what is cultural evolution essay

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In This Article Expand or collapse the "in this article" section Cultural Evolution

Introduction, general overviews.

• Mathematical Models
• Laboratory Experiments
• Ethnographic Field Studies
• Phylogenetic Methods
• Comparative Studies of Nonhuman Culture
• Cultural Macroevolution
• Cultural Microevolution
• The Evolution of Cultural Evolution
• Cultural Drift
• Gene-Culture Coevolution
• Cultural Group Selection
• Language Evolution
• Evolutionary Economics
• Technological Evolution
• Evolutionary Synthesis in the Social Sciences

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Cultural Evolution by Alex Mesoudi LAST REVIEWED: 11 January 2012 LAST MODIFIED: 11 January 2012 DOI: 10.1093/obo/9780199766567-0038

“Cultural evolution” is the idea that human cultural change––that is, changes in socially transmitted beliefs, knowledge, customs, skills, attitudes, languages, and so on––can be described as a Darwinian evolutionary process that is similar in key respects (but not identical) to biological/genetic evolution. More specifically, just as Darwin described biological/genetic evolution as comprising three key components––variation, competition (or selection), and inheritance––cultural change also comprises these same phenomena. Yet while cultural evolution can be described as Darwinian in this sense, the details of the processes (e.g., how variation is generated, or how information is transmitted) are likely to be different in the cultural case compared to the details of biological/genetic evolution. Bearing these differences in mind, cultural evolution researchers have taken many of the same methods, tools, and concepts that biologists have developed to explain biological diversity and complexity and used them to explain similar diversity and complexity in cultural systems. These include phylogenetic methods to reconstruct “macroevolutionary” historical relations between cultural traits (e.g., languages or tools), ethnographic field studies to document and explain contemporary cross-cultural variation, laboratory experiments to determine the small-scale details of cultural “microevolution” (e.g., how cognitive biases favor certain ideas over others or whether we preferentially learn from certain people within a group), and mathematical models to explore the long-term and population-level consequences of those microevolutionary processes. Given this interdisciplinary breadth, it has been suggested that evolutionary theory may serve as a synthetic framework for unifying the social sciences, just as evolutionary theory synthesized the biological sciences during the early 20th century.

Mesoudi, et al. 2004 reviews evidence that cultural evolution is Darwinian by drawing an explicit analogy with Darwin’s original argument in On the Origin of Species published in 1859. Mesoudi, et al. 2006 then provides a more detailed overview of the field of cultural evolution, from phylogenetic analyses of cultural macroevolution to experimental and theoretical explorations of cultural microevolution. This overview is expanded in Mesoudi 2011 . Henrich and McElreath 2003 provides a similar brief overview of cultural evolution research, as does Richerson and Boyd 2005 . Laland and Brown 2011 provides an accessible overview of cultural evolution theory and research within the wider context of other evolutionary approaches to human behavior. A more detailed and empirically focused account of cultural evolution is provided by Durham 1991 .

Durham, W. H. 1991. Coevolution: Genes, culture, and human diversity . Stanford, CA: Stanford Univ. Press.

This book is a comprehensive and detailed account of how genetic and cultural evolution can interact, such as the coevolution of lactose tolerance alleles and dairy farming, or yam cultivation and sickle cell anemia.

Henrich, J., and R. McElreath. 2003. The evolution of cultural evolution. Evolutionary Anthropology 12:123–135.

DOI: 10.1002/evan.10110

This review of cultural evolution theory covers key issues such as when and why culture is biologically adaptive, and the cognitive mechanisms underlying cultural evolution.

Laland, K. N., and G. R. Brown. 2011. Sense and nonsense . 2d ed. Oxford: Oxford Univ. Press.

This book is a highly readable overview of evolutionary approaches to human behavior, including chapters on cultural evolution and gene-culture coevolution.

Mesoudi, A. 2011. Cultural evolution: How Darwinian theory can explain human culture and synthesize the social sciences . Chicago: Univ. of Chicago Press.

This volume is an accessible overview of contemporary cultural evolution theory and research.

Mesoudi, A., A. Whiten, and K. N. Laland. 2004. Is human cultural evolution Darwinian? Evidence reviewed from the perspective of The Origin of Species . Evolution 58:1–11.

This paper argues that cultural evolution can be described as Darwinian if it comprises variation, competition, and inheritance, and reviews evidence for each of these processes in human culture.

Mesoudi, A., A. Whiten, and K. N. Laland. 2006. Towards a unified science of cultural evolution. Behavioral and Brain Sciences 29:329–383.

This review of contemporary cultural evolution research covers the key methods used to study both cultural microevolution and macroevolution, along with brief commentaries from key researchers in the field.

Richerson, P. J., and R. Boyd. 2005. Not by genes alone: How culture transformed human evolution . Chicago: Univ. of Chicago Press.

This book is an accessible overview of the authors’ work on cultural evolution, which has defined the field for more than twenty-five years.

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Cultural Evolution: A Review of Theory, Findings and Controversies

• Synthesis Paper
• Published: 22 April 2015
• Volume 43 , pages 481–497, ( 2016 )

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• Alex Mesoudi 1 , 2  

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The last two decades have seen an explosion in research analysing cultural change as a Darwinian evolutionary process. Here I provide an overview of the theory of cultural evolution, including its intellectual history, major theoretical tenets and methods, key findings, and prominent criticisms and controversies. ‘Culture’ is defined as socially transmitted information. Cultural evolution is the theory that this socially transmitted information evolves in the manner laid out by Darwin in The Origin of Species , i.e. it comprises a system of variation, differential fitness and inheritance. Cultural evolution is not, however, neo-Darwinian, in that many of the details of genetic evolution may not apply, such as particulate inheritance and random mutation. Following a brief history of this idea, I review theoretical and empirical studies of cultural microevolution, which entails both selection-like processes wherein some cultural variants are more likely to be acquired and transmitted than others, plus transformative processes that alter cultural information during transmission. I also review how phylogenetic methods have been used to reconstruct cultural macroevolution, including the evolution of languages, technology and social organisation. Finally, I discuss recent controversies and debates, including the extent to which culture is proximate or ultimate, the relative role of selective and transformative processes in cultural evolution, the basis of cumulative cultural evolution, the evolution of large-scale human cooperation, and whether social learning is learned or innate. I conclude by highlighting the value of using evolutionary methods to study culture for both the social and biological sciences.

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If we are all cultural Darwinians what’s the fuss about? Clarifying recent disagreements in the field of cultural evolution

Cultural Evolution and the Evolution of Cultural Information

Darwinism Without Selection? A Lesson from Cultural Evolutionary Theory

Confusingly, the terms ‘social learning’, ‘social transmission’, ‘cultural transmission’, ‘cultural inheritance’ and variants thereof are used interchangeably within the field, to denote the passing of information non-genetically from one individual to another. Here I stick to the term ‘social learning’, although this may differ from cited sources.

Some of this latter school (e.g. Claidière et al. 2014 ) have argued that the existence of these transformative processes requires a major revision of the standard approach to cultural evolution presented in this article; I deal with this critique separately in a later section.

Earlier I discussed nineteenth century progressive Spencerian theories of cultural evolution. Currie et al.’s ( 2010 ) analysis presents an interesting empirical test of a version of those claims that societies increase in complexity, although it should be noted that (1) Currie et al.’s analysis is an empirical test, whereas Tylor and Morgan offered little empirical support for their progressive schemes; (2) Currie et al. precisely defined ‘complexity’ in terms of political hierarchy, whereas Tylor and Morgan were vague and conflated social organisation, technology and many other traits into a single scheme; and (3) Currie et al. showed that cultural evolution is not inevitably progressive, in that societies often lost social hierarchical levels.

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Mesoudi, A. Cultural Evolution: A Review of Theory, Findings and Controversies. Evol Biol 43 , 481–497 (2016). https://doi.org/10.1007/s11692-015-9320-0

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What is cultural evolution?

The core idea of cultural evolution is that cultural change constitutes an evolutionary process that shares fundamental similarities with – but also differs in key ways from – genetic evolution. Humans and other cultural species are the joint product of both our genetic and cultural inheritances.

To understand exactly what we mean by cultural evolution, first we need to define ‘culture’ and ‘evolution’.

What is culture?

When we use the term culture we don’t mean art, opera and novels like you find in the ‘Culture’ section of a newspaper. We mean something much broader. We define culture as any information that is passed from one individual to another via social learning , rather than genetically. Social learning (aka cultural transmission) can take the form of observation and imitation, spoken or written language, or teaching.

Culture therefore encompasses all of the knowledge, ideas, attitudes, opinions, languages, norms, institutions, music, art and technology that people learn from other people. Culture in this sense is not limited to humans. For example, chimpanzees have culture when they learn how to crack nuts by watching other chimpanzees, while birds and whales have culture when they learn songs from other members of their species.

What is evolution?

Charles Darwin is celebrated because he came up with the first workable theory of evolution. In The Origin of Species , Darwin laid out three key principles for evolution: variation, inheritance and selection. He applied these three principles to biological organisms. First, there is variation amongst individuals. For example, different finches might have differently shaped beaks. Second, this variation is inherited genetically. For example, a finch’s beak shape resembles that of its parents because of the transmission of genes. And third, there is sometimes selection of certain variants. For example, finches with larger beaks might out-reproduce smaller-beaked finches when a drought reduces the availability of smaller seeds in the environment.

Over time, this process leads to adaptation , as organisms become better suited to their environments, and diversification into different species, as different environments favour different forms. For example, some finches might evolve large beaks to open nuts and other finches evolve small beaks to feed on insects. Eventually the two populations might become different species. Darwin therefore saw evolution as tree-like, with different forms diversifying over time. Evolution is not progress along a linear ladder of increasing complexity or perfection.

Combining these two concepts gives us cultural evolution. When cultural information varies, when it is inherited via social learning, and when sometimes certain cultural variants spread more effectively than others, then we can say that culture evolves.

For example, say there are different forms of the past tense of the verb ‘to chide’ (principle of variation ). Some people say ‘chid’, others say ‘chided’. These forms are socially transmitted as people hear others say one or the other form and copy it (principle of inheritance ). Then imagine that the regular form ‘chided’ is easier to remember than the irregular form ‘chid’ (principle of selection ). Then the regular form ‘chided’ spreads over time. This is cultural evolution.

'chided', the regular past tense form of the verb 'to chide', has increased in frequency in literature over the last 200 years, while the irregular past tense form 'chid' has decreased. Source: Google ngrams.

Over time cultural evolution leads to adaptation , as cultural traits come to fit their environments (which can be physical, psychological or social), and diversification , as different populations converge on different cultural adaptations. Languages, for example, diversify over time in a broadly tree-like fashion to create diverse language families. English, French, Hindi and Bengali are all related by descent from a common Indo-European ancestor that existed around 8000-9000 years ago. Following biologists, we call these trees cultural phylogenies .

Just like genetic evolution, cultural evolution does not involve progress up a ladder. There is no sense in which entire societies are ‘more evolved’ or ‘less evolved’ than other societies, just like one species cannot be ‘more evolved’ or ‘less evolved’ than another species.

And just like in genetic evolution, not all cultural change is due to selection. Culture can change due to random chance or population structures such as bottlenecks. This is called cultural drift , analogous to genetic drift. Or migration and mutation can bring new cultural traits into a population, just as it brings in new genes.

Similar but different

So far, we have focused on the similarities between genetic and cultural evolution. But there are also important differences. Here are a few:

– in humans, genetic information flows from parents to children. Cultural inheritance has many more pathways. We get our culture not just from our parents but also from our peers, teachers, books, the internet and so on.

– genes are inherited with very high fidelity, whereas cultural traits are often transformed and reconstructed as they are passed from person to person. 

– genetic mutation is undirected, whereas cultural innovation is often directed towards specific goals. 

– cultural evolution features transmission biases such as conformity (following the majority) and prestige bias (copying high status individuals) which are absent in genetic evolution. 

