory psychology courses due to its simple representation of the central purpose of heuristics: the use of reliable problem-solving frameworks to reduce cognitive load.

Anchoring and Adjustment Heuristic

Anchoring refers to the tendency to formulate expectations relating to new scenarios relative to an already ingrained piece of information.

Put simply, this anchoring one to form reasonable estimations around uncertainties. For example, if asked to estimate the number of days in a year on Mars, many people would first call to mind the fact the Earth’s year is 365 days (the “anchor”) and adjust accordingly.

This tendency can also help explain the observation that ingrained information often hinders the learning of new information, a concept known as retroactive inhibition.

Familiarity Heuristic

This technique can be used to guide actions in cognitively demanding situations by simply reverting to previous behaviors successfully utilized under similar circumstances.

The familiarity heuristic is most useful in unfamiliar, stressful environments.

For example, a job seeker might recall behavioral standards in other high-stakes situations from her past (perhaps an important presentation at university) to guide her behavior in a job interview.

Many psychologists interpret this technique as a slightly more specific variation of the availability heuristic.

How to Make Better Decisions

Heuristics are ingrained cognitive processes utilized by all humans and can lead to various biases.

Both of these statements are established facts. However, this does not mean that the biases that heuristics produce are unavoidable. As the wide-ranging impacts of such biases on societal institutions have become a popular research topic, psychologists have emphasized techniques for reaching more sound, thoughtful and fair decisions in our daily lives.

Ironically, many of these techniques are themselves heuristics!

To focus on the key details of a given problem, one might create a mental list of explicit goals and values. To clearly identify the impacts of choice, one should imagine its impacts one year in the future and from the perspective of all parties involved.

Most importantly, one must gain a mindful understanding of the problem-solving techniques used by our minds and the common mistakes that result. Mindfulness of these flawed yet persistent pathways allows one to quickly identify and remedy the biases (or otherwise flawed thinking) they tend to create!

Further Information

• Shah, A. K., & Oppenheimer, D. M. (2008). Heuristics made easy: an effort-reduction framework. Psychological bulletin, 134(2), 207.
• Marewski, J. N., & Gigerenzer, G. (2012). Heuristic decision making in medicine. Dialogues in clinical neuroscience, 14(1), 77.
• Del Campo, C., Pauser, S., Steiner, E., & Vetschera, R. (2016). Decision making styles and the use of heuristics in decision making. Journal of Business Economics, 86(4), 389-412.

What is a heuristic in psychology?

A heuristic in psychology is a mental shortcut or rule of thumb that simplifies decision-making and problem-solving. Heuristics often speed up the process of finding a satisfactory solution, but they can also lead to cognitive biases.

Bobadilla-Suarez, S., & Love, B. C. (2017, May 29). Fast or Frugal, but Not Both: Decision Heuristics Under Time Pressure. Journal of Experimental Psychology: Learning, Memory, and Cognition .

Bowes, S. M., Ammirati, R. J., Costello, T. H., Basterfield, C., & Lilienfeld, S. O. (2020). Cognitive biases, heuristics, and logical fallacies in clinical practice: A brief field guide for practicing clinicians and supervisors. Professional Psychology: Research and Practice, 51 (5), 435–445.

Dietrich, C. (2010). “Decision Making: Factors that Influence Decision Making, Heuristics Used, and Decision Outcomes.” Inquiries Journal/Student Pulse, 2(02).

Groenewegen, A. (2021, September 1). Kahneman Fast and slow thinking: System 1 and 2 explained by Sue. SUE Behavioral Design. Retrieved March 26, 2022, from https://suebehaviouraldesign.com/kahneman-fast-slow-thinking/

Kahneman, D., Lovallo, D., & Sibony, O. (2011). Before you make that big decision .

Kahneman, D. (2011). Thinking, fast and slow . Macmillan.

Pratkanis, A. (1989). The cognitive representation of attitudes. In A. R. Pratkanis, S. J. Breckler, & A. G. Greenwald (Eds.), Attitude structure and function (pp. 71–98). Hillsdale, NJ: Erlbaum.

Simon, H.A., 1956. Rational choice and the structure of the environment. Psychological Review .

Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases. Science, 185 (4157), 1124–1131.

Related Articles

Cognitive Psychology

Automatic Processing in Psychology: Definition & Examples

Controlled Processing in Psychology: Definition & Examples

How Ego Depletion Can Drain Your Willpower

What is the Default Mode Network?

Theories of Selective Attention in Psychology

Availability Heuristic and Decision Making

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

What Are Heuristics?

These mental shortcuts can help people make decisions more efficiently

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Steven Gans, MD is board-certified in psychiatry and is an active supervisor, teacher, and mentor at Massachusetts General Hospital.

Verywell / Cindy Chung 

• History and Origins
• Heuristics vs. Algorithms
• Heuristics and Bias

How to Make Better Decisions

Heuristics are mental shortcuts that allow people to solve problems and make judgments quickly and efficiently. These rule-of-thumb strategies shorten decision-making time and allow people to function without constantly stopping to think about their next course of action.

However, there are both benefits and drawbacks of heuristics. While heuristics are helpful in many situations, they can also lead to  cognitive biases . Becoming aware of this might help you make better and more accurate decisions.

Press Play for Advice On Making Decisions

Hosted by therapist Amy Morin, LCSW, this episode of The Verywell Mind Podcast shares a simple way to make a tough decision. Click below to listen now.

Follow Now : Apple Podcasts / Spotify / Google Podcasts

The History and Origins of Heuristics

Nobel-prize winning economist and cognitive psychologist Herbert Simon originally introduced the concept of heuristics in psychology in the 1950s. He suggested that while people strive to make rational choices, human judgment is subject to cognitive limitations. Purely rational decisions would involve weighing all the potential costs and possible benefits of every alternative.

But people are limited by the amount of time they have to make a choice as well as the amount of information they have at their disposal. Other factors such as overall intelligence and accuracy of perceptions also influence the decision-making process.

During the 1970s, psychologists Amos Tversky and Daniel Kahneman presented their research on cognitive biases. They proposed that these biases influence how people think and the judgments people make.

As a result of these limitations, we are forced to rely on mental shortcuts to help us make sense of the world. Simon's research demonstrated that humans were limited in their ability to make rational decisions, but it was Tversky and Kahneman's work that introduced the study of heuristics and the specific ways of thinking that people rely on to simplify the decision-making process.

How Heuristics Are Used

Heuristics play important roles in both  problem-solving  and  decision-making , as we often turn to these mental shortcuts when we need a quick solution.

Here are a few different theories from psychologists about why we rely on heuristics.

• Attribute substitution : People substitute simpler but related questions in place of more complex and difficult questions.
• Effort reduction : People use heuristics as a type of cognitive laziness to reduce the mental effort required to make choices and decisions.
• Fast and frugal : People use heuristics because they can be fast and correct in certain contexts. Some theories argue that heuristics are actually more accurate than they are biased.

In order to cope with the tremendous amount of information we encounter and to speed up the decision-making process, our brains rely on these mental strategies to simplify things so we don't have to spend endless amounts of time analyzing every detail.

You probably make hundreds or even thousands of decisions every day. What should you have for breakfast? What should you wear today? Should you drive or take the bus? Fortunately, heuristics allow you to make such decisions with relative ease and without a great deal of agonizing.

There are many heuristics examples in everyday life. When trying to decide if you should drive or ride the bus to work, for instance, you might remember that there is road construction along the bus route. You realize that this might slow the bus and cause you to be late for work. So you leave earlier and drive to work on an alternate route.

Heuristics allow you to think through the possible outcomes quickly and arrive at a solution.

Are Heuristics Good or Bad?

Heuristics aren't inherently good or bad, but there are pros and cons to using them to make decisions. While they can help us figure out a solution to a problem faster, they can also lead to inaccurate judgments about other people or situations.

Types of Heuristics

There are many different kinds of heuristics. While each type plays a role in decision-making, they occur during different contexts. Understanding the types can help you better understand which one you are using and when.

Availability

The availability heuristic  involves making decisions based upon how easy it is to bring something to mind. When you are trying to make a decision, you might quickly remember a number of relevant examples. Since these are more readily available in your memory, you will likely judge these outcomes as being more common or frequently occurring.

For example, if you are thinking of flying and suddenly think of a number of recent airline accidents, you might feel like air travel is too dangerous and decide to travel by car instead. Because those examples of air disasters came to mind so easily, the availability heuristic leads you to think that plane crashes are more common than they really are.

Familiarity

The familiarity heuristic refers to how people tend to have more favorable opinions of things, people, or places they've experienced before as opposed to new ones. In fact, given two options, people may choose something they're more familiar with even if the new option provides more benefits.

Representativeness

The representativeness heuristic  involves making a decision by comparing the present situation to the most representative mental prototype. When you are trying to decide if someone is trustworthy, you might compare aspects of the individual to other mental examples you hold.

A soft-spoken older woman might remind you of your grandmother, so you might immediately assume that she is kind, gentle, and trustworthy. However, this is an example of a heuristic bias, as you can't know someone trustworthy based on their age alone.

The affect heuristic involves making choices that are influenced by the emotions that an individual is experiencing at that moment. For example, research has shown that people are more likely to see decisions as having benefits and lower risks when they are in a positive mood. Negative emotions, on the other hand, lead people to focus on the potential downsides of a decision rather than the possible benefits.

The anchoring bias involves the tendency to be overly influenced by the first bit of information we hear or learn. This can make it more difficult to consider other factors and lead to poor choices. For example, anchoring bias can influence how much you are willing to pay for something, causing you to jump at the first offer without shopping around for a better deal.

Scarcity is a principle in heuristics in which we view things that are scarce or less available to us as inherently more valuable. The scarcity heuristic is one often used by marketers to influence people to buy certain products. This is why you'll often see signs that advertise "limited time only" or that tell you to "get yours while supplies last."

Trial and Error

Trial and error is another type of heuristic in which people use a number of different strategies to solve something until they find what works. Examples of this type of heuristic are evident in everyday life. People use trial and error when they're playing video games, finding the fastest driving route to work, and learning to ride a bike (or learning any new skill).

Difference Between Heuristics and Algorithms

Though the terms are often confused, heuristics and algorithms are two distinct terms in psychology.

Algorithms are step-by-step instructions that lead to predictable, reliable outcomes; whereas heuristics are mental shortcuts that are basically best guesses. Algorithms always lead to accurate outcomes, whereas, heuristics do not.

Examples of algorithms include instructions for how to put together a piece of furniture or a recipe for cooking a certain dish. Health professionals also create algorithms or processes to follow in order to determine what type of treatment to use on a patient.

How Heuristics Can Lead to Bias

While heuristics can help us solve problems and speed up our decision-making process, they can introduce errors. As in the examples above, heuristics can lead to inaccurate judgments about how commonly things occur and about how representative certain things may be.

Just because something has worked in the past does not mean that it will work again, and relying on a heuristic can make it difficult to see alternative solutions or come up with new ideas.

Heuristics can also contribute to stereotypes and  prejudice . Because people use mental shortcuts to classify and categorize people, they often overlook more relevant information and create stereotyped categorizations that are not in tune with reality.

While heuristics can be a useful tool, there are ways you can improve your decision-making and avoid cognitive bias at the same time.

We are more likely to make an error in judgment if we are trying to make a decision quickly or are under pressure to do so. Whenever possible, take a few deep breaths . Do something to distract yourself from the decision at hand. When you return to it, you may find you have a fresh perspective, or notice something you didn't before.

Identify the Goal

We tend to focus automatically on what works for us and make decisions that serve our best interest. But take a moment to know what you're trying to achieve. Are there other people who will be affected by this decision? What's best for them? Is there a common goal that can be achieved that will serve all parties?

Process Your Emotions

Fast decision-making is often influenced by emotions from past experiences that bubble to the surface. Is your decision based on facts or emotions? While emotions can be helpful, they may affect decisions in a negative way if they prevent us from seeing the full picture.

Recognize All-or-Nothing Thinking

When making a decision, it's a common tendency to believe you have to pick a single, well-defined path, and there's no going back. In reality, this often isn't the case.

Sometimes there are compromises involving two choices, or a third or fourth option that we didn't even think of at first. Try to recognize the nuances and possibilities of all choices involved, instead of using all-or-nothing thinking .

Rachlin H. Rational thought and rational behavior: A review of bounded rationality: The adaptive toolbox . J Exp Anal Behav . 2003;79(3):409–412. doi:10.1901/jeab.2003.79-409

Shah AK, Oppenheimer DM. Heuristics made easy: An effort-reduction framework . Psychol Bull. 2008;134(2):207-22. doi:10.1037/0033-2909.134.2.207

Marewski JN, Gigerenzer G. Heuristic decision making in medicine .  Dialogues Clin Neurosci . 2012;14(1):77–89. PMID: 22577307

Schwikert SR, Curran T. Familiarity and recollection in heuristic decision making .  J Exp Psychol Gen . 2014;143(6):2341-2365. doi:10.1037/xge0000024

Finucane M, Alhakami A, Slovic P, Johnson S. The affect heuristic in judgments of risks and benefits . J Behav Decis Mak . 2000; 13(1):1-17. doi:10.1002/(SICI)1099-0771(200001/03)13:1<1::AID-BDM333>3.0.CO;2-S

Cheung TT, Kroese FM, Fennis BM, De Ridder DT. Put a limit on it: The protective effects of scarcity heuristics when self-control is low . Health Psychol Open . 2015;2(2):2055102915615046. doi:10.1177/2055102915615046

Mohr H, Zwosta K, Markovic D, Bitzer S, Wolfensteller U, Ruge H. Deterministic response strategies in a trial-and-error learning task . Inman C, ed. PLoS Comput Biol. 2018;14(11):e1006621. doi:10.1371/journal.pcbi.1006621

Lang JM, Ford JD, Fitzgerald MM.  An algorithm for determining use of trauma-focused cognitive-behavioral therapy .  Psychotherapy   (Chic) . 2010;47(4):554-69. doi:10.1037/a0021184

Bigler RS, Clark C. The inherence heuristic: A key theoretical addition to understanding social stereotyping and prejudice. Behav Brain Sci . 2014;37(5):483-4. doi:10.1017/S0140525X1300366X

del Campo C, Pauser S, Steiner E, et al.  Decision making styles and the use of heuristics in decision making .  J Bus Econ.  2016;86:389–412. doi:10.1007/s11573-016-0811-y

Marewski JN, Gigerenzer G. Heuristic decision making in medicine .  Dialogues Clin Neurosci . 2012;14(1):77-89. doi:10.31887/DCNS.2012.14.1/jmarewski

Zheng Y, Yang Z, Jin C, Qi Y, Liu X. The influence of emotion on fairness-related decision making: A critical review of theories and evidence .  Front Psychol . 2017;8:1592. doi:10.3389/fpsyg.2017.01592

Bazerman MH. Judgment and decision making. In: Biswas-Diener R, Diener E, eds.,  Noba Textbook Series: Psychology.  DEF Publishers.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

22 Heuristics Examples (The Types of Heuristics)

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

Learn about our Editorial Process

A heuristic is a mental shortcut that enables people to make quick but less-than-optimal decisions.

The benefit of heuristics is that they allow us to make fast decisions based upon approximations, fast cognitive strategies, and educated guesses. The downside is that they often lead us to come to inaccurate conclusions and make flawed decisions.

The most common examples of heuristics are the availability, representativeness, and affect heuristics. However, there are many more possible examples, as shown in the 23 listed below.

Heuristics Definition

Psychologists Amos Tversky and Daniel Kahneman created the concept of heuristics in the early 1970s. They can be described in the following way:

“[They are] judgmental shortcuts that generally get us where we need to go – and quickly – but at the cost of occasionally sending us off course.”

Thus, we can see heuristics as being both positive and negative for our lives. But most interestingly, they can be leveraged in marketing situations to manipulate people’s purchasing decisions, as discussed below.

Types of Heuristics with Examples

1. availability heuristic.

Quick Definition: Making decisions based upon information that is easily available.

We often rely upon and place greater emphasis upon information that is easily available when making decisions.

We might make a decision based solely on what we know about a topic rather than conducting deeper research in order to make a more informed decision. This causes mistakes in our thinking and leads us to make decisions that are flawed or not sufficiently thought out.

This bias is one reason why political parties try to be the last person who talks to a voter before they go into a polling booth. The newness of the information may cause someone to vote for that part because the party’s arguments are closest to the top of mind.

> Check out these 15 availability heuristic examples

2. Representativeness Heuristic

Quick Definition: Making judgments based upon the similarity of one thing to its archetype. In social situations, this leads to prejudice.

We often make a snap judgment about something by placing it into a category based on its surface appearance. For example, we might see a tree and immediately assume it’s in the oak family based upon the color of its bark or size of its leaves.

In social sciences, we can also see that people make judgements about other people based upon their race, gender, class, or other aspects of their identity. In these situations, we are using stereotypes to come to snap judgements about others.

In these situations, our stereotypical assumptions about others can lead to bias, prejudice , and even discrimination .

> Check out these 11 representativeness heuristic examples

3. Affect Heuristic

Quick Definition: We often make decisions based on emotions, moods, and “gut feelings” rather than logic.

Emotions, moods, and feelings impact our thoughts. This simple fact can lead people into making emotional decisions that they may regret later on when they reflect using logic.

One affect heuristic example is the fact that we often make emotional outbursts that we regret later on. Yelling at a cashier at the shops, for example, may be followed up with regret when we reflect and realize it really wasn’t the cashier’s fault.

Similarly, shoppers make impulse purchases based on the feelings they have about the handbag or new dress. These purchases may be regretted later on when we use logic and realize we have overspent our budgets.

4. Anchoring Heuristic

Quick Definition: We often make decisions based upon a subjective anchoring point that influences all subsequent thinking on a topic.

An anchoring point is often the original piece of information that we are given. Based upon this original piece of information, all future thinking and decisions look good or bad.

An anchoring heuristic example is when a company sets the cost of their goods high before setting a discount. If a high price is set, then a discount is applied, then people would see the price as a bargain rather than high .

Similarly, if you were looking at two highly-priced products, the product that is a few dollars less than the other is seen as a good deal, even if its price is also inflated.

5. Base Rate Heuristic

Quick Definition: We neglect the base statistics in favor of other more proximate statistics when making a judgment.

Base rate neglect occurs when someone forgets the base rate, or a basic fact about information, and instead makes decisions based upon other information that they place too much importance upon.

For example, we may predict that the next person to walk into a hospital is a man if the last three people who entered were all males.

This assumption neglects the fact that 50% of all people who enter hospitals are women.

Here, we are privileging immediate information: that there appears to be a lot of men entering the hospital right now., instead of the base rate fact: that you’ve generally got a 50% chance of a woman walking into the store.

6. Absurdity Heuristic

Quick Definition: We tend to classify things that are improbably as absurd rather than giving them proper consideration.

Many people who believe themselves to be highly logical fall prey to the absurdity heuristic. This occurs when you hear a claim that is improbable, so you instantly dismiss it out of hand.

The ability to filter out absurdity has been highly useful to humans – allowing us to keep our focus on reality and not get caught up in conspiracy theories day and night.

But this becomes a problem when we dismiss things that are serious problems. For example, rejection of climate change science based on the fact that it seems extreme, or a doctor dismissing symptoms of a rare disease, are cases when absurdity bias leads us to make overly dismissive decisions.

7. Contagion Heuristic

Quick Definition: We can sometimes see people, ideas, and things as being either positively or negatively contagious despite lack of logic.

Sometimes, people will try to avoid contact with something or someone that has been the victim of bad luck. For example, a person may feel uncomfortable touching a cancer patient despite the fact they are not at all contagious.

On the positive end, we may believe lucky people will remain lucky and may even spread good luck if we spend time with them. Sometimes, this could be called the halo effect and horns effect.

8. Effort Heuristic

Quick Definition: Assuming the quality of something correlates with the amount of effort put into it.

We will often think something is more valuable or higher quality if it took a great deal of effort to create it. This assumption may be correct, but it doesn’t always turn out to be true.

For example, a person may spend 20 hours a day, 365 days a year, working on a startup business and it may still fail due to flaws in the business model. Another person may build a business in a week and see instant success.

Here, there is no positive correlation between effort and quality.

Nevertheless, the effort heuristic is utilized by advertisers all the time. Advertisements might talk about the amount of hours spent testing products, the research and development money put into it, and so on, in order to show that a lot of effort was put into it. The insinuation here is that the effort has led to a higher-quality product, when this is not necessarily always true.

9. Familiarity Heuristic

Quick Definition: We can often take mental shortcuts where we decide things that are most familiar to us are better than things that are less familiar.

Humans tend to see safety in the familiar and risk in the unfamiliar. In reality, familiar things may be just as risky, if not more, than unfamiliar things. Nevertheless, we know how to navigate familiar situations and therefore find them less risky.

A good example of this is travel. We may look to a country overseas and see it as potentially dangerous or scary. But, looking at data, our hometown or home city may be far more dangerous!

Similarly, we’re much more likely to die in a car crash than a plane crash. Nevertheless, fear may overcome you getting on a plane despite the fact that you didn’t put a moment’s thought into the drive to the airport.

10. Fluency Heuristic

Quick Definition: If an idea is communicated more fluently or skillfully then it is given more credence than an idea that is clumsily communicated, regardless of the merit of the idea.

The fluency with which an idea is communicated can directly impact how we perceive the idea. This mental shortcut allows us to bypass direct assessment of the merits of a case. Instead, we rely more on the charisma of the communicator.

For example, leaders with charismatic authority can often command a high vote during elections because of their ability to connect with voters moreso than their actual policy positions.

11. Gaze Heuristic

Quick Definition: Animals and humans have developed the ability to fixate on an estimated position rather than conducting complex calculations. Generally, this is in relation to motion.

The most common example of the gaze heuristic is the process humans go through to estimate where a ball will land. We don’t do all the calculations to understand trajectory and angle. Instead, we’ve developed an uncanny ability to identify where the ball will land through mental shortcuts based on previous experience.

Similarly, predatory animals can predict where their prey will flee to in order to intercept it, bats can use it during echolocation to estimate the location of obstacles, and hockey goalkeepers can use it to estimate the eventual position of a puck flying towards the goals.

12. Recognition Heuristic

Quick Definition: We assume that things we recognize have more value than things we do not recognize.

Recognition is an important facet of product marketing. Brand recognition alone can help a brand to thrive among a field of other products on a shelf.

The recognition heuristic states that we take mental shortcuts when looking at a range of options by assuming that the most recognizable option holds greater value. Thus, we assume a well-known household brand is higher-quality than a lesser-known brand.

Similarly, a study in psychology found that people assume cities whose names they recognize have larger populations than those that they don’t recognize. This assumption is based on the mental shortcut that larger cities are more likely to have recognizable names than smaller cities. This mental shortcut is often accurate, showing how heuristics can be beneficial (we call this the “less is more effect”).

13. Scarcity Heuristic

Quick Definition: When something is scarce , we see it as more valuable.

False scarcity is a widely-utilized method in marketing psychology because it encourages consumers to see a product as having greater value than it really does.

When a product is framed as being scarce, it is seen as having value because only a certain number of people can have it. As a result, people want it more. Sometimes, we call this the framing effect .

One way marketers use false scarcity is that they create limited-time discounts. In this case, the low price is a point of scarcity. Another way they can create false scarcity is to have open and closed cart periods so the product is only available for a short period of time.

This is a heuristic because people are encouraged to bypass making cold contemplative decisions about the product and, instead, make rushed decisions based on fear of missing out.

14. Similarity Heuristic

Quick Definition: Similarity between past and present situations impacts decision-making, allowing people to bypass making objective comparisons of two alternatives.

We tend to rely on past experiences to shape future experiences. If we liked something previously, then we may seek out similar situations in the future. If we didn’t like it in the past ,then we may avoid those situations in the future.

This logic allows people to bypass a thorough assessment of something and, instead, make fast decisions based on past experience.

Marketers can take advantage of this tendency. For example, a new fast food restaurant may use colors and a menu similar to McDonald;s in order to lull consumers into seeing the restaurant as similar to their previous positive experiences at McDonald’s, and therefore more likely to give it a go.

Similarly, Netflix may show you shows and movies similar to previous ones you watched to the end, because Netflix knows that you are going to be partisan toward a similar experience to the ones you previously enjoyed.

15. Simulation Heuristic

Quick Definition: We tend to overestimate the likelihood of an event based upon how easy it is to visualize it.

If our minds are able to visualize something happening, then we overstimate its probability.

Generally, the simulation heuristic occurs in relation to regret or near misses. A great example of this is buying a lottery ticket. If you found out that someone bought a winning lottery ticket one hour after you bought your ticket, then you’d easily be able to visualize the potentiality that you had gotten stuck in traffic that day and turned up to buy the ticket an hour later.

In this example, the probability of you ever turning up to buy the lottery ticket at the right time and place remains extremely low. However, because you can so easily visualize that eventuality, it feels as if you were truly very close to winning the lottery.

16. Social Proof Heuristic

Quick Definition: We use social proof as a mental shortcut to verify the quality or veracity of something instead of investigating it ourselves.

The social proof heuristic occurs both in social norms and product marketing.

In social norms, people tend to accept something as normal, correct, or appropriate because the rest of society does.

We could imagine, for example, 200 years ago many people thought the idea of the women’s right to vote as an idea that is strange or worthy of serious critique before being implemented. There weren’t many people supportive of the idea, so it was unquestioned. Today, because women’s right to vote is a social norm, it seems absurd that anyone would take it away.

In both of the above situations, people relied on broader society’s views (i.e. social proof) as an anchoring point for their own thinking on the topic.

Similarly, in marketing, marketers often go to great lengths to get quotes from “average joes” who have used a product in order to provide social proof in their advertisements.

17. Authority Heuristic

Quick Definition: We tend to defer to authorities as a shortcut rather than doing the thinking and research ourselves.

Society is structured in such a way that we defer to authorities and experts constantly. For example, we will defer to doctors on medical issues, engineers when building bridges, and lawyers on legal issues.

It’s just impossible to go about life trying to be an expert and authority on every topic. Instead, we will need to team up with authorities to make intelligent decisions. So, this heuristic is necessary.

However, mistakes can often be made when we see a person as an authority in one topic and, therefore, assume they’re an authority in entirely unrelated topics.

18. Hot-Hand Fallacy

Quick Definition: We overestimate our chances of success after a string of recent successes.

The hot-hand fallacy assumes that successful people will continue to experience success in the future.

The phrase “hot-hand” refers to gambling where a person rolling a dice has a “hot-hand” if they keep rolling the right numbers.

But we can apply this concept to a range of other situations. For example, we can apply it to investment funds, where investors will invest in a fund if it recently saw a lot of success.

However, past success does not guarantee future results. The more important thing would be to look at their investment philosophy rather than take the mental shortcut of “if they have recently been successful, then they will be in the future, too.”

19. Occam’s Razor

Quick Definition: The assumption that the most straightforward explanation is the most accurate.

Occam’s razor refers to the preferencing of more straightforward explanations as opposed to more complex ones. One logical justification for this is that the straightforward explanation has the least possible variables where mistakes in logic can occur.

However, critics of this approach highlight that, by definition, Occam’s razor fails to contemplate all possible variables and therefore causes oversimplification of explanations. Nevertheless, invoking Occam’s razor allows people to step back from a situation and contemplate whether they have over-complicated a simple situation.

>Check out these 15 occam’s razor examples

20. Naive Diversification

Quick Definition: Longer-term planning tends to involve more diversification than shorter-term planning.

Consider a situation where you are asked to purchase 5 weeks’ worth of groceries at once. In this situation, you’re more likely to buy a diverse range of fruit and vegetables for the forthcoming five weeks.

By contrast, if you were to go shopping once a week for five weeks, you’re less likely to diversify. Rather, you would buy a narrow range of products that you want in the short term.

In this example, people tend to diversify when faced with longer-term plans than shorter-term plans.

Naive diversification teaches us a lesson in business and investment. It teaches us that sometimes we are too soon to diversify when making plans because of our inability to make longer-term decisions in the shorter-term. As a result, we try to hedge by diversifying.