Models, experiments and analyses of cultural evolution often incorporate these and other differences.

Many methods, many disciplines

Because our concept of culture is so broad, cultural evolution research encompasses many academic disciplines, including anthropology, archaeology, biology, computer science, economics, ethology, history, linguistics, neuroscience, politics, psychology and sociology. We also use many methods, including mathematical and computer models, lab and field experiments, ethnographic fieldwork, and historical and archaeological analyses.

As such, cultural evolution research is highly interdisciplinary and often draws on multiple methods to tackle the same problem. Just as evolutionary theory synthesised the biological sciences in the mid-20th century, so too we hope it might synthesise the social sciences and humanities.

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A widely accepted view in the cultural evolutionary literature is that culture forms a dynamic system of elements (or ‘traits’) linked together by a variety of relationships. Despite this, large families of models within the cultural evolutionary literature tend to represent only a small number of traits, or traits without interrelationships. As such, these models may be unable to capture complex dynamics resulting from multiple interrelated traits. Here we put forward a systems approach to cultural evolutionary research—one that explicitly represents numerous cultural traits and their relationships to one another. Basing our discussion on simple graph-based models, we examine the implications of the systems approach in four domains: (i) the cultural evolution of decision rules (‘filters’) and their influence on the distribution of cultural traits in a population; (ii) the contingency and stochasticity of system trajectories through a structured state space; (iii) how trait interrelationships can modulate rates of cultural change; and (iv) how trait interrelationships can contribute to understandings of inter-group differences in realised traits. We suggest that the preliminary results presented here should inspire greater attention to the role of multiple interrelated traits on cultural evolution, and should motivate attempts to formalise the rich body of analyses and hypotheses within the humanities and social science literatures.

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Introduction.

Research in cultural evolution aims at understanding and explaining cultural change at multiple causal levels (e.g., Mesoudi, 2011 ; Colleran and Mace, 2015 ; Gjesfjeld et al., 2016 ). Culture, like many targets in science, is complex, with multiple processes interacting at a variety of spatial and temporal scales. This is evident both in the multiple definitions of culture, many of which selectively highlight features and processes of culture and cultural change (Kroeber and Kluckhohn, 1952 ; Weiss, 1973 ; Keesing, 1974 ; Mesoudi, 2011 ), and in the variety of methods used to decompose and analyse the constituent causal processes of culture. Footnote 1 Despite variation among these attempts at describing and understanding the complexity of human culture, there has long been consensus on its key features: that culture is composed of a number of distinct elements (or traits), that these traits bear varying relationships to one another, and that these traits are realised in overlapping yet heterogeneous ways by different populations in the world.

In calling this a consensus, we draw attention to the long history of viewing culture as a complex dynamic system, composed of multiple traits and their relationships, which can change over time. This is a view arguably as old as the discipline of anthropology itself: clear precursors of such thinking can be found in the writings of British sociocultural evolutionists (Stocking, 1987 ) and the various schools of nineteenth century German anthropology (Smith, 1991 ). This consensus view persisted in the works of twentieth century American evolutionary anthropologists (Carneiro, 2003 ), as well as in anthropology’s interpretivist, structuralist, and post-structuralist traditions (Kuper, 1999 ). More relevant for current considerations, this consensus view is also evident in the qualitative descriptions accompanying early cultural evolutionary models (e.g., Cavalli-Sforza and Feldman, 1981 ; Boyd and Richerson, 1985 ) and in banner claims about the scope and power of cultural evolutionary theory (e.g., Sperber, 1996 ; Henrich, 2016 ).

Nonetheless, formal modelling within the contemporary cultural evolution literature has tended to idealise away key features of this consensus picture. Large families of models represent culture via a small number of traits, and, further, represent such traits as ‘atomic’ elements with no substantial interaction between them (e.g., Durham, 1991 ; Henrich, 2001 ; Kitcher, 2001 ; Henrich and Boyd, 2002 ; Rogers, 2010 )—with a few notable exceptions (e.g., Enquist et al., 2011 ; Kolodny et al., 2015 ). Typically, when multiple traits are represented, they are taken to vary along a single dimension (e.g., Cavalli-Sforza and Feldman, 1981 ; Boyd and Richerson, 1985 ; Henrich, 2004 ), or function as an index of some other feature of interest (e.g., Fogarty and Creanza, 2017 ; Fogarty, 2018 ). While these models are all significant achievements, by idealising away multiple traits and trait interrelationships, they may be unable to represent a range of phenomena; notably those where the clustering of traits influences the downstream origination, distribution, and change in the trait pool over time.

Consider, as an illustration of the complex relationships among traits, communities of the Tyva Republic. The Tyva are pastoralists who engage in seasonal migrations. As they migrate from pasture to pasture, the Tyva engage in costly rituals around cairns that mark out pasture boundaries, regional borders, and salient geographical landmarks. These costly rituals involve offerings of food, tobacco, money, and the performance of ritualised behaviour. As experimental and ethnographic evidence shows, a plausible explanation for the origin and persistence of these costly rituals appeals to the Tyva’s pastoral subsistence strategy. The rituals demonstrate to nearby populations the acknowledgement of local norms, and in so doing, may diffuse potential tensions about the use of common resources—such as pasture lands—by unfamiliar and potentially untrustworthy economic free riders. The costly rituals, then, signal trustworthiness and cooperation to the groups whose land may be being crossed and grazed (Sosis, 2005 ; Purzycki, 2010 , 2011 , 2016 ; Purzycki and Arakchaa, 2013 ).

This example shows how a rich system of interlocking religious practices, moral judgments, and patterns of subsistence can jointly explain the origin, organisation and persistence of costly rituals as a solution to intergroup relationships and the management of resources. Such a complex explanation, however, requires explicit consideration of multiple cultural traits, specific ecological circumstances, and salient interrelationships between the two.

The case of the Tyvan pastoralists is illustrative of the need for a broad theoretical and empirical endeavour aimed at capturing the dynamics of multiple cultural traits and their interrelationships. Here we motivate a systems approach as such an endeavour. We do so by examining implications of such an approach for key features of social transmission and the acquisition of traits, and how these generate macroevolutionary patterns and features. We illustrate these with simple models, and draw on a range of empirical and theoretical literatures to suggest how such models might be expanded into a broader research program. Though we here adopt a graph representation of trait interdependencies for modelling culture and cultural change, we nonetheless think there may be multiple ways of modelling cultural systems that better represent the complexity and heterogeneity of its constituent parts. Given this, the current paper may best be understood as offering one avenue through which a more fully-fleshed systems approach—that is, a distinctive approach encompassing novel models, concepts, and research questions—may be realised. The major contribution of this paper is to lay the conceptual foundation for such a research endeavour.

Despite the limited aspirations of the current piece, the conceptual ground-clearing we undertake here does suggest some immediate methodological and epistemological benefits that come with adopting a systems approach. Importantly, the explicit representation of traits and their interrelationships highlights how traits themselves function as a novel medium through which causes of cultural change can intersect at multiple levels. As we suggest below, the traits agents acquire can change how they learn, modulating the overall behaviour of the population in which they are a part. At the same time, the aggregate behaviour of the population can influence the availability and valence of such traits. The systems approach thus highlights how individual (micro) and population (macro) levels can influence one another through effects on trait relationships and availability. Here we predominantly focus on the first of these levels, looking at the effects of multiple traits and their interrelationships at the individual level. Yet we expect these models to complement the growing body of macro-level models (Kandler et al., 2012 ), and we return to consider multilevel causation and macro-level phenomena more fully in the discussion section.

A second important upshot is that a systems approach allows for the modelling of processes of path dependence and self-organisation (Enquist et al., 2011 ). Already well-recognised within evolutionary and systems biology (e.g., Kauffman, 1993 , Carroll, 2005 , Sansom, 2011 ), network interactions can impose structural and situational constraints that influence the synchronic behaviour and diachronic constitution of such networks. The graph-based models we adopt here provide some of the first links between this literature and cultural evolutionary theory—links that we also consider in more detail in the discussion.

The plan for the paper is as follows. After a brief introduction to the approach in the next section (§2 ‘What is a cultural system’), we highlight four domains of phenomena for which the systems approach has implications at both the microlevel and macrolevel: the cultural evolution of decision rules (‘filters’) and their influence on the distribution of cultural traits in a population (§3 ‘Cultural filters’); the contingency and stochasticity of system trajectories through a structured state space (§4 ‘Evolutionary trajectories and historical dependencies’), where trait interrelationships modulate rates of cultural change (§5 ‘Stability versus change’); and, where trait interrelationships contribute to inter-group differences in realised traits (§6 ‘Group phenomena’). We conclude by highlighting a number of possible avenues for future research, noting that a systems approach is poised to formalise and make explicit theories and hypotheses concerning culture that have been made in the humanities and social sciences.

What is a cultural system?

Researchers identify a wide variety of entities as candidate cultural traits. Typical lists include such diverse things as beliefs, myths, stories, and material artefacts, and often include larger societal structures like practices, norms, and institutions, like kinship systems or subsistence strategies (see e.g., Cavalli-Sforza and Feldman, 1981 ; Boyd and Richerson, 1985 ; Mesoudi, 2011 ; Henrich, 2016 ). Many of these elements bear connections, or relational properties, to one another that impact the acquisition, maintenance, and transmission of other traits. Beliefs, for instance, bear evidential and entailment relationships to other beliefs. If I believe that the dice are loaded, then I should change how often I expect to roll a seven. Material artefacts bear relationships to one another, often in ways that affect their functioning. Tin and copper, for instance, combine to make an alloy suitable for weapons and cookware, while tin and mercury make an amalgam suited for silvering mirrors. Speaking generally, models adopting a systems approach aim at capturing three key features: an explicit representation of multiple traits (perhaps of multiple trait types); trait relationships of different valence and character; and how traits and their relationships generate dynamic interactions over time. To put the motivation for a systems approach briefly, in human cultures, traits bear a wide range of relationships to one another, and these can have a variety of important consequences.

In the illustrations of this paper, we represent traits and their relationships as weighted graphs, where the nodes are the cultural traits, and there is a weighted edge with a positive value between two nodes if the traits are compatible, and with a negative value if they are incompatible. Relationships can also be asymmetric and represented with directed edges. In our simulations, there is a well-mixed population of agents, who are gradually replaced through a birth-death process. Agents can acquire traits either by inventing, through sampling from the universe of available traits, or by copying other agents. Agents copy traits with a probability proportional to how compatible the observed trait is to all other traits in the agent’s current repertoire. The ideas in this paper are most clearly illustrated using small cultural systems and trait universes, so we will typically include only a few traits in the models, but our approach is general and could easily be scaled up to include many traits, with a range of asymmetric compatibilities on a continuous scale. For a specification of the simulation model and the parameter values used in the different examples, along with Python code implementing it, see the Supplementary information.

One important kind of consequence of a systems approach bears upon how traits may be distributed in a population. To see this, consider a simple model with four trait types: A, B, C, and D. Assume that these trait types begin with equal starting frequencies in a generational model with random copying. On the assumption that traits are acquired independently of one another, one would expect the frequency of trait types to be autocorrelated over time, varying only with the vagaries of random copying. Yet when pairwise relations are introduced—for instance, where traits pairs (A, B) and (C, D) (or AB and CD for short) facilitate the acquisition of their partners and inhibit the acquisition of other traits (e.g., C and D inhibit the acquisition both of A and of B, and vice versa) (Fig. 1 )—this simple arrangement generates very different dynamics, ones that eventually settle into an equilibrium state where most agents have either AB or CD trait pairs (Fig. 2 ).

Cultural system with simple attraction and repulsion. The left panel shows which pairs of traits attract and repel, and the right panel shows an example with individual repertoires and relationships between individuals

Number of individuals with 0 to 4 traits, over time. Two traits can either be compatible or incompatible

Of course, the nature and effects of trait interrelations themselves may change over time. This too is an important consequence of approaching culture as a system constituted by linked elements. Note that a preference (for a cultural trait) can also be considered a cultural trait. Shifts in preferences and beliefs are particularly noteworthy, as these both govern behaviour and change constantly in the face of exposure to new evidence and ideas (Fig. 3 ). In our modelling framework, preferences could be modelled with a positively weighted edge from the preference trait to the preferred trait.

Cultural system with preference traits. The left panel shows examples of traits relationships, where + indicates a preference for a trait, and – a preference against it. The right panel shows examples of three individual repertoires, where II acts as a cultural model. Individual I is more likely to copy II, including the preference for B, since I prefers A, while individual III has an aversion to II due to trait A

The complex tangle of changing traits and relationships can be illustrated by looking to the work of Heidi Colleran and colleagues ( 2015 ; Colleran, 2016 ; Colleran and Snopkowski, 2018 ) on the demographic transition—the decline in fertility that has been observed in multiple human populations over the previous two centuries. The demographic transition is a striking trend, with families around the world increasingly limiting themselves to two or fewer children. It is also an unusual trend, evolutionarily speaking, since standard evolutionary reasoning would hold that organisms should produce as many viable offspring as their resources allow.

As Colleran articulates it, the demographic transition is a complex phenomenon, with tangled and imbricated causal processes interacting at multiple levels. Decisions on childrearing are influenced by the makeup of social networks, the prevailing social norms, ties among kin groups, socioeconomic classes, and more encompassing structures such as the regulations and institutions of the local polity and state. Nonetheless, distinct causal pathways and their effects can be discerned. For instance, combining ethnographic work with sophisticated network and statistical analyses, Colleran ( 2016 ) and Colleran and Mace ( 2015 ) were able to chart the distribution of contraceptive strategies used (if any) among a group of communities in Poland—separating out general contraceptive strategies (any decision or strategy for controlling fertility) from ‘artificial’ contraceptives (encompassing a range of modern contraceptive technologies).

Colleran’s explanation highlights both individual-level and population-level causes. At the individual level, agents exerted variable influence: knowledge and use of contraception strategies by close kin and friends were key causal factors in determining not only whether any particular individual would use contraception, but also the particular strategy adopted. Yet community level indicators such as religiosity and education played an important role modulating and changing both the rate at which contraceptive strategies diffused through populations, and the particular strategies adopted. Highly educated populations accelerated the adoption of contraceptive strategies in general, but had limited effects on the spread of artificial contraceptives. Highly religious populations, on the other hand, tended to slow down the adoption of artificial contraceptive use, but not the diffusion of contraceptive strategies and decision-making more generally. Thus while individual networks and the transmission of knowledge and preferences are important, average population-level characteristics also influence the diffusion of contraceptive strategies by changing the background conditions against which individual transmission occurs (Colleran and Mace, 2015 ).

This example illustrates both the aspirations and the difficulties of a systems approach to culture: there is an enormous range of possible traits and trait relationships that are affected by wide-ranging causes. We cannot hope to offer an exhaustive taxonomy of such entities and effects in this paper. Nor do we suggest that the models we develop here provide more than thumbnail sketches as to how multiple interrelated traits might influence the composition and structure of culture over time. Nonetheless, by combining illustrations using graphs and simulation models with existing empirical research, we hope to articulate a number of implications of such models, sketch a number of compelling research objectives, and provide the conceptual tools for developing a distinctive systems approach to culture.

Importantly, we see the humanities and social sciences as playing an important role in the development of a systems approach. From Marxist approaches to postmodernism, researchers in philosophy, anthropology, literary studies, sociology, and many more besides have developed a range of theories and hypotheses about how best to describe cultural traits, their interrelationships, and the structures that they produce. It would be an overwhelming task to summarise the riches of the many fields in the humanities and social sciences, but we suggest that these resources have mostly not been integrated into the datasets or everyday theorising of cultural evolutionary research.

The reasons for this lack of integration may be a number of disciplinary and methodological features. One might be reticence on the parts of humanities and social scientific scholars regarding the past history of unilinear theory, which promulgated racist and Eurocentric accounts of cultural development and change (Steward, 1955 ). Another might be the failure of current work in cultural evolution to speak to the phenomena that interest researchers within the humanities and social sciences, perhaps because of mutual ignorance of the rich literatures within the humanities and social sciences (Ingold, 2007 ) and of cultural evolution (Lewens, 2015 ). Or, perhaps, the lack of integration may reflect methodological differences, with many of the theories and results of the humanities being resistant to formulation in formal, quantified models (Mesoudi, 2011 ).

These are all legitimate explanations for the lack of integration and conversation between the cultural evolutionary literature and other scholars within the humanities, social sciences, and natural sciences. Yet we think one roadblock not sufficiently addressed concerns the family of models used by many cultural evolutionary researchers. While humanities and social science scholars are interested in complex phenomena—often involving the interaction between behaviour rich in semantic information, networks of social interactions, material artefacts and persisting institutions—many prominent cultural evolutionary models focus on the evolution of a few select cultural traits, or traits that vary along a single dimension (Cavalli-Sforza and Feldman, 1981 ; Boyd and Richerson, 1985 ; Durham, 1991 ; Mesoudi et al., 2006 ; Rogers, 2010 ). Moreover, when such models do build in more traits, these typically are taken to evolve independently of one another (Hahn and Bentley, 2003 ; Henrich, 2004 ; Bentley and Shennan, 2005 ; Enquist and Ghirlanda, 2007 ; Enquist et al., 2008 ; Strimling et al., 2009 ; Eriksson et al., 2010 ; Aoki et al., 2011 ). Though these families of models are impressive, and have generated a rich body of research, they represent a substantial epistemic gambit, one akin to that undertaken by mid-twentieth century work in population genetics (Provine, 1971 ). Within cultural evolutionary theory, this strategy holds that the dynamics and structure of cultural evolutionary phenomena can be extrapolated from models that represent a small number of cultural traits interacting in independent (or non-epistatic) processes. This kind of strategy licences the modelling of simple trait systems, either with an eye to describing the kinematics of those simple systems, or to illuminate the evolution and operation of mechanisms underpinning their transmission (e.g., Boyd and Richerson, 1985 ; Henrich, 2004 ).

To be clear, many, but by no means all, modelling families in contemporary cultural evolution are based on equations and results drawn from populations genetics. Yet, even those that do not tend to adopt the epistemic gambit of extrapolating from simple trait systems that model only a few, independent entities. These models have produced an exceptional range of compelling theoretical and empirical results. Yet what we are stressing here is that these models need to be complemented by those that explicitly represent how multiple traits and their interrelationships together affect the downstream distribution and structure of the cultural trait pool. In these circumstances, a systems approach that explicitly represents these elements and their relationships is needed. We turn to highlight these scenarios in the next four sections.

Cultural filters

A common view among many cultural evolutionary researchers is that the cognitive architecture implicated in cultural evolution is composed of special-purpose evolved cognitive mechanisms (Sperber, 1996 ; Sperber and Hirschfeld 2004 , 2006 ; Boyd and Richerson, 2005 ; Richerson and Boyd, 2005 ; Mesoudi, 2011 ; Sperber and Mercier, 2017 ). As a case in point, early cultural evolutionary models (e.g., Boyd and Richerson, 1985 ) explicitly assumed that mechanisms for social learning and selective social learning strategies were under genetic control. Subsequent modelling and empirical work continued to assume the innate nature of these strategies—like prestige bias (Henrich and Gil-White, 2001 ) and conformity bias (Henrich, 2001 )—usually on the basis of their perceived ubiquity in human populations (cf. Henrich, 2016 ).

Yet recent empirical research challenges many of these assumptions. Consider the recent work on selective social learning—the capacities involved in adopting particular strategies for learning from others. Work in both experimental and developmental psychology plausibly suggests that selective social learning strategies emerge from simple associative learning, where learners acquire links between certain individuals or cues and the value of information (reviewed by Heyes, 2018 ). This dovetails with developmental results that suggest that children preferentially attend to models on the basis of a number of cues, including competency, reliability, status, and certainty, as well as features including relative age, resemblance, and sex (Wood et al., 2013 ). Other evidence suggests that the nature of the cues, and their weighting in particular circumstances, is also controlled by associative mechanisms (Behrens et al., 2008 ; Heyes, 2018 ). Selective social learning may thus result from simple mechanisms of learning conjoined with exposure to the local structure of the informational landscape. Such exposure leads to the association between simple cues and the identification of agents bearing useful information across a range of situations.

More generally, there is growing empirical research supporting the claim that even central capacities of human social learning may be culturally evolved. Philosophers and psychologists have recently argued that the plasticity of human psychology provides opportunity for the acquisition not only of strategies for learning (as above) but also of novel cognitive functions. Kim Sterelny ( 2003 ), for instance, has argued that mindreading capabilities—the capacity to attribute and explain behaviour using mental state attributions—are assembled in development in an environment “soaked not just by behaviourally complex agents, but with agents interpreting one another” (p. 222). Such an assertion is backed up by a range of empirical results that suggest that the acquisition of key capacities differs in sequence and rate across different developmental and cultural circumstances (Siegal and Peterson, 2008 ; Wellman and Peterson, 2013 ; Shahaeian et al., 2013 ; Peterson et al., 2017 ). More recently, Cecilia Heyes ( 2018 ) has argued that not only mindreading, but also imitation, selective social learning strategies, and language may be the result of simple domain-general learning capacities occurring within culturally enriched, and perhaps designed, learning environments.

These accounts suggest that cultural evolution may be critically involved in the evolution of what we call filters : ‘decision rules’ that modulate the flow of traits in a cultural system. Footnote 2 These filters not only include those involved in acquiring traits—such as is the case with selective social learning, which sifts and sorts different sources of information—but also those involved in innovating (deciding whether to introduce a trait or set of traits to a system) and diffusing traits (deciding, out of many traits, which to express). We call these capacities ‘filters’ because they do just that: they filter out some traits while letting others through.

At this point, it is helpful to distinguish between origin explanations and distribution explanations (Godfrey-Smith, 2012 ). The accounts emphasised above provide origin explanations, which aim at explaining how a particular trait came about, often by pointing to studies in palaeoanthropology, developmental and experimental psychology, and cognitive neuroscience that lay out the evolutionary and developmental circumstances required for certain capacities to come about. Sterelny ( 2003 ) and Heyes ( 2018 ) are exemplary in this regard in bringing together a wealth of such data in their synthetic cultural evolutionary accounts of the origin of critical cognitive capacities of human beings.

Distribution explanations, by contrast, explain the distribution of traits in a population, or across populations. Food preferences represent one domain where filters may contribute to a distribution explanation. There is great between-culture variation in patterns of acceptance and rejection of food, and individuals are often strongly influenced by their cultural backgrounds in what foods they come to like or find distasteful (Rozin, 1988 ). Though only supported anecdotally, acceptance of fermented foods—for example, the slimy Japanese soybean ferment called natto or the strongly ammonia-scented fermented shark kæstur hákarl from Iceland—is often highly regionalised and culture specific (Katz, 2012 ). This may be because food acceptance or rejection is often tightly linked to culture-specific norms around what is considered disgusting (Rozin et al., 2016 ). Fermented foods are, after all, foods in a controlled process of decomposition. In this example, culture-specific norms influence individuals in filtering out possible traits ( natto, kæstur hákarl ) as incompatible with those they already possess.

In the discussion, we offer some speculations as to how a systems approach may contribute to origin explanations. But by and large, the graph operationalisation of cultural systems adopted here is apt for providing distribution explanations that demonstrate how cultural filters might modulate downstream distributions of traits.

We close this section by considering two ways in which such modulation might occur. The first is through direct , or trait, filtering , where the relationships between traits influence the distribution of other traits. The case of food preferences is case in point. Here, the history of trait sampling by a population means that only some traits are available for individuals to acquire. These realised traits then influence decision making: some foods are desirable, while others are filtered out in virtue of being disgusting.