21. Peak–End Rule

Quick Definition: People tend to remember and pass judgment on an event based upon its most intense moment of finality rather than the average.

The peak-end rule refers to situations where the peak and end of a situation are the most important in our memories. When describing situations in the past tense, our minds shortcut to the peak and the end and fail to contemplate the other parts of the memory.

For example, a book or movie may be boring for 75% of the film, but the last 25% are excellent. You then go away and tell people how excellent it was, forgetting that there were long boring periods.

This is because our minds are most stimulated at the highly emotive parts of a situation, searing them in our memories.

This rule can be applied in vacation packages, movies, and other experince-based services where the experience is curated so the peak (and end) are highly stimulating to create a ‘wow experience’ that shapes people’s memories.

22. Mere Exposure Effect

Quick Definition: The mere exposure effect occurs when people develop a preference for a stimulus (such as a brand) simply because it is familiar. It is sometimes referred to as the familiarity principle.

The more frequently a person sees, experiences, or is otherwise exposed to something, the more likely it is that they will begin to like and favor it.

This is a cognitive heuristic because it involves a mental shortcut where something that is familiar is assumed to be safer and more trustworthy than unfamiliar things, regardless of the facts of the case.

This is used extensively in advertising, for example, where repeated exposure to advertisements from a particular brand, such as a restaurant, might make people more inclined to go to that restaurant next time they are hungry.

>See our full article on the Mere Exposure Effect

Heuristics are rules of thumb that help us make decisions quickly. They are useful in many situations, and in fact have helped us evolutionarily by filtering out bad information and making decisions quickly.

However, they can can also lead to biases and errors in our thinking. In the worst-case scenarios they can lead to stereotyping and significant social harm. The most common types of heuristics are availability heuristics, representativeness heuristics, and anchoring and adjustment.

Knowing about these biases in our thinking can help marketers to sell products and help reflective people to make better decisions by knowing when and when not to use heuristics.

See Also: Fundamental Attribution Error Examples

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 15 Self-Actualization Examples (Maslow's Hierarchy)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ Forest Schools Philosophy & Curriculum, Explained!
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ Montessori's 4 Planes of Development, Explained!
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ Montessori vs Reggio Emilia vs Steiner-Waldorf vs Froebel

2 thoughts on “22 Heuristics Examples (The Types of Heuristics)”

Really interesting reading about the types of Heuristics and thank you for the concise explanations.

The information was very concise is used it for a presentation I had. Thank you

Leave a Comment Cancel Reply

Your email address will not be published. Required fields are marked *

Reviewed by Psychology Today Staff

A heuristic is a mental shortcut that allows an individual to make a decision, pass judgment, or solve a problem quickly and with minimal mental effort. While heuristics can reduce the burden of decision-making and free up limited cognitive resources, they can also be costly when they lead individuals to miss critical information or act on unjust biases.

• Understanding Heuristics
• Different Heuristics
• Problems with Heuristics

As humans move throughout the world, they must process large amounts of information and make many choices with limited amounts of time. When information is missing, or an immediate decision is necessary, heuristics act as “rules of thumb” that guide behavior down the most efficient pathway.

Heuristics are not unique to humans; animals use heuristics that, though less complex, also serve to simplify decision-making and reduce cognitive load.

Generally, yes. Navigating day-to-day life requires everyone to make countless small decisions within a limited timeframe. Heuristics can help individuals save time and mental energy, freeing up cognitive resources for more complex planning and problem-solving endeavors.

The human brain and all its processes—including heuristics— developed over millions of years of evolution . Since mental shortcuts save both cognitive energy and time, they likely provided an advantage to those who relied on them.

Heuristics that were helpful to early humans may not be universally beneficial today . The familiarity heuristic, for example—in which the familiar is preferred over the unknown—could steer early humans toward foods or people that were safe, but may trigger anxiety or unfair biases in modern times.

The study of heuristics was developed by renowned psychologists Daniel Kahneman and Amos Tversky. Starting in the 1970s, Kahneman and Tversky identified several different kinds of heuristics, most notably the availability heuristic and the anchoring heuristic.

Since then, researchers have continued their work and identified many different kinds of heuristics, including:

Familiarity heuristic

Fundamental attribution error

Representativeness heuristic

Satisficing

The anchoring heuristic, or anchoring bias , occurs when someone relies more heavily on the first piece of information learned when making a choice, even if it's not the most relevant. In such cases, anchoring is likely to steer individuals wrong .

The availability heuristic describes the mental shortcut in which someone estimates whether something is likely to occur based on how readily examples come to mind . People tend to overestimate the probability of plane crashes, homicides, and shark attacks, for instance, because examples of such events are easily remembered.

People who make use of the representativeness heuristic categorize objects (or other people) based on how similar they are to known entities —assuming someone described as "quiet" is more likely to be a librarian than a politician, for instance. 

Satisficing is a decision-making strategy in which the first option that satisfies certain criteria is selected , even if other, better options may exist.

Heuristics, while useful, are imperfect; if relied on too heavily, they can result in incorrect judgments or cognitive biases. Some are more likely to steer people wrong than others.

Assuming, for example, that child abductions are common because they’re frequently reported on the news—an example of the availability heuristic—may trigger unnecessary fear or overprotective parenting practices. Understanding commonly unhelpful heuristics, and identifying situations where they could affect behavior, may help individuals avoid such mental pitfalls.

Sometimes called the attribution effect or correspondence bias, the term describes a tendency to attribute others’ behavior primarily to internal factors—like personality or character— while attributing one’s own behavior more to external or situational factors .

If one person steps on the foot of another in a crowded elevator, the victim may attribute it to carelessness. If, on the other hand, they themselves step on another’s foot, they may be more likely to attribute the mistake to being jostled by someone else .

Listen to your gut, but don’t rely on it . Think through major problems methodically—by making a list of pros and cons, for instance, or consulting with people you trust. Make extra time to think through tasks where snap decisions could cause significant problems, such as catching an important flight.

Discover how the anchoring effect, a subtle cognitive bias, shapes our decisions across life's domains.

Artificial intelligence already plays a role in deciding who’s getting hired. The way to improve AI is very similar to how we fight human biases.

Think you are avoiding the motherhood penalty by not having children? Think again. Simply being a woman of childbearing age can trigger discrimination.

Psychological experiments on human judgment under uncertainty showed that people often stray from presumptions about rational economic agents.

Psychology, like other disciplines, uses the scientific method to acquire knowledge and uncover truths—but we still ask experts for information and rely on intuition. Here's why.

We all experience these 3 cognitive blind spots at work, frequently unaware of their costs in terms of productivity and misunderstanding. Try these strategies to work around them.

How do we make sense of new experiences? Ultimately, it's about how we categorize them—which we often do by "lumping" or "splitting" them.

Have you ever fallen for fake news? This toolkit can help you easily evaluate whether a claim is real or phony.

An insidious form of prejudice occurs when a more powerful group ignores groups with less power and keeps them out of the minds of society.

Learn how to boost mental shortcuts through the effective use of heuristics.

• Find a Therapist
• Find a Treatment Center
• Find a Psychiatrist
• Find a Support Group
• Find Online Therapy
• United States
• Brooklyn, NY
• Chicago, IL
• Houston, TX
• Los Angeles, CA
• New York, NY
• Portland, OR
• San Diego, CA
• San Francisco, CA
• Seattle, WA
• Washington, DC
• Asperger's
• Bipolar Disorder
• Chronic Pain
• Eating Disorders
• Passive Aggression
• Personality
• Goal Setting
• Positive Psychology
• Stopping Smoking
• Low Sexual Desire
• Relationships
• Child Development
• Self Tests NEW
• Therapy Center
• Diagnosis Dictionary
• Types of Therapy

At any moment, someone’s aggravating behavior or our own bad luck can set us off on an emotional spiral that threatens to derail our entire day. Here’s how we can face our triggers with less reactivity so that we can get on with our lives.

• Emotional Intelligence
• Gaslighting
• Affective Forecasting
• Neuroscience

7.3 Problem-Solving

Learning objectives.

By the end of this section, you will be able to:

• Describe problem solving strategies
• Define algorithm and heuristic
• Explain some common roadblocks to effective problem solving

   People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.

The study of human and animal problem solving processes has provided much insight toward the understanding of our conscious experience and led to advancements in computer science and artificial intelligence. Essentially much of cognitive science today represents studies of how we consciously and unconsciously make decisions and solve problems. For instance, when encountered with a large amount of information, how do we go about making decisions about the most efficient way of sorting and analyzing all the information in order to find what you are looking for as in visual search paradigms in cognitive psychology. Or in a situation where a piece of machinery is not working properly, how do we go about organizing how to address the issue and understand what the cause of the problem might be. How do we sort the procedures that will be needed and focus attention on what is important in order to solve problems efficiently. Within this section we will discuss some of these issues and examine processes related to human, animal and computer problem solving.

PROBLEM-SOLVING STRATEGIES

   When people are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.

Problems themselves can be classified into two different categories known as ill-defined and well-defined problems (Schacter, 2009). Ill-defined problems represent issues that do not have clear goals, solution paths, or expected solutions whereas well-defined problems have specific goals, clearly defined solutions, and clear expected solutions. Problem solving often incorporates pragmatics (logical reasoning) and semantics (interpretation of meanings behind the problem), and also in many cases require abstract thinking and creativity in order to find novel solutions. Within psychology, problem solving refers to a motivational drive for reading a definite “goal” from a present situation or condition that is either not moving toward that goal, is distant from it, or requires more complex logical analysis for finding a missing description of conditions or steps toward that goal. Processes relating to problem solving include problem finding also known as problem analysis, problem shaping where the organization of the problem occurs, generating alternative strategies, implementation of attempted solutions, and verification of the selected solution. Various methods of studying problem solving exist within the field of psychology including introspection, behavior analysis and behaviorism, simulation, computer modeling, and experimentation.

A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them (table below). For example, a well-known strategy is trial and error. The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

   Another type of strategy is an algorithm. An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

• When one is faced with too much information
• When the time to make a decision is limited
• When the decision to be made is unimportant
• When there is access to very little information to use in making the decision
• When an appropriate heuristic happens to come to mind in the same moment

Working backwards is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

Further problem solving strategies have been identified (listed below) that incorporate flexible and creative thinking in order to reach solutions efficiently.

Additional Problem Solving Strategies :

• Abstraction – refers to solving the problem within a model of the situation before applying it to reality.
• Analogy – is using a solution that solves a similar problem.
• Brainstorming – refers to collecting an analyzing a large amount of solutions, especially within a group of people, to combine the solutions and developing them until an optimal solution is reached.
• Divide and conquer – breaking down large complex problems into smaller more manageable problems.
• Hypothesis testing – method used in experimentation where an assumption about what would happen in response to manipulating an independent variable is made, and analysis of the affects of the manipulation are made and compared to the original hypothesis.
• Lateral thinking – approaching problems indirectly and creatively by viewing the problem in a new and unusual light.
• Means-ends analysis – choosing and analyzing an action at a series of smaller steps to move closer to the goal.
• Method of focal objects – putting seemingly non-matching characteristics of different procedures together to make something new that will get you closer to the goal.
• Morphological analysis – analyzing the outputs of and interactions of many pieces that together make up a whole system.
• Proof – trying to prove that a problem cannot be solved. Where the proof fails becomes the starting point or solving the problem.
• Reduction – adapting the problem to be as similar problems where a solution exists.
• Research – using existing knowledge or solutions to similar problems to solve the problem.
• Root cause analysis – trying to identify the cause of the problem.

The strategies listed above outline a short summary of methods we use in working toward solutions and also demonstrate how the mind works when being faced with barriers preventing goals to be reached.

One example of means-end analysis can be found by using the Tower of Hanoi paradigm . This paradigm can be modeled as a word problems as demonstrated by the Missionary-Cannibal Problem :

Missionary-Cannibal Problem

Three missionaries and three cannibals are on one side of a river and need to cross to the other side. The only means of crossing is a boat, and the boat can only hold two people at a time. Your goal is to devise a set of moves that will transport all six of the people across the river, being in mind the following constraint: The number of cannibals can never exceed the number of missionaries in any location. Remember that someone will have to also row that boat back across each time.

Hint : At one point in your solution, you will have to send more people back to the original side than you just sent to the destination.

The actual Tower of Hanoi problem consists of three rods sitting vertically on a base with a number of disks of different sizes that can slide onto any rod. The puzzle starts with the disks in a neat stack in ascending order of size on one rod, the smallest at the top making a conical shape. The objective of the puzzle is to move the entire stack to another rod obeying the following rules:

• 1. Only one disk can be moved at a time.
• 2. Each move consists of taking the upper disk from one of the stacks and placing it on top of another stack or on an empty rod.
• 3. No disc may be placed on top of a smaller disk.

  Figure 7.02. Steps for solving the Tower of Hanoi in the minimum number of moves when there are 3 disks.

Figure 7.03. Graphical representation of nodes (circles) and moves (lines) of Tower of Hanoi.

The Tower of Hanoi is a frequently used psychological technique to study problem solving and procedure analysis. A variation of the Tower of Hanoi known as the Tower of London has been developed which has been an important tool in the neuropsychological diagnosis of executive function disorders and their treatment.

GESTALT PSYCHOLOGY AND PROBLEM SOLVING

As you may recall from the sensation and perception chapter, Gestalt psychology describes whole patterns, forms and configurations of perception and cognition such as closure, good continuation, and figure-ground. In addition to patterns of perception, Wolfgang Kohler, a German Gestalt psychologist traveled to the Spanish island of Tenerife in order to study animals behavior and problem solving in the anthropoid ape.

As an interesting side note to Kohler’s studies of chimp problem solving, Dr. Ronald Ley, professor of psychology at State University of New York provides evidence in his book A Whisper of Espionage  (1990) suggesting that while collecting data for what would later be his book  The Mentality of Apes (1925) on Tenerife in the Canary Islands between 1914 and 1920, Kohler was additionally an active spy for the German government alerting Germany to ships that were sailing around the Canary Islands. Ley suggests his investigations in England, Germany and elsewhere in Europe confirm that Kohler had served in the German military by building, maintaining and operating a concealed radio that contributed to Germany’s war effort acting as a strategic outpost in the Canary Islands that could monitor naval military activity approaching the north African coast.

While trapped on the island over the course of World War 1, Kohler applied Gestalt principles to animal perception in order to understand how they solve problems. He recognized that the apes on the islands also perceive relations between stimuli and the environment in Gestalt patterns and understand these patterns as wholes as opposed to pieces that make up a whole. Kohler based his theories of animal intelligence on the ability to understand relations between stimuli, and spent much of his time while trapped on the island investigation what he described as  insight , the sudden perception of useful or proper relations. In order to study insight in animals, Kohler would present problems to chimpanzee’s by hanging some banana’s or some kind of food so it was suspended higher than the apes could reach. Within the room, Kohler would arrange a variety of boxes, sticks or other tools the chimpanzees could use by combining in patterns or organizing in a way that would allow them to obtain the food (Kohler & Winter, 1925).

While viewing the chimpanzee’s, Kohler noticed one chimp that was more efficient at solving problems than some of the others. The chimp, named Sultan, was able to use long poles to reach through bars and organize objects in specific patterns to obtain food or other desirables that were originally out of reach. In order to study insight within these chimps, Kohler would remove objects from the room to systematically make the food more difficult to obtain. As the story goes, after removing many of the objects Sultan was used to using to obtain the food, he sat down ad sulked for a while, and then suddenly got up going over to two poles lying on the ground. Without hesitation Sultan put one pole inside the end of the other creating a longer pole that he could use to obtain the food demonstrating an ideal example of what Kohler described as insight. In another situation, Sultan discovered how to stand on a box to reach a banana that was suspended from the rafters illustrating Sultan’s perception of relations and the importance of insight in problem solving.

Grande (another chimp in the group studied by Kohler) builds a three-box structure to reach the bananas, while Sultan watches from the ground.  Insight , sometimes referred to as an “Ah-ha” experience, was the term Kohler used for the sudden perception of useful relations among objects during problem solving (Kohler, 1927; Radvansky & Ashcraft, 2013).

Solving puzzles.

   Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below (see figure) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.

How long did it take you to solve this sudoku puzzle? (You can see the answer at the end of this section.)

   Here is another popular type of puzzle (figure below) that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:

Did you figure it out? (The answer is at the end of this section.) Once you understand how to crack this puzzle, you won’t forget.

   Take a look at the “Puzzling Scales” logic puzzle below (figure below). Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).

What steps did you take to solve this puzzle? You can read the solution at the end of this section.

Pitfalls to problem solving.

   Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A mental set is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.

Functional fixedness is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. During the Apollo 13 mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.

   Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and nonindustrialized cultures (German & Barrett, 2005).

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An anchoring bias occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.

The confirmation bias is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Representative bias describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Finally, the availability heuristic is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision . Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in the table below.

Were you able to determine how many marbles are needed to balance the scales in the figure below? You need nine. Were you able to solve the problems in the figures above? Here are the answers.

   Many different strategies exist for solving problems. Typical strategies include trial and error, applying algorithms, and using heuristics. To solve a large, complicated problem, it often helps to break the problem into smaller steps that can be accomplished individually, leading to an overall solution. Roadblocks to problem solving include a mental set, functional fixedness, and various biases that can cloud decision making skills.

References:

Openstax Psychology text by Kathryn Dumper, William Jenkins, Arlene Lacombe, Marilyn Lovett and Marion Perlmutter licensed under CC BY v4.0. https://openstax.org/details/books/psychology

Review Questions:

1. A specific formula for solving a problem is called ________.

a. an algorithm

b. a heuristic

c. a mental set

d. trial and error

2. Solving the Tower of Hanoi problem tends to utilize a  ________ strategy of problem solving.

a. divide and conquer

b. means-end analysis

d. experiment

3. A mental shortcut in the form of a general problem-solving framework is called ________.

4. Which type of bias involves becoming fixated on a single trait of a problem?

a. anchoring bias

b. confirmation bias

c. representative bias

d. availability bias

5. Which type of bias involves relying on a false stereotype to make a decision?

6. Wolfgang Kohler analyzed behavior of chimpanzees by applying Gestalt principles to describe ________.

a. social adjustment

b. student load payment options

c. emotional learning

d. insight learning

7. ________ is a type of mental set where you cannot perceive an object being used for something other than what it was designed for.

a. functional fixedness

c. working memory

Critical Thinking Questions:

1. What is functional fixedness and how can overcoming it help you solve problems?

2. How does an algorithm save you time and energy when solving a problem?

Personal Application Question:

1. Which type of bias do you recognize in your own decision making processes? How has this bias affected how you’ve made decisions in the past and how can you use your awareness of it to improve your decisions making skills in the future?

anchoring bias

availability heuristic

confirmation bias

functional fixedness

hindsight bias

problem-solving strategy

representative bias

trial and error

working backwards

Answers to Exercises

algorithm:  problem-solving strategy characterized by a specific set of instructions

anchoring bias:  faulty heuristic in which you fixate on a single aspect of a problem to find a solution

availability heuristic:  faulty heuristic in which you make a decision based on information readily available to you

confirmation bias:  faulty heuristic in which you focus on information that confirms your beliefs

functional fixedness:  inability to see an object as useful for any other use other than the one for which it was intended

heuristic:  mental shortcut that saves time when solving a problem

hindsight bias:  belief that the event just experienced was predictable, even though it really wasn’t

mental set:  continually using an old solution to a problem without results

problem-solving strategy:  method for solving problems

representative bias:  faulty heuristic in which you stereotype someone or something without a valid basis for your judgment

trial and error:  problem-solving strategy in which multiple solutions are attempted until the correct one is found

working backwards:  heuristic in which you begin to solve a problem by focusing on the end result

Share This Book

• Increase Font Size

Heuristic Problem Solving: A comprehensive guide with 5 Examples

What are heuristics, advantages of using heuristic problem solving, disadvantages of using heuristic problem solving, heuristic problem solving examples, frequently asked questions.

• Speed: Heuristics are designed to find solutions quickly, saving time in problem solving tasks. Rather than spending a lot of time analyzing every possible solution, heuristics help to narrow down the options and focus on the most promising ones.
• Flexibility: Heuristics are not rigid, step-by-step procedures. They allow for flexibility and creativity in problem solving, leading to innovative solutions. They encourage thinking outside the box and can generate unexpected and valuable ideas.
• Simplicity: Heuristics are often easy to understand and apply, making them accessible to anyone regardless of their expertise or background. They don’t require specialized knowledge or training, which means they can be used in various contexts and by different people.
• Cost-effective: Because heuristics are simple and efficient, they can save time, money, and effort in finding solutions. They also don’t require expensive software or equipment, making them a cost-effective approach to problem solving.
• Real-world applicability: Heuristics are often based on practical experience and knowledge, making them relevant to real-world situations. They can help solve complex, messy, or ill-defined problems where other problem solving methods may not be practical.
• Potential for errors: Heuristic problem solving relies on generalizations and assumptions, which may lead to errors or incorrect conclusions. This is especially true if the heuristic is not based on a solid understanding of the problem or the underlying principles.
• Limited scope: Heuristic problem solving may only consider a limited number of potential solutions and may not identify the most optimal or effective solution.
• Lack of creativity: Heuristic problem solving may rely on pre-existing solutions or approaches, limiting creativity and innovation in problem-solving.
• Over-reliance: Heuristic problem solving may lead to over-reliance on a specific approach or heuristic, which can be problematic if the heuristic is flawed or ineffective.
• Lack of transparency: Heuristic problem solving may not be transparent or explainable, as the decision-making process may not be explicitly articulated or understood.
• Trial and error: This heuristic involves trying different solutions to a problem and learning from mistakes until a successful solution is found. A software developer encountering a bug in their code may try other solutions and test each one until they find the one that solves the issue.
• Working backward: This heuristic involves starting at the goal and then figuring out what steps are needed to reach that goal. For example, a project manager may begin by setting a project deadline and then work backward to determine the necessary steps and deadlines for each team member to ensure the project is completed on time.
• Breaking a problem into smaller parts: This heuristic involves breaking down a complex problem into smaller, more manageable pieces that can be tackled individually. For example, an HR manager tasked with implementing a new employee benefits program may break the project into smaller parts, such as researching options, getting quotes from vendors, and communicating the unique benefits to employees.
• Using analogies: This heuristic involves finding similarities between a current problem and a similar problem that has been solved before and using the solution to the previous issue to help solve the current one. For example, a salesperson struggling to close a deal may use an analogy to a successful sales pitch they made to help guide their approach to the current pitch.
• Simplifying the problem: This heuristic involves simplifying a complex problem by ignoring details that are not necessary for solving it. This allows the problem solver to focus on the most critical aspects of the problem. For example, a customer service representative dealing with a complex issue may simplify it by breaking it down into smaller components and addressing them individually rather than simultaneously trying to solve the entire problem.

Test your problem-solving skills for free in just a few minutes.

The free problem-solving skills for managers and team leaders helps you understand mistakes that hold you back.

What are the three types of heuristics?

What are the four stages of heuristics in problem solving.

Other Related Blogs

Top 15 Tips for Effective Conflict Mediation at Work

Top 10 games for negotiation skills to make you a better leader, manager effectiveness: a complete guide for managers in 2024, 5 proven ways managers can build collaboration in a team.

• Media Center

Why do we take mental shortcuts?

What are heuristics.

Heuristics are mental shortcuts that can facilitate problem-solving and probability judgments. These strategies are generalizations, or rules-of-thumb, that reduce cognitive load. They can be effective for making immediate judgments, however, they often result in irrational or inaccurate conclusions.

Where this bias occurs

Debias your organization.

Most of us work & live in environments that aren’t optimized for solid decision-making. We work with organizations of all kinds to identify sources of cognitive bias & develop tailored solutions.

We use heuristics in all sorts of situations. One type of heuristic, the availability heuristic , often happens when we’re attempting to judge the frequency with which a certain event occurs. Say, for example, someone asked you whether more tornadoes occur in Kansas or Nebraska. Most of us can easily call to mind an example of a tornado in Kansas: the tornado that whisked Dorothy Gale off to Oz in Frank L. Baum’s The Wizard of Oz . Although it’s fictional, this example comes to us easily. On the other hand, most people have a lot of trouble calling to mind an example of a tornado in Nebraska. This leads us to believe that tornadoes are more common in Kansas than in Nebraska. However, the states actually report similar levels. 1

Individual effects

The thing about heuristics is that they aren’t always wrong. As generalizations, there are many situations where they can yield accurate predictions or result in good decision-making. However, even if the outcome is favorable, it was not achieved through logical means. When we use heuristics, we risk ignoring important information and overvaluing what is less relevant. There’s no guarantee that using  heuristics will work out and, even if it does, we’ll be making the decision for the wrong reason. Instead of basing it on reason, our behavior is resulting from a mental shortcut with no real rationale to support it.

Systemic effects

Heuristics become more concerning when applied to politics, academia, and economics. We may all resort to heuristics from time to time, something that is true even of members of important institutions who are tasked with making large, influential decisions. It is necessary for these figures to have a comprehensive understanding of the biases and heuristics that can affect our behavior, so as to promote accuracy on their part.

How it affects product

Heuristics can be useful in product design. Specifically, because heuristics are intuitive to us, they can be applied to create a more user-friendly experience and one that is more valuable to the customer. For example, color psychology is a phenomenon explaining how our experiences with different colors and color families can prime certain emotions or behaviors. Taking advantage of the representativeness heuristic, one could choose to use passive colors (blue or green) or more active colors (red, yellow, orange) depending on the goals of the application or product. 18 For example, if a developer is trying to evoke a feeling of calm for their app that provides guided meditations, they may choose to make the primary colors of the program light blues and greens. Colors like red and orange are more emotionally energizing and may be useful in settings like gyms or crossfit programs. 

By integrating heuristics into products we can enhance the user experience. If an application, device, or item includes features that make it feel intuitive, easy to navigate and familiar, customers will be more inclined to continue to use it and recommend it to others. Appealing to those mental shortcuts we can minimize the chances of user error or frustration with a product that is overly complicated.

Heuristics and AI

Artificial intelligence and machine learning tools already use the power of heuristics to inform its output. In a nutshell, simple AI tools operate based on a set of built in rules and sometimes heuristics! These are encoded within the system thus aiding in decision-making and the presentation of learning material. Heuristic algorithms can be used to solve advanced computational problems, providing efficient and approximate solutions.  Like in humans, the use of heuristics can result in error, and thus must be used with caution. However, machine learning tools and AI can be useful in supporting human decision-making, especially when clouded by emotion, bias or irrationality due to our own susceptibility to heuristics. 

Why it happens

In their paper “Judgment Under Uncertainty: Heuristics and Biases” 2 , Daniel Kahneman and Amos Tversky identified three different kinds of heuristics: availability, representativeness, as well as anchoring and adjustment. Each type of heuristic is used for the purpose of reducing the mental effort needed to make a decision, but they occur in different contexts.

Availability heuristic

The availability heuristic, as defined by Kahneman and Tversky, is the mental shortcut used for making frequency or probability judgments based on “the ease with which instances or occurrences can be brought to mind”. 3 This was touched upon in the previous example, judging the frequency with which tornadoes occur in Kansas relative to Nebraska. 3

The availability heuristic occurs because certain memories come to mind more easily than others. In Kahneman and Tversky’s example participants were asked if more words in the English language start with the letter K or have K as the third letter  Interestingly, most participants responded with the former when in actuality, it is the latter that is true. The idea being that it is much more difficult to think of words that have K as the third letter than it is to think of words that start with K. 4 In this case,  words that begin with K are more readily available to us than words with the K as the third letter.

Representativeness heuristic

Individuals tend to classify events into categories, which, as illustrated by Kahneman and Tversky, can result in our use of the representativeness heuristic. When we use this heuristic, we categorize events or objects based on how they relate to instances we are already familiar with.  Essentially, we have built our own categories, which we use to make predictions about novel situations or people. 5 For example, if someone we meet in one of our university lectures looks and acts like what we believe to be a stereotypical medical student, we may judge the probability that they are studying medicine as highly likely, even without any hard evidence to support that assumption.

The representativeness heuristic is associated with prototype theory. 6 This prominent theory in cognitive science, the prototype theory explains object and identity recognition. It suggests that we categorize different objects and identities in our memory. For example, we may have a category for chairs, a category for fish, a category for books, and so on. Prototype theory posits that we develop prototypical examples for these categories by averaging every example of a given category we encounter. As such, our prototype of a chair should be the most average example of a chair possible, based on our experience with that object. This process aids in object identification because we compare every object we encounter against the prototypes stored in our memory. The more the object resembles the prototype, the more confident we are that it belongs in that category. 

Prototype theory may give rise to the representativeness heuristic as it is in situations when a particular object or event is viewed as similar to the prototype stored in our memory, which leads us to classify the object or event into the category represented by that prototype. To go back to the previous example, if your peer closely resembles your prototypical example of a med student, you may place them into that category based on the prototype theory of object and identity recognition. This, however, causes you to commit the representativeness heuristic.

Anchoring and adjustment heuristic

Another heuristic put forth by Kahneman and Tversky in their initial paper is the anchoring and adjustment heuristic. 7 This heuristic describes how, when estimating a certain value, we tend to give an initial value, then adjust it by increasing or decreasing our estimation. However, we often get stuck on that initial value – which is referred to as anchoring – this results in us making insufficient adjustments. Thus, the adjusted value is biased in favor of the initial value we have anchored to.

In an example of the anchoring and adjustment heuristic, Kahneman and Tversky gave participants questions such as “estimate the number of African countries in the United Nations (UN).” A wheel labeled with numbers from 0-100 was spun, and participants were asked to say whether or not the number the wheel landed on was higher or lower than their answer to the question. Then, participants were asked to estimate the number of African countries in the UN, independent from the number they had spun. Regardless, Kahneman and Tversky found that participants tended to anchor onto the random number obtained by spinning the wheel. The results showed that  when the number obtained by spinning the wheel was 10, the median estimate given by participants was 25, while, when the number obtained from the wheel was 65, participants’ median estimate was 45.8.

A 2006 study by Epley and Gilovich, “The Anchoring and Adjustment Heuristic: Why the Adjustments are Insufficient” 9 investigated the causes of this heuristic. They illustrated that anchoring often occurs because the new information that we anchor to is more accessible than other information Furthermore, they provided empirical evidence to demonstrate that our adjustments tend to be insufficient because they require significant mental effort, which we are not always motivated to dedicate to the task. They also found that providing incentives for accuracy led participants to make more sufficient adjustments. So, this particular heuristic generally occurs when there is no real incentive to provide an accurate response.

Quick and easy

Though different in their explanations, these three types of heuristics allow us to respond automatically without much effortful thought. They provide an immediate response and do not use up much of our mental energy, which allows us to dedicate mental resources to other matters that may be more pressing. In that way, heuristics are efficient, which is a big reason why we continue to use them. That being said, we should be mindful of how much we rely on them because there is no guarantee of their accuracy.

Why it is important

As illustrated by Tversky and Kahneman, using heuristics can cause us to engage in various cognitive biases and commit certain fallacies. 10 As a result, we may make poor decisions, as well as inaccurate judgments and predictions. Awareness of heuristics can aid us in avoiding them, which will ultimately lead us to engage in more adaptive behaviors.

How to avoid it

Heuristics arise from automatic System 1 thinking. It is a common misconception that errors in judgment can be avoided by relying exclusively on System 2 thinking. However, as pointed out by Kahneman, neither System 2 nor System 1 are infallible. 11   While System 1 can result in relying on heuristics leading to certain biases, System 2 can give rise to other biases, such as the confirmation bias . 12 In truth, Systems 1 and 2 complement each other, and using them together can lead to more rational decision-making. That is, we shouldn’t make judgments automatically, without a second thought, but we shouldn’t overthink things to the point where we’re looking for specific evidence to support our stance. Thus, heuristics can be avoided by making judgments more effortfully, but in doing so, we should attempt not to overanalyze the situation.

How it all started

The first three heuristics – availability, representativeness, as well as anchoring and adjustment – were identified by Tverksy and Kahneman in their 1974 paper, “Judgment Under Uncertainty: Heuristics and Biases”. 13 In addition to presenting these heuristics and their relevant experiments, they listed the respective biases each can lead to.

For instance, upon defining the availability heuristic, they demonstrated how it may lead to illusory correlation , which is the erroneous belief that two events frequently co-occur. Kahneman and Tversky made the connection by illustrating how the availability heuristic can cause us to over- or under-estimate the frequency with which certain events occur. This may result in drawing correlations between variables when in reality there are none.  

Referring to our tendency to overestimate our accuracy making probability judgments, Kahneman and Tversky also discussed how the illusion of validity is facilitated by the representativeness heuristic. The more representative an object or event is, the more confident we feel in predicting certain outcomes. The illusion of validity, as it works with the representativeness heuristic, can be demonstrated by our assumptions of others based on past experiences. If you have only ever had good experiences with people from Canada, you will be inclined to judge most Canadians as pleasant. In reality, your small sample size cannot account for the whole population. Representativeness is not the only factor in determining the probability of an outcome or event, meaning we should not be as confident in our predictive abilities.

Example 1 – Advertising

Those in the field of advertising should have a working understanding of heuristics as consumers often rely on these shortcuts when making decisions about purchases. One heuristic that frequently comes into play in the realm of advertising is the scarcity heuristic . When assessing the value of something, we often fall back on this heuristic, leading us to believe that the rarity or exclusiveness of an object contributes to its value.

A 2011 study by Praveen Aggarwal, Sung Yul Jun, and Jong Ho Huh evaluated the impact of “scarcity messages” on consumer behavior. They found that both “limited quantity” and “limited time” advertisements influence consumers’ intentions to purchase, but “limited quantity” messages are more effective. This explains why people get so excited over the one-day-only Black Friday sales, and why the countdowns of units available on home shopping television frequently lead to impulse buys. 14

Knowledge of the scarcity heuristic can help businesses thrive, as “limited quantity” messages make potential consumers competitive and increase their intentions to purchase. 15 This marketing technique can be a useful tool for bolstering sales and bringing attention to your business.

Example 2 – Stereotypes

One of the downfalls of heuristics is that they have the potential to lead to stereotyping, which is often harmful. Kahneman and Tversky illustrated how the representativeness heuristic might result in the propagation of stereotypes. The researchers presented participants with a personality sketch of a fictional man named Steve followed by a list of possible occupations. Participants were tasked with ranking the likelihood of each occupation being Steve’s. Since the personality sketch described Steve as shy, helpful, introverted, and organized, participants tended to indicate that it was probable that he was a  librarian. 16 In this particular case the stereotype is less harmful than many others, however it accurately illustrates the link between heuristics and stereotypes.

Published in 1989, Patricia Devine’s paper “Stereotypes and Prejudice: Their Automatic and Controlled Components” illustrates how, even among people who are low in prejudice, rejecting stereotypes requires a certain level of motivation and cognitive capacity. 17 We typically use heuristics in order to avoid exerting too much mental energy, specifically when we are not sufficiently motivated to dedicate mental resources to the task at hand. Thus, when we lack the mental capacity to make a judgment or decision effortfully, we may rely upon automatic heuristic responses and, in doing so, risk propagating stereotypes.

Stereotypes are an example of how heuristics can go wrong. Broad generalizations do not always apply, and their continued use can have serious consequences. This underscores the importance of effortful judgment and decision-making, as opposed to automatic.

Heuristics are mental shortcuts that allow us to make quick judgment calls based on generalizations or rules of thumb.

Heuristics, in general, occur because they are efficient ways of responding when we are faced with problems or decisions. They come about automatically, allowing us to allocate our mental energy elsewhere. Specific heuristics occur in different contexts; the availability heuristic happens because we remember certain memories better than others, the representativeness heuristic can be explained by prototype theory, and the anchoring and adjustment heuristic occurs due to lack of incentive to put in the effort required for sufficient adjustment.

The scarcity heuristic, which refers to how we value items more when they are limited, can be used to the advantage of businesses looking to increase sales. Research has shown that advertising objects as “limited quantity” increases consumers' competitiveness and their intentions to buy the item.

While heuristics can be useful, we should exert caution, as they are generalizations that may lead us to propagate stereotypes ranging from inaccurate to harmful.

Putting more effort into decision-making instead of making decisions automatically can help us avoid heuristics. Doing so requires more mental resources, but it will lead to more rational choices.

Related TDL articles

What are heuristics.

This interview with The Decision Lab’s Managing Director Sekoul Krastev delves into the history of heuristics, their applications in the real world, and their consequences, both positive and negative.

10 Decision-Making Errors that Hold Us Back at Work

In this article, Dr. Melina Moleskis examines the common decision-making errors that occur in the workplace. Everything from taking in feedback provided by customers to cracking the problems of on-the-fly decision-making, Dr. Moleskis delivers workable solutions that anyone can implement. 

• Gilovich, T., Keltner, D., Chen. S, and Nisbett, R. (2015).  Social Psychology  (4th edition). W.W. Norton and Co. Inc.
• Tversky, A. and Kahneman, D. (1974). Judgment Under Uncertainty: Heuristics and Biases.  Science . 185(4157), 1124-1131.
• Mervis, C. B., & Rosch, E. (1981). Categorization of natural objects.  Annual Review of Psychology ,  32 (1), 89–115. https://doi.org/10.1146/annurev.ps.32.020181.000513
• Epley, N., & Gilovich, T. (2006). The anchoring-and-adjustment heuristic.  Psychological Science -Cambridge- ,  17 (4), 311–318.
• System 1 and System 2 Thinking.  The Marketing Society.  https://www.marketingsociety.com/think-piece/system-1-and-system-2-thinking
• Aggarwal, P., Jun, S. Y., & Huh, J. H. (2011). Scarcity messages.  Journal of Advertising ,  40 (3), 19–30.
• Devine, P. G. (1989). Stereotypes and prejudice: their automatic and controlled components.  Journal of Personality and Social Psychology ,  56 (1), 5–18. https://doi.org/10.1037/0022-3514.56.1.5
• Kuo, L., Chang, T., &amp; Lai, C.-C. (2022). Research on product design modeling image and color psychological test. Displays, 71, 102108. https://doi.org/10.1016/j.displa.2021.102108

About the Authors

Dan is a Co-Founder and Managing Director at The Decision Lab. He is a bestselling author of Intention - a book he wrote with Wiley on the mindful application of behavioral science in organizations. Dan has a background in organizational decision making, with a BComm in Decision & Information Systems from McGill University. He has worked on enterprise-level behavioral architecture at TD Securities and BMO Capital Markets, where he advised management on the implementation of systems processing billions of dollars per week. Driven by an appetite for the latest in technology, Dan created a course on business intelligence and lectured at McGill University, and has applied behavioral science to topics such as augmented and virtual reality.

Dr. Sekoul Krastev

Sekoul is a Co-Founder and Managing Director at The Decision Lab. He is a bestselling author of Intention - a book he wrote with Wiley on the mindful application of behavioral science in organizations. A decision scientist with a PhD in Decision Neuroscience from McGill University, Sekoul's work has been featured in peer-reviewed journals and has been presented at conferences around the world. Sekoul previously advised management on innovation and engagement strategy at The Boston Consulting Group as well as on online media strategy at Google. He has a deep interest in the applications of behavioral science to new technology and has published on these topics in places such as the Huffington Post and Strategy & Business.

Hindsight Bias

Why do unpredictable events only seem predictable after they occur, hot hand fallacy, why do we expect previous success to lead to future success, hyperbolic discounting, why do we value immediate rewards more than long-term rewards.

Eager to learn about how behavioral science can help your organization?

Get new behavioral science insights in your inbox every month..

[show_hide]

[live_search]

✅ Verified by World Mental Healthcare Association

Table of Contents

What is heuristics, history of heuristics, types of heuristics, advantages of heuristics, heuristics & cognitive bias, making quick decisions with heuristics, heuristics at a glance.

Heuristics are mental shortcut techniques used to solve problems and make decisions efficiently. These techniques are used to reduce the decision making time and allow the individual to function without interrupting their next course of action.

Heuristics are a time-saving approach to solving problems and making decisions efficiently. Heuristics processes are usually used to find quick answers and solutions to problems. However, decisions based on this mindset are not always accurate. They serve as quick mental references that are used for everyday problems and experiences.

Humans and animals resort to this mindset because processing every information that comes into the brain takes time and effort. With the help of these shortcut techniques, the brain can make faster and efficient decisions despite the consequences. This is known as the accuracy effort trade-off theory. This theory works because not every decision requires the same amount of time and energy.

Hence, people use it as a means to save time. Another reason why people resort to heuristics is that the brain simply doesn’t have the capacity to process everything and so they must resort to these mental shortcuts to make quick decisions. A 2014 study 1 Mousavi, S., & Gigerenzer, G. (2014). Risk, uncertainty, and heuristics.  Journal of Business Research ,  67 (8), 1671-1678.  https://doi.org/10.1016/j.jbusres.2014.02.013 demonstrated that in case of uncertainty and a lack of information, heuristics allows a “less is more effect” wherein less information leads to more accuracy. It is worth mentioning that the applicability and usefulness of heuristics depend on the situation.

A 2011 study 2 Gigerenzer G, Gaissmaier W. Heuristic decision making. Annu Rev Psychol. 2011;62:451-82. doi: 10.1146/annurev-psych-120709-145346. PMID: 21126183. pointed out that there may be two reasons for relying on heuristics. They are:

• Individuals and organizations often rely on simple heuristics in an adaptive way
• Ignoring part of the information can lead to more accurate judgments than weighing and adding all information

Although heuristics are useful, sometimes they can be inaccurate. In case an individual relies on it too heavily, it may result in incorrect judgments or cognitive biases. Understanding commonly unfavorable heuristics and identifying situations that may affect behavior can help individuals avoid mental pitfalls. It is important to assess major problems by making a list of pros and cons. In order to avoid inaccurate decisions, you can consult trusted individuals, take time to think through things where quick decisions may cause significant problems such as catching an important flight. Hence, it is important to be mindful of the information that is being processed in the brain to make accurate decisions.

Nobel prize-winning psychologist, Herbert Simon suggested that although people attempt to make rational decisions, humans are subject to cognitive limitations. A 2013 study 3 Rachlin, H. (2003). Rational thought and rational behavior: A review of bounded rationality: The adaptive toolbox. Journal of the Experimental Analysis of Behavior, 79(3), 409-412. https://doi.org/10.1901/jeab.2003.79-409 pointed out that rational decisions involve weighing different factors such as potential costs against potential benefits. People are often limited by time to make choices as well as the amount of information we have at our disposal. Other factors that influence our thinking are overall intelligence and accuracy of perceptions.

Psychologists Amos Tversky and Daniel Kahneman proposed that cognitive biases influence how people think and the judgements people make about events. Due to these limitations, we are often forced to rely on our instinctive shortcuts i.e heuristics to make sense of the world. Simon’s research indicated that humans have a limited ability to make rational decisions. On the other hand, Tversky and Kahneman’s research 4 Kahneman, D., & Tversky, A. (1977). Prospect theory. An analysis of decision making under risk. https://doi.org/10.21236/ada045771 represented how people have specific ways to simplify the decision-making process.

Some of the common heuristics may include the following:

1. Availability Heuristics

This involves making decisions based on the information that is readily available in one’s mind. When an individual makes a decision, they immediately refer to a number of relevant examples. Since the relevant information is readily available in their memory, they are more likely to conclude that these outcomes are common. For example, dramatic, violent deaths are usually more highly publicized and hence have higher availability.

Another instance where availability heuristics may work is if an individual is thinking about taking a trip and thinks of a number of recent airline accidents. This may lead them to think that air travel is dangerous. This may also enable them to resort to traveling by car instead. Since airline disasters came to their mind easily, the availability heuristics lead them to think that plane crashes are more common even though it may not be entirely true.

2. Representative Heuristics

This involves making a decision based on the comparison of the present situation and the most relevant mental prototype. In case an individual is trying to decide if someone is trustworthy they may compare the incident with other mental examples. For instance, an older woman sitting beside you at a train station may remind you of your grandmother. You may immediately assume that she may be kind, gentle, and trustworthy. People tend to believe in the existing mental information since the traits match up to the individual’s mental prototype.

3. Affect Heuristics

This involves making choices that are influenced by emotions that an individual is experiencing at that moment. Research 5 Finucane, M. L., Alhakami, A., Slovic, P., & Johnson, S. M. (2000). The affect heuristic in judgments of risks and benefits. Journal of Behavioral Decision Making, 13(1), 1-17. https://doi.org/10.1002/(sici)1099-0771(200001/03)13:1<1::aid-bdm333>3.0.co;2-s has demonstrated that people are more likely to view decisions as having benefits and lower risks when their mood is positive. However, negative emotions lead people to focus on the potential downfall of a decision rather than the possible benefits.

4. Satisficing Heuristics

This is a decision making strategy wherein the first option that fulfills the criteria is selected even if there are better alternatives available. Hebert Simon formulated the concept of satisficing. This theory 6 Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99. https://doi.org/10.2307/1884852 is used to choose one alternative from a set of alternatives in situations of uncertainty. In this case, uncertainty refers to the total set of alternatives and their consequences that cannot be known or foreseen. For instance, professional real estate entrepreneurs rely on this theory to decide where they should invest to develop new commercial areas. Although there may be better alternatives available, they resort to the first option that fulfills their criteria.

Some of the most common advantages of using this cognitive approach are:

• Facilitates timely decisions
• Makes decision making simpler
• Less information, more accuracy
• Quick answers to problems
• Reduces complex information into simple and manageable set of choices
• Frees up cognitive resources for more complex planning

Although heuristics can advance our problems and decision-making process, it can even cause errors. It can often lead to inaccurate judgments based on how common things can occur and how certain events influence our decisions. It is important to realize that even though something worked in the past, it doesn’t necessarily mean that it will work again. Relying on existing heuristics can make it difficult to see alternatives or brainstorm new ideas. A 2014 study pointed out that heuristics can also contribute to other things such as stereotypes and prejudice. Due to this people often overlook more relevant information and create stereotypical categorization that is not entirely true.

Read More About Cognitive Bias Here

Heuristics allow us to make quick decisions and make our life easier. It is often accurate. However, it is important to be aware of what is influencing our decisions in order to avoid potential cognitive biases. This will allow us to make more accurate decisions.

• Heuristics are mental shortcut techniques used to solve problems and make decisions efficiently.
• Heuristics processes are usually used to find quick answers and solutions to problems.
• They serve as quick mental references that are used for everyday problems and experiences.
• With the help of these shortcut techniques, the brain can make faster and efficient decisions despite the consequences.
• Sometimes it may result in incorrect judgments or cognitive biases.
• It is important to be aware of what is influencing our decisions in order to avoid potential cognitive biases.

References:

• 1 Mousavi, S., & Gigerenzer, G. (2014). Risk, uncertainty, and heuristics.  Journal of Business Research ,  67 (8), 1671-1678.  https://doi.org/10.1016/j.jbusres.2014.02.013
• 2 Gigerenzer G, Gaissmaier W. Heuristic decision making. Annu Rev Psychol. 2011;62:451-82. doi: 10.1146/annurev-psych-120709-145346. PMID: 21126183.
• 3 Rachlin, H. (2003). Rational thought and rational behavior: A review of bounded rationality: The adaptive toolbox. Journal of the Experimental Analysis of Behavior, 79(3), 409-412. https://doi.org/10.1901/jeab.2003.79-409
• 4 Kahneman, D., & Tversky, A. (1977). Prospect theory. An analysis of decision making under risk. https://doi.org/10.21236/ada045771
• 5 Finucane, M. L., Alhakami, A., Slovic, P., & Johnson, S. M. (2000). The affect heuristic in judgments of risks and benefits. Journal of Behavioral Decision Making, 13(1), 1-17. https://doi.org/10.1002/(sici)1099-0771(200001/03)13:1<1::aid-bdm333>3.0.co;2-s
• 6 Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99. https://doi.org/10.2307/1884852

— Share —

• Share on Pinterest
• Share on Facebook
• Email this Page
• Share on LinkedIn
• Share on Telegram
• Share on Tumblr
• Share on Reddit
• Print this Page
• Share on Pocket
• Share on WhatsApp

Types of Heuristics in Psychology

Categories Cognition

When you are trying to solve a problem or make a decision, you don’t always have time to examine every possible answer or possibility. Sometimes, you have to rely on the information you already have to make the best guess or estimate in a limited amount of time.

This is an example of using heuristics, or mental ‘rules of thumb,’ that help you make choices quickly and easily. 

There are many different ways to solve problems, but some take more time than others. Heuristics can be thought of as mental shortcuts that often help us make educated guesses.

Definition : A heuristic in psychology refers to a mental shortcut or rule of thumb that helps individuals make decisions or solve problems more efficiently. It is a cognitive strategy that allows us to simplify complex information and make judgments quickly. 

Table of Contents

3 Key Types of Heuristics

There are several different types of heuristics that we commonly use in our everyday lives. 

Availability Heuristic

This heuristic involves making judgments based on the ease with which examples or instances come to mind. For example, if we can easily recall instances of successful outcomes from a particular option, we are more likely to choose it. 

Representativeness Heuristic

This heuristic involves making judgments based on how well an object or event matches a particular prototype or stereotype. For example, if someone fits our mental image of a successful entrepreneur, we may assume they are more likely to be successful. 

Anchoring and Adjustment Heuristic

The anchoring and adjustment heuristic heuristic involves starting with an initial anchor or reference point and then adjusting our judgment based on additional information. For example, when negotiating a price, we may start with a high anchor and then adjust downward based on the seller’s counteroffer. 

Why We Use Different Types of Heuristics

We don’t always have the time–or the resources–to consider every possible option for every decision we make. People make hundreds of decisions each day. If we had to meticulously use trial and error or other methods to make each choice, we’d never get anything done.

Heuristics are often used when we don’t have the time or resources to gather all the necessary information for a decision.

Of course, heuristics aren’t perfect. They can also be biased and may lead to inaccurate decisions. Despite their limitations, heuristics are valuable because they allow us to make decisions quickly and with minimal effort. 

There are different types of heuristics that individuals use in various situations. Some common types include availability heuristics, representativeness heuristics, and anchoring and adjustment heuristics. 

Each type of heuristic has its own set of characteristics and biases. Understanding heuristics is important because they can contribute to cognitive biases, which are systematic errors in thinking. By recognizing these biases, we can become more aware of our decision-making processes and make more informed choices. 

How Were These Types of Heuristics Discovered? 

While we may like to believe that our choices are rooted in rationality and logic, psychologists have shown that there are certain patterns that tend to dictate how we solve the problems we face. During the 1970s, [sychologists Amos Tversky and Daniel Kahneman conducted groundbreaking studies on how people make judgments in the face of uncertainty.

Their work challenged the traditional view that humans always make rational choices based on complete information. 

Tversky and Kahneman suggested that heuristics are mental shortcuts that individuals use to simplify complex problems and make judgments quickly. They identified several common heuristics, such as the availability heuristic, which involves making judgments based on how easily examples come to mind, and the representativeness heuristic, which involves making judgments based on how closely something resembles a typical example. 

This research provided valuable insights into the limitations and biases associated with heuristics. They showed that heuristics can lead to systematic errors in thinking known as cognitive biases . 

How We Use Different Types of Heuristics to Make Decisions?

Heuristics are an inextricable part of our daily lives, even though we are rarely aware of them. In decisions both large and small, we use heuristics to help narrow down our choices and determine which option is right for us, often based on very limited information. 

For example, the availability heuristic helps us make decisions based on the ease with which examples come to mind. If we can easily recall instances of successful outcomes from a particular option, we are more likely to choose it. 

Heuristics also aid in problem-solving by providing shortcuts to finding solutions. Instead of exhaustively analyzing every possible solution, heuristics allow us to quickly identify potential options based on past experiences or general rules of thumb. This can save time and mental effort, especially when we need to act quickly.

Advertisers often employ heuristics to create persuasive messages that appeal to consumers’ cognitive biases. By framing information in a certain way or using social proof, they can influence our decision-making and encourage specific actions. 

Examples of Types of Heuristics 

There are examples of heuristics all around us. Examining some of these examples can help give greater insight into how they shape our thinking and influence our choices. 

• For example, imagine that you are going to be flying to another country for vacation. In the weeks before your flight, you find yourself recalling numerous news stories of plane crashes. Because these examples spring to mind so readily, you may overestimate the likelihood that a plane crash will occur. This is an example of the availability heuristic.
• Or imagine you deciding who to vote for in an upcoming election. You might look at the candidates and pick one based on your expectations about good leadership traits. Basing your decision on how well the candidate fits your expectations rather than on their voting record or policy platform is an example of the representativeness heuristic.
• Or imagine that you are thinking about buying a new house. You look at the list price, and then use that number as an ‘anchor’ to base your offer on. It may not necessarily indicate what the house is worth or what other similar houses are going for, but you’re still likely to use that initial number as a reference point for all future negotiations. 

These examples highlight how heuristics can simplify decision-making but also demonstrate their potential limitations. By recognizing these heuristics in action, we can become more aware of their influence and make more informed choices. 

Heuristics vs. Other Decision-Making Strategies 

Heuristics are just one of the many strategies people utilize to make decisions. We may be more likely to rely on heuristics when:

• When need to make decisions quickly
• When we don’t have the cognitive resources to use other strategies
• When we have relevant past experience

It is important to note that heuristics and other decision-making strategies are not mutually exclusive. In fact, individuals often use a combination of heuristics and other strategies depending on the context and the specific decision at hand. 

Understanding the differences between heuristics and other decision-making strategies can help individuals become more aware of their decision-making processes and make more informed choices. By recognizing the strengths and limitations of each approach, individuals can develop a more balanced and effective decision-making toolkit.

Using Different Types of Heuristics to Make Better Decisions

Using various types of heuristics can be a valuable tool for making better decisions. By understanding how heuristics work and being aware of their potential biases, individuals can harness the power of heuristics to improve their decision-making processes. 

• Recognize when they are appropriate . Heuristics are particularly useful when time is limited or when there is a lack of information. Mental shortcuts can help individuals make quick and efficient decisions in these cases. 
• Combine them with other decision-making strategies . While heuristics provide shortcuts, they are not foolproof and can lead to cognitive biases. By incorporating other strategies such as weighing pros and cons or conducting research, individuals can mitigate the potential biases associated with heuristics. 
• Be aware of the specific heuristics being used and their potential limitations . Different types of heuristics, such as availability heuristics or representativeness heuristics, have their own biases and may not always lead to accurate judgments. 

By understanding these biases, individuals can make more informed decisions and avoid common pitfalls. In conclusion, heuristics can be a powerful tool for making better decisions. Recognizing when to use them, combining them with other strategies, and being aware of their limitations can help you make better decisions.

Bobadilla-Suarez, S., & Love, B. C. (2018). Fast or frugal, but not both: Decision heuristics under time pressure . Journal of Experimental Psychology: Learning, Memory, and Cognition , 44(1), 24–33. https://doi.org/10.1037/xlm0000419

Lindström, B., Jangard, S., Selbing, I., & Olsson, A. (2018). The role of a “common is moral” heuristic in the stability and change of moral norms . Journal of Experimental Psychology: General , 147(2), 228–242. https://doi.org/10.1037/xge0000365

7.3 Problem Solving

Learning objectives.

By the end of this section, you will be able to:

• Describe problem solving strategies
• Define algorithm and heuristic
• Explain some common roadblocks to effective problem solving and decision making

People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.

Problem-Solving Strategies

When you are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.

A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them ( Table 7.2 ). For example, a well-known strategy is trial and error . The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

Another type of strategy is an algorithm. An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

• When one is faced with too much information
• When the time to make a decision is limited
• When the decision to be made is unimportant
• When there is access to very little information to use in making the decision
• When an appropriate heuristic happens to come to mind in the same moment

Working backwards is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

Everyday Connection

Solving puzzles.

Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below ( Figure 7.7 ) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.

Here is another popular type of puzzle ( Figure 7.8 ) that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:

Take a look at the “Puzzling Scales” logic puzzle below ( Figure 7.9 ). Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).