Yet traits might also be modulated through indirect filtering , for instance, where such filters determine with whom one associates. One such indirect filter is the example of selective social learning (or model-based filtering) given above, where individuals selectively choose from whom to learn on the basis of informational cues. With such a filter, the traits one acquires will be skewed by the model one is oriented towards. Another indirect filter is a similarity filter , where individuals associate with others who bear similar traits (sometimes called homophily ), either through deliberate choice of association, or by pruning their social networks of individuals with dissimilar traits (Axelrod, 1997 ; Centola et al., 2007 ). Unsurprisingly, similarity filters decrease the within-group heterogeneity while increasing the across-group heterogeneity of realised traits.

Evolutionary trajectories and historical dependencies

Interdependencies among traits reduce the number of sets of cultural combinations that are likely or even possible, and as a consequence, the number of likely or possible evolutionary trajectories that lead to those assemblages. For clarity of illustration, we will here consider strict dependencies, such that traits are not only facilitated by, but also contingent on, the existence of other traits. This can be illustrated by a simple unidirectional example. Consider ten traits, labelled by the first letters of the alphabet. Were the traits to be unilinearly dependent, as in Fig. 4a , subject to stepwise acquisition—such that B was contingent on the existence of A, C on B, and so on—then there are only ten possible cultural combinations: one for each trait, including all the preceding traits it is contingent on. There is also only one trajectory for each combination: the one that passes through each trait in alphabetical order, up to the last possible addition.

Example dependencies between traits. Here, traits are ( a ) unilinearly dependent, ( b ) arranged in a tree structure, ( c ) combined in different ways. Traits can also be acquired in different sequences ( d , e ) and inhibit other traits

Compare this to the case where traits are independent, with no limitations on the order of acquisition. In this case, the state space of possible combinations explodes. Any combination of traits is possible, so the state space equals the power set of the ten cultural traits, meaning that the number of potential combinations is 2 10  = 1024 (minus one if we exclude the case of having no culture), and doubles for every trait added. The number of evolutionary trajectories that lead to such states is almost ten million ( \(\mathop {\sum}\nolimits_{i = 1}^{10} {\mathop {\prod}\nolimits_{j = 1}^{10} {j = 9,864,100}}\) ). Interdependencies thus provide a path dependence that can significantly facilitate the emergence of a particular cultural system on several occasions.

Relationships between traits can also lead to more complex and diverse cumulative culture, beyond the trivial accumulation of making culture larger by adding independent elements to a collection of traits, and beyond the predetermined stepwise acquisition of the previous example.

Cumulative culture is likely to be a significant contributor to path dependence in cultural evolution. When traits are preserved and build upon past innovations, culture generates traditions —historical chains of cultural variants linked through patterns of cultural transmission. Cultural evolutionary researchers often use the metaphor of a ‘ratchet’ to describe this historical process, since like a ratchet, things move steadily in a single direction—changes are kept, ‘ratcheted’, into the future rather than ‘slipping back’ over multiple transmission events (Tomasello, 1999 ; Dean et al., 2014 ). This ratcheting metaphor is meant to capture the way that cumulative culture differs from a range of possible (cultural) evolutionary scenarios—for instance, where evolution occurs stochastically, moves cyclically through a range of variants, or merely tracks environmental features in ways that do not involve building upon priorly held cultural variation. In so doing, cumulative culture can explain the production of climate appropriate clothing (Boyd and Richerson, 2005 ), counter-intuitive food extraction and processing techniques (Henrich, 2016 ), social organisation and institutions (Bowles and Gintis, 2013 ; Richerson et al., 2014 ), the differentiation and specialisation of tools (Basalla, 1988 ), and culturally evolved cognitive novelties (Heyes, 2018 ). Because cumulative culture produces traditions where future states of the tradition depend on the past states of that tradition, it is the kind of process that generates path dependence.

There have been only a few attempts within the cultural evolution literature that describe or model path dependence, partly since most previous models cannot describe historical processes of ‘ratcheting’ in ways that account for dependencies between traits. One recent exception is a model of the cumulative evolution of technology (Kolodny et al., 2015 ). Central to the model is a highly structured description of a cultural state space, which delimits three kinds of cultural innovations. The structure involves a central ‘main-axis’ with stepwise modification as in the unidirectional example above, but each trait on the main-axis can also be modified in a separate direction, to create ‘toolkit innovations’, and traits on the main-axis can be combined. While this sequential and combinatorial structure may be apt for understanding the evolution of (some aspects of) technological evolution, it seems less apt for characterising the opportunistic and creative processes involved in myth and storytelling (Morin, 2016 ; Acerbi et al., 2017 ), ritual and religions (Whitehouse, 2000 ), or social norms and institutions (Sperber, 1996 ; Bowles, 2004 ).

The combinatorial combination of traits in Kolodny and colleagues’ model draws attention to the various relationships between traits. As illustrated by Enquist et al. ( 2011 ), two important kinds of interdependencies that can structure the cultural state space includes the combination and differentiation of elements. A sweater consists of a combination of cloth and thread, items which can be used also for other purposes. Even though a needle is not part of a sweater, it vastly facilitates the creation of one. With the further introduction of cultural traits, for example, dyes or pigments, we can have a differentiation of sweaters, such as different colours. Simple graphs exemplifying such relationships are given in Fig. 4b, c . Representing relationships between traits in graphs like these enable us to easily describe facilitative and inhibitory relations, characterise the possible and likely trajectories of cultural evolution, and to consider how such relationships among traits themselves might produce new kinds of path dependent phenomena.

The existence of an organised—that is, structured—cumulative culture means that culture can carry traces of its historical trajectory, and, thus, has deep history (Sterelny, 2014 ; Sterelny and Hiscock, 2014 ). To illustrate this, consider the graph in Fig. 4d , with four traits, A, B, C and D, of which the latter three depend on the existence of another trait, and which can all be inhibited by another trait. For clarity of illustration, let us assume that the inhibition is strong enough to completely suppress the inhibited trait, such that the carrier loses it. Footnote 3 Were these traits to be independent, there would exist 2 4  = 16 possible cultural states (including the possibility of having no culture). The traits of such independent assemblages may have occurred in any order (and if traits can disappear and reappear, then potential evolutionary trajectories are boundless), and as a result, the state of a particular system contains no information on its history, except that its constituent elements must all have occurred (at least once) at some point. The relationships between traits, posited in Fig. 4d , halves the number of possible states, and there is one unique trajectory leading to each of these states. The possible states that include at least one trait (and the corresponding trajectories) are: {A} (A), {B} (A → C → B), {C} (A → C), {D} (A → C → B → D), {A, B} (A → B), {A, D} (A → B → D), and {C, D} (A → C → D). Even if the present state of a cultural system does not include all traits that had (at some point) been acquired over their evolutionary trajectory, the scheme of relations makes it possible to recreate their evolutionary history. The fact that culture, due to these structural constraints, often carries so much of its history also enables cultural evolution to have complex path dependence while having the Markov property in terms of predictability: while historical events dictate where we are now, the future cultural states depend only on the present state.

It is a straightforward conclusion from the fact that cultural traits can have downstream effects arising from their interrelations, or compatibility, that acquiring certain traits can have vast effects on which traits can be acquired later on, and thus potentially lead to cultural systems that differ in most of the traits they include. For an extreme example, consider the tree-like structure in Fig. 4c . Each new acquired trait prevents the acquisition of the traits on the other branch, by making them unreachable.

Yet it is not only which traits are acquired that determines which cultural states are accessible, but also the sequences of events can determine which traits can coexist. Let the traits B and C be dependent on A, and B be compatible with C but inhibit A, as in Fig. 4e . The two traits B and C can then be maintained simultaneously, in the same system, provided that B is acquired first. If, on the contrary, C is acquired first, then there are no traits allowing for the acquisition of B. As an example, A may be a generic or non-explanatory answer to a politically charged issue, B a populist answer, and C a complex answer providing a real explanation. B could then be attractive enough not to be lost in a population even in face of a real answer, and even if it would not appear if there already existed such an answer, while C could easily replace the unsatisfactory answer A. The importance of the sequence of acquisition is further amplified if B and C enable different clusters of traits down the line.

For a more concrete example based on Fig. 4e , consider the Lancet MMR autism fraud. In 1998, former physician Andrew Wakefield (A) submitted a paper linking the MMR vaccine to colitis and autism spectrum disorders. (B) The paper was accepted and led to a drop in vaccination rates and a loss of confidence in their safety, with a concomitant increase in anti-vaccination propaganda (e.g., Gross, 2009 ). However, the paper was filled with flaws, the results had been misinterpreted, it had been conducted unethically, and its main findings were later refuted, which led to (C) a late rejection (a retraction) of the paper by the Lancet twelve years later (Dyer, 2010 ). Even so, the strengthening of the anti-vaccination movement that B sparked, and the spread of anti-vaccination ideas it caused, was not cancelled out by C. Had the paper been rejected, C, directly, without publication, then that would have inhibited B and its consequences.

Stability versus change

Empirical observations of cultural phenomena reveal extensive variation in the rate at which culture changes. These rates can range from traits and systems that remain more or less the same over many generations, to traits and systems that change rapidly within a single generation. For instance, there are many examples of religious beliefs and social norms that have remained similar over long periods of time (Geertz, 1973 ; Glenn, 2010 ). At the same time, however, clothing styles may be subject to fast changes (Shepard, 1972 ; Belleau, 1987 , Herzog et al., 2004 ). Not only are there diverse rates of change, but these rates themselves may also change over time. To give one example, Gjesfjeld and colleagues ( 2016 ) show how changing rates of origination and extinction rates have changed the landscape of car models, with competition between manufacturers being a substantial driver of a decreased diversification in automobile models. And, of course, different elements within culture may vary in their rates of change. Comparative and phylogenetic studies of language evolution, for instance, demonstrate both fast and slow changes in different lexical and grammatical elements (e.g., Greenhill et al., 2017 ).

A number of explanations have been suggested for the variation in the rate of change in cultural evolution, including external factors such as the physical and ecological environment (Vegvari and Foley, 2014 ), demographic factors (Powell et al., 2009 ), and cultural complexity (Querbes et al., 2014 ). Here, we explore how trait relationships and a systems view of culture could potentially explain variation in the pace of cultural change. Two factors seem important to consider. One is the intrinsic properties of traits that determine their relationships with other traits, and the other is filtering processes that may favour collections of traits that either promote stability or drive change. We first consider trait relationships that can promote stability and then relationships that can drive changes. We end with describing systems with fashions or fad-like dynamics, in which traits may change more quickly than when they are modelled as independent traits.

It is a plausible extension of the idea that traits are more or less compatible with one another that traits which mutually support each other’s transmission could form stable cultural clusters that are maintained over many generations. We have investigated this idea in a series of simulations similar to those in the other sections. Here we generated a situation with 20 traits with predominantly negative relationships, and explored how groups of two, three or four mutually supporting traits could influence each other’s existence in such a trait environment. Examples of these simulations are illustrated in Fig. 5 (see the Supplementary information for more details). It shows, in the situation explored, that two mutually supporting traits promote each other only ephemerally, with three traits the effect was stronger, and finally with four traits a stable cluster was formed. Note that two traits have only one relationship; three traits have three relationships; and four traits have six relationships that can support stability (in general, n ( n  − 1)/2, where n is the number of supporting traits).

Mutual support may maintain system configurations over a long time (where a time step is one round of interactions). The number of traits supporting each other is two in the top panel, three in the middle panel and four in the bottom panel, from a total of twenty traits. The figure shows the frequencies of the supporting traits and the average frequency of the other traits included in the simulations

Though the model emphasises the stability brought about through compatibilities between traits, incompatibilities or negative relationships could also contribute to a stable cluster if these inhibit traits outside the present cluster. This would decrease the likelihood of new traits invading the cluster in question.