Pitfalls to Problem Solving

Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but they just need to go to another doorway, instead of trying to get out through the locked doorway. A mental set is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.

Functional fixedness is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. Duncker (1945) conducted foundational research on functional fixedness. He created an experiment in which participants were given a candle, a book of matches, and a box of thumbtacks. They were instructed to use those items to attach the candle to the wall so that it did not drip wax onto the table below. Participants had to use functional fixedness to overcome the problem ( Figure 7.10 ). During the Apollo 13 mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.

Link to Learning

Check out this Apollo 13 scene about NASA engineers overcoming functional fixedness to learn more.

Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and nonindustrialized cultures (German & Barrett, 2005).

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An anchoring bias occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.

The confirmation bias is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Representative bias describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Finally, the availability heuristic is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision . Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in Table 7.3 .

Watch this teacher-made music video about cognitive biases to learn more.

Were you able to determine how many marbles are needed to balance the scales in Figure 7.9 ? You need nine. Were you able to solve the problems in Figure 7.7 and Figure 7.8 ? Here are the answers ( Figure 7.11 ).

As an Amazon Associate we earn from qualifying purchases.

This book may not be used in the training of large language models or otherwise be ingested into large language models or generative AI offerings without OpenStax's permission.

Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute OpenStax.

Access for free at https://openstax.org/books/psychology-2e/pages/1-introduction

• Authors: Rose M. Spielman, William J. Jenkins, Marilyn D. Lovett
• Publisher/website: OpenStax
• Book title: Psychology 2e
• Publication date: Apr 22, 2020
• Location: Houston, Texas
• Book URL: https://openstax.org/books/psychology-2e/pages/1-introduction
• Section URL: https://openstax.org/books/psychology-2e/pages/7-3-problem-solving

© Jan 6, 2024 OpenStax. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution License . The OpenStax name, OpenStax logo, OpenStax book covers, OpenStax CNX name, and OpenStax CNX logo are not subject to the Creative Commons license and may not be reproduced without the prior and express written consent of Rice University.

Heuristics: The Psychology of Mental Shortcuts

• Archaeology
• Ph.D., Materials Science and Engineering, Northwestern University
• B.A., Chemistry, Johns Hopkins University
• B.A., Cognitive Science, Johns Hopkins University

Heuristics (also called “mental shortcuts” or “rules of thumb") are efficient mental processes that help humans solve problems and learn new concepts. These processes make problems less complex by ignoring some of the information that’s coming into the brain, either consciously or unconsciously. Today, heuristics have become an influential concept in the areas of judgment and decision-making.

Key Takeaways: Heuristics

• Heuristics are efficient mental processes (or "mental shortcuts") that help humans solve problems or learn a new concept.
• In the 1970s, researchers Amos Tversky and Daniel Kahneman identified three key heuristics: representativeness, anchoring and adjustment, and availability.
• The work of Tversky and Kahneman led to the development of the heuristics and biases research program.

History and Origins

Gestalt psychologists postulated that humans solve problems and perceive objects based on heuristics. In the early 20th century, the psychologist Max Wertheimer identified laws by which humans group objects together into patterns (e.g. a cluster of dots in the shape of a rectangle).

The heuristics most commonly studied today are those that deal with decision-making. In the 1950s, economist and political scientist Herbert Simon published his A Behavioral Model of Rational Choice , which focused on the concept of on bounded rationality : the idea that people must make decisions with limited time, mental resources, and information.

In 1974, psychologists Amos Tversky and Daniel Kahneman pinpointed specific mental processes used to simplify decision-making. They showed that humans rely on a limited set of heuristics when making decisions with information about which they are uncertain—for example, when deciding whether to exchange money for a trip overseas now or a week from today. Tversky and Kahneman also showed that, although heuristics are useful, they can lead to errors in thinking that are both predictable and unpredictable.

In the 1990s, research on heuristics, as exemplified by the work of Gerd Gigerenzer’s research group, focused on how factors in the environment impact thinking–particularly, that the strategies the mind uses are influenced by the environment–rather than the idea that the mind uses mental shortcuts to save time and effort.

Significant Psychological Heuristics

Tversky and Kahneman’s 1974 work, Judgment under Uncertainty: Heuristics and Biases , introduced three key characteristics: representativeness, anchoring and adjustment, and availability. 

The  representativeness  heuristic allows people to judge the likelihood that an object belongs in a general category or class based on how similar the object is to members of that category.

To explain the representativeness heuristic, Tversky and Kahneman provided the example of an individual named Steve, who is “very shy and withdrawn, invariably helpful, but with little interest in people or reality. A meek and tidy soul, he has a need for order and structure, and a passion for detail.” What is the probability that Steve works in a specific occupation (e.g. librarian or doctor)? The researchers concluded that, when asked to judge this probability, individuals would make their judgment based on how similar Steve seemed to the stereotype of the given occupation.

The anchoring and adjustment heuristic allows people to estimate a number by starting at an initial value (the “anchor”) and adjusting that value up or down. However, different initial values lead to different estimates, which are in turn influenced by the initial value.

To demonstrate the anchoring and adjustment heuristic, Tversky and Kahneman asked participants to estimate the percentage of African countries in the UN. They found that, if participants were given an initial estimate as part of the question (for example, is the real percentage higher or lower than 65%?), their answers were rather close to the initial value, thus seeming to be "anchored" to the first value they heard.

The availability heuristic allows people to assess how often an event occurs or how likely it will occur, based on how easily that event can be brought to mind. For example, someone might estimate the percentage of middle-aged people at risk of a heart attack by thinking of the people they know who have had heart attacks.

Tversky and Kahneman's findings led to the development of the heuristics and biases research program. Subsequent works by researchers have introduced a number of other heuristics.

The Usefulness of Heuristics

There are several theories for the usefulness of heuristics. The  accuracy-effort trade-off   theory  states that humans and animals use heuristics because processing every piece of information that comes into the brain takes time and effort. With heuristics, the brain can make faster and more efficient decisions, albeit at the cost of accuracy. 

Some suggest that this theory works because not every decision is worth spending the time necessary to reach the best possible conclusion, and thus people use mental shortcuts to save time and energy. Another interpretation of this theory is that the brain simply does not have the capacity to process everything, and so we  must  use mental shortcuts.

Another explanation for the usefulness of heuristics is the  ecological rationality theory. This theory states that some heuristics are best used in specific environments, such as uncertainty and redundancy. Thus, heuristics are particularly relevant and useful in specific situations, rather than at all times.

• Gigerenzer, G., and Gaissmeier, W. “Heuristic decision making.” Annual Review of Psychology , vol. 62, 2011, pp. 451-482.
• Hertwig, R., and Pachur, T. “Heuristics, history of.” In International Encyclopedia of the Social & Behavioral Sciences, 2 Edition nd , Elsevier, 2007.
• “Heuristics representativeness.” Cognitive Consonance.
• Simon. H. A. “A behavioral model of rational choice.” The Quarterly Journal of Economics , vol. 69, no. 1, 1955, pp. 99-118.
• Tversky, A., and Kahneman, D. “Judgment under uncertainty: Heuristics and biases.” Science , vol. 185, no. 4157, pp. 1124-1131.
• What Is Cognitive Bias? Definition and Examples
• Critical Thinking Definition, Skills, and Examples
• What Is Behavioral Economics?
• What Is a Schema in Psychology? Definition and Examples
• Heuristics in Rhetoric and Composition
• Introduction to Evolutionary Psychology
• Understanding the Triarchic Theory of Intelligence
• Status Quo Bias: What It Means and How It Affects Your Behavior
• What Is the Elaboration Likelihood Model in Psychology?
• Psychodynamic Theory: Approaches and Proponents
• Dream Interpretation According to Psychology
• What Is Self-Concept in Psychology?
• Criminology Definition and History
• How Psychology Defines and Explains Deviant Behavior
• Information Processing Theory: Definition and Examples
• What Is a Human's Psychological Makeup for Ergonomics?

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

8.2 Problem-Solving: Heuristics and Algorithms

Learning objectives.

• Describe the differences between heuristics and algorithms in information processing.

When faced with a problem to solve, should you go with intuition or with more measured, logical reasoning? Obviously, we use both of these approaches. Some of the decisions we make are rapid, emotional, and automatic. Daniel Kahneman (2011) calls this “fast” thinking. By definition, fast thinking saves time. For example, you may quickly decide to buy something because it is on sale; your fast brain has perceived a bargain, and you go for it quickly. On the other hand, “slow” thinking requires more effort; applying this in the same scenario might cause us not to buy the item because we have reasoned that we don’t really need it, that it is still too expensive, and so on. Using slow and fast thinking does not guarantee good decision-making if they are employed at the wrong time. Sometimes it is not clear which is called for, because many decisions have a level of uncertainty built into them. In this section, we will explore some of the applications of these tendencies to think fast or slow.

We will look further into our thought processes, more specifically, into some of the problem-solving strategies that we use. Heuristics are information-processing strategies that are useful in many cases but may lead to errors when misapplied. A heuristic is a principle with broad application, essentially an educated guess about something. We use heuristics all the time, for example, when deciding what groceries to buy from the supermarket, when looking for a library book, when choosing the best route to drive through town to avoid traffic congestion, and so on. Heuristics can be thought of as aids to decision making; they allow us to reach a solution without a lot of cognitive effort or time.

The benefit of heuristics in helping us reach decisions fairly easily is also the potential downfall: the solution provided by the use of heuristics is not necessarily the best one. Let’s consider some of the most frequently applied, and misapplied, heuristics in the table below.

In many cases, we base our judgments on information that seems to represent, or match, what we expect will happen, while ignoring other potentially more relevant statistical information. When we do so, we are using the representativeness heuristic . Consider, for instance, the data presented in the table below. Let’s say that you went to a hospital, and you checked the records of the babies that were born on that given day. Which pattern of births do you think you are most likely to find?

Most people think that list B is more likely, probably because list B looks more random, and matches — or is “representative of” — our ideas about randomness, but statisticians know that any pattern of four girls and four boys is mathematically equally likely. Whether a boy or girl is born first has no bearing on what sex will be born second; these are independent events, each with a 50:50 chance of being a boy or a girl. The problem is that we have a schema of what randomness should be like, which does not always match what is mathematically the case. Similarly, people who see a flipped coin come up “heads” five times in a row will frequently predict, and perhaps even wager money, that “tails” will be next. This behaviour is known as the gambler’s fallacy . Mathematically, the gambler’s fallacy is an error: the likelihood of any single coin flip being “tails” is always 50%, regardless of how many times it has come up “heads” in the past.

The representativeness heuristic may explain why we judge people on the basis of appearance. Suppose you meet your new next-door neighbour, who drives a loud motorcycle, has many tattoos, wears leather, and has long hair. Later, you try to guess their occupation. What comes to mind most readily? Are they a teacher? Insurance salesman? IT specialist? Librarian? Drug dealer? The representativeness heuristic will lead you to compare your neighbour to the prototypes you have for these occupations and choose the one that they seem to represent the best. Thus, your judgment is affected by how much your neibour seems to resemble each of these groups. Sometimes these judgments are accurate, but they often fail because they do not account for base rates , which is the actual frequency with which these groups exist. In this case, the group with the lowest base rate is probably drug dealer.

Our judgments can also be influenced by how easy it is to retrieve a memory. The tendency to make judgments of the frequency or likelihood that an event occurs on the basis of the ease with which it can be retrieved from memory is known as the availability heuristic (MacLeod & Campbell, 1992; Tversky & Kahneman, 1973). Imagine, for instance, that I asked you to indicate whether there are more words in the English language that begin with the letter “R” or that have the letter “R” as the third letter. You would probably answer this question by trying to think of words that have each of the characteristics, thinking of all the words you know that begin with “R” and all that have “R” in the third position. Because it is much easier to retrieve words by their first letter than by their third, we may incorrectly guess that there are more words that begin with “R,” even though there are in fact more words that have “R” as the third letter.

The availability heuristic may explain why we tend to overestimate the likelihood of crimes or disasters; those that are reported widely in the news are more readily imaginable, and therefore, we tend to overestimate how often they occur. Things that we find easy to imagine, or to remember from watching the news, are estimated to occur frequently. Anything that gets a lot of news coverage is easy to imagine. Availability bias does not just affect our thinking. It can change behaviour. For example, homicides are usually widely reported in the news, leading people to make inaccurate assumptions about the frequency of murder. In Canada, the murder rate has dropped steadily since the 1970s (Statistics Canada, 2018), but this information tends not to be reported, leading people to overestimate the probability of being affected by violent crime. In another example, doctors who recently treated patients suffering from a particular condition were more likely to diagnose the condition in subsequent patients because they overestimated the prevalence of the condition (Poses & Anthony, 1991).

The anchoring and adjustment heuristic is another example of how fast thinking can lead to a decision that might not be optimal. Anchoring and adjustment is easily seen when we are faced with buying something that does not have a fixed price. For example, if you are interested in a used car, and the asking price is $10,000, what price do you think you might offer? Using $10,000 as an anchor, you are likely to adjust your offer from there, and perhaps offer $9000 or $9500. Never mind that $10,000 may not be a reasonable anchoring price. Anchoring and adjustment does not just happen when we’re buying something. It can also be used in any situation that calls for judgment under uncertainty, such as sentencing decisions in criminal cases (Bennett, 2014), and it applies to groups as well as individuals (Rutledge, 1993).

In contrast to heuristics, which can be thought of as problem-solving strategies based on educated guesses, algorithms are problem-solving strategies that use rules. Algorithms are generally a logical set of steps that, if applied correctly, should be accurate. For example, you could make a cake using heuristics — relying on your previous baking experience and guessing at the number and amount of ingredients, baking time, and so on — or using an algorithm. The latter would require a recipe which would provide step-by-step instructions; the recipe is the algorithm. Unless you are an extremely accomplished baker, the algorithm should provide you with a better cake than using heuristics would. While heuristics offer a solution that might be correct, a correctly applied algorithm is guaranteed to provide a correct solution. Of course, not all problems can be solved by algorithms.

As with heuristics, the use of algorithmic processing interacts with behaviour and emotion. Understanding what strategy might provide the best solution requires knowledge and experience. As we will see in the next section, we are prone to a number of cognitive biases that persist despite knowledge and experience.

Key Takeaways

• We use a variety of shortcuts in our information processing, such as the representativeness, availability, and anchoring and adjustment heuristics. These help us to make fast judgments but may lead to errors.
• Algorithms are problem-solving strategies that are based on rules rather than guesses. Algorithms, if applied correctly, are far less likely to result in errors or incorrect solutions than heuristics. Algorithms are based on logic.

Bennett, M. W. (2014). Confronting cognitive ‘anchoring effect’ and ‘blind spot’ biases in federal sentencing: A modest solution for reforming and fundamental flaw. Journal of Criminal Law and Criminology , 104 (3), 489-534.

Kahneman, D. (2011). Thinking, fast and slow. New York, NY: Farrar, Straus and Giroux.

MacLeod, C., & Campbell, L. (1992). Memory accessibility and probability judgments: An experimental evaluation of the availability heuristic.  Journal of Personality and Social Psychology, 63 (6), 890–902.

Poses, R. M., & Anthony, M. (1991). Availability, wishful thinking, and physicians’ diagnostic judgments for patients with suspected bacteremia.  Medical Decision Making,  11 , 159-68.

Rutledge, R. W. (1993). The effects of group decisions and group-shifts on use of the anchoring and adjustment heuristic. Social Behavior and Personality, 21 (3), 215-226.

Statistics Canada. (2018). Ho micide in Canada, 2017 . Retrieved from https://www150.statcan.gc.ca/n1/en/daily-quotidien/181121/dq181121a-eng.pdf

Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability.  Cognitive Psychology, 5 , 207–232.

Psychology - 1st Canadian Edition Copyright © 2020 by Sally Walters is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

Home Blog Business Using Heuristic Problem-Solving Methods for Effective Decision-Making

Using Heuristic Problem-Solving Methods for Effective Decision-Making

Problem-solving capability and effective decision making are two of the most prized capabilities of any leader. However, one cannot expect these traits to be simply present by default in an individual, as both require extensive analysis of the root cause of issues and to know what to look for when anticipating a gain. In a previous article, we brought you  5 Problem-Solving Strategies to Become a Better Problem Solver . This time we have something that can help you dig deep to resolve problems, i.e. using heuristic problem-solving methods for effective decision-making.

What are Heuristics?

Heuristics are essentially problem-solving tools that can be used for solving non-routine and challenging problems. A heuristic method is a practical approach for a short-term goal, such as solving a problem. The approach might not be perfect but can help find a quick solution to help move towards a reasonable way to resolve a problem.

Example: A computer that is to be used for an event to allow presenters to play PowerPoint presentations via a projector malfunctions due to an operating system problem. In such a case a system administrator might quickly refresh the system using a backup to make it functional for the event. Once the event concludes the system administrator can run detailed diagnostic tests to see if there are any further underlying problems that need to be resolved.

In this example, restoring the system using a backup was a short-term solution to solve the immediate problem, i.e. to make the system functional for the event that was to start in a few hours. There are a number of heuristic methods that can lead to such a decision to resolve a problem. These are explained in more detail in the sections below.

Examples of Heuristic Methods Used for Challenging and Non-Routine Problems

Heuristic methods can help ease the cognitive load by making it easy to process decisions. These include various basic methods that aren’t rooted in any theory per se but rather rely on past experiences and common sense. Using heuristics one can, therefore, resolve challenging and non-routine problems. Let’s take a look at some examples.

A Rule of Thumb

This includes using a method based on practical experience. A rule of thumb can be applied to find a short-term solution to a problem to quickly resolve an issue during a situation where one might be pressed for time.

Example: In the case of the operating system failure mentioned earlier, we assume that the PC on which PowerPoint presentations are to be run by presenters during an event is getting stuck on the start screen. Considering that the event is about to start in 2 hours, it is not practical for the system administrator to reinstall the operating system and all associated applications, hotfixes and updates, as it might take several hours. Using a rule of thumb, he might try to use various tried and tested methods, such as trying to use a system restore point to restore the PC without deleting essential files or to use a backup to restore the PC to an earlier environment.

An Educated Guess

An educated guess or guess and check can help resolve a problem by using knowledge and experience. Based on your knowledge of a subject, you can make an educated guess to resolve a problem.

Example: In the example of the malfunctioning PC, the system administrator will have to make an educated guess regarding the best possible way to resolve the problem. The educated guess, in this case, can be to restore the system to a backup instead of using system restore, both of which might take a similar amount of time; however, the former is likely to work better as a quick fix based on past experience and knowledge of the system administrator.

Trial and Error

This is another heuristic method to problem-solving where one might try various things that are expected to work until a solution is achieved.

Example: The system administrator might try various techniques to fix the PC using trial and error. He might start with checking if the system is accessible in safe mode. And if so, does removing a newly installed software or update solve the problem? If he can’t access the system at all, he might proceed with restoring it from a backup. If that too fails, he might need to quickly opt for a wipe and load installation and only install PowerPoint to ensure that at least presenters can run presentations on the PC. In this case he can perform other required software installations after the event.

An Intuitive Judgment

Intuitive judgment does not result from a rational analysis of a situation or based on reasoning. It is more of a feeling one has which may or may not lead to the desired outcome. Sometimes, intuitive judgement can help resolve problems. Perhaps the most rational way to describe an intuition is that it is some type of calculation at the subconscious level, where you can’t put your finger on the reason why you think something might be the way it is.

Example: The system administrator might have a feeling that the PC is not working because the hard drive has failed. This might be an intuitive judgment without hard evidence. He might quickly replace the hard drive to resolve the problem. Later, after he runs diagnostics on the old hard drive, he might realize that it was indeed that hard drive that was faulty and trying to fix it would have been a waste of time. In this case, he might be able to solve a problem using intuitive judgment.

Stereotyping

A stereotype is an opinion which is judgmental rather than rational. Certain types of possessions for example create a stereotype of social status. A person who wears an expensive watch might be deemed rich, although he might simply have received it as a gift from someone, instead of being rich himself.

Example: A certain company might have developed a bad reputation of developing faulty hard drives. If the systems administrator sees the name of that company on the hard drive when opening the faulty PC, he might think that the hard drive is faulty based on stereotyping and decide to replace it.

Profiling is used to systematically analyze data to understand its dynamics. Profiling as a heuristic method for problem-solving might entail analyzing data to understand and resolve a problem or to look for patterns, just like a root cause analysis .

Example: To solve the issue of the faulty PC, a system administrator might look for similar patterns which might have led to the problem. He might search online for solutions via online forums to understand what might have caused the issue. He might also look at the information associated with recently installed software and updates to see if something conflicted with the operating system. During the profiling process, he might realize that software he installed yesterday before shutting down the PC is the cause of the problem, since similar issues have been reported by other users. He might try to remove the software using Safe Mode or by removing its files by running the computer from a bootable disc drive.

Common Sense

Common sense is the use of practical judgment to understand something. The use of common sense is also a heuristic method used for problem-solving.

Example: When dealing with a faulty PC the system administrator sees smoke coming out of the PC. In this case, it is common sense that a hardware component is faulty. He shuts down the PC, removes the power cord and investigates the issue further based on common sense. This is because keeping the system linked to a power socket amidst smoke emitting from the PC can only make things worse. It is common sense to turn off everything and take the necessary precautions to investigate the issue further.

How are Heuristic Methods Used in Decision-Making?

There are a number of formal and informal models of heuristics used for decision making. Let’s take a look at a few of the formal models of heuristics used for decision making.

Formal Models of Heuristics

Fast-and-frugal tree.

A fast-and-frugal tree is a classification or decision tree. It is a graphical form that helps make decisions. For example, a fast-and-frugal tree might help doctors determine if a patient should be sent to a regular ward or for an emergency procedure. fast-and-frugal trees are methods for making decisions based on hierarchical models, where one has to make a decision based on little information.

Fluency Heuristic

In psychology, fluency heuristic implies an object that can be easily processed and deemed to have a higher value, even if it is not logical to assume this. Understanding the application of fluency heuristic can help make better decisions in a variety of fields. Fluency heuristic is more like sunk cost fallacy .

For example, a designer might design a user interface that is easier for users to process, with fewer buttons and easily labeled options. This can help them think fast, work quicker and improve productivity. Similarly, the concept might be used in marketing to sell products using effective marketing techniques. Even if two products are identical, a consumer might pick one over the other based on fluency heuristic. The consumer might deem the product to be better for his needs, even if it is the same as the other one.

Gaze Heuristic

Assume that you aim to catch a ball. Based on your judgment you would leap to catch the ball. If you were to leave yourself to instinct, you will end up at the same spot to catch the ball at a spot you would predict it to fall. This is essentially gaze heuristic. The concept of gaze heuristic is thought to be applied for simple situations and its applications are somewhat limited.

Recognition Heuristic

If there are two objects, one recognizable and the one isn’t, the person is likely to deem the former to be of greater value. A simple example of recognition heuristic is branding. People get used to brand logos, assuming them to be of high quality. This helps brands to sell multiple products using recognition heuristic. So, if you are looking to buy an air conditioner and come across two products, A and B, where A is a brand you know and B is a new company you don’t recognize, you might opt for A. Even if B is of better quality, you might simply trust A because you have been buying electronics from the brand for many years and they have been of good quality.

Satisficing

Satisficing entails looking for alternatives until an acceptable threshold can be ensured. Satisficing in decision making implies selecting an option which meets most needs or the first option which can meet a need, even if it is not the optimal solution. For example, when choosing between early retirement or continuing service for 2 or 3 more years, one might opt for early retirement assuming that it would meet the individual’s needs.

Similarity Heuristic

Similarity heuristic is judgment based on which is deemed similar, if something reminds someone of good or bad days, something similar might be considered the same. Similarity heuristics is often used by brands to remind people of something that they might have sentimental value for.

Someone might buy a limited-edition bottle of perfume that is being sold in a packaging style that was replaced 20 years ago. Assuming that sales were great in those days, the company might sell such limited-edition perfume bottles in the hope of boosting sales. Consumers might buy them simply because they remind them of the ‘good old days’, even though the product inside might not even be of the same but rather similar to what it used to be. Many consumers claim to buy these types of products claiming that it reminds them of a fond memory, such as their youth, marriage or  first job, when they used the product back in the day.

Final Words

Heuristics play a key role in decision making and affect the way we make decisions. Understanding heuristics can not only help resolve problems but also understand biases that affect effective decision making. A business decision or one that affects one’s health, life, or well-being cannot rely merely on a hunch. Understanding heuristics and applying them effectively can therefore help make the best possible decisions. Heuristic methods are not only used in different professions and personal decision making but are also used in artificial intelligence and programming.

Modern anti-virus software for instance uses heuristic methods to dig out the most elusive malware. The same rule can be essentially applied to decision making, by effectively using heuristics to resolve problems and to make decisions based on better judgment.

Like this article? Please share

Common Sense, Decision Making, Educated Guess, Heuristics, Judgment, Problem Solving, Profiling, Rule of Thumb, Stereotyping, Trial and Error Filed under Business

Related Articles

Filed under Business • January 16th, 2024

The OODA Loop Decision-Making Model and How to Use it for Presentations

OODA Loop is a model that supports people and companies when defining important decisions in teams or individuals. See here how to apply it in presentation slide design.

Filed under Business • October 5th, 2023

SCAMPER Technique & Ideation Method (Quick Guide for Interactive Presentations)

SCAMPER is a technique that provides a structured approach towards thinking outside the box. In this article, we explore how this technique can be used.

Filed under Business • October 2nd, 2023

How to Write a Problem Statement: Hands-On Guide With Examples

A well-written problem statement defines the stage for successful solution development and garnering support from stakeholders. Helpful tips here.

Leave a Reply

Problem-solving techniques that result in a quick and practical solution

What are Heuristics?

Heuristics are problem-solving techniques that result in a quick and practical solution. In contrast to business decisions that involve extensive analysis, heuristics are used in situations where a short-term solution is required.

Although heuristics may not result in the most optimal and ideal solution, it allows companies to speed up their decision-making process and achieve an adequate solution for the short term.

In situations where perfect solutions may be improbable, heuristics can be used to achieve imperfect but satisfactory decisions. Heuristics can also include mental shortcuts that help speed up the decision-making process.

• Heuristics are problem-solving techniques that result in a quick and practical solution.
• In situations where perfect solutions may be improbable, heuristics can be used to achieve imperfect but satisfactory decisions.
• Most heuristic methods involve using mental shortcuts to make decisions based on prior experiences.

Understanding Heuristics

When facing complex situations with limited time and resources, heuristics can help companies make quick decisions by using shortcuts and approximated calculations. Most heuristic methods involve using mental shortcuts to make decisions based on prior experiences.

Some of the most common fundamental heuristic methods include trial and error, historical data analysis, guesswork, and the process of elimination. Such methods typically involve easily accessible information that is not specific to the problem but is broadly applicable. It provides an opportunity to make imperfect decisions that can adequately address the problem in the short term.

Depending on the context, there may be several different heuristic methods, which correlate to the scope of the problem. They can include affect, representative, and availability heuristics.

Types of Heuristics

Affect Heuristics

Affect heuristics are based on positive and negative feelings that are associated with a certain stimulus. It typically involves quick, reactionary feelings that are based on prior beliefs. The theory of affect heuristics is that one’s emotional response to a stimulus can affect an individual’s decisions.

When people face little time to reflect and evaluate a situation carefully, they may base their decision on their immediate emotional reactions. Rather than conducting a cost-benefit analysis, affect heuristics focus on eliciting an automatic, reactionary response.

For example, it’s been shown that advertisements can influence consumers’ emotions and therefore affect their purchasing decisions. One of the most common examples is advertisements for products such as fast food. When fast-food companies run ads, they hope to elicit a positive emotional response that encourages you to view their products positively.

If individuals were to analyze the risks and benefits of consuming fast food carefully, they might decide that it is an unhealthy option. However, people rarely take the time to evaluate everything they see and often base their decisions on their automatic, emotional response. Fast-food ads rely on such a type of affect heuristic to generate a positive emotional response, which results in sales.

Availability Heuristics

Availability heuristics are judgments people make regarding the likelihood of an event based on information that comes to mind quickly. When people make decisions, they typically rely on prior knowledge of an event. As a result, we tend to overestimate the likelihood of an event occurring simply because it comes to mind quickly. Such mental shortcuts allow us to make decisions quickly, but they can also be inaccurate.

One example of the availability heuristic is stock prices, especially for newly public companies. Many investors tend to invest in new IPOs in the hopes that the stock price will increase significantly in the next few years. Rather than analyzing the company’s fundamentals, the investors remember IPOs that have become tremendously successful, such as Amazon or Apple.

Although it has been shown that most IPOs underperform, investors tend to overestimate the chances of landing a successful IPO based on prior examples that come to mind. It demonstrates a clear example of availability heuristics.

Representative Heuristics

Representative heuristics occur when we evaluate the probability of an event based on its similarity to another event. In general, people tend to overestimate the likelihood of an event occurring based on their perceived similarity with another event. When it happens, we tend to ignore the base rate, which is the actual probability of an event occurring, independent of its similarity to other events.

An example of the representative heuristic is product packaging, as consumers tend to associate quality products with their external packaging. If a generic brand packages its products in a way that resembles a well-known, high-quality product, then consumers will associate the generic product as having the same quality as the branded product.

Instead of evaluating the quality of the products, consumers are correlating the quality of the products based on the similarity in packaging.

More Resources

CFI is the official provider of the global Business Intelligence & Data Analyst (BIDA)® certification program, designed to help anyone become a world-class financial analyst. To keep advancing your career, the additional CFI resources below will be useful:

• Action Learning
• Data Anonymization
• Decision Tree
• Distributed Ledger Technology
• See all wealth management resources
• Share this article

Create a free account to unlock this Template

Access and download collection of free Templates to help power your productivity and performance.

Already have an account? Log in

Supercharge your skills with Premium Templates

Take your learning and productivity to the next level with our Premium Templates.

Upgrading to a paid membership gives you access to our extensive collection of plug-and-play Templates designed to power your performance—as well as CFI's full course catalog and accredited Certification Programs.

Already have a Self-Study or Full-Immersion membership? Log in

Access Exclusive Templates

Gain unlimited access to more than 250 productivity Templates, CFI's full course catalog and accredited Certification Programs, hundreds of resources, expert reviews and support, the chance to work with real-world finance and research tools, and more.

Already have a Full-Immersion membership? Log in

Thinking and Intelligence

Solving Problems

Learning objectives.

• Describe problem solving strategies, including algorithms and heuristics

People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.

Problem-Solving Strategies

When you are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.

A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them. For example, a well-known strategy is trial and error . The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

Another type of strategy is an algorithm. An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

• When one is faced with too much information
• When the time to make a decision is limited
• When the decision to be made is unimportant
• When there is access to very little information to use in making the decision
• When an appropriate heuristic happens to come to mind in the same moment

Working backwards is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

What problem-solving method could you use to solve Einstein’s famous riddle?

https://youtube.com/watch?v=1rDVz_Fb6HQ%3Flist%3DPLUmyCeox8XCwB8FrEfDQtQZmCc2qYMS5a

You can view the transcript for “Can you solve “Einstein’s Riddle”? – Dan Van der Vieren” here (opens in new window) .

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

Everyday Connections: Solving Puzzles

Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below (Figure 1) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.

Here is another popular type of puzzle that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:

Take a look at the “Puzzling Scales” logic puzzle below (Figure 3). Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).

Were you able to determine how many marbles are needed to balance the scales in the Puzzling Scales? You need nine. Were you able to solve the other problems above? Here are the answers:

CC licensed content, Original

• Modification and adaptation. Provided by : Lumen Learning. License : CC BY: Attribution

CC licensed content, Shared previously

• Problem-Solving. Authored by : OpenStax College. Located at : https://openstax.org/books/psychology-2e/pages/7-3-problem-solving . License : CC BY: Attribution . License Terms : Download for free at https://openstax.org/books/psychology-2e/pages/1-introduction

All rights reserved content

• Can you solve Einsteinu2019s Riddle? . Authored by : Dan Van der Vieren. Provided by : Ted-Ed. Located at : https://www.youtube.com/watch?v=1rDVz_Fb6HQ&index=3&list=PLUmyCeox8XCwB8FrEfDQtQZmCc2qYMS5a . License : Other . License Terms : Standard YouTube License

method for solving problems

problem-solving strategy in which multiple solutions are attempted until the correct one is found

problem-solving strategy characterized by a specific set of instructions

mental shortcut that saves time when solving a problem

heuristic in which you begin to solve a problem by focusing on the end result

General Psychology Copyright © by OpenStax and Lumen Learning is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

Share This Book

• Social Media and SadFishing
• Empty Nest Syndrome
• Fardeen Khan Discusses Mental Health Struggles During Career Comeback

What is the Hawthorne effect?

• NEET Controversy and Re-examination: What about Students?

Your cart is empty

Exploring Heuristics: Understanding Mental Shortcuts

• by Psychologs Magazine News
• June 13, 2024
• 8 minutes read

Sadly, humans do not have an 8GB RAM module in their processing system as the computer has, to assess the information quickly, so they need some other way to narrow down the workable solution. The mental shortcut used to draw inferences, make decisions, and solve problems is called Heuristics and is often referred to as the “rule of thumb”.  

Heuristics and Its History 

Drawing from our experiences, a well-informed estimation helps us narrow down potential solutions to problems, facilitating quick decisions in complex situations. However, this approach often leads to judgment errors. These strategies are frequently utilized in everyday social interactions and various professional domains such as medicine, law, behavioural science,  economics, political science etc.

In the 1950s, Herbert Simon, a Nobel laureate in economics and cognitive psychology, introduced the notion of heuristics. He argued that while individuals strive for rational decision-making, cognitive constraints limit human judgment. Ideally, rational choices involve thoroughly evaluating the costs and benefits of all alternatives. However, time constraints and limited information hinder this process. Psychologists Amos Tversky and Daniel Kahneman further explored cognitive biases in the 1970s, highlighting their influence on thinking patterns and decision-making .

Consequently, individuals resort to mental shortcuts to navigate complex scenarios. This behaviour is demonstrated by Simon’s research and expanded upon by Tversky and Daniel’s work. Their contributions have shaped the study of heuristics, simplifying decision-making processes.

Types of Heuristics: 

1. representative heuristics .

This is essentially saying that if someone or something has traits similar to those in a certain group, then it is considered part of that group as well. For example, assume that a man has moved into the next house door, has a book-filled shelf, he organized his house neatly, and has the biggest desire to help others find information. Now here is the point,  guess his occupation, a librarian, an engineer, or a bank manager. Most of us would tell the librarian, right? Because most of the characteristics of that man represent the characteristics of a librarian. This is known as representative heuristics. 

Representative heuristics are effective for categorizing plants but less so when applied to people. They can lead to mistakes by overlooking the actual probability of an event. For example, assuming all individuals with certain traits behave a certain way. For example, assumptions that all red-haired people have a bad temper, or all dark-skinned people are from Africa, and blue eyes and blondes are from Sweden. 

2. Availability Heuristics 

The availability heuristics means making decisions based on how easily something comes to mind. When faced with a decision, you might recall several relevant examples quickly.  Because these are easily remembered, you might think these outcomes are more common or more often. In other words, judging how often something occurs by how we can recall relevant information from memory or think of examples.

For instance, a friend of yours living in a coastal area calls to share alarming news about people in their region losing their lives, the conversation abruptly ends as your friend loses network coverage, before the reason is revealed for fatalities. Guess the causes of the people’s death. We can assume that the fatalities are to be tsunamis because of the information that we know about tsunamis and their catastrophic nature and the familiarity makes tsunamis more easily accessible to our mind when we think about disasters in coastal areas.  

3. Anchoring and Adjustment heuristics 

A cognitive inclination to heavily depend on the initial information encountered is termed the “anchor” when making decisions or estimations. This often results in insufficient adjustments being made from this initial value. For example, during shopping, if the initial price of an item is much lower than the subsequent price we encounter, we are unlikely to be willing to pay a higher price for it later on.

The anchoring bias is partially attributed to the primacy effect, where people tend to recall information learned first better than later information. This leads individuals to assign greater significance to the initial value, often overlooking subsequent information, thus contributing to the anchoring bias. Essentially, if people remember the initial value more strongly than later details, they are inclined to perceive it as more important, often without consciously recognizing this cognitive process.

4. Working backwards 

A helpful strategy is often used in working backwards from the desired outcomes. It is a problem-solving approach where one mentally envisions having already solved the problem they’re facing. By imagining the problem as already resolved, they can mentally backtrack, eventually visualizing a solution. For example, solving a maze by beginning at the endpoint and retracing steps back to the starting point. This type of heuristics is mainly used in solving mathematical problems.

Uses of Heuristics: 

• Heuristics are useful to professionals in various fields like medicine, law, behavioural science, economics, political science etc., with their expertise and the available information and also providing the simplest decision-making framework. 
• Making rational and informed choices often requires time and analysis, which may not always be feasible. Heuristics offer a reliable method of decision-making in intricate situations.  
• When only a limited amount of information or options are accessible, heuristics enable efficient decision-making, contributing to enhanced efficiency, creativity, and problem-solving. 
• Heuristics offer an economical approach to decision-making by allowing for quick and effective choices considering the available data and variables.  
• Heuristics are used in mathematical problems and help to find solutions easily.

Heuristics and Cognitive Bias 

Although heuristics aid in problem-solving and expediting decision-making, they also carry the risk of introducing errors. Heuristics can result in inaccurate assessments of frequency and representation. Additionally, heuristics can fuel stereotypes and biases as individuals use mental shortcuts to categorize others, often neglecting pertinent information and forming skewed perceptions not aligned with reality. 

Status Quo bias: 

The status quo is where individuals prefer things to remain unchanged or maintain the current state of affairs. While this bias helps minimize the perceived risks of change, it also leads people to overlook potential benefits that could outweigh these risks. For example, consistently ordering the same food item at the same restaurant, choosing the same seat in the classroom and always sleeping in the same spot at home.  

This happens because of some reason, firstly, loss aversion bias, when making decisions,  individuals tend to concentrate more on potential losses rather than potential gains. Secondly, the mere exposure effect, frequently, our preferences are influenced by familiarity, as we tend to favour things we are more acquainted with. The status quo has the potential to have smaller to bigger impacts, as we discussed in the example, if we consistently order the same food we might miss out on the chance to enjoy the taste of other food and we also might lose the chances and opportunities in our life. 

To Avoid Heuristics:

It’s crucial to be aware of its impact and actively engage in analytical thinking. Here are some strategies: 

• Seek out diverse perspectives to challenge your initial emotional reactions and achieve a more balanced decision. 
• Take a moment to pause and reflect before making decisions, considering whether emotions are influencing judgment. 
• Considering the broader goals beyond personal interest and processing emotions can lead to more balanced decisions. 
• Practice mindfulness to increase awareness of your emotional state and reduce the likelihood of being swayed by immediate reactions. 
• Develop emotional intelligence to recognize when emotions are disproportionately affecting your decision-making and adjust accordingly. 
• Implement a relaxation period for significant decisions to allow emotions to settle and rational processes to take over before finalizing choices.  
• Utilize analytical tools like decision matrices and cost-benefit analyses to structure your thinking and bring a rational perspective to your choices. 

In summary, heuristics provide quick decision-making shortcuts, but they can also lead to errors due to cognitive biases. To mitigate these risks, it’s essential to be mindful of our emotional state, seek diverse perspectives, and use analytical tools. By doing so, we can make more informed decisions and achieve better outcomes in our personal and professional lives.

• Chen, J. (2024, May 2). Heuristics: Definition, Pros & Cons, and Examples. Investopedia.  https://www.investopedia.com/terms/h/heuristics.asp 
• Hammond, J. S. (2023, May 3). The Hidden Traps in Decision Making. Harvard Business  Review. https://hbr.org/1998/09/the-hidden-traps-in-decision-making-2 
• Heuristics – the Decision lab. (n.d.). The Decision Lab.  https://thedecisionlab.com/biases/heuristics 
• Hoffman, B. (2024a, April 8). Availability heuristic: What it is and how to overcome it. Forbes.  https://www.forbes.com/sites/brycehoffman/2024/04/06/availability-heuristic-what-it-is-and how-to-overcome-it/ 
• Hoffman, B. (2024, May 9). Affect heuristic: What it is and how to avoid it. Forbes.  https://www.forbes.com/sites/brycehoffman/2024/02/19/affect-heuristic-what-it-is-and-how-to avoid 
• it/#:~:text=Six%20ways%20to%20avoid%20the%20affect%20heuristic&text=1.,considered%20 all%20the%20relevant%20information 
• MSEd, K. C. (2022, November 8). What are heuristics? Verywell Mind.  https://www.verywellmind.com/what-is-a-heuristic-2795235#toc-how-heuristics-can-lead-to bias 
• MSEd, K. C. (2023, December 13). How the Status Quo Bias Affects Our Decisions. Verywell  Mind. https://www.verywellmind.com/status-quo-bias-psychological-definition-4065385 
• Myers, E. (2023). Anchoring Bias & Adjustment Heuristic: Definition and Examples. Simply  Psychology. https://www.simplypsychology.org/what-is-the-anchoring-bias.html#Anchoring Bias-Heuristic 
• Nordstrom, S. (2021, December 14). Avoiding bias in product Decision-Making: Recognition  heuristic. Medium. https://medium.com/agileinsider/avoiding-bias-in-product-decision-making recognition-heuristic-9069553a664e 
• Raeburn, A. (2024, January 15). Heuristics: How Mental Shortcuts Help us Make decisions  [2024] • Asana. Asana. https://asana.com/resources/heuristics 
• Working Backward Heuristic definition | Psychology Glossary | AlleyDog.com. (n.d.).  https://www.alleydog.com/glossary/definition.php?term=Working+Backward+Heuristic#:~:text =The%20working%20backward%20heuristic%20is%20a%20method%20of%20problem%20sol ving,a%20solution%20to%20the%20problem

Share This Post:

Madras hc orders state to provide lifelong care for 20-year-old with bipolar disorder, uk teen’s lungs collapse after vaping 400 cigarettes a week, leave feedback about this cancel reply.

• Rating 5 4 3 2 1

Related Post

Perception: Approaches and Perceptual Organisation 

The Asch Conformity Experiment and Its Implications

Pragmatism as the Philosophy of modern times?

Suicide Through the Eyes of a College Student

• Games & Quizzes
• History & Society
• Science & Tech
• Biographies
• Animals & Nature
• Geography & Travel
• Arts & Culture
• On This Day
• One Good Fact
• New Articles
• Lifestyles & Social Issues
• Philosophy & Religion
• Politics, Law & Government
• World History
• Health & Medicine
• Browse Biographies
• Birds, Reptiles & Other Vertebrates
• Bugs, Mollusks & Other Invertebrates
• Environment
• Fossils & Geologic Time
• Entertainment & Pop Culture
• Sports & Recreation
• Visual Arts
• Demystified
• Image Galleries
• Infographics
• Top Questions
• Britannica Kids
• Saving Earth
• Space Next 50
• Student Center
• Introduction
• Elements of thought
• The process of thought
• Motivational aspects of thinking
• The problem-solving cycle in thinking
• Structures of problems

Algorithms and heuristics

Obstacles to effective thinking.

• Expert thinking and novice thinking
• Concept attainment
• Creative thinking

Our editors will review what you’ve submitted and determine whether to revise the article.

• Internet Encyclopedia of Philosophy - Language of Thought Hypothesis
• Psychology Today - What actually is a thought? And how is information physical?
• National Center for Biotechnology Information - PubMed Central - Constructive and Unconstructive Repetitive Thought
• Stanford Encyclopedia of Philosophy - The Language of Thought Hypothesis
• College of DuPage Digital Press - Introduction to Psychology - Thinking, Reasoning, and Problem-Solving
• Verywell Mind - What Happens to Your Body When You're Thinking?
• University of Southern Queensland - Academic Success - Thinking
• Table Of Contents

Other means of solving problems incorporate procedures associated with mathematics, such as algorithms and heuristics , for both well- and ill-structured problems. Research in problem solving commonly distinguishes between algorithms and heuristics, because each approach solves problems in different ways and with different assurances of success.

A problem-solving algorithm is a procedure that is guaranteed to produce a solution if it is followed strictly. In a well-known example, the “ British Museum technique,” a person wishes to find an object on display among the vast collections of the British Museum but does not know where the object is located. By pursuing a sequential examination of every object displayed in every room of the museum, the person will eventually find the object, but the approach is likely to consume a considerable amount of time. Thus, the algorithmic approach, though certain to succeed, is often slow.

A problem-solving heuristic is an informal, intuitive, speculative procedure that leads to a solution in some cases but not in others. The fact that the outcome of applying a heuristic is unpredictable means that the strategy can be either more or less effective than using an algorithm . Thus, if one had an idea of where to look for the sought-after object in the British Museum, a great deal of time could be saved by searching heuristically rather than algorithmically. But if one happened to be wrong about the location of the object, one would have to try another heuristic or resort to an algorithm.

Although there are several problem-solving heuristics, a small number tend to be used frequently. They are known as means-ends analysis, working forward, working backward, and generate-and-test.

In means-ends analysis , the problem solver begins by envisioning the end, or ultimate goal, and then determines the best strategy for attaining the goal in his current situation. If, for example, one wished to drive from New York to Boston in the minimum time possible, then, at any given point during the drive, one would choose the route that minimized the time it would take to cover the remaining distance, given traffic conditions, weather conditions, and so on.

In the working-forward approach, as the name implies, the problem solver tries to solve the problem from beginning to end. A trip from New York City to Boston might be planned simply by consulting a map and establishing the shortest route that originates in New York City and ends in Boston. In the working-backward approach, the problem solver starts at the end and works toward the beginning. For example, suppose one is planning a trip from New York City to Paris. One wishes to arrive at one’s Parisian hotel. To arrive, one needs to take a taxi from Orly Airport. To arrive at the airport, one needs to fly on an airplane; and so on, back to one’s point of origin.

Often the least systematic of the problem-solving heuristics, the generate-and-test method involves generating alternative courses of action, often in a random fashion, and then determining for each course whether it will solve the problem. In plotting the route from New York City to Boston, one might generate a possible route and see whether it can get one expeditiously from New York to Boston; if so, one sticks with that route. If not, one generates another route and evaluates it. Eventually, one chooses the route that seems to work best, or at least a route that works. As this example suggests, it is possible to distinguish between an optimizing strategy, which gives one the best path to a solution, and a satisficing strategy, which is the first acceptable solution one generates. The advantage of optimizing is that it yields the best possible strategy; the advantage of satisficing is that it reduces the amount of time and energy involved in planning.

A better understanding of the processes of thought and problem solving can be gained by identifying factors that tend to prevent effective thinking. Some of the more common obstacles, or blocks, are mental set, functional fixedness, stereotypes , and negative transfer.

A mental set, or “entrenchment,” is a frame of mind involving a model that represents a problem, a problem context , or a procedure for problem solving. When problem solvers have an entrenched mental set, they fixate on a strategy that normally works well but does not provide an effective solution to the particular problem at hand. A person can become so used to doing things in a certain way that, when the approach stops working, it is difficult for him to switch to a more effective way of doing things.

Functional fixedness is the inability to realize that something known to have a particular use may also be used to perform other functions. When one is faced with a new problem, functional fixedness blocks one’s ability to use old tools in novel ways. Overcoming functional fixedness first allowed people to use reshaped coat hangers to get into locked cars, and it is what first allowed thieves to pick simple spring door locks with credit cards.

Another block involves stereotypes . The most common kinds of stereotypes are rationally unsupported generalizations about the putative characteristics of all, or nearly all, members of a given social group . Most people learn many stereotypes during childhood. Once they become accustomed to stereotypical thinking, they may not be able to see individuals or situations for what they are.

Negative transfer occurs when the process of solving an earlier problem makes later problems harder to solve. It is contrasted with positive transfer, which occurs when solving an earlier problem makes it easier to solve a later problem. Learning a foreign language, for example, can either hinder or help the subsequent learning of another language.

Advisory boards aren’t only for executives. Join the LogRocket Content Advisory Board today →

• Product Management
• Solve User-Reported Issues
• Find Issues Faster
• Optimize Conversion and Adoption

Heuristic evaluation: Definition, case study, template

Imagine yourself faced with the challenge of assembling a tricky puzzle but not knowing where to start. Elements such as logical reasoning and meticulous attention to detail become essential, requiring an approach that goes beyond the surface level to achieve effectiveness. When evaluating the user experience of an interface, it is no different.

In this article, we will cover the fundamental concepts of heuristic evaluation, how to properly perform a heuristic evaluation, and the positive effects it can bring to your UX design process. Let’s learn how you can solve challenges with heuristic evaluation.

What is heuristic evaluation?

The heuristic evaluation principles, understanding nielsen’s 10 usability heuristics, essential steps in heuristic evaluation, prioritization criteria in the analysis of usability problems, communicating heuristic evaluation results effectively, dropbox’s heuristic evaluation approach, incorporating heuristic evaluation into the ux process.

The heuristic evaluation method’s main goal is to evaluate the usability quality of an interface based on a set of principles, based on UX best practices. From the identification of the problems, it is possible to provide practical recommendations and consequently improve the user experience.

So where did heuristic evaluation come from, and how do we use these principles? Read on.

Heuristic evaluation was created by Jakob Nielsen, recognized worldwide for his significant contributions to the field of UX. The method created by Nielsen is based on a set of heuristics from human-computer interaction (HCI) and psychology to inspect the usability of user interfaces.

Therefore, Nielsen’s 10 usability heuristics make up the principles of heuristic evaluation by establishing carefully established foundations. These foundations serve as a practical guide to cover the main usability problems of projects. These heuristics work as cognitive shortcuts used by the brain for efficient decision making, especially in redesign projects. Heuristics also help to complement the UX process when understanding user problems and supporting UX research and evaluation.

When you are getting ready to conduct a heuristic evaluation, the first step is to set clear goals. Then, during the evaluation, you should make notes on what you find considering usability issues, always based on the criteria. Once this is done, you can prepare a report that might include information on which issues to tackle first, which makes the evaluation even better. All these steps matter because they help make sure interfaces match what users want and expect, leading to better interactions overall.

Preparation for the heuristic evaluation: Defining usability objectives and criteria

As with the puzzle example in the intro, fully understanding the problem is critical to applying heuristic evaluation effectively. Thus, during the preparation phase, you need to establish the evaluation criteria, also defining how these criteria will be evaluated.

Select evaluators based on their experience. By involving a diverse set of evaluators, you can obtain different perspectives on the same challenge. Although an expert is able to point out most of the problems in a heuristic evaluation, collaboration is essential to generate more comprehensive recommendations.

Although it follows a set of heuristics, the evaluation is less formal and less expensive than a user test, making it faster and easier to conduct. Therefore, heuristic evaluation can be performed in the early stages of design and development when making changes is more cost effective.

Nielsen’s usability heuristics are like a tactical set for methodically making things work, providing valuable clues that designers and creators follow to piece together the usability puzzle. These heuristics act as master guides, helping us intelligently fit each piece of the puzzle together so that everything makes sense and is easy to understand to create amazing experiences in the products and websites we use.

Over 200k developers and product managers use LogRocket to create better digital experiences

Here are Nielsen’s 10 usability heuristics, each with its own relevance and purpose:

1. System status visibility

Continuously inform the user about what is happening.

2. Correspondence between the system and the real world

Use words and concepts familiar to the user.

3. User control and freedom

Allow users to undo actions and explore the system without fear of making mistakes.

4. Consistency and standards

Maintain a consistent design throughout the system, so users can apply what they learned in one part to the rest.

5. Error prevention

Design in a manner that prevents users from committing mistakes or provides means to easily correct wrong decisions.

6. Recognition instead of memorization

Provide contextual hints and tips to help users accomplish tasks without needing to remember specific information.

7. Flexibility and efficiency of use

Allow users to customize keyboard shortcuts or create custom profiles to streamline their interactions.

8. Aesthetics and minimalist design

Keep the design clean and simple, focusing on the most relevant information to avoid overwhelming users using proper spacing, colors, and typography.

9. Help and documentation

Provide helpful and accessible support in case users need extra guidance.

10. User feedback

Give immediate feedback to users when they take an action.

Together, these pieces of the usability heuristics puzzle help us build a complete picture of digital experiences. Thus, by following these guidelines, evaluators can identify problems and prioritize them for correction at the evaluation stage.

In the evaluation phase, evaluators should look at the product or system interface and document any usability issues based on heuristics. By using heuristics consistently across different parts of the interface, it is still possible to balance conflicting heuristics to find optimal design solutions.

There may be challenges during the evaluation phase, which is why it is important that evaluators suggest strategies to overcome them from the definition of priorities. Evaluators should therefore, in consensus, discuss how these heuristics can be applied to identify and address usability problems.

One of the interesting ways to do heuristic evaluation is through real-time collaboration tools like Miro. On the template below, you will be able to collaborate in real-time to conduct heuristic evaluations of your project with your team, evaluating the problems by criteria and dividing them by colors, based on the level of complexity to be solved.

You can download the Miro Heuristic Evaluation template for free .

After performing a heuristic assessment, evaluators should analyze the findings and prioritize usability issues, trying to identify the underlying causes of usability issues rather than just addressing surface symptoms.

Usability issues discovered during the assessment can be given severity ratings to prioritize fixes.

Below is an example of categorization by severity according to the challenge presented:

• High severity : Prevents the user from performing one or more tasks
• Medium severity : Requires user effort and affects performance
• Low severity : May be noticeable to the user but does not impede execution or performance

The classification will help the team to have greater clarity regarding what is most relevant to be faced considering the impact on the user experience. By prioritizing the most critical issues based on their impact on the user experience, it will be easier to effectively allocate them throughout the project.

Finally, during the reporting phase, evaluators should present their findings and recommendations to stakeholders and facilitate discussions on identified issues.

Evaluators typically conduct multiple iterations of the assessment to uncover different issues in subsequent rounds based on the need for the project and the issues identified.

Heuristic evaluation provides qualitative data, making it important to interpret the results with a deeper understanding of user behavior. When reporting and communicating the results of a heuristic assessment, assessors should follow best practices by presenting findings in visual representations that are easy to read and understand, and that highlight key findings, whether using interactive boards, tables or other visuals.

Problem descriptions should be clear and concise so they can be actionable. Instead of generating generic problems, for example, break the problems into distinct parts to be easier to deal with. If necessary, try to analyze the interface component and its details, thinking not only analytically in an abstract way but also understanding that that problem will be solved by a UX Designer, considering all its elements. In this scenario, a well-applied context makes all the difference.

It is also important to involve stakeholders and facilitate discussions around identified issues. As a popular saying goes: a problem communicated is a problem half solved.

The Dropbox team really nails it when it comes to giving users a smooth and user friendly experience. Let’s dive into a few ways they have put these heuristic evaluation principles to work in their platform:

Dropbox keeps things clear by using concise labels to show the status of your uploaded files. They also incorporate a convenient progress bar that provides a time estimate for the completion of the upload. This real-time feedback keeps you informed about the ongoing status of your uploads on the platform:

The ease of moving, deleting, and renaming files between different folders and sharing with other people means that Dropbox offers users control over fundamental actions, allowing them to work in a personalized way, increasing their sense of ownership:

Making it a breeze for users to navigate no matter if they’re on a computer or a mobile device, Dropbox keeps things consistent in both: website and mobile app design:

To prevent errors from happening, Dropbox has implemented an interesting feature. If a user attempts to upload a file that’s too large, Dropbox triggers an error message. This message is quite helpful, as it guides the user to select a smaller file and clearly explains the issue. It’s a nifty feature that ensures users know exactly which steps to take next:

Dropbox cleverly employs affordances to ensure that users can easily figure out how to navigate the app. Take, for instance, the blue button located at the top of the screen — it’s your go-to for creating new files and folders. This is a familiar and intuitive pattern that users can quickly grasp:

Consider now flexibility and efficiency. On Dropbox, the user can access their files from any gadget, and they can keep working even when they are offline without worrying about losing anything. It makes staying productive a breeze, no matter where the users finds themselves:

Dropbox has a clean and minimalist design that’s a breeze to use and get around in. Plus, it’s available in different languages, ensuring accessibility for people all around the world:

Dropbox goes the extra mile by using additional methods alongside heuristic evaluation, demonstrating a high positive impact on their services. All this dedication to applying the best heuristics on their products has made Dropbox one of the most popular storage services globally.