This supports previous work showing that such conservative tendencies can easily evolve (Ghirlanda et al., 2006 ; Acerbi et al., 2009 ; Acerbi et al., 2014 ). In these models, individuals are born open and acquire traits in interactions with other individuals. Whether copying occurs depends on how compatible the observed trait is with the other traits already acquired by the individual (this is what we call trait filtering above). The reason why conservatism evolves in these models is that open individuals are more likely to acquire traits that make them more conservative while conservative individuals are less likely to acquire traits that make them more open. Over generations, increasingly conservative systems become established.

As one can see, the stability of cultural systems—or as is more likely to be the case, specific trait assemblages—is plausibly promoted both by mutual relationships among its parts, and potentially by incompatible relationships with traits not part of the system. This describes one kind of evolutionary history; here, trait assemblages successively increase internal compatibility and decrease external compatibility. If such features were to characterise most traits of a cultural system, then one would expect such a system to eventually enter a basin of attraction where little subsequent change could occur. However, as we will see, there are also circumstances and arrangements of trait relationships that promote change.

While mutual support can give rise to stable cultural systems, there are other relationship distributions that will promote change rather than stability. Some arrangements may even give rise to rates of change that are higher than for independently evolving traits. One type of trait relationship that would promote change rather than stability is an asymmetric relationship between two traits: for instance, trait A may facilitate the acquisition of a trait B while B has the opposite effect on A (inhibiting its acquisition). Such an asymmetric relationship could lead to a succession of trait replacement events. If A appears first, then it will promote B, but when B becomes common, it will cause A to disappear. The processes are directly dependent on properties of the current cultural system and can lead to an accelerating generation of new cultural traits (Lehman, 1947 ; Ogburn, 1950 ; Enquist et al., 2011 ; Kolodny et al., 2015 ).

Theoretical work also shows how fluctuations in the rate of change may arise, with periods of rapid change interspersed with periods of slow change (Aoki, 2015 ). Within the humanities, a classical model for such fluctuations in the rate of change is the ideas of dialectic processes (Cohen, 1978 ), which recognises that cultural systems may give rise to internal contradictions that promote substantial changes to the system. This idea seems fully compatible with the theory of cultural systems suggested in this paper. However, we are not aware of any theoretical work demonstrating for instance a correlation between the rate of change and the degree of internal conflict or incompatibility in a system.

Evolving traits relationships may, under certain assumptions, give rise to very high rates of change typical of fashion or fashion-like phenomena (Acerbi et al., 2012 , Michaud, 2019). To see this, suppose that there are two kinds of traits, both of which can be transmitted between individuals. The first kind is composed of display traits like a colour or a style, and the other preference traits, which are linked to specific display traits. During their lifetime, individuals acquire and display traits and preferences through their interactions with other individuals. This set-up has two consequences for the individual. First, the acquisition of display traits will increase or modify the individual’s efficacy as a cultural model. If an individual’s display traits are popular, then that individual will be copied more frequently than an individual with less popular display traits. Second, preferences acquired by the individual determines which individuals it will tend to learn from. Note that with these assumptions, there are no absolute or permanent standards for what makes a display trait popular.

Among a group of individuals, these social learning processes will give rise to a highly unstable but clearly patterned scenario of cultural evolution, in which systems of preferences and display traits change quickly in cycles of outburst and decay (Acerbi et al., 2012 ). A cycle starts with a preference for a particular display trait stochastically becoming common among currently popular individuals. This increases the spread of the preference in the population, which in turn spreads the trait. However, as soon as the trait starts to be common, the preference starts to disappear. The reason for this is that individuals with the preference change faster than individuals without the preference (see the discussion about evolution of conservatism above). Thus, more individuals with the preference will lose it than individuals without the preference will gain it. An example from the model of Acerbi and colleagues is shown in Fig. 6 with the lag between preference and the corresponding display trait. The changes that occur in this model can be faster than the rate of change occurring when traits evolve independently of each other, because both the rise and fall of the display trait are actively driven by the fast changes in preferences.

Example of a fashion cycle generated by an evolving mixture of display and preference traits. The example is based on the model of Acerbi et al. ( 2012 )

In most examples in this paper, trait relationships are assumed to be fixed and exogenously given, for instance by the nature of traits, logical constraints, interaction with reality or genetic predispositions. However, in the simulation model of Fig. 6 , the relationships between traits themselves are subject to cultural evolution.

Group phenomena

Cultural systems may also help to explain the emergence of cultural groups. A single trait may suffice to distinguish between members of different groups. Yet for the existence of such group-defining traits to be a causal factor influencing the behaviour of others—for instance, to serve as a signal for intra-group and inter-group biases or for overt prejudice towards other groups—and for the existence of the trait to be formed and maintained, such a trait needs to be interdependent with those that induce the relevant behaviour.

There are numerous examples of how important groups are for dispersal of ideas about the world and our behaviours towards other people, and how easily they form. A famous example is the minimal group paradigm (Tajfel, 1970 ; Tajfel et al., 1971 ), where discrimination emerges between groups based on arbitrary divisions. There is a vast empirical literature on group phenomena that we cannot cover here, but there are for example metastudies on group biases across cultures (Balliet et al., 2014 ; Romano et al., 2017 ), and surveys on how opinions and beliefs are reinforced in groups, polarising views on the societal level (Lamm and Myers, 1978 ; Isenberg, 1986 ; Abrams and Hogg, 1990 ) and on the importance of sharing several cultural traits for emotional closeness between individuals (Curry and Dunbar, 2013 ), showing that a cultural systems approach to understanding group formation may be viable.

Small systems of two or a few more traits may explain the maintenance of pre-existing groups in specific situations. In the modelling literature, there is typically an underlying strategic situation in the specified form of a game, usually a prisoners’ dilemma, where the group structure supposedly facilitates altruistic behaviour by coupling the trait of a group marker with cooperative behaviour towards individuals with that marker. The objective of such studies is to find a mechanism for an ingroup bias. Typically such models are based on a biologically inherited marker (e.g., Riolo et al., 2001 ; Hammond and Axelrod, 2006 ; Jansen and van Baalen, 2006 ) that also requires spatial assortment and kin selection (see Read, 2010 ; Jansson, 2013 ) or rapidly changing markers (Fu et al., 2012 ). Changing the underlying game can replace the spatial assortment (Jansson, 2015 ) and also allow for cultural nonstatic markers to coevolve with behaviour (McElreath et al., 2003 ; Efferson et al., 2008 ). Typically, the models are based on strategic situations and try to explain cooperative behaviour exclusively to members of your group through some kind of greenbeard effect (Dawkins, 1976 , 1982 ), group selection (Choi and Bowles, 2007 ), direct reciprocity through spatial structure or making group traits more flexible than behavioural traits, or reputation (Masuda and Ohtsuki, 2007 ; Grey et al., 2014 ) (for a review, see Masuda and Fu, 2015 ). As will be illustrated below, a cultural systems approach may contrast with such approaches by moving beyond strategic situations, pre-existing groups and biological inheritance.

There are also models implicitly based on simple cultural systems. Examples include polarisation and clustering based on shared traits (Schelling, 1971 ; Axelrod, 1997 ), set structured populations (Tarnita et al., 2009 ), and individuals structuring into groups (Grey et al., 2014 ). Schelling’s ( 1971 ) segregation model, for instance, entails simple systems that can maintain homogeneous views among actors, or opposing views that are somewhat balanced in numbers of advocates. The latter systems are unstable, and the population ends up segregating into cliques of homogenous sub-populations. Similar patterns emerge when agents copy more from the agents in the vicinity with whom they already have the most in common (Axelrod, 1997 ). This idea is a bit more generalised and explicitly connected to cultural systems in what is referred to as evolutionary set theory (Tarnita et al., 2009 ). Here, agents can become and stop being members of any number of available sets, that is, they have a number of cultural traits, and they interact more with agents with whom they share many traits, again in a strategic situation facilitating cooperation between similar agents. An even more bottom-up approach to group formation is one where the ideas are about the other agents, and agents interact more when they gain positive experience from previous interactions, and where they also exchange views on third parties, leading to clustering (Grey et al., 2014 ). Apart from cooperative interactions, there are also models of how social network structure emerges from similarity in several cultural traits (Centola et al., 2007 ; Centola, 2015 ).

Using the framework of cultural systems suggested here, we can potentially generalise mechanisms of group formation, polarisation and prejudice further, as by-products of relationships between the traits that agents possess.

Consider the previous example of a cultural system with simple attraction between traits A and B, and C and D, and repulsion between all other pairs (Fig. 1 ). We saw that the cultural evolution of such a system leads to a dominance of compatible traits. Looking at the prevalence of the individual traits over time (Fig. 7 ), we see that, typically, the population has not converged on sharing the same pair of compatible traits, but the two systems, AB and CD, tend to coexist (for further details on the simulation, see the Supplementary information). The relationships between traits have thus led to the spontaneous formation of two incompatible cultural groups.

Prevalence of cultural systems over time. Most agents have either traits A and B, or C and D

Contrasting with the previous modelling approaches described above, there are no utilities involved. Groups have not been formed because it is rational or there is a selective pressure on the individuals, nor of spatial or social assortment, and the groups are defined only by cultural traits. This illustration merely points at the potential of explaining various group phenomena, and this particular example pertains more to polarisation into two camps than ethnic groups. However, with more clusters of mutually compatible traits, the population could polarise into several, and potentially overlapping, groups.

Relationships can also vary and be endogenous. A recent and related model specifically representing preferences (Goldberg and Stein, 2018 ) finds cultural variation divided into two clusters also when the compatibility between traits evolves culturally, through associative diffusion that takes place by pairwise displays and observations of cultural traits. When agents see two traits used together, they increase their association between them and make their preferences for them more similar, resulting in a cluster of traits that a part of the population likes and another cluster that they dislike, with the other part of the population having opposite preferences.

These cultural systems also provide opportunities for path dependence at the group level. New traits that enter the population might be absorbed by individual members of only one of the groups, depending on how they relate to existing traits, or even more groups may form. When clusters of compatible traits grow, the groups that are defined by them become more stable, and limit exchange between groups. Trait dependencies should thus not only allow for groups to form, but also for them to be maintained over time, and eventually be associated with beliefs, as well as behavioural and phenotypic traits. Examples may include prejudice, group biases and closeness between individuals.

At a more abstract level, this illustrates how multiple cultural systems can exist in parallel, also when the relationships between the traits are exogenously given (e.g., set by physical reality or logical constraints) and the potentially available traits are the same for all individuals in the population. At a higher-order level, cultural systems may themselves regulate the relationships between traits (e.g., having A and B may regulate how compatible C and D are). Cultural evolution may thus also give rise to multiple cultural systems that differ not only in what traits are included in a cluster, but also in how compatible those traits and potential traits outside that cluster are.

The cultural systems approach articulated here highlights a range of novel and emerging research areas in the cultural evolutionary literature. We have here focused on its implications for four such areas: (i) the cultural evolution of ‘filters’ that modulate processes involved in acquisition, invention, and transmission; (ii) the path dependent trajectories of cultural systems that carry signals of that system’s history; (iii) the rates of cultural change and diversification; and (iv) the formation and dynamics of cultural groups.

A noteworthy feature of these domains is that they display self-organisation: that the relationships between traits play a large part in which trait combinations are realised (in individuals, groups), and that these may, in turn, influence the downstream acquisition, innovation, and diffusion of traits. So, for instance, filters may themselves be culturally evolved decision rules aimed at optimising various goals, and path dependent explorations of trait pools may depend on the relationships holding between traits.

The modulating effects of self-organisation can be ephemeral, systematic, and everything in between—with the effect and duration of self-organised features contingent upon the vagaries of cultural evolution. Above we focused on the possibility of systematic influences, where cultural evolution itself provides the circumstances for the reliable acquisition of trait complexes and their effects in populations. This is for the simple reason that such complexes are likely to have pervasive and long-term effects, with broad implications.