Heuristic evaluation fits into the broader UX design process and can be conducted iteratively throughout the design lifecycle, despite being commonly used early in the design process.

It provides valuable insights to inform design decisions and improvements and enables UX designers to effectively identify and address usability issues.

Conclusion and key takeaways

In this article, we have seen that heuristic evaluation is a systematic and valuable approach to identifying usability problems in systems and products. Through the use of general usability guidelines, it is possible to highlight gaps in the user experience, addressing areas such as clarity, consistency and control. This evaluation is conducted by a multidisciplinary team, and the problems identified are recorded in detail, allowing for further prioritization and refinement.

Much like a complex puzzle, improving usability and user experience requires identifying patterns and providing instructive feedback when working collaboratively.

Checking interfaces using heuristic evaluation can uncover many issues, but it’s not a replacement for what you learn from watching actual users. Think of it as an extra tool to understand users better.

Remember that heuristic evaluation not only reveals challenges but also empowers you as a UX professional to create more intuitive and impactful solutions.

When you mix in heuristic evaluation while making your designs, you can end up with products and systems that are more helpful and user-friendly without spending too much. This helps make your products or services even better by following good user experience tips.

So don’t hesitate: make the most of the potential of heuristic evaluation to push usability to the next level in your UX project.

LogRocket : Analytics that give you UX insights without the need for interviews

LogRocket lets you replay users' product experiences to visualize struggle, see issues affecting adoption, and combine qualitative and quantitative data so you can create amazing digital experiences.

See how design choices, interactions, and issues affect your users — get a demo of LogRocket today .

Share this:

• Click to share on Twitter (Opens in new window)
• Click to share on Reddit (Opens in new window)
• Click to share on LinkedIn (Opens in new window)
• Click to share on Facebook (Opens in new window)
• #ux research

Stop guessing about your digital experience with LogRocket

Recent posts:.

Validation research techniques to test value and viability assumptions

Learn the different types of value and viability assumptions and a few methods for validation research testing.

14 principles for designing products for teenagers

Teens are an exciting group to design products for, but we need to consider age, lifestyle, and generational differences.

Creating UI animations: Tutorial, tools, and best practices

Explore a few different types of UI animations, how they differ from motion graphics, some principles of animation to follow, and more.

Using Visily’s AI wireframing and prototyping

Discover what Visily is, why it’s disrupting the UX tools market, and the AI capabilities it enables you to use.

Leave a Reply Cancel reply

Thinking and Intelligence

Solving problems, learning objectives.

• Describe problem solving strategies, including algorithms and heuristics

People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.

Problem-Solving Strategies

When you are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.

A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them. For example, a well-known strategy is trial and error . The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

Another type of strategy is an algorithm. An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

• When one is faced with too much information
• When the time to make a decision is limited
• When the decision to be made is unimportant
• When there is access to very little information to use in making the decision
• When an appropriate heuristic happens to come to mind in the same moment

Working backwards is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

You can view the transcript for “Can you solve “Einstein’s Riddle”? – Dan Van der Vieren” here (opens in new window) .

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

Everyday Connections: Solving Puzzles

Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below (Figure 1) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.

Figure 1 . How long did it take you to solve this sudoku puzzle? (You can see the answer at the end of this section.)

Here is another popular type of puzzle that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:

Figure 2 . Did you figure it out? (The answer is at the end of this section.) Once you understand how to crack this puzzle, you won’t forget.

Take a look at the “Puzzling Scales” logic puzzle below (Figure 3). Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).

Figure 3 . The puzzle reads, “Since the scales now balance…and balance when arranged this way, then how many marbles will it require to balance with that top?

Were you able to determine how many marbles are needed to balance the scales in the Puzzling Scales? You need nine. Were you able to solve the other problems above? Here are the answers:

• Modification and adaptation. Provided by : Lumen Learning. License : CC BY: Attribution
• Problem-Solving. Authored by : OpenStax College. Located at : https://openstax.org/books/psychology-2e/pages/7-3-problem-solving . License : CC BY: Attribution . License Terms : Download for free at https://openstax.org/books/psychology-2e/pages/1-introduction
• Can you solve Einsteinu2019s Riddle? . Authored by : Dan Van der Vieren. Provided by : Ted-Ed. Located at : https://www.youtube.com/watch?v=1rDVz_Fb6HQ&index=3&list=PLUmyCeox8XCwB8FrEfDQtQZmCc2qYMS5a . License : Other . License Terms : Standard YouTube License

An efficient ODE-solving method based on heuristic and statistical computations: αII-(2 + 3)P method

• Published: 31 May 2024

Cite this article

• Mehdi Babaei 1  

22 Accesses

Explore all metrics

The main goal of this paper is to develop a new class of sixth- and fourth-order implicit Runge–Kutta methods to treat linear and nonlinear initial value ordinary differential equations (ODEs) in applied science and engineering. In this regard, a completely unconventional approach is taken to derive the new formulations. They are derived through a computational approach without involving in the rigorous theory of order conditions. The new formulation has two distinctive parts, namely integration and interpolation. In the first part, we introduce a form of multi-stage single-step numerical integrator with unknown weights. These weights are determined by four simple interconnectivity rules that relate them to minimize the error of the integrator. Some of these rules are based on our expertise gathered from the available prominent quadratures. However, the most important one, the so-called fundamental weighting rule (FWR), is obtained from evolutionary and statistical computations. The second part of this research focuses on developing efficient tools for evaluating the internal stages of the integrator. Accordingly, a series of strong Hermite interpolators has been extended to approximate the solution values at the internal stages. They effectively collaborate with the integrator in several numerical algorithms, the so-called \(\alpha II-(q+r)P\) algorithms, to solve applied ODEs. Development of the integrator, its weighting rules, and the interpolators constitutes the central contribution of this research. Numerical studies demonstrate that the presented algorithms exhibit a high level of accuracy when approximating high-frequency problems as well as long-term solutions of linear and nonlinear ODEs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations

The Finite Volume Method

Arbitrary High Order ADER-DG Method with Local DG Predictor for Solutions of Initial Value Problems for Systems of First-Order Ordinary Differential Equations

Availability of data and material.

Not applicable.

Abelman S, Patidar KC (2008) Comparison of some recent numerical methods for initial-value problems for stiff ordinary differential equations. Comput Math Appl 55(4):733–744

Article   MathSciNet   Google Scholar  

Anastassi ZA, Simos TE (2005) An optimized Runge-Kutta method for the solution of orbital problems. J Comput Appl Math 175(1):1–9

Soroushian A (2008) A technique for time integration analysis with steps larger than the excitation steps. Commun Numer Methods Eng 24(12):2087–2111

Soroushian A, Farjoodi J (2003) More reliable responses for time integration analyses. Struct Eng Mech Int J 16(2):219–240

Article   Google Scholar  

Babaei M (2013) A general approach to approximate solutions of nonlinear differential equations using particle swarm optimization. Appl Soft Comput 13(7):3354–3365

Babaei M (2024) A Swarm-Intelligence Based Formulation for Solving Nonlinear ODEs: γβII-(2+ 3) P method. Appl Soft Comput, 111424. https://doi.org/10.1016/j.asoc.2024.111424

Babaei M (2022) Numerical solution of beam equation using neural networks and evolutionary optimization tools. Adv Comput Des 7(1):1–17. https://doi.org/10.12989/acd.2022.7.1.001

MathSciNet   Google Scholar  

Babaei M, Jalilkhani M, Ghasemi SH, Mollaei S (2022) New methods for dynamic analysis of structural systems under earthquake loads. J Rehabilit Civil Eng 10(3):81–99. https://doi.org/10.22075/JRCE.2021.23323.1506

Google Scholar  

Babaei M, Farzi J (2023) Derivation of weighting rules for developing a class of A-stable numerical integration scheme: αI-(2+3)P method. J Difference Equat Appl, pp 1–30. https://doi.org/10.1080/10236198.2023.2219785

Babaei M (2024) Optimized Gauss–Legendre–Hermite 2-point (O-GLH-2P) method for nonlinear time-history analysis of structures. Mechanica, pp 1–28, https://doi.org/10.1007/s11012-023-01752-4

Babaei M, Hanafi MR (2024) A novel method for nonlinear time-history analysis of structural systems: improved Newton–Cotes-Hermite-5P Method. (in press). https://doi.org/10.1007/s40996-024-01345-5

Boyce WE, DiPrima RC, Meade DB (2021) Elementary differential equations and boundary value problems. Wiley, New York

Burden Richard L (2011) Numerical analysis. Brooks/Cole Cengage Learning.

Burrage K, Butcher JC (1979) Stability criteria for implicit Runge-Kutta methods. SIAM J Numer Anal 16(1):46–57

Butcher JC (1964) Implicit Runge-Kutta processes. Math Comp 18(85):50–64

Butcher JC (1996) A history of Runge-Kutta methods. Appl Numer Math 20(3):247–260

Butcher JC, Wanner G (1996) Runge-Kutta methods: some historical notes. Appl Numer Math 22(1–3):113–151

Butcher JC (2000) Numerical methods for ordinary differential equations in the 20th century. J Comput Appl Math 125(1–2):1–29

Butcher JC (2016) Numerical methods for ordinary differential equations. John Wiley & Sons, Wiley

Book   Google Scholar  

Carpenter MH, Gottlieb D, Abarbanel S, Don WS (1993) The theoretical accuracy of Runge-Kutta time discretizations for the initial boundary value problem: a careful study of the boundary error (No. NAS 1.26: 191561). NASA.

Cash JR, Considine S (1992) An MEBDF code for stiff initial value problems. ACM Trans Math Softw (TOMS) 18(2):142–155

Chan RP, Tsai AY (2010) On explicit two-derivative Runge-Kutta methods. Numer Algorithms 53:171–194

Chicone C (2006) Ordinary differential equations with applications (Vol. 34). Springer, Cham.

Chopra AK (2012) Dynamics of structures: theory and applications to earthquake engineering, 4th edn. Prentice Hall, Upper Saddle River, NJ

Clough RW, Penzien J (2003) Dynamics of structures. Computers and Structures, Berkeley, CA

Coddington EA (2012) An introduction to ordinary differential equations. Courier Corporation.

Cooper G (1987) Stability of Runge-Kutta methods for trajectory problems. IMA J Numer Anal 7(1):1–13

Cooper GJ, Vignesvaran R (1993) Some schemes for the implementation of implicit Runge-Kutta methods. J Comput Appl Math 45(1–2):213–225

Craig Jr RR, Kurdila AJ (2006) Fundamentals of structural dynamics. Wiley, New York

D'Alembert JBLR (1767) Mélanges de littérature, d'histoire, et de philosophie: Part 5 (Vol. 5). Z. Chatelain & fils.

Davis PJ (1975) Interpolation and approximation. Courier Corporation.

Dowdy S, Wearden S, Chilko D (2011) Statistics for research. Wiley, New York

Epperson JF (2021) An introduction to numerical methods and analysis. Wiley, New York

Euler L (1824) Institutionum calculi integralis (Vol. 1). Impensis Academiae imperialis scientiarum.

Fathoni MF, Wuryandari AI (2015) Comparison between Euler, Heun, Runge-Kutta and Adams-Bashforth-Moulton integration methods in the particle dynamic simulation. In: 2015 4th International Conference on Interactive Digital Media (ICIDM) (pp. 1–7). IEEE, New York.

Gadisa G, Garoma H (2017) Comparison of higher order Taylor’s method and Runge-Kutta methods for solving first order ordinary differential equations. J Comp Math Sci 8(1):12–23

Gatti PL (2014) Applied structural and mechanical vibrations: theory and methods. CRC Press, Boca Raton.

Gautschi W (2011) Numerical analysis. Springer, Cham

Gear CW (1971) Numerical initial value problems in ordinary differential equations. Prentice-Hall series in automatic computation, New Jersey.

Greenberg MD (2012) Ordinary differential equations. Wiley, New York

Griffiths DF, Higham DJ (2010) Numerical methods for ordinary differential equations: initial value problems, vol 5. Springer, London

Gupta SC, Kapoor VK (2020) Fundamentals of mathematical statistics. Sultan Chand & Sons, Delhi

Hairer E, Wanner G, Nørsett SP (2008) Solving ordinary differential equations I: Nonstiff problems. Springer, Berlin Heidelberg, Germany

Hairer E, Wanner G (1973) Multistep-multistage-multiderivative methods for ordinary differential equations. Computing 11(3):287–303

Hairer E, Wanner G (2015) Runge–Kutta methods, explicit, implicit. Encyclopedia of Applied and Computational Mathematics, pp 1282–1285.

Hermann M, Saravi M (2016) Nonlinear ordinary differential equations. Springer, Berlin

Islam MA (2015) A comparative study on numerical solutions of initial value problems (IVP) for ordinary differential equations (ODE) with Euler and Runge Kutta Methods. Am J Comput Math 5(03):393

Islam MA (2015) Accurate solutions of initial value problems for ordinary differential equations with the fourth order Runge Kutta method. J Math Res 7(3):41

Islam MA (2015) Accuracy Analysis of Numerical solutions of initial value problems (IVP) for ordinary differential equations (ODE). IOSR J Math 11(3):18–23

Jator SN (2010) Solving second order initial value problems by a hybrid multistep method without predictors. Appl Math Comput 217(8):4036–4046

Jeon Y, Bak S, Bu S (2019) Reinterpretation of multi-Stage methods for stiff systems: a comprehensive review on current perspectives and recommendations. Mathematics 7(12):1158

Kalogiratou Z, Monovasilis T, Simos TE (2010) New modified Runge–Kutta–Nyström methods for the numerical integration of the Schrödinger equation. Comput Math Appl 60(6):1639–1647

Katsikadelis JT (2013) A new direct time integration scheme for the nonlinear equations of motion in structural dynamics. In: 10 the HSTAM international congress on mechanics, May, pp. 25–27.

Katsikadelis JT (2021) A new method for numerical integration of higher-order ordinary differential equations without losing the periodic responses. Front Built Environ 7:40

Katsikadelis JT (2020) Dynamic analysis of structures, 1st edn. Elsevier Amsterdam.

Kutta M (1901) Beitrag zur näherungsweisen Integration totaler Differentialgleichungen. Zeitschrift für Mathematik und Physik 46:435–453

Lambert JD (1973) Computational methods in ordinary differential equations. (No Title).

Lapidus L, Seinfeld JH (1971) Numerical solution of ordinary differential equations. Academic Press, New York.

Larsen RJ, Marx ML (2005) An introduction to mathematical statistics. Prentice Hall, Hoboken, NJ

Lian J, Hui G (2024) Human evolutionary optimization algorithm. Expert Syst Appl 241:122638

Lotkin M (1951) On the accuracy of Runge-Kutta’s method. Math Tables Other Aids Comput 5(35):128–133

Mechee M, Senu N, Ismail F, Nikouravan B, Siri Z (2013) A three-stage fifth-order Runge-Kutta method for directly solving special third-order differential equation with application to thin film flow problem. Math Problems Eng. 2013, ID 795397: 1-7

Meriam JL, Kraige LG, Bolton JN (2020) Engineering mechanics: dynamics. Wiley, New York

Mohammed AA, Hamza A, Mohammed U (2013) A self-starting hybrid linear multistep method for a direct solution of the general second-order initial value problem, IOSR Journal of Mathematics (IOSR-JM) 4(6): 7-13

Montiel O, Castillo O, Melin P, Díaz AR, Sepúlveda R (2007) Human evolutionary model: a new approach to optimization. Inf Sci 177(10):2075–2098

Murphy GM (2011) Ordinary differential equations and their solutions. Courier Corporation.

Ogunrinde RB, Fadugba SE, Okunlola JT (2012) On some numerical methods for solving initial value problems in ordinary differential equations. IOSR Journal of Mathematics (IOSRJM) 1(3): 25-31

Omar Z, Adeyeye O (2016) Numerical solution of first order initial value problems using a self-starting implicit two-step Obrechkoff-type block method. J Math Stat 12(2):127–134

Paz M, Leigh W (2004) Structural dynamics: theory and computation, 5th edn. Springer, New York

Rabiei F, Ismail F, Suleiman M (2013) Improved Runge-Kutta methods for solving ordinary differential equations. Sains Malaysiana 42(11):1679–1687

Runge C (1895) Über die numerische Auflösung von Differentialgleichungen. Math Ann 46(2):167–178

Sakhnovich LA (2012) Interpolation theory and its applications, Vol. 428. Springer, Cham.

Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

Sasser JE (1992) History of ordinary differential equations: the first hundred years. In: Proceedings of the Midwest Mathematics History Society, p 1.

Simmons GF (2016) Differential equations with applications and historical notes. CRC Press, Boca Raton

Stoer J, Bulirsch R, Bartels R, Gautschi W, Witzgall C (1980) Introduction to numerical analysis, vol 1993. Springer, New York

Sunday J, Odekunle MR (2012) A New Numerical integrator for the solution of initial value problems in ordinary differential equations. Pacific J Sci Technol 13(1):221–227

Tedesco J, McDougal WG, Ross CA (2000) Structural dynamics. Pearson Education, London, UK.

Tsitouras C, Famelis IT, Simos TE (2011) On modified Runge-Kutta trees and methods. Comput Math Appl 62(4):2101–2111

Thomson W(2018) Theory of vibration with applications. CRC Press, Boca Raton.

Walter W (2013) Ordinary differential equations, Vol. 182. Springer, Cham.

Wang ZQ, Guo BY (2012) Legendre-Gauss-Radau collocation method for solving initial value problems of first order ordinary differential equations. J Sci Comput 52(1):226–255

Wanner G, Hairer E (1996) Solving ordinary differential equations II (Vol. 375). Computing 52(1):226–255.

Witte RS, Witte JS (2017) Statistics. Wiley, New York

Wright K (1964) Chebyshev collocation methods for ordinary differential equations. Comput J 6(4):358–365

Zervoudakis K, Tsafarakis S (2022) A global optimizer inspired from the survival strategies of flying foxes. Eng Comp, pp 1–34.

Download references

Acknowledgements

I would like to express my sincere thanks to the reviewers of this paper for their valuable feedback and constructive comments. Their insightful suggestions greatly improved the scientific quality of this work. I am truly appreciative of their time and expertise in reviewing this manuscript.

No funding was received for conducting this study.

Author information

Authors and affiliations.

Department of Civil Engineering, University of Bonab, P.O. Box 55513-95133, Bonab, Iran

Mehdi Babaei

You can also search for this author in PubMed   Google Scholar

Contributions

The whole paper is worked by M. Babaei, the single author of the paper.

Corresponding author

Correspondence to Mehdi Babaei .

Ethics declarations

Conflict of interests.

There is no conflict of interest.

Ethics approval and consent to participate

Consent for publication, additional information, publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Babaei, M. An efficient ODE-solving method based on heuristic and statistical computations: αII-(2 + 3)P method. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06137-2

Download citation

Accepted : 09 April 2024

Published : 31 May 2024

DOI : https://doi.org/10.1007/s11227-024-06137-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

•  Initial value problem (IVP)
•  Ordinary differential equation (ODE)
• Runge–Kutta method
• Numerical Integration
• Hermite Interpolation
• Long-term solution
• Optimization
• Find a journal
• Publish with us
• Track your research

• Corpus ID: 270380137

Fast Adaptive Meta-Heuristic for Large-Scale Facility Location Problem

• B. Alidaee , Haibo Wang
• Published 11 June 2024
• Engineering, Computer Science

Tables from this paper

40 References

A method for solving to optimality uncapacitated location problems.

• Highly Influential

A new hybrid-heuristic for large-scale combinatorial optimization: A case of quadratic assignment problem

An adaptive parallel evolutionary algorithm for solving the uncapacitated facility location problem, a fast and efficient discrete evolutionary algorithm for the uncapacitated facility location problem, location planning of electric vehicle charging station with users’ preferences and waiting time: multi-objective bi-level programming model and hnsga-ii algorithm, a hybrid multistart heuristic for the uncapacitated facility location problem, introduction to location science, facility location in logistics and transportation: an enduring relationship, solving uncapacitated p-median problem with reinforcement learning assisted by graph attention networks, related papers.

Showing 1 through 3 of 0 Related Papers

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Thinking and Intelligence

Problem Solving

OpenStaxCollege

[latexpage]

Learning Objectives

By the end of this section, you will be able to:

• Describe problem solving strategies
• Define algorithm and heuristic
• Explain some common roadblocks to effective problem solving

People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.

PROBLEM-SOLVING STRATEGIES

When you are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.

A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them ( [link] ). For example, a well-known strategy is trial and error . The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

Another type of strategy is an algorithm. An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

• When one is faced with too much information
• When the time to make a decision is limited
• When the decision to be made is unimportant
• When there is access to very little information to use in making the decision
• When an appropriate heuristic happens to come to mind in the same moment

Working backwards is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below ( [link] ) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.

Here is another popular type of puzzle ( [link] ) that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:

Take a look at the “Puzzling Scales” logic puzzle below ( [link] ). Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).

PITFALLS TO PROBLEM SOLVING

Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A mental set is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.

Functional fixedness is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. During the Apollo 13 mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.

Check out this Apollo 13 scene where the group of NASA engineers are given the task of overcoming functional fixedness.

Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and nonindustrialized cultures (German & Barrett, 2005).

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An anchoring bias occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.

The confirmation bias is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Representative bias describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Finally, the availability heuristic is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision . Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in [link] .

Please visit this site to see a clever music video that a high school teacher made to explain these and other cognitive biases to his AP psychology students.

Were you able to determine how many marbles are needed to balance the scales in [link] ? You need nine. Were you able to solve the problems in [link] and [link] ? Here are the answers ( [link] ).

Many different strategies exist for solving problems. Typical strategies include trial and error, applying algorithms, and using heuristics. To solve a large, complicated problem, it often helps to break the problem into smaller steps that can be accomplished individually, leading to an overall solution. Roadblocks to problem solving include a mental set, functional fixedness, and various biases that can cloud decision making skills.

Review Questions

A specific formula for solving a problem is called ________.

• an algorithm
• a heuristic
• a mental set
• trial and error

A mental shortcut in the form of a general problem-solving framework is called ________.

Which type of bias involves becoming fixated on a single trait of a problem?

• anchoring bias
• confirmation bias
• representative bias
• availability bias

Which type of bias involves relying on a false stereotype to make a decision?

Critical Thinking Questions

What is functional fixedness and how can overcoming it help you solve problems?

Functional fixedness occurs when you cannot see a use for an object other than the use for which it was intended. For example, if you need something to hold up a tarp in the rain, but only have a pitchfork, you must overcome your expectation that a pitchfork can only be used for garden chores before you realize that you could stick it in the ground and drape the tarp on top of it to hold it up.

How does an algorithm save you time and energy when solving a problem?

An algorithm is a proven formula for achieving a desired outcome. It saves time because if you follow it exactly, you will solve the problem without having to figure out how to solve the problem. It is a bit like not reinventing the wheel.

Personal Application Question

Which type of bias do you recognize in your own decision making processes? How has this bias affected how you’ve made decisions in the past and how can you use your awareness of it to improve your decisions making skills in the future?

Problem Solving Copyright © 2014 by OpenStaxCollege is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

Ideas: Solving network management puzzles with Behnaz Arzani

Published June 13, 2024

By Gretchen Huizinga , Executive Producer and Host of the Microsoft Research Podcast Behnaz Arzani , Principal Researcher

Share this page

• Share on Facebook
• Share on Twitter
• Share on LinkedIn
• Share on Reddit
• Subscribe to our RSS feed

Behind every emerging technology is a great idea propelling it forward. In the new Microsoft Research Podcast series, Ideas , members of the research community at Microsoft discuss the beliefs that animate their research, the experiences and thinkers that inform it, and the positive human impact it targets. 

In this episode, host Gretchen Huizinga talks with Principal Researcher Behnaz Arzani . Arzani has always been attracted to hard problems, and there’s no shortage of them in her field of choice—network management—where her contributions to heuristic analysis and incident diagnostics are helping the networks people use today run more smoothly. But the criteria she uses to determine whether a challenge deserves her time has evolved. These days, a problem must appeal across several dimensions: Does it answer a hard technical question? Would the solution be useful to people? And … would she enjoy solving it?

Learn more:

• Solving Max-Min Fair Resource Allocations Quickly on Large Graphs Publication, February 2024
• Finding Adversarial Inputs for Heuristics using Multi-level Optimization Publication, February 2024
• MetaOpt: Examining, explaining, and improving heuristic performance Microsoft Research blog, January 2024
• A Holistic View of AI-driven Network Incident Management Publication, October 2023
• Behnaz Arzani: Painting, storytelling, and other hobbies Microsoft Research bio page

Subscribe to the Microsoft Research Podcast :

• Apple Podcasts

[TEASER] 

[MUSIC PLAYS UNDER DIALOGUE]

BEHNAZ ARZANI: I guess the thing I’m seeing is that we are freed up to dream more—in a way. Maybe that’s me being too … I’m a little bit of a romantic, so this is that coming out a little bit, but it’s, like, because of all this, we have the time to think bigger, to dream bigger, to look at problems where maybe five years ago, we wouldn’t even dare to think about.

[TEASER ENDS]

GRETCHEN HUIZINGA: You’re listening to Ideas , a Microsoft Research Podcast that dives deep into the world of technology research and the profound questions behind the code. I’m Dr. Gretchen Huizinga. In this series, we’ll explore the technologies that are shaping our future and the big ideas that propel them forward.

[MUSIC FADES]

My guest today is Behnaz Arzani. Behnaz is a principal researcher at Microsoft Research, and she’s passionate about the systems and networks that provide the backbone to nearly all our technologies today. Like many in her field, you may not know her , but you know her work: when your networks function flawlessly, you can thank people like Behnaz Arzani. Behnaz, it’s been a while. I am so excited to catch up with you today. Welcome to Ideas !

BEHNAZ ARZANI: Thank you. And I’m also excited to be here.

HUIZINGA: So since the show is about ideas and leans more philosophical, I like to start with a little personal story and try to tease out anything that might have been an inflection point in your life, a sort of aha moment, or a pivotal event, or an animating “what if,” we could call it. What captured your imagination and got you inspired to do what you’re doing today?

ARZANI: I think that it was a little bit of an accident and a little bit of just chance, I guess, but for me, this happened because I don’t like being told what to do! [LAUGHTER] I really hate being told what to do. And so, I got into research by accident, mostly because it felt like a job where that wouldn’t happen. I could pick what I wanted to do. So, you know, a lot of people come talking about how they were the most curious kids and they all—I wasn’t that. I was a nerd , but I wasn’t the most curious kid. But then I found that I’m attracted to puzzles and hard puzzles and things that I don’t know how to answer, and so that gravitated me more towards what I’m doing today. Things that are basically difficult to solve … I think are difficult to solve.

HUIZINGA: So that’s your inspiring moment? “I’m a bit of a rebel, and …”

ARZANI: Yup!

HUIZINGA: … I like puzzles … ”?

ARZANI: Yup! [LAUGHTER] Which is not really a moment. Yeah, I can’t point to a moment. It’s just been a journey, and it’s just, like, been something that has gradually happened to me, and I love where I am …

HUIZINGA: Yeah …

ARZANI: … but I can’t really pinpoint to like this , like this inspiring awe-drop—no.

HUIZINGA: OK. So let me ask you this: is there nobody in this building that tells you what to do? [LAUGHS]

ARZANI: There are people who have tried, [LAUGHS] but …

HUIZINGA: Oh my gosh!

ARZANI: No, it doesn’t work. And I think if you ask them, they will tell you it hasn’t worked.

HUIZINGA: OK. The other side question is, have you encountered a puzzle that has confounded you?

ARZANI: Have I encountered a puzzle? Yes. Incident management. [LAUGHTER]

HUIZINGA: And we’ll get there in the next couple of questions. Before we do, though, I want to know about who might have influenced you earlier. I mean, it’s interesting. Usually if you don’t have a what , there might not be a who attached to it …

ARZANI: No. But I have a who . I have multiple “whos” actually.

HUIZINGA: OK! Wonderful. So tell us a little bit about the influential people in your life.

ARZANI: I think the first and foremost is my mom. I have a necklace I’m holding right now. This is something my dad gave my mom on their wedding day. On one side of it is a picture of my mom and dad; on the other side is both their names on it. And I have it on every day. To my mom’s chagrin. [LAUGHTER] She is like, why? But it’s, like, it helps me stay grounded. And my mom is a person that … she had me while she was an undergrad. She got her master’s. She got into three different PhD programs in her lifetime. Every time, she gave it up for my sake and for my brother’s sake. But she’s a woman that taught me you can do anything you set your mind to and that you should always be eager to learn. She was a chemistry teacher, and even though she was a chemistry teacher, she kept reading new books. She came to the US to visit me in 2017, went to a Philadelphia high school, and asked, can I see your chemistry books? I want to see what you’re teaching your kids. [LAUGHTER] So that’s how dedicated she is to what she does. She loves what she does. And I could see it on her face on a daily basis. And at some point in my life a couple of years ago, I was talking to my mom about something, and she said, tell yourself, “I’m stronger than my mom.”

HUIZINGA: Oh my gosh.

ARZANI: And that has been, like, the most amazing thing to have in the back of my head because I view my mom as one of the strongest people I’ve ever met, and she’s my inspiration for everything I do.

HUIZINGA: Tell yourself you’re stronger than your mom. … Did you?

ARZANI: I’m not stronger than my mom, I don’t think … [LAUGHS]

HUIZINGA: [LAUGHS] You got to change that narrative!

ARZANI: But, yes, I think it’s just this thing of, like, “What would Mom do?” is a great thing to ask yourself, I think.

HUIZINGA: I love that. Well, and so I would imagine, though, that post-, you know, getting out of the house, you’ve had instructors, you’ve had professors, you’ve had other researchers. I mean, anyone else that’s … ?

ARZANI: Many! And in different stages of your life, different people step into that role, I feel like. One of the first people for me was Jen Rexford, and she is just an amazing human being. She’s an amazing researcher, hands down. Her work is awesome, but also, she’s an amazing human being, as well. And that just makes it better.

HUIZINGA: Yeah.

ARZANI: And then another person is Mohammad Alizadeh, who’s at MIT. And actually, let’s see, I’m going to keep going …

HUIZINGA: Good.

ARZANI: … a little with people—Mark Handley. When I was a PhD student, I would read their papers, and I’d be like, wow! And, I want to be like you!

HUIZINGA: So linking that back to your love of puzzles, were these people that you admired good problem solvers or … ?

ARZANI: Oh, yeah! I think Jen is one of those who … a lot of her work is also practical, like, you know, straddles a line between both solving the puzzle and being practical and being creative and working with theorists and working with PL people. So she’s also collaborative, which is, kind of, my style of work, as well. Mohammad is more of a theorist, and I love … like more the theoretical aspect of problems that I solve. And so, like, just the fact that he was able to look at those problems and thinks about those problems in those ways. And then Mark Handley’s intuition about problems—yeah, I can’t even speak to that!

HUIZINGA: That’s so fascinating because you’ve identified three really key things for a researcher. And each one is embodied in a person. I love that. And because I know who you are, I know we’re going to get to each of those things probably in the course of all these questions that I’ll ask you. [LAUGHTER] So we just spent a little time talking about what got you here and who influenced you along the way. But your life isn’t static. And at each stage of accomplishment, you get a chance to reflect and, sort of, think about what you got right, what you got wrong, and where you want to go next. So I wonder if you could take a minute to talk about the evolution of your values as a researcher, collaborator, and colleague and then a sort of “how it started/how it’s going” thing.

ARZANI: Hmm … For me, I think what I’ve learned is to be more mindful—about all of it. But I think if I talk about the evolution, when you’re a PhD student, especially if you’re a PhD student from a place that’s not MIT, that’s not Berkeley, which is where I was from, [1] my main focus was proving myself. I mean, for women, always , we have to prove ourselves. But, like, I think if you’re not from one of those schools, it’s even more so. At least that’s how I felt. That might not be the reality, but that’s how you feel. And so you’re always running to show this about yourself. And so you don’t stop to think how you’re showing up as a person, as a researcher, as a collaborator. You’re not even, like, necessarily reflecting on, are these the problems that I enjoy solving? It’s more of, will solving this problem help me establish myself in this world that requires proving yourself and is so critical and all of that stuff? I think now I stop more. I think more, is this a problem that I would enjoy solving? I think that’s the most important thing. Would other people find it useful? Is it solving a hard technical question? And then, in collaborations, I’m being more mindful that I show up in a way that basically allows me to be a good person the way I want to be in my collaboration. So as researchers, we have to be critical because that’s how science evolves. Not all work is perfect. Not all ideas are the best ideas. That’s just fundamental truth. Because we iterate on each other’s ideas until we find the perfect solution to something. But you can do all of these things in a way that’s kind, in a way that’s mindful, in a way that respects other people and what they bring to the table. And I think what I’ve learned is to be more mindful about those things.

HUIZINGA: How would you define mindful? That’s an interesting word. It has a lot of baggage around it, you know, in terms of how people do mindfulness training. Is that what you’re talking about, or is it more, sort of, intentional?

ARZANI: I think it’s both. So I think one of the things I said—I think when I got into this booth even—was, I’m going to take a breath before I answer each question. And I think that’s part of it, is just taking a breath to make sure you’re present is part of it. But I think there is more to it than that, which is I don’t think we even think about it. I think if I … when you asked me about the evolution of how I evolved, I never thought about it.

HUIZINGA: No.

ARZANI: I was just, like, running to get things done, running to solve the question, running to, you know, find the next big thing, and then you’re not paying attention to how you’re impacting the world in the process.

HUIZINGA: Right.

ARZANI: And once you start paying attention, then you’re like, oh, I could do this better. I can do that better. If I say this to this person in that way, that allows them to do so much more, that encourages them to do so much more.

HUIZINGA: Yeah, yeah.

ARZANI: So …

HUIZINGA: You know, when you started out, you said, is this a problem I would enjoy solving? And then you said, is this a problem that somebody else needs to have solved? Which is sort of like “do I like it?”—it goes back to Behnaz at the beginning: don’t tell me what to do; I want to do what I want to do. Versus—or and is this useful to the world? And I feel like those two threads are really key to you.

ARZANI: Yes. Basically, I feel like that defines me as a researcher, pretty much. [LAUGHS] Which is, you know, I was one of the, you know, early people … I wouldn’t say first. I’m not the first, I don’t think, but I was one of the early people who was talking about using machine learning in networking. And after a while, I stopped because I wasn’t finding it fun anymore, even though there was so much hype about, you know, let’s do machine learning in networking. And it’s not because there’s not a lot of technical stuff left to do. You can do a lot of other things there. There’s room to innovate. It’s just that I got bored.

HUIZINGA: I was just going to say, it’s still cool, but Behnaz is bored! [LAUGHTER] OK, well, let’s start to talk a little bit about some of the things that you’re doing. And I like this idea of a researcher, even a person, having a North Star goal. It sounds like you’ve got them in a lot of areas of your life, and you’ve said your North Star goal, your research goal , is to make the life of a network operator as painless as possible. So I want to know who this person is. Walk us through a day in the life of a network operator and tell us what prompted you to want to help them.

ARZANI: OK, so it’s been years since I actually, like, sat right next to one of them for a long extended period of time because now we’re in different buildings, but back when I was an intern, I was actually, like, kind of, like right in the middle of a bunch of, you know, actual network operators. And what I observed … and see, this was not, like, I’ve never lived that experience, so I’m talking about somebody else’s experience, so bear that in mind …

HUIZINGA: Sure, but at least you saw it …

ARZANI: Yeah. What they do is, there’s a lot of, “OK, we design the network, configure it.” A lot of it goes into building new systems to manage it. Building new systems to basically make it better, more efficient, all of that. And then they also have to be on call so that when any of those things break, they’re the ones who have to look at their monitoring systems and figure out what happened and try to fix it. So they do all of this in their day-to-day lives.

HUIZINGA: That’s tough …

ARZANI: Yeah.

HUIZINGA: OK. So I know you have a story about what prompted you, at the very beginning, to want to help this person. And it had some personal implications. [LAUGHS]

ARZANI: Yeah! So my internship mentor, who’s an amazing person, I thought—and this is, again, my perception as an intern—the day after he was on call, he was so tired, I felt. And so grumpy … grumpier than normal! [LAUGHTER] And, like, my main motivation initially for working in this space was just, like, make his life better!

HUIZINGA: Make him not grumpy.

ARZANI: Yeah. Pretty much. [LAUGHS]

HUIZINGA: Did you have success at that point in your life? Or was this just, like, setting a North Star goal that I’m going to go for that?

ARZANI: I mean, I had done a lot of work in monitoring space, but back then—again, going back to the talk we were having about how to be mindful about problems you pick—back then it was just like, oh, this was a problem to solve, and we’ll go solve it, and then what’s the next thing? So there was not an overarching vision, if you will. It was just, like, going after the next, after the next. I think that’s a point where, like, it all came together of like, oh, all of the stuff that I’m doing can help me achieve this bigger thing.

HUIZINGA: Right. OK, Behnaz, I want to drop anchor, to use a seafaring analogy, for a second and contextualize the language that these operators use. Give us a “networking for neophytes” overview of the tools they rely on and the terminology they use in their day-to-day work so we’re not lost when we start to unpack the problems, projects, and papers that are central to your work.

ARZANI: OK. So I’m going to focus on my pieces of this just because of the context of this question. But a lot of operators … just because a lot of the problems that we work on these days to be able to manage our network, the optimal form of these problems tend to be really, really hard. So a lot of the times, we use algorithms and solutions that are approximate forms of those optimal solutions in order to just solve those problems faster. And a lot of these heuristics, some of them focus on our wide area network, which we call a WAN . Our WANs, basically what they do is they move traffic between datacenters in a way that basically fits the capacity of our network. And, yeah, I think for my work, my current work, to understand it, that’s, I think, enough networking terminology.

HUIZINGA: OK. Well, so you’ve used the term heuristic and optimal. Not with an “s” on the end of it. Or you do say “optimals,” but it’s a noun …

ARZANI: Well, so for each problem definition, usually, there’s one way to formulate an optimal solution. There might be multiple optima that you find, but the algorithm that finds the optimum usually is one. But there might be many, I guess. The ones that I’ve worked on generally have been one.

HUIZINGA: Yeah, yeah. And so in terms of how things work on a network, can you give us just a little picture of how something moves from A to B that might be a problem?

ARZANI: So, for example, we have these datacenters that generate terabytes of traffic and—terabytes per second of traffic—that wants to move from point A to point B, right. And we only have finite network capacity, and these, what we call, “demands” between these datacenters—and you didn’t see me do the air quotes, but I did the air quotes—so they go from point A to point B, and so in order to fit this demand in the pipes that we have—and these pipes are basically links in our network—we have to figure out how to send them. And there’s variations in them. So, like, it might be the case that at a certain time of the day, East US would want to send more traffic to West US, and then suddenly, it flips. And that’s why we solve this problem every five minutes! Now assume one of these links suddenly goes down. What do I do? I have to resolve this problem because maybe the path that I initially picked for traffic to go through goes exactly through that failed link. And now that it’s disappeared, all of that traffic is going to fall on the floor. So I have to re -solve that problem really quickly to be able to re -move my traffic and move it to somewhere else so that I can still route it and my customers aren’t impacted. What we’re talking about here is a controller, essentially, that the network operators built. And this controller solves this optimization problem that figures out how traffic should move. When it’s failed, then the same controller kicks in and reroutes traffic. The people who built that controller are the network operators.

HUIZINGA: And so who does the problem-solving or the troubleshooting on the fly?

ARZANI: So hopefully—and this, most of the times, is the case—is we have monitoring systems in place that the operators have built that, like, kind of, signal to this controller that, oh, OK, this link is down; you need to do something.

[MUSIC BREAK]

HUIZINGA: Much of your recent work represents an effort to reify the idea of automated network management and to try to understand the performance of deployed algorithms. So talk about the main topics of interest here in this space and how your work has evolved in an era of generative AI and large language models.

ARZANI: So if you think about it, what generative AI is going to enable, and I’m using the term “going to enable” a little bit deliberately because I don’t think it has yet . We still have to build on top of what we have to get that to work. And maybe I’ll reconsider my stance on ML now that, you know, we have these tools. Haven’t yet but might. But essentially, what they enable us to do is take automated action on our networks. But if we’re allowing AI to do this, we need to be mindful of the risks because AI in my, at least in my head of how I view it, is a probabilistic machine, which, what that means is that there is some probability, maybe a teeny tiny probability, it might get things wrong. And the thing that you don’t want is when it gets things wrong, it gets things catastrophically wrong. And so you need to put guardrails in place, ensure safety, figure out, like, for each action be able to evaluate that action and the risks it imposes long term on your network and whether you’re able to tolerate that risk. And I think there is a whole room of innovation there to basically just figure out the interaction between the AI and the network and where … and actually strategic places to put AI, even.

ARZANI: The thing that for me has evolved is I used to think we just want to take the human out of the equation of network management. The way I think about it now is there is a place for the human in the network management operation because sometimes human has context and that context matters. And so I think what the, like, for example, we have this paper in HotNets 2023 where we talk about how to put an LLM in the incident management loop, and then there, we carefully talk about, OK, these are the places a human needs to be involved, at least given where LLMs are right now, to be able to ensure that everything happens in a safe way.

HUIZINGA: So go back to this “automated network management” thing. This sounds to me like you’re in a space where it could be, but it isn’t ready yet …

HUIZINGA: … and without, sort of, asking you to read a crystal ball about it, do you feel like this is something that could be eventually?

ARZANI: I hope so. This is the best thing about research. You get to be like, yeah!

HUIZINGA: Yeah, why not?

ARZANI: Why not? And, you know, maybe somebody will prove me wrong, but until they do, that’s what I’m working towards!

HUIZINGA: Well, right now it’s an animating “what if?”

HUIZINGA: Right?

HUIZINGA: This is a problem Behnaz is interested in right now. Let’s go!

ARZANI: Yeah. Pretty much. [LAUGHTER]

HUIZINGA: OK. Behnaz, the systems and networks that we’ve come to depend on are actually incredibly complex. But for most of us, most of the time, they just work. There’s only drama when they don’t work, right? But there’s a lot going on behind the scenes. So I want you to talk a little bit about how the cycle of configuring, managing, reconfiguring, etc., helps keep the drama at bay.

ARZANI: Well … you reminded me of something! So when I was preparing my job … I’m going to tell this story really, really quickly. But when I was preparing my job talk, somebody showed me a tweet. In 2014, I think, people started calling 911 when Facebook was down! Because of a networking problem! [LAUGHS] Yeah. So that’s a thing. But, yeah, so network availability matters, and we don’t notice it until it’s actually down. But that aside, back to your question. So I think what operators do is they build systems in a way that tries to avoid that drama as much as possible. So, for example, they try to build systems that these systems configure the network. And one of my dear friends, Ryan Beckett, works on intent-driven networking that essentially tries to ensure that what the operators intend with their configurations matches what they actually push into the network. They also monitor the network to ensure that as soon as something bad happens, automation gets notified. And there’s automation also that tries to fix these problems when they happen as much as possible. There’s a couple of problems that happen in the middle of this. One of them is our networks continuously change, and what we use in our networks changes. And there’s so many different pieces and components of this, and sometimes what happens is, for example, a team decides to switch from one protocol to a different protocol, and by doing that, it impacts another team’s systems and monitoring and what expectations they had for their systems, and then suddenly it causes things to go bad …

ARZANI: And they have to develop new solutions taking into account the changes that happened. And so one of the things that we need to account for in this whole process is how evolution is happening. And like evolution-friendly, I guess, systems, maybe, is how you should be calling it.

ARZANI: But that’s one. The other part of it that goes into play is, most of the time you expect a particular traffic characteristic, and then suddenly, you have one fluke event that, kind of, throws all of your assumptions out the window, so …

HUIZINGA: Right. So it’s a never-ending job …

ARZANI: Pretty much.

HUIZINGA: It’s about now that I ask all my guests what could possibly go wrong if, in fact, you got everything right. And so for you, I’d like to earth this question in the broader context of automation and the concerns inherent in designing machines to do our work for us. So at an earlier point in your career—we talked about this already—you said you believed you could automate everything. Cool. Now you’re not so much on that. Talk about what changed your thinking and how you’re thinking now.

ARZANI: OK, so the shallow answer to that question—there’s a shallow answer, and there’s a deeper answer—the shallow answer to that question is I watched way too many movies where robots took over the world. And honestly speaking, there’s a scenario that you can imagine where automation starts to get things wrong and then keeps getting things wrong, and wrong, not by the definition of automation. Maybe they’re doing things perfectly by the objectives and metrics that you used to design them …

HUIZINGA: Sure.

ARZANI: … but they’re screwing things up in terms of what you actually want them to do.

HUIZINGA: Interesting.

ARZANI: And if everything is automated and you don’t leave yourself an intervention plan, how are you going to take control back?

HUIZINGA: Right. So this goes back to the humans-in-the-loop/humans-out-of-the-loop. And if I remember in our last podcast, we were talking about humans out of the loop.

HUIZINGA: And you’ve already talked a bit about what the optimal place for a human to be is. Is the human always going to have to be in the loop, in your opinion?

ARZANI: I think it’s a scenario where you always give yourself a way to interrupt. Like, always put a back door somewhere. When we notice things go bad, we have a way that’s foolproof that allows us to shut everything down and take control back to ourselves. Maybe that’s where we go.

HUIZINGA: How do you approach the idea of corner cases?

ARZANI: That’s essentially what my research right now is, actually! And I love it, which is essentially figuring out, in a foolproof way, all the corner cases.

HUIZINGA: Yeah?

ARZANI: Can you build a tool that will tell you what the corner cases are? Now, granted, what we focus on is performance corner cases. Nikolaj Bjørner, in RiSE—so RiSE is Research in Software Engineering—is working on, how do you do verification corner cases? But all of them, kind of, have a hand-in-hand type of, you know, Holy Grail goal, which is …

ARZANI: … how do you find all the corner cases?

HUIZINGA: Right. And that, kind of, is the essence of this “What could possibly go wrong?” question, is looking in every corner …

ARZANI: Correct.

HUIZINGA: … for anything that could go wrong. So many people in the research community have observed that the speed of innovation in generative AI has shrunk the traditional research-to-product timeline, and some people have even said everyone’s an applied researcher now. Or everyone’s a PM. [LAUGHS] Depends on who you are! But you have an interesting take on this Behnaz, and it reminds me of a line from the movie Nanny McPhee : “When you need me but do not want me, then I will stay. When you want me but no longer need me, I have to go.” So let’s talk a little bit about your perspective on this idea-to-ideation pipeline. How and where are researchers in your orbit operating these days, and how does that impact what we might call “planned obsolescence” in research?

ARZANI: I guess the thing I’m seeing is that we are freed up to dream more—in a way. Maybe that’s me being too … I’m a little bit of a romantic, so this is that coming out a little bit, but it’s, like, because of all this, we have the time to think bigger, to dream bigger, to look at problems where maybe five years ago, we wouldn’t even dare to think about. We have amazingly, amazingly smart, competent people in our product teams. Some of them are actually researchers. So there’s, for example, the Azure systems research group that has a lot of people that are focused on problems in our production systems. And then you have equivalents of those spread out in the networking sphere, as well. And so a lot of complex problems that maybe like 10 years ago Microsoft Research would look at nowadays they can handle themselves. They don’t need us. And that’s part of what has allowed us to now go and be like, OK, I’m going to think about other things. Maybe things that, you know, aren’t relevant to you today, but maybe in five years, you’ll come in and thank me for thinking about this!

HUIZINGA: OK. Shifting gears here! In a recent conversation, I heard a colleague refer to you as an “idea machine.” To me, that’s one of the greatest compliments you could get. But it got me wondering, so I’ll ask you: how does your brain work, Behnaz, and how do you get ideas?

ARZANI: Well, this has been, to my chagrin, one of the realities of life about my brain apparently. So I never thought of this as a strength. I always thought about it as a weakness. But nowadays, I’m like, oh, OK, I’m just going to embrace this now! So I have a random brain. It’s completely ran—so, like, it actually happens, like, you’re talking, and then suddenly, I say something that seems to other people like it came out of left field. I know how I got there. It’s essentially kind of like a Markov chain. [LAUGHTER] So a Markov chain is essentially a number of states, and there’s a certain probability you can go from one state to the other state. And, actually, one of the things I found out about myself is I think through talking for this exact reason. Because people see this random Markov chain by what they say, and it suddenly goes into different places, and that’s how ideas come about. Most of my ideas have actually come through when I’ve been talking to someone.

HUIZINGA: Really?

HUIZINGA: Them talking or you talking?

ARZANI: Both.

ARZANI: So it’s, like, basically, I think the thing that has recently … like, I’ve just noticed more—again, being more mindful does that to you—it’s like I’m talking to someone. I’m like, I have an idea. And it’s usually they said something, or I was saying something that triggered that thought coming up. Which doesn’t happen when … I’m not one of those people that you can put in a room for three days—somebody actually once told me this— [LAUGHTER] like, I’m not one of those people you can put in a room for three days and I come out with these brilliant ideas. It’s like you put me in a room with five other people, then I come out with interesting ideas.

HUIZINGA: Right. … It’s the interaction.

HUIZINGA: I want to link this idea of the ideas that you get to the conversations you have and maybe go back to linking it to the work you’ve recently done. Talk about some of the projects, how they came from idea to paper to product even …

ARZANI: Mm-hm. So like one of the works that we were doing was this work on, like, max-min fair resource allocation that recently got published in NSDI and is actually in production. So the way that came out is I was working with a bunch of other researchers on risk estimation, actually, for incident management of all things, which was, how do you figure out if you want to mitigate a particular problem in a certain way, how much risk it induces as a problem. And so one of the people who was originally … one of the original researchers who built our wide-area traffic engineering controller, which we were talking about earlier, he said, “You’re solving the max-min fair problem.” We’re like, really? And then this caused a whole, like, one-year collaboration where we all sat and evolved this initial algorithm we had into a … So initially it was not a multipath problem. It had a lot of things that didn’t fully solve the problem of max-min fair resource allocation, but it evolved into that. Then we deployed it, and it improved the SWAN solver by a factor of three in terms of how fast it solved the problem and didn’t have any performance impact, or at least very little. And so, yeah, that’s how it got born.

HUIZINGA: OK. So for those of us who don’t know, what is max-min fair resource allocation, and why is it such a problem?

ARZANI: Well, so remember I said that in our wide area network, we route traffic from one place to the other in a way that meets capacity. So one of the objectives we try to meet is we try to be fair in a very specific metric. So max-min is just the metric of fairness we use. And that basically means you cannot improve what you allocated to one piece of traffic in a way that would hurt anybody who has gotten less. So there’s a little bit of a, like, … it’s a mind bend to wrap your head a little bit around the max-min fair definition. But the reason making it faster is important is if something fails, we need to quickly recompute what the paths are and how we route traffic. So the faster we can solve this problem, the better we can adapt to failures.

HUIZINGA: So talk a little bit about some of the work that started as an idea and you didn’t even maybe know that it was going to end up in production.

ARZANI: There was this person from Azure Networking came and gave a talk in our group. And he’s a person I’ve known for years, so I was like, hey, do you want to jump on a meeting and talk? So he came into that meeting, and I was like, OK, what are some of the things you’re curious about these days? You want to answer these days? And it was like, yeah, we have this heuristic we’re using in our traffic engineering solution, and essentially what it does is to make the optimization problem we solve smaller. If a piece of traffic is smaller than a particular, like, arbitrary threshold, we just send it on a shortest path and don’t worry about it. And then we optimize everything else. And I just want to know, like, what is the optimality gap of this heuristic? How bad can this heuristic be? And then I had worked on Stackelberg games before, in my PhD. It never went anywhere, but it was an idea I played around with, and it just immediately clicked in my head that this is the same problem. So Stackelberg games are a leader-follower game where in this scenario a leader has an objective function that they’re trying to maximize, and they control one or multiple of the inputs that their followers get to operate over. The followers, on the other hand, don’t get to control anything about this input. They have their own objective that they’re trying to maximize or minimize, but they have other variables in their control, as well. And what their objective is, is going to control the leader’s payoff. And so this game is happening where the leader has more control in this game because it’s, kind of, like the followers are operating in subject to whatever the leader says, right. But the leader is impacted by what the followers do. And so this dynamic is what they call a Stackelberg game. And the way we map the MetaOpt problem to this is the leader in our problem wants to maximize the difference between the optimal and the heuristic. It controls the inputs to both the optimal and the heuristic. And now this optimal and heuristic algorithms are the followers in that game. They don’t get to control the inputs, but they have other variables they control, and they have objectives that they want to maximize or minimize.

ARZANI: And so that’s how the Stackelberg-game dynamic comes about. And then we got other researchers in the team involved, and then we started talking, and then it just evolved into this beast right now that is a tool, MetaOpt, that we released, I think, a couple of months ago. And another piece that was really cool was people from ETH Zürich came to us and were like, oh, you guys analyzed our heuristic! We have a better one! Can you analyze this one? And that was a whole fun thing we did where we analyzed their heuristics for them. And, then, yeah …

HUIZINGA: Yeah. So all these things that you’re mentioning, are they findable as papers? Were they presented …

ARZANI: Yes.

HUIZINGA: … at conferences, and where are they in anybody’s usability scenario?

ARZANI: So the MetaOpt tool that I just mentioned, that one is in … it’s an open-source tool. You can go online and search for MetaOpt. You’ll find the tool. We’re here to support anything you need; if you run into issues, we’ll help you fix it.

HUIZINGA: Great. You can probably find all of these papers under publications …

HUIZINGA: … on your bio page on the website, Microsoft Research website.

HUIZINGA: Cool. If anyone wants to do that. So, Behnaz, the idea of having ideas is cool to me, but of course, part of the research problem is identifying which ones you should go after [LAUGHS] and which ones you shouldn’t. So, ironically, you’ve said you’re not that good at that part of it, but you’re working at getting better.

HUIZINGA: So first of all, why do you say that you’re not very good at it? And second of all, what are you doing about it?

ARZANI: So I, as I said, get attracted to puzzles, to hard problems. So most of the problems that I go after are problems I have no idea how to solve. And that tends to be a risk.

ARZANI: Where I think people who are better at selecting problems are those who actually have an idea of whether they’ll be able to solve this problem or not. And I never actually asked myself that question before this year. [LAUGHTER] So now I’m trying to get a better sense of, how do I figure out if a problem is solvable or not before I try to solve it? And also, just what makes a good research problem? So what I’m doing is, I’m going back to the era that I thought had the best networking papers, and I’m just trying to dissect what makes those papers good, just to understand better for myself, to be like, OK, what do I want to replicate? Replicate, not in terms of techniques, but in terms of philosophy.

HUIZINGA: So what you’re looking at is how people solve problems through the work that they did in this arena. So what are you finding? Have you gotten any nuggets of …

ARZANI: So a couple. So one of my favorite papers is Van Jacobson’s TCP paper. The intuition is amazing to me. It’s almost like he has a vision of what’s happening, is the best I can describe it. And another example of this is also early-on papers by people like Ratul Mahajan, Srikanth Kandula, those guys, where you see that they start with a smaller example that, kind of, shows how this problem is going to happen and how they’re going to solve it. I mean, I did this in my work all the time, too, but it was never conscious. It’s more of like that goes to that mindfulness thing that I said before, too. It’s like you might be doing some of these already, but you don’t notice what you’re doing. It more of is, kind of, like putting of like, oh, this is what they did. And I do this, too. And this might be a good habit to keep but cultivate into a habit as opposed to an unconscious thing that you’re just doing.