The phenomenon of self-organisation is underappreciated in the modelling work of cultural evolutionary theory—even if the idea itself has some currency in the broader anthropological, philosophical, and evolutionary literature (e.g., Kauffman, 1993 ; Deacon, 1997 ; Sterelny, 2012 ). This may be because self-organising structures are only visible in approaches that represent multiple traits and their interrelationships. As we hope to have shown above, even when a few traits are employed, trait relationships can generate a wide range of interesting and novel dynamics. A cultural systems approach thus not only makes conspicuous self-organising phenomena, but provides a flexible set of tools for investigating and understanding them.

Another important feature of the systems approach is that it can address questions at multiple levels. We have here illustrated how cultural systems identify distinctive features at the trait, individual, and population level. As illustrated in Figs 5 – 7 , the consideration of relationships between traits can enrich the dynamics of population-level outcomes through microlevel models. We also saw how such relationships could channel the characteristics of individuals, modulate homogeneity and heterogeneity, and alter the pace of cultural change. Such processes might also bring about group formation in stable clusters or fashion cycles, and can explain aggregate measures at the group level that are difficult to generate with independent traits.

To take one example, when discussing rates of change in fashions or fads, we highlighted how the acquisition of preference and preferred display traits can generate rapidly fluctuating dynamics at the aggregate level. A cultural systems approach can thus complement already existing strategies at employing ‘population thinking’ (Lewens, 2015 ) by exploring how the endogenous links within cultural systems interact with individuals to realise population-level phenomena. Mapping the link between microlevel mechanisms and macrolevel outcomes to the scheme of Coleman ( 1986 ), cultural systems along with frequencies of traits in the population pose structural and situational constraints on agents, who adopt traits selectively through copying and filtering, producing updated frequencies and a new subset of associated structural and situational constraints.

Thus, a systems approach provides a framework for understanding how individual actions generate macroevolutionary causes, and how these can feed back to influence microevolutionary interactions, via their influence on trait availability and interrelationships (such as preferences). Our models have focused predominantly on the first of these mechanisms, pointing to areas where a systems approach can illuminate how individual-level behaviour generates population-level patterns. For instance, trait interrelationships can drive differentiation between cultural groups and modulate the tempo and mode of cultural evolution. We have further suggested that cultural filters may be an important mechanism at play to change macroevolutionary patterns by influencing and modifying these trait interrelationships.

At the same time, we pointed to empirical work showing how population-level causes can influence individual-level behaviour by modulating trait availability and desirability. Heidi Colleran’s work on the diffusion of contraceptive technology demonstrates how the influence of the average behaviour of the population (here, religiosity and education) can influence the availability and attitudes towards contraceptive knowledge and use. Group dynamics too can polarise and cause clustering of traits among distinct populations, further altering trait availability and desirability.

It is also possible that systems themselves may interact at the macro-level. Though we have not focused on such a possibility in this paper, we above highlighted the work of Erik Gjesfjeld and colleagues ( 2016 ) who explored the changing rates of origination and extinction in the production of car models. The system-level properties that feed into such origination and extinction rates—broad relationships between manufacturing strategies, state policies, demand cycles, oil production, and the like—provide yet another avenue of potential investigation for the systems approach.

As we hope to have stressed above, the idea of cultural systems is not a new one. It is not only a consensus view, but one that has long been subject to analysis and theorising in anthropological thought, especially where a range of thinkers have described cultures as systems subject to evolutionary change (Steward, 1955 ; Sahlins, 1960 ; Kroeber and Parsons, 1958 ; Geertz, 1973 ; Diener, 1980 ; for a general review, see Carneiro, 2003 ). Yet for the most part, these researchers deployed systems thinking in a qualitative way—often drawing a variety of analogies between cultures and specific systems like organisms or species. What is distinct about the approach motivated here—and what it adds to the already existing use of systems thinking—is that it employs the tools of formal modelling. The bottom-up style of systems modelling used in our examples is flexible and open-ended, providing the opportunity to explore a wide range of hypotheses by creatively modifying and combining different combinations of trait universes and agent properties.

This approach complements and generalises some recent models that have also adopted a strategy of modelling multiple traits and their interrelationships. Goldberg and Stein ( 2018 ), for instance, employ a similar framework to explore the role of what they call ‘constraint satisfaction’ in changing the trait interrelationships in a small trait pool (what they call a ‘semantic network’). This work explores how the compatibility and incompatibility of traits can be socially constructed and modified over time. In a different vein, Claidière and colleagues ( 2014 ) employ ‘evolutionary causal matrices’ to explore the effect that trait types have on the absolute number of said trait types over time. This is mostly analogous to what we have discussed as selective trait filters. Without explicitly representing the compatibility or incompatibility between traits, or the specific decision rules that determine the acquisition or modification of traits, these matrices directly model the filtering effects that traits have on the downstream composition of both individual and group systems.

Speaking generally, we have here illustrated how a systems approach—particularly one that builds upon the strategy of investigating the strategies of acquiring, innovating, and diffusing culture in a rich trait universe—generates new tools for explaining cultural evolutionary phenomena. Already, the results given above reveal multiple areas for future enquiry. In particular, exploration that goes beyond disjunctive compatibility or incompatibility has the potential to generate a richer set of dynamics. At the same time, building in different kinds of trait relationships—such as those necessitating the sequential acquisition of certain traits—offers the possibility of exploring more realistic trait universes.

As we have suggested in numerous places above, a systems approach also has the potential to connect with, and help to explore, other issues in cultural evolutionary theory. In particular, it seems apt for exploring issues at the intersection of demography, population size, and the size of population-level cultural systems (Henrich, 2004 ; Powell et al., 2009 . Cf. Vaesen et al., 2016 ). Along the same lines, it seems apt for connecting with the palaeoanthropological literature on the rates of change in cultural traits over time, where this includes both stasis and rapid change. The radical stasis of lithic technologies in the lower and middle Pleistocene and the radical change in culture that occurs in the Holocene (Mithen, 2005 . Cf. McBrearty and Brooks, 2000 ) provide a rich set of phenomena for exploration by a systems approach.

As we noted above, many of the extant cultural evolutionary models are based on those developed in evolutionary biology. Researchers in cultural evolution motivate the adoption of such models by means of analogy: the seeming similarity of transmission processes in cultural and genetic evolution has given warrant for the exploration of cultural evolutionary dynamics based on models using replicator dynamics or other population-genetic tools. We do not here wish to contribute to the growing literature that explores how researchers have developed analogy (e.g., Sperber, 1996 ; Lewens, 2015 ). Instead, we merely wish to point out that analogies often function to highlight salient avenues of empirical research, and that there are many such fruitful avenues.

We have here been inspired in part by work in systems biology—particularly that which describes the evolution of organisation and constraint within complex dynamic systems (Kauffman, 1993 ). To illustrate this analogy, consider HOX genes—an important class of deeply conserved homeobox genes that regulate patterns of development across almost all eukaryotes (Bürglin and Affolter, 2016 ). HOX genes regulate the site-specific development of morphology, so that limbs grow in species-typical fashion (Krumlauf, 1994 ), and manipulation of these genes can lead to odd mutations, such as Drosophila with legs where antennae normally form (Carroll, 2005 ). HOX genes are one instance of a structure that, once it has arisen, persists over time—forming a set of tools that can be tweaked to generate diversity. They serve as a signal and explanation for the similarity in body plan across different evolutionary groups.

Systems biology studies such homeobox genes as an instance of ‘constraint-based generality’ (Green, 2015 )—here understood as the ways in which systems tend to self-assemble a structure that constrains the possibilities in which it can change in the future (O’Malley, 2012 ). Some of these structural constraints that researchers have identified include core components and weak regulatory linkage (Kirschner and Gerhart, 2006 ), generative entrenchment (Wimsatt, 2001 ), and network robustness (Jaeger et al., 2015 ). These are structures that limit the evolutionary trajectories likely to occur, but in so doing, minimise the risk of lethal mutations, and, perhaps, increase the tempo of evolution.

Our guiding thought is that similar kinds of constraint-based principles and self-assembling features can help in understanding cultural systems. Like HOX genes, it seems likely that at least some cultural traditions are tightly integrated in virtue of their role in ensuring the socioeconomic viability of cultures over time (cf. Boyd et al., 1997 ). We expect these ‘cultural cores’ (Steward, 1955 ) to share several features, given their role in mitigating recurrent socioecological problems concerning resource allocation, free-rider problems, warfare, and the like (Sterelny, 2012 , 2016 ). Such cores could be usefully explored using a constraint-based approach that investigates the likely trajectories that populations will traverse over evolutionary time frames.

Yet here we also urge caution. Along with other researchers (e.g., Richerson and Boyd, 2005 ; Mesoudi, 2011 ), we would stress that cultural evolution works differently from biological evolution. As has often been noted, the social nature of culture means that ideas, traditions, beliefs, and technologies can readily diffuse between populations. The free-flowing transmissibility of culture—though analogous to a limited extent with horizontal gene transfer—is likely to generate unique dynamics and rates of change. Cultural traits are not necessarily transmitted as one package, but are acquired and lost in multiple steps, with the consequence that they can be individually selected on how compatible they are to other acquired traits. This suggests that realised cultural traits in a population may have radically different histories and transmission dynamics.

Beyond developing new analogies to on-going empirical research, a systems approach to culture has the potential to connect with the wide range of humanities and social science literature that have made general hypotheses about the formation, nature, and dynamics of cultural change. As we suggested above, the idea that culture can be understood as a system has been a mainstay of anthropological thinking in the twentieth century. Yet these ideas are also found in the classical works of sociology, linguistics, and economics (Marx, 1867 /1990, Saussure, 1959 , Durkheim, 1995 ), and are now widespread throughout the humanities and social sciences. To touch on just a few areas, systems thinking seems to be implicated in understanding gender structures (e.g., Walby, 1989 ), social norms, attitudes and ideology (e.g., Boutyline and Vaisey, 2017 ; Inglehart, 2018 ; Jansson et al., 2013 ; Strimling et al., 2019 ), and systems of language (e.g., Greenhill et al., 2017 ), technology (e.g., Franklin, 1999 ), economy (e.g., Wallerstein, 1974 ), and religion (e.g., Geertz, 1973 ). A systems approach provides a promising bridge to the as of yet unexplored wealth of theorising about culture coming from within the humanities and social sciences.

Of course, a systems approach brings with it a distinct set of challenges. Compared to population-level models of single or independent traits, incorporating relationships between traits introduces a higher level of complexity. This decreases the tractability of cultural evolution models, while simultaneously increasing degrees of freedom. Given the wide variety of possible outcomes, modifying parameters might induce significant changes to modelling results.

It is uncontroversial that culture is an organised system. What we have argued here is that explanations of several cultural phenomena are sensitive to the relationships between traits, and, further, that empirical and theoretical research suggests that these phenomena are central to culture and cultural change. In other words, acknowledging trait interrelationships opens up rich dynamics that can generate empirically observable patterns unattainable for models that represent traits in isolation. This suggests that we should not shy away from the challenge of adding this extra layer of complexity to our cultural evolutionary models.

Simulation model

In the simulations, there is a universe of cultural traits with relationships between them. A relationship between two traits consists of a compatibility score of 1 if the traits are compatible, and −1 if they are incompatible. A universe is specified for each illustration in the manuscript (see below). There are 100 agents, each with an individual cultural repertoire, consisting of a subset of traits from the universe. At the outset, the agents are naive, with empty repertoires, but acquire traits through innovation (of traits from the universe) and copying from other agents.

The agents meet in random interactions. One round of interactions, or a time step, includes copying, invention and a birth death process. First, each agent, the receiver, samples one other agent as a cultural model. The model randomly selects one of the traits, i, in its repertoire for display to the receiver. The receiver copies the trait with a probability determined by the average compatibility score s of the trait with the receiver’s current repertoire, that is,

where \(c_{ij}\) is the compatibility between i and j , and \(R \;\ne\; \emptyset\) is the set of traits in the receiver’s repertoire. If the receiver has no traits, \(R = \emptyset\) , then s  ≔  0. The probability of copying is determined by the logistic equation

The constant 10 was arbitrarily chosen, but values below around 5 give the score a small influence and the results were not sensitive to scores above that value.