HUIZINGA: Right. You know, this whole idea of going back to what’s been done before, I think that’s a lesson about looking at history, as well, and to say, you know, what can we learn from that? What are we trying to reinvent …

HUIZINGA: … that maybe doesn’t need to be reinvented? Has it helped you to get more targeted on the kinds of problems that you say, “I’m not going to work on that. I am going to work on that”?

ARZANI: To be very, very, very fair, I haven’t done this for a long time yet! This has been …

HUIZINGA: A new thing.

ARZANI: I started this this month, yeah.

HUIZINGA: Oh my goodness!

ARZANI: So we’ll see how far I get and how useful it ends up being! [LAUGHS]

HUIZINGA: One of my favorite things to talk about on this show is what my colleague Kristina calls “outrageous” lines of research. And so I’ve been asking all my guests about their most outrageous ideas and how they turned out. So sometimes these ideas never got off the ground. Sometimes they turned out great. And other times, they’ve failed spectacularly. Do you have a story for the “Microsoft Research Outrageous Ideas” file?

ARZANI: I had this question of, if language has grammar, and grammar is what LLMs are learning, which, to my understanding of what people who are experts in this field say, this maybe isn’t that, but if it is the case that grammar is what allows these LLMs to learn how language works, then in networking, we have the equivalent of that, and the equivalent of that is essentially network protocols. And everything that happens in a network, you can define it as an event that happens in a network. You can think of those, like, the events are words in a language. And so, is it going to be the case, and this is a question which is, if you take an event abstraction and encode everything that happens in a network in that event abstraction, can you build an equivalent of an LLM for networks? Now what you would use it for—this is another reason I’ve never worked on this problem—I have no idea! [LAUGHTER] But what this would allow you to do is build the equivalent of an LLM for networking, where actually you just translate that network’s events into, like, this event abstraction, and then the two understand each other. So like a universal language of networking, maybe . It could be cool. Never tried it. Probably a dumb idea! But it’s an idea.

HUIZINGA: What would it take to try it?

ARZANI: Um … I feel like bravery is, I think, one because with any risky idea, there’s a probability that you will fail.

HUIZINGA: As a researcher here at Microsoft Research, when you have this idea, um … and you say, well, I’m not brave enough … even if you were brave enough, who would you have to convince that they should let you do it?

ARZANI: I don’t think anybody!

ARZANI:  That’s the whole … that’s the whole point of me being here! I don’t like being told what to do! [LAUGHS]

HUIZINGA: Back to the beginning!

ARZANI: Yeah. The only thing is that, maybe, like, people would be like, what have you been doing in the past six months? And I wouldn’t have … that’s the risk. That’s where bravery comes in.

ARZANI: The bravery is more of there is a possibility that I have to devote three years of my life into this, to figuring out how to make that work, and I might not be able to.

HUIZINGA: Yes …

ARZANI: And there’s other things. So it’s a tradeoff also of where you put your time.

ARZANI: So there. Yeah.

HUIZINGA: And if, but … part of it would be explaining it in a way to convince people: if it worked, it would be amazing!

ARZANI: And that’s the other problem with this idea. I don’t know what you would use it for. If I knew what you would use it for, maybe then it would make it worth it.

HUIZINGA: All right. Sounds like you need to spend some more time …

HUIZINGA: …ruminating on it. Um, yeah. The whole cliché of the solution in search of a problem.

HUIZINGA: [LAUGHS] As we close, I want to talk a little bit about some fun things. And so, aside from your research life, I was intrigued by the fact, on your bio page, that you have a rich artistic life, as well, and that includes painting, music, writing, along with some big ideas about the value of storytelling. So I’ll take a second to plug the bio page. People, go look at it because she’s got paintings and cool things that you can link to. As we close, I wonder if you could use this time to share your thoughts on this particular creative pursuit of storytelling and how it can enhance our relationships with our colleagues and ultimately make us better researchers and better people?

ARZANI: I think it’s not an understatement to say I had a life-changing experience through storytelling. The first time I encountered it, it was the most horrific thing I had ever seen! I had gone on Meetup—this was during COVID—to just, like, find places to meet people, build connections and all that, and I saw this event called “Storytelling Workshop,” and I was like, good! I’m good at making up stories, and, you know, that’s what I thought it was. Turns out it’s, you go and tell personal stories about your life that only involve you, that make you deeply vulnerable. And, by the way, I’m Iranian. We don’t do vulnerability. It’s just not a thing. So it was the most scary thing I’ve ever done in my life. But you go on stage and basically talk about your life. And the thing it taught me by both telling my own stories and listening to other people’s stories is that it showed me that you can connect to people through stories, first of all. The best ideas come when you’re actually in it together. Like one of the things that now I say that I didn’t used to say, we, we’re all human. And being human essentially means we have good things about ourselves and bad things about ourselves. And as researchers, we have our strengths as researchers, and we have our weaknesses as researchers. And so when we collaborate with other people, we bring all of that. And collaboration is a sacred thing that we do where we’re basically trusting each other with bringing all of that to the table and being that vulnerable. And so our job as collaborators is essentially to protect that, in a way, and make it safe for everybody to come as they are. And so I think that’s what it taught me, which is, like, basically holding space for that.

HUIZINGA: Yeah. How’s that working?

ARZANI: First of all, I stumbled into it, but there are people who are already “that” in this building …

ARZANI: … that have been for years. It’s just that now I can see them for what they bring, as opposed to before, I didn’t have the vocabulary for it.

HUIZINGA: Gotcha …

ARZANI: But people who don’t, it’s like what I’ve seen is almost like they initially look at you with skepticism, and then they think it’s a gimmick, and then they are like, what is that? And then they become curious, and then they, too, kind of join you, which is very, very interesting to see. But, like, again, it’s something that already existed. It’s just me not being privileged enough to know about it or, kind of, recognize it before.

HUIZINGA: Yeah. Can that become part of a culture, or do you feel like it is part of the culture here at Microsoft Research, or … ?

ARZANI: I think this depends on how people individually choose to show up. And I think we’re all, at the end of the day, individuals. And a lot of people are that way without knowing they are that way. So maybe it is already part of the culture. I haven’t necessarily sat down and thought about it deeply, so I can’t say.

HUIZINGA: Yeah, yeah. But it would be a dream to have the ability to be that vulnerable through storytelling as part of the research process?

ARZANI: I think so. We had a storytelling coach that would say, “Tell your story, change the world.” And as researchers, we are attempting to change the world, and part of that is our stories. And so maybe, yeah! And basically, what we’re doing here is, I’m telling my story. So …

ARZANI: … maybe you’re changing the world!

HUIZINGA: You know, I’m all in! I’m here for it, as they say. Behnaz Arzani. It is such a pleasure—always a pleasure—to talk to you. Thanks for sharing your story with us today on Ideas .

ARZANI: Thank you.

[1] For clarification, Arzani notes that she attended and received her PhD from the University of Pennsylvania. By “which is where I was from,” Arzani meant outside of those academic institutions well known for their technical programs.

Related publications

Solving max-min fair resource allocations quickly on large graphs, a holistic view of ai-driven network incident management, finding adversarial inputs for heuristics using multi-level optimization, meet the authors.

Gretchen Huizinga

Executive Producer and Host of the Microsoft Research Podcast

Behnaz Arzani

Principal Researcher

Continue reading

What’s Your Story: Weishung Liu

Abstracts: April 16, 2024

What’s Your Story: Ranveer Chandra

TLA+ Foundation aims to bring math-based software modeling to the mainstream

Research areas.

Research Groups

• Networking Research Group

Related tools

• MetaOpt: Towards efficient heuristic design with quantifiable and confident performance

Related projects

• MetaOpt: A Comprehensive Heuristic Analysis and Optimization Tool

Related labs

• Microsoft Research Lab - Redmond
• Follow on Twitter
• Like on Facebook
• Follow on LinkedIn
• Subscribe on Youtube
• Follow on Instagram

Share this page:

What is AI (artificial intelligence)?

Humans and machines: a match made in productivity  heaven. Our species wouldn’t have gotten very far without our mechanized workhorses. From the wheel that revolutionized agriculture to the screw that held together increasingly complex construction projects to the robot-enabled assembly lines of today, machines have made life as we know it possible. And yet, despite their seemingly endless utility, humans have long feared machines—more specifically, the possibility that machines might someday acquire human intelligence  and strike out on their own.

Get to know and directly engage with senior McKinsey experts on AI

Sven Blumberg is a senior partner in McKinsey’s Düsseldorf office; Michael Chui is a partner at the McKinsey Global Institute and is based in the Bay Area office, where Lareina Yee is a senior partner; Kia Javanmardian is a senior partner in the Chicago office, where Alex Singla , the global leader of QuantumBlack, AI by McKinsey, is also a senior partner; Kate Smaje and Alex Sukharevsky are senior partners in the London office.

But we tend to view the possibility of sentient machines with fascination as well as fear. This curiosity has helped turn science fiction into actual science. Twentieth-century theoreticians, like computer scientist and mathematician Alan Turing, envisioned a future where machines could perform functions faster than humans. The work of Turing and others soon made this a reality. Personal calculators became widely available in the 1970s, and by 2016, the US census showed that 89 percent of American households had a computer. Machines— smart machines at that—are now just an ordinary part of our lives and culture.

Those smart machines are also getting faster and more complex. Some computers have now crossed the exascale threshold, meaning they can perform as many calculations in a single second as an individual could in 31,688,765,000 years . And beyond computation, which machines have long been faster at than we have, computers and other devices are now acquiring skills and perception that were once unique to humans and a few other species.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

AI is a machine’s ability to perform the cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem-solving, and even exercising creativity. You’ve probably interacted with AI even if you don’t realize it—voice assistants like Siri and Alexa are founded on AI technology, as are some customer service chatbots that pop up to help you navigate websites.

Applied AI —simply, artificial intelligence applied to real-world problems—has serious implications for the business world. By using artificial intelligence, companies have the potential to make business more efficient and profitable. But ultimately, the value of AI isn’t in the systems themselves. Rather, it’s in how companies use these systems to assist humans—and their ability to explain to shareholders and the public what these systems do—in a way that builds trust and confidence.

For more about AI, its history, its future, and how to apply it in business, read on.

Learn more about QuantumBlack, AI by McKinsey .

Introducing McKinsey Explainers : Direct answers to complex questions

What is machine learning.

Machine learning is a form of artificial intelligence that can adapt to a wide range of inputs, including large sets of historical data, synthesized data, or human inputs. (Some machine learning algorithms are specialized in training themselves to detect patterns; this is called deep learning. See Exhibit 1.) These algorithms can detect patterns and learn how to make predictions and recommendations by processing data, rather than by receiving explicit programming instruction. Some algorithms can also adapt in response to new data and experiences to improve over time.

The volume and complexity of data that is now being generated, too vast for humans to process and apply efficiently, has increased the potential of machine learning, as well as the need for it. In the years since its widespread deployment, which began in the 1970s, machine learning has had an impact on a number of industries, including achievements in medical-imaging analysis  and high-resolution weather forecasting.

The volume and complexity of data that is now being generated, too vast for humans to process and apply efficiently, has increased the potential of machine learning, as well as the need for it.

What is deep learning?

Deep learning is a more advanced version of machine learning that is particularly adept at processing a wider range of data resources (text as well as unstructured data including images), requires even less human intervention, and can often produce more accurate results than traditional machine learning. Deep learning uses neural networks—based on the ways neurons interact in the human brain —to ingest data and process it through multiple neuron layers that recognize increasingly complex features of the data. For example, an early layer might recognize something as being in a specific shape; building on this knowledge, a later layer might be able to identify the shape as a stop sign. Similar to machine learning, deep learning uses iteration to self-correct and improve its prediction capabilities. For example, once it “learns” what a stop sign looks like, it can recognize a stop sign in a new image.

What is generative AI?

Case study: vistra and the martin lake power plant.

Vistra is a large power producer in the United States, operating plants in 12 states with a capacity to power nearly 20 million homes. Vistra has committed to achieving net-zero emissions by 2050. In support of this goal, as well as to improve overall efficiency, QuantumBlack, AI by McKinsey worked with Vistra to build and deploy an AI-powered heat rate optimizer (HRO) at one of its plants.

“Heat rate” is a measure of the thermal efficiency of the plant; in other words, it’s the amount of fuel required to produce each unit of electricity. To reach the optimal heat rate, plant operators continuously monitor and tune hundreds of variables, such as steam temperatures, pressures, oxygen levels, and fan speeds.

Vistra and a McKinsey team, including data scientists and machine learning engineers, built a multilayered neural network model. The model combed through two years’ worth of data at the plant and learned which combination of factors would attain the most efficient heat rate at any point in time. When the models were accurate to 99 percent or higher and run through a rigorous set of real-world tests, the team converted them into an AI-powered engine that generates recommendations every 30 minutes for operators to improve the plant’s heat rate efficiency. One seasoned operations manager at the company’s plant in Odessa, Texas, said, “There are things that took me 20 years to learn about these power plants. This model learned them in an afternoon.”

Overall, the AI-powered HRO helped Vistra achieve the following:

• approximately 1.6 million metric tons of carbon abated annually
• 67 power generators optimized
• $60 million saved in about a year

Read more about the Vistra story here .

Generative AI (gen AI) is an AI model that generates content in response to a prompt. It’s clear that generative AI tools like ChatGPT and DALL-E (a tool for AI-generated art) have the potential to change how a range of jobs  are performed. Much is still unknown about gen AI’s potential, but there are some questions we can answer—like how gen AI models are built, what kinds of problems they are best suited to solve, and how they fit into the broader category of AI and machine learning.

For more on generative AI and how it stands to affect business and society, check out our Explainer “ What is generative AI? ”

What is the history of AI?

The term “artificial intelligence” was coined in 1956  by computer scientist John McCarthy for a workshop at Dartmouth. But he wasn’t the first to write about the concepts we now describe as AI. Alan Turing introduced the concept of the “ imitation game ” in a 1950 paper. That’s the test of a machine’s ability to exhibit intelligent behavior, now known as the “Turing test.” He believed researchers should focus on areas that don’t require too much sensing and action, things like games and language translation. Research communities dedicated to concepts like computer vision, natural language understanding, and neural networks are, in many cases, several decades old.

MIT physicist Rodney Brooks shared details on the four previous stages of AI:

Symbolic AI (1956). Symbolic AI is also known as classical AI, or even GOFAI (good old-fashioned AI). The key concept here is the use of symbols and logical reasoning to solve problems. For example, we know a German shepherd is a dog , which is a mammal; all mammals are warm-blooded; therefore, a German shepherd should be warm-blooded.

The main problem with symbolic AI is that humans still need to manually encode their knowledge of the world into the symbolic AI system, rather than allowing it to observe and encode relationships on its own. As a result, symbolic AI systems struggle with situations involving real-world complexity. They also lack the ability to learn from large amounts of data.

Symbolic AI was the dominant paradigm of AI research until the late 1980s.

Neural networks (1954, 1969, 1986, 2012). Neural networks are the technology behind the recent explosive growth of gen AI. Loosely modeling the ways neurons interact in the human brain , neural networks ingest data and process it through multiple iterations that learn increasingly complex features of the data. The neural network can then make determinations about the data, learn whether a determination is correct, and use what it has learned to make determinations about new data. For example, once it “learns” what an object looks like, it can recognize the object in a new image.

Neural networks were first proposed in 1943 in an academic paper by neurophysiologist Warren McCulloch and logician Walter Pitts. Decades later, in 1969, two MIT researchers mathematically demonstrated that neural networks could perform only very basic tasks. In 1986, there was another reversal, when computer scientist and cognitive psychologist Geoffrey Hinton and colleagues solved the neural network problem presented by the MIT researchers. In the 1990s, computer scientist Yann LeCun made major advancements in neural networks’ use in computer vision, while Jürgen Schmidhuber advanced the application of recurrent neural networks as used in language processing.

In 2012, Hinton and two of his students highlighted the power of deep learning. They applied Hinton’s algorithm to neural networks with many more layers than was typical, sparking a new focus on deep neural networks. These have been the main AI approaches of recent years.

Traditional robotics (1968). During the first few decades of AI, researchers built robots to advance research. Some robots were mobile, moving around on wheels, while others were fixed, with articulated arms. Robots used the earliest attempts at computer vision to identify and navigate through their environments or to understand the geometry of objects and maneuver them. This could include moving around blocks of various shapes and colors. Most of these robots, just like the ones that have been used in factories for decades, rely on highly controlled environments with thoroughly scripted behaviors that they perform repeatedly. They have not contributed significantly to the advancement of AI itself.

But traditional robotics did have significant impact in one area, through a process called “simultaneous localization and mapping” (SLAM). SLAM algorithms helped contribute to self-driving cars and are used in consumer products like vacuum cleaning robots and quadcopter drones. Today, this work has evolved into behavior-based robotics, also referred to as haptic technology because it responds to human touch.

• Behavior-based robotics (1985). In the real world, there aren’t always clear instructions for navigation, decision making, or problem-solving. Insects, researchers observed, navigate very well (and are evolutionarily very successful) with few neurons. Behavior-based robotics researchers took inspiration from this, looking for ways robots could solve problems with partial knowledge and conflicting instructions. These behavior-based robots are embedded with neural networks.

Learn more about  QuantumBlack, AI by McKinsey .

What is artificial general intelligence?

The term “artificial general intelligence” (AGI) was coined to describe AI systems that possess capabilities comparable to those of a human . In theory, AGI could someday replicate human-like cognitive abilities including reasoning, problem-solving, perception, learning, and language comprehension. But let’s not get ahead of ourselves: the key word here is “someday.” Most researchers and academics believe we are decades away from realizing AGI; some even predict we won’t see AGI this century, or ever. Rodney Brooks, an MIT roboticist and cofounder of iRobot, doesn’t believe AGI will arrive until the year 2300 .

The timing of AGI’s emergence may be uncertain. But when it does emerge—and it likely will—it’s going to be a very big deal, in every aspect of our lives. Executives should begin working to understand the path to machines achieving human-level intelligence now and making the transition to a more automated world.

For more on AGI, including the four previous attempts at AGI, read our Explainer .

What is narrow AI?

Narrow AI is the application of AI techniques to a specific and well-defined problem, such as chatbots like ChatGPT, algorithms that spot fraud in credit card transactions, and natural-language-processing engines that quickly process thousands of legal documents. Most current AI applications fall into the category of narrow AI. AGI is, by contrast, AI that’s intelligent enough to perform a broad range of tasks.

How is the use of AI expanding?

AI is a big story for all kinds of businesses, but some companies are clearly moving ahead of the pack . Our state of AI in 2022 survey showed that adoption of AI models has more than doubled since 2017—and investment has increased apace. What’s more, the specific areas in which companies see value from AI have evolved, from manufacturing and risk to the following:

• marketing and sales
• product and service development
• strategy and corporate finance

One group of companies is pulling ahead of its competitors. Leaders of these organizations consistently make larger investments in AI, level up their practices to scale faster, and hire and upskill the best AI talent. More specifically, they link AI strategy to business outcomes and “ industrialize ” AI operations by designing modular data architecture that can quickly accommodate new applications.

What are the limitations of AI models? How can these potentially be overcome?

We have yet to see the longtail effect of gen AI models. This means there are some inherent risks involved in using them—both known and unknown.

The outputs gen AI models produce may often sound extremely convincing. This is by design. But sometimes the information they generate is just plain wrong. Worse, sometimes it’s biased (because it’s built on the gender, racial, and other biases of the internet and society more generally).

It can also be manipulated to enable unethical or criminal activity. Since gen AI models burst onto the scene, organizations have become aware of users trying to “jailbreak” the models—that means trying to get them to break their own rules and deliver biased, harmful, misleading, or even illegal content. Gen AI organizations are responding to this threat in two ways: for one thing, they’re collecting feedback from users on inappropriate content. They’re also combing through their databases, identifying prompts that led to inappropriate content, and training the model against these types of generations.

But awareness and even action don’t guarantee that harmful content won’t slip the dragnet. Organizations that rely on gen AI models should be aware of the reputational and legal risks involved in unintentionally publishing biased, offensive, or copyrighted content.

These risks can be mitigated, however, in a few ways. “Whenever you use a model,” says McKinsey partner Marie El Hoyek, “you need to be able to counter biases  and instruct it not to use inappropriate or flawed sources, or things you don’t trust.” How? For one thing, it’s crucial to carefully select the initial data used to train these models to avoid including toxic or biased content. Next, rather than employing an off-the-shelf gen AI model, organizations could consider using smaller, specialized models. Organizations with more resources could also customize a general model based on their own data to fit their needs and minimize biases.

It’s also important to keep a human in the loop (that is, to make sure a real human checks the output of a gen AI model before it is published or used) and avoid using gen AI models for critical decisions, such as those involving significant resources or human welfare.

It can’t be emphasized enough that this is a new field. The landscape of risks and opportunities is likely to continue to change rapidly in the coming years. As gen AI becomes increasingly incorporated into business, society, and our personal lives, we can also expect a new regulatory climate to take shape. As organizations experiment—and create value—with these tools, leaders will do well to keep a finger on the pulse of regulation and risk.

What is the AI Bill of Rights?

The Blueprint for an AI Bill of Rights, prepared by the US government in 2022, provides a framework for how government, technology companies, and citizens can collectively ensure more accountable AI. As AI has become more ubiquitous, concerns have surfaced  about a potential lack of transparency surrounding the functioning of gen AI systems, the data used to train them, issues of bias and fairness, potential intellectual property infringements, privacy violations, and more. The Blueprint comprises five principles that the White House says should “guide the design, use, and deployment of automated systems to protect [users] in the age of artificial intelligence.” They are as follows:

• The right to safe and effective systems. Systems should undergo predeployment testing, risk identification and mitigation, and ongoing monitoring to demonstrate that they are adhering to their intended use.
• Protections against discrimination by algorithms. Algorithmic discrimination is when automated systems contribute to unjustified different treatment of people based on their race, color, ethnicity, sex, religion, age, and more.
• Protections against abusive data practices, via built-in safeguards. Users should also have agency over how their data is used.
• The right to know that an automated system is being used, and a clear explanation of how and why it contributes to outcomes that affect the user.
• The right to opt out, and access to a human who can quickly consider and fix problems.

At present, more than 60 countries or blocs have national strategies governing the responsible use of AI (Exhibit 2). These include Brazil, China, the European Union, Singapore, South Korea, and the United States. The approaches taken vary from guidelines-based approaches, such as the Blueprint for an AI Bill of Rights in the United States, to comprehensive AI regulations that align with existing data protection and cybersecurity regulations, such as the EU’s AI Act, due in 2024.

There are also collaborative efforts between countries to set out standards for AI use. The US–EU Trade and Technology Council is working toward greater alignment between Europe and the United States. The Global Partnership on Artificial Intelligence, formed in 2020, has 29 members including Brazil, Canada, Japan, the United States, and several European countries.

Even though AI regulations are still being developed, organizations should act now to avoid legal, reputational, organizational, and financial risks. In an environment of public concern, a misstep could be costly. Here are four no-regrets, preemptive actions organizations can implement today:

• Transparency. Create an inventory of models, classifying them in accordance with regulation, and record all usage across the organization that is clear to those inside and outside the organization.
• Governance. Implement a governance structure for AI and gen AI that ensures sufficient oversight, authority, and accountability both within the organization and with third parties and regulators.
• Data management. Proper data management includes awareness of data sources, data classification, data quality and lineage, intellectual property, and privacy management.
• Model management. Organizations should establish principles and guardrails for AI development and use them to ensure all AI models uphold fairness and bias controls.
• Cybersecurity and technology management. Establish strong cybersecurity and technology to ensure a secure environment where unauthorized access or misuse is prevented.
• Individual rights. Make users aware when they are interacting with an AI system, and provide clear instructions for use.

How can organizations scale up their AI efforts from ad hoc projects to full integration?

Most organizations are dipping a toe into the AI pool—not cannonballing. Slow progress toward widespread adoption is likely due to cultural and organizational barriers. But leaders who effectively break down these barriers will be best placed to capture the opportunities of the AI era. And—crucially—companies that can’t take full advantage of AI are already being sidelined by those that can, in industries like auto manufacturing and financial services.

To scale up AI, organizations can make three major shifts :

• Move from siloed work to interdisciplinary collaboration. AI projects shouldn’t be limited to discrete pockets of organizations. Rather, AI has the biggest impact when it’s employed by cross-functional teams with a mix of skills and perspectives, enabling AI to address broad business priorities.
• Empower frontline data-based decision making . AI has the potential to enable faster, better decisions at all levels of an organization. But for this to work, people at all levels need to trust the algorithms’ suggestions and feel empowered to make decisions. (Equally, people should be able to override the algorithm or make suggestions for improvement when necessary.)
• Adopt and bolster an agile mindset. The agile test-and-learn mindset will help reframe mistakes as sources of discovery, allaying the fear of failure and speeding up development.

Learn more about QuantumBlack, AI by McKinsey , and check out AI-related job opportunities if you’re interested in working at McKinsey.

Articles referenced:

• “ As gen AI advances, regulators—and risk functions—rush to keep pace ,” December 21, 2023, Andreas Kremer, Angela Luget , Daniel Mikkelsen , Henning Soller , Malin Strandell-Jansson, and Sheila Zingg
• “ What is generative AI? ,” January 19, 2023
• “ Tech highlights from 2022—in eight charts ,” December 22, 2022
• “ Generative AI is here: How tools like ChatGPT could change your business ,” December 20, 2022, Michael Chui , Roger Roberts , and Lareina Yee  
• “ The state of AI in 2022—and a half decade in review ,” December 6, 2022, Michael Chui , Bryce Hall , Helen Mayhew , Alex Singla , and Alex Sukharevsky  
• “ Why businesses need explainable AI—and how to deliver it ,” September 29, 2022, Liz Grennan , Andreas Kremer, Alex Singla , and Peter Zipparo
• “ Why digital trust truly matters ,” September 12, 2022, Jim Boehm , Liz Grennan , Alex Singla , and Kate Smaje
• “ McKinsey Technology Trends Outlook 2023 ,” July 20, 2023, Michael Chui , Mena Issler, Roger Roberts , and Lareina Yee  
• “ An AI power play: Fueling the next wave of innovation in the energy sector ,” May 12, 2022, Barry Boswell, Sean Buckley, Ben Elliott, Matias Melero , and Micah Smith  
• “ Scaling AI like a tech native: The CEO’s role ,” October 13, 2021, Jacomo Corbo, David Harvey, Nicolas Hohn, Kia Javanmardian , and Nayur Khan
• “ What the draft European Union AI regulations mean for business ,” August 10, 2021, Misha Benjamin, Kevin Buehler , Rachel Dooley, and Peter Zipparo
• “ Winning with AI is a state of mind ,” April 30, 2021, Thomas Meakin , Jeremy Palmer, Valentina Sartori , and Jamie Vickers
• “ Breaking through data-architecture gridlock to scale AI ,” January 26, 2021, Sven Blumberg , Jorge Machado , Henning Soller , and Asin Tavakoli  
• “ An executive’s guide to AI ,” November 17, 2020, Michael Chui , Brian McCarthy, and Vishnu Kamalnath
• “ Executive’s guide to developing AI at scale ,” October 28, 2020, Nayur Khan , Brian McCarthy, and Adi Pradhan
• “ An executive primer on artificial general intelligence ,” April 29, 2020, Federico Berruti , Pieter Nel, and Rob Whiteman
• “ The analytics academy: Bridging the gap between human and artificial intelligence ,” McKinsey Quarterly , September 25, 2019, Solly Brown, Darshit Gandhi, Louise Herring , and Ankur Puri  

This article was updated in April 2024; it was originally published in April 2023.

Want to know more about AI?

Related articles.

Ten unsung digital and AI ideas shaping business

Driving innovation with generative AI

As gen AI advances, regulators—and risk functions—rush to keep pace