Each agent then invents a new trait with probability 0.001; that is, it randomly selects a trait from the universe and adds it to its repertoire (if the agent does not already possess the trait). Finally, each agent dies with probability 0.01 (0.0025 in Section 6 – the lower rate provides more stability), and is replaced by a new naive agent.

In the sections ‘What is a cultural system?’ and ‘Group phenomena’, the universe consists of four traits, A, B, C and D, where A and B are mutually compatible, and C and D are also mutually compatible, but all other pairs of traits are mutually incompatible.

In the section ‘Stability versus change’, the universe consists of 20 traits. Four of these are named, A, B, C and D. In the first simulation A and B are compatible, in the second A, B and C are all compatible, and in the third all four are compatible. The remaining trait pairs (including C and D in the first, and D in the second simulation) are set to be mutually compatible with probability 0.1, and otherwise they are mutually incompatible.

See data availability to access the code (in Python).

Data availability

The models used in this paper were implemented in Python. The program along with code to generate data for the figures are available in a Dataverse repository: https://doi.org/10.7910/DVN/KKDZX8 .

For some recent reviews of this interdisciplinary literature and the variety of empirical and theoretical methods employed, see Mesoudi, 2011 , and Henrich, 2016 .

While the cognitive capacity for such ‘filters’ would be genetically evolved, as cultural traits are acquired, they will be increasingly shaped by cultural evolution. However, this is not to discount the likely existence and relevance of innate biases that modulate and influence processes involved in cultural acquisition, innovation, and change. For some discussion of these issues, see Cowie, 1999 , Sterelny, 2012 , Lewens, 2015 , and Heyes, 2018 .

Losing a trait can represent different actions depending on what is being studied. An individual can forget a piece of information, lose a skill or a preference, or suppress the use and display of the trait (if the focus is on visible culture). If, for example, trait A is a preference for X and B a preference against it, then B replaces A.

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This work was supported by the Knut and Alice Wallenberg Foundation (grant number 2015.0005), the Leverhulme Trust (grant number RG95309), and the Isaac Newton Trust (grant number G101655). Open access funding provided by Stockholm University.

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Buskell, A., Enquist, M. & Jansson, F. A systems approach to cultural evolution. Palgrave Commun 5 , 131 (2019). https://doi.org/10.1057/s41599-019-0343-5

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The Cultural Evolution of Human Nature

Mark stanford.

Institute of Cognitive and Evolutionary Anthropology, University of Oxford, Oxford, UK

Recent years have seen the growing promise of cultural evolutionary theory as a new approach to bringing human behaviour fully within the broader evolutionary synthesis. This review of two recent seminal works on this topic argues that cultural evolution now holds the potential to bring together fields as disparate as neuroscience and social anthropology within a unified explanatory and ontological framework.

Essay review of Cecilia Heyes: Cognitive Gadgets: The Cultural Evolution of Thinking , Harvard University Press, 2018, 304 pp; and Robert Boyd: A Different Kind of Animal: How Culture Transformed Our Species , Princeton University Press, 2018, 248 pp.

Introduction

What makes humans different from other animals? It is an old question, and one which many have given up as meaningless or unanswerable. The simple fact of our unparalleled range and adaptability as a species cannot be denied, and indeed we seem to feel we are special for many more reasons than that. One response to this instinct is to resist it: having evolved like all other animals, our weird and wonderful panoply of behaviours is the result of neither individual creativity nor cultural supervenience, but is instead the simple product of millions of years of evolution, like the mating dance of birds or the territorial displays of octopi. If they seem unintelligible or even maladaptive, that is simply because evolution is very slow; our minds are adapted not to our present environment, but to the long vanished world of our ancestors (Cosmides and Tooby 2003 ). It may seem to us that behaviours such as joining religious cults, suicide bombing and racial prejudice are as irrational as they are undesirable, but in fact, on the account offered by standard evolutionary psychology, they are perfectly intelligible as the results of our ‘stone age minds’ misfiring and triggering behaviours that were adaptive long ago (Cosmides and Tooby 1987 ).

But this account has never sat entirely well with the vast cultural variation of our species, and in recent years, culture itself has come under a novel evolutionary lens (Henrich 2017 ; Richerson and Boyd 2005 ). Based on the insight that the class of mathematical models specified by evolutionary theory can be applied not only to genes, but to many other forms of information transmission, cultural evolutionary theory posits that human cultures do not vary, change and remain stable at random, but that cultural information is itself subject to evolutionary forces. Practices and ideas are differentially imitated and transmitted depending on unconscious cues such as the status or success of the individuals who bear them, and at times based on the way they interact with aspects of our cognitive apparatus. Cultural evolution proceeds much more quickly than genetic evolution, because individuals can change behaviours within one lifetime, and there is no need to wait for death or reproduction for the distribution of cultural traits to change. What is more, genetic and cultural evolution can interact; cultural adaptation can remove the pressure for genetic adaptation, and even change the adaptive environment to which genetic change responds.

The Cultural Evolutionary Synthesis

In A Different Kind of Animal ( 2018 ), Robert Boyd, one of the founding fathers of the discipline of cultural evolution, sketches the current state of the field, and outlines his current theoretical position. Boyd places particular emphasis on blind imitation, as opposed to intelligent design, as an explanation for the diffusion of innovations throughout a population. That is, he shows that we become better adapted to our environments not by thinking consciously about how to solve problems better, but simply through evolutionary processes triggered by blind imitation.

Much evolutionary research in recent decades has been dedicated to attempting to explain co-operation, and until recently, many have argued that human co-operation is principally to be explained by reference to the small size and high degree of relatedness of hunter-gatherer groups. Large-scale co-operation may then be a result of a ‘misfiring’ of mechanisms developed to cope with that ancient environment. But it has become increasingly clear many human hunter-gatherer groups do not fit this description, and that even in the case of smaller-scale co-operation problems, many hunter-gatherer bands appear to organise co-operation through mechanisms other than kin helping and direct reciprocity (Henrich 2018 ). Indeed, as Boyd shows, contra the common intuition, large scale co-operation with non-kin has been extraordinarily common throughout our history as a species. Far from contributions to the public good being a ‘misfiring’ caused by evolutionary environments in which no one was a stranger, this sort of co-operation has always been with us.

The implications for evolutionary theory are profound. Boyd argues that the bulk of human co-operation is in fact undergirded not by kin helping or direct reciprocity, but by social norms: humans are extraordinarily well-adapted rule enforcers, often intrinsically motivated not only to enforce rules themselves, but also to punish others who do not punish violators. Boyd argues that the so-called ‘second-order free-rider problem’ is not a concern here; so long as only some fraction of individuals fail to punish those who do not punish, while others do so, the proportion of non-punishers should decrease with each successive iteration of the problem. Thus our strong tendency to follow and enforce social norms turns them into a powerful tool for enforcing co-operation at a large scale.

Why, then, are many norms detrimental to individuals and even maladaptive from the point of view of the individual or the group? For Boyd, the explanation lies in our exquisite ability to enforce and stabilise any norm at all; like our tendency to imitate successful others, it is content-neutral. And while our norm psychology has evolved genetically because it is highly advantageous, its content neutrality means that we are prone at times to adopt norms which themselves may not be advantageous, and may even be detrimental. The selection of adaptive versus maladaptive norms is a distinct process that takes place, Boyd argues, through cultural group selection. Those groups that adopt advantageous norms will be more likely to survive, to attract new members, and to be imitated. Thus at any given time, all manner of norms may be present across multiple groups, but cultural evolution suggests there is an ongoing process of selection and adaptation.

Neither must it be the case that maladaptive behaviours were adaptive at some point in the past. A large part of evolutionary psychology has been predicated on the assertion that much of our present-day behaviour is to be explained by reference to adaptive fitness in a distant, imagined past environment. Thus although behaviours may appear maladaptive now, they are present because in that past environment, they were advantageous. Against this, Boyd suggests that many apparently maladaptive traits may be explained without reference to a hypothetical past. For if many cultural practices spread through blind imitation, then at least for a time, random variation will result in the spread of some maladaptive behaviours. More importantly, he argues that the strength of social norm enforcement is such that maladaptive social norms may remain stable for extended periods of time. Genetic evolution has made humans into excellent norm enforcers, but it has not specified the content of those norms; it is up to cultural evolution to do that.

This approach sheds new light on debates going back, among other things, to the founding of social anthropology. In the nineteenth century, ethnologists adhering to stadial theories of human progress noted the presence in multiple cultures of practices and ideas which appeared not to cohere with the culture as a whole, but instead seemed to constitute vestigial forms left over from the past. While later anthropology discarded the notion that cultures progressed through a series of fixed stages, diffusionists continued to argue that cultural traits could often, or largely, be explained by reference to a history of transmission from other cultures. Over time, diffusionist explanation came increasingly to be ignored in favour of synchronic and functional analysis—that is, anthropology came to base itself on the methodological assumption that every cultural trait either might have a function, or did have one (Kuper 1983 , pp 1–9). According to Tylor’s ( 1920 , p 16) ‘doctrine of survivals’, vestigial cultural traits continued on in new generations simply by ‘force of habit’; but the functionalists rejected this, arguing that traits would only continue, or diffuse, so long as they continued to play a functional role (Stocking 1984 , p 152). Thus although diffusion was not deemed unreal, it was relegated at best to a minor explanatory role in comparison to that of social function.

If Boyd is correct, however, diffusion and ‘vestiges’ may be underpinned by more plausible mechanisms than mere force of habit. Individuals and groups may copy each other blindly, or as a result of some other cue, as with prestige-biased transmission. A social norm may at one point have provided a fitness advantage in the context of cultural group selection, but once that context falls away, the norm may live on, stabilised by powerful enforcement mechanisms. Even in the presence of selection, local fitness maxima may result in the preservation of traits which may be globally suboptimal. This theory has the benefit not only of being more plausible than a common sense appeal to force of habit, but, because it is rooted in precise evolutionary models, holds the prospect of generating precise predictions about when and to what extent ‘survivals’ will last, depending on selection pressures (Tehrani 2010 ).

Moreover, Boyd shows how cultural evolutionary research can already test the functionalist claim that cultural traits survive only so long as they serve a social function. In the context of his argument for blind imitation as opposed to intelligent design in cultural evolution, he points out that if intelligence drives adaptation, then the shadow of history should be short. That is, intelligent individuals designing cultural solutions to problems should pay little heed to the practices of their ancestors, unless it happens that those practices are still optimal. The same argument, of course, can be applied to functionalism: if cultural traits are tightly tied to social function, then the shadow of history should be correspondingly short. But as Boyd argues, evidence from the field of cultural phylogenetics shows that this is not the case. Many cultural traits are strongly determined by history, or diffusion, even in the face of apparently contravening present or local functional demands. The shadow of history is long, but finite. It is now possible, then, to give a more nuanced picture than anthropologists on either side of the nineteenth century debate. Imitation and norm enforcement create path dependencies, but the extent of these dependencies depends closely on selection pressures.

Thus as Radcliffe-Brown ( 1952 , pp 1–14) argued, historical and functional explanation in anthropology are, indeed, complementary. But as in evolutionary analysis more generally, the two are interdependent. Historical explanation requires reference to function and selection pressures in the past, while synchronic functional explanation must always be qualified by path dependency and the presence of mutations and transmission errors. An anthropology that takes cultural evolution seriously is one which systematically combines these approaches; for if cultural evolutionary theory is correct, it is just as fruitless to separate them as it is to analyse the functions of an animal’s organs and the evolutionary history of those organs in isolation from one another.

It thus seems that cultural evolution holds the promise of helping to resolve—or dissolve—old debates about whether anthropology should be functionalist, diffusionist, evolutionist, or none of these. If that is the case, then the same may apply similarly to other social sciences, too. Cultural evolution requires no rigid adherence to either functional or historical explanation, materialism or idealism, and so on; instead it offers a toolkit for the study of how a variety of forces may shape any given social phenomenon. Likewise, cultural evolution entails no metaphysical commitment to social wholes, atomistic rational individuals, or similar exotic beasts; instead, it simply promises a social science in which what happens inside brains, and what happens between people, form two inseparable parts of a dynamic, but metaphysically unmysterious causal story (Sperber 1985 ).

Extending the Synthesis: Radically Encultured Brains

But while the cultural evolutionists are keen to point out that, contrary to the tendency of evolutionary psychologists to treat human minds as ‘hard-wired’ computers, much human behaviour and concepts are malleable and subject to a much faster form of evolution, it is commonly assumed that for cultural evolution to take place, certain innate mental capacities must be present, such as the ability to imitate, or elements of ‘theory of mind’. Thus with the evolutionary psychologists, many assume that these are mental modules, produced by genetic evolution in some ancient adaptive environment, after which cultural evolution became possible, and built on the foundation of these modules, but did not replace them.

Not so Heyes ( 2018 ). On the basis not of any theoretical or ideological opposition, but on reams of empirical evidence collected from recent research in cognitive psychology, Heyes argues that cultural evolution goes much deeper: we are born not with a set of pre-programmed, computer-like modules, but with a set of behavioural tendencies, subtly different from our primate cousins, which, coupled with copious amounts of information from our environment, enables us to develop such mental modules as those required by theory of mind, language, and even motor skills. But that is not to say that these modules do not evolve, for the lesson of cultural evolution is that the very environmental scaffolding which gives rise to them may itself be the carrier of information that is subject to evolutionary forces. Our sophisticated cognitive capacities look like they have evolved because they have done, over many thousands of years, but not by genes alone. We are not blank slates—we do begin life with a ‘starter kit’ which prepares us to develop in interaction with our environment—but neither is what is written on the slate an arbitrary scribble, created anew with each generation. Instead, cultural evolution goes ‘all the way down’.

Heyes acknowledges an affinity to a number of other recent constructivist positions in psychology. Among these, Tomasello’s ( 2014 ) ‘shared intentionality hypothesis’ has been particularly influential for scholars of cultural evolution. Tomasello argues that cultural evolution is made possible by a package of genetic adaptations which endow children with the ability to engage in a sharing of intentions, perceptions, and other mental objects. Heyes contrasts her theory with that of Tomasello in part by claiming that, while her arguments are based on cognitive science, his are rooted in Vygotskian psychology. More specifically, while Heyes shows how complex cognitive functions can be constructed out of a basic ‘starter kit’ of subpersonal mechanisms, Tomasello assumes that specifically human cognition must begin with shared intentionality, which must therefore have evolved genetically—with Vygotsky, he is ‘resolutely focused on the most complex kinds of thinking’ (Heyes 2014 ).

This interpretation of Vygotskian psychology is not without textual support. Much of Vygotsky’s work was preoccupied with the claim that language acquisition leads to a reorganisation of cognitive function; and on several occasions, he appears to claim that preverbal children are akin to apes (Vygotsky 1978 , p 24, Vygotsky 1986 , pp 86–87). Thus on one interpretation, the Vygotskian view is that the acquisition of language is what enables enculturation to get going, and therefore justifies the existence of cultural-historical psychology (Cole 2005 ). Tomasello’s innovation here lies in his claim—strongly contested by some self-described Vygotskians (Fernyhough 2005 , 2008 )—that shared intentionality is a prerequisite for the development of language. If that is the case, and if cultural transmission begins only with language acquisition, then the capacity for shared intentionality must precede cultural transmission, and is therefore presumably genetically inherited.

This is not, however, the only interpretation of Vygotskian psychology. Prominent members of the Vygotskian school, such as the neuropsychologist Luria, claimed repeatedly that the influence of culture on cognition began from the moment of birth, notably including the shaping of motor and perceptual functions through interaction with cultural artefacts (Homskaya 2001 , p 87; Luria 1966 , p 30; Luria 1976 , p 9). Indeed, it might be said that the foundational principle of the whole Soviet school of neuropsychology—itself a precursor of neuropsychology in the West (Goldberg and Bougakov 2009 )—was the notion that the ontogeny of the human brain is, from beginning to end, necessarily shaped by interaction with the individual’s social environment (Cagigas and Bilder 2009 ). Vygotsky and Luria’s preoccupation with ‘higher’ mental functions may simply reflect the paucity of evidence available at the time concerning the role of environmental influences in preverbal development. On this view, then, Heyes’ accomplishment is not to undermine the Vygotskian school—at least in its original, Soviet form—but to bring its key insights up to date with evidence from modern cognitive science.

Heyes does this in two principal ways. First, she shows in detail how cognitive accomplishments as basic as motor skills, and as complex as language, appear to arise during development on the basis of a few simple principles, rather than depending on pre-programmed genetic modules. Imitation, often seen as a task so complex it can only be carried out by virtue of an innate, cognitive black box, is instead explicable as a result of ‘associative sequence learning’—a combination of vertical associations between motor sequences and sensory inputs, themselves formed by the same simple associative learning which enables the development of motor skills. Even language, an area on which Heyes demurs, as a non-specialist, from making a definitive pronouncement, appears to be amenable to this form of explanation; Heyes shows how recent neurological, developmental and mathematical evidence provides a strong case against the Chomskyan view that language must necessarily depend on a universal language module of some form. In all these respects, just as Boyd brings old social scientific intuitions into precise relief in the light of contemporary research, Heyes does the same for developmental psychology; we can now do far better than a vague debate about ‘nature’ versus ‘nurture’.

Secondly, Heyes’ theory represents an important advance on earlier intuitions because while denying that cognitive modules are the direct product of genetic evolution, she shows why it looks as if such modules have evolved. For they have indeed done so, but through cultural evolution, rather than genetic. While traditional evolutionary psychology, motivated in part by the assumption that genetic evolution takes place at a glacial pace, has been preoccupied with hypothetical accounts of the evolutionary pressures under which our prehistoric ancestors lived, the cognitive gadgets model instead suggests that evolutionary forces relevant to psychology are far more recent and rapid. As our understanding of the complex interaction between genes and environment grows, the metaphor of genetic transmission as a process of downloading modules wholesale, as in transmission between two computers, becomes less and less persuasive (Sasaki and Kim 2017 ). If the cognitive gadgets theory is correct, it implies not a divorce of psychology from evolution, but a correction of a deep misunderstanding of how evolution, and ontogeny, actually take place.

Quite apart from the evolutionary argument, it is worth appreciating what the cognitive gadgets theory might tell us about the direction of psychology as a whole. Half a century ago, the cognitive revolution upended the discipline, drawing in part on the metaphor of the mind as computer to motivate attempts to analyse the inner workings of that computer. As fruitful as the metaphor has been, it must perhaps be held partly responsible, too, for notions such as the ‘hard-wiring’ of the brain, or indeed of hard-wired mental modules. These notions have been strained by modern neuroscientific research, which has increasingly shown the brain not only to be highly plastic, but also to be radically shaped by its changing social environment. Of course, Heyes’ theory retains the modular framework beloved of cognitive scientists, and indeed, far from breaking with cognitive science, she builds incrementally upon it. But her approach arguably represents a deeper shift, which may yet prove as significant as the original cognitive revolution—a shift to a psychology inspired not by metaphors of ‘hard-wiring’ and ‘software’, but by the brain itself, in all its dynamism and variety.

Redrawing the Disciplinary Map of the Human Sciences

The accounts offered by Boyd and Heyes are largely complementary. Boyd’s argument for a form of cultural evolution led by blind imitation and cultural group selection can clearly be extended to include the evolution of cognitive mechanisms themselves. Indeed, Heyes comes down firmly on the side of cultural group selection, arguing that cognitive gadgets which are today universal may have evolved by virtue of advantages they conferred to those groups which exhibited the cultural norms necessary to scaffold them.

But while Boyd is keen to emphasise that the key to human success is blind imitation, rather than intelligence, if Heyes is right, then it may be that much of what we think of as intelligence is itself a product of the same forces. On that account, the question of whether cultural change proceeds by intelligence or by blind imitation may pose a false dichotomy. Indeed, Heyes’ extension of cultural evolution into the psychological domain goes so far as to call into question most of what cultural evolutionists have hitherto assumed to be innate; she suggests even that, contrary to Boyd and others (Kelly and Davis 2018 ), normativity and moral psychology itself may be a cognitive gadget.

Nevertheless, while Boyd is concerned mainly with explaining cultural variation, Heyes’ theory is explicitly aimed at explaining a package of cognitive capacities which she takes to be universal, but which she postulates became universal through a process of cultural evolution. But the theory leaves open the possibility, too, of systematically investigating variations in cognitive mechanisms of the kind of which anthropologists have long spoken. In recent decades, the fields of cross-cultural psychology, cognitive anthropology, and related disciplines have begun to document often fine-grained psychological differences between populations, ranging from differences in social cognition to more fundamental variations, such as in visual perception (Keith 2019 ). A promising extension of Heyes’ thinking would be to begin to fit these findings within the broad cultural evolutionary approach of Boyd and others—allowing us in this way not only to explain cross-cultural psychological variation, but to draw more precisely the boundaries between culturally specific cognitive mechanisms and those which have become universal, and which we might therefore think of as constituting something like ‘human nature’, or at least the nature of humans as we currently stand.

A further possible fusion between Heyes’ thinking and that of broader culture-gene coevolutionary theory is in the telling of the story of human origins. Throughout human prehistory, we find take-off points, at which we suddenly seem to see a leap in cognitive complexity. Evolutionary theorists have often thought of these as resulting from genetic changes, which suddenly endowed humans of the time with new capabilities. But Heyes’ theory offers the possibility for a more nuanced view, in which some of these take-off points might have been the result of sudden cultural shifts—new practices or technologies which provided the scaffolding for the development of new cognitive capacities. Cultural group selection, as in Boyd’s account, may then have led to rapid shifts to new equilibria, producing the sorts of take-off points we find in the archaeological record. Bearing in mind the likely interactions between genetic and cultural evolution, this may be reason to look again at putative explanations for the sudden development of such capabilities as speech, figurative art, and ritual.

But beyond the story of human origins, this approach holds the exciting prospect for both psychology and anthropology that future investigation may be based neither on attempting to work out which human characteristics are ‘hard-wired’, and which are not, nor even on simply estimating the contribution made to characteristics by various forms of inheritance, but on the broader evolutionary stability, trajectory and interaction of both psychological mechanisms and the cultural practices which sustain and result from them. While it will remain important to investigate the role of various forms of inheritance in ontogeny, this approach suggests that, as with developments in epigenetics, the lines between phylogeny and ontogeny may soon begin to blur (Colagè and d’Errico 2018 ).

In the closing passages of Darwin’s Origin of Species ( 1861 , pp 420–425), he outlined with uncanny accuracy the implications his theory would have for the biological sciences over the century to come. In particular, where once these fields had existed as disparate, unconnected endeavours, as diverse as butterfly collecting and palaeontology, Darwin foresaw that evolutionary theory would unite them, by placing them under a single explanatory umbrella. Not only did evolution unite fields by relating the study of different species and genera within a larger family tree of life; it united the study of phylogeny and ontogeny, motivating the latter as a quest for solutions to evolutionary problems, and the former as a means to explain the development of the latter.

However, one key link in Darwin’s chain has remained outside the broader umbrella of the biological sciences. That is, of course, humankind itself. This is not for want of trying; numerous attempts have been made since Darwin’s own time to subsume the study of human behaviour into biology. But such attempts, when not badly misconstruing the concept of evolution and natural selection, have consistently been based on the idea that evolutionary explanation applies only to inheritance by reproduction. Thus they either attempt to fit human behaviour into a procrustean bed of questionable universals, or they provide only a highly circumscribed role for evolutionary explanation.

Cultural evolution promises to change this. For the field as a whole is pointing increasingly toward the potential for a synthesis of just the kind Darwin predicted, in at least two ways. Firstly, if the cultural evolution of cognition turns out to be right, this would mean that already questionable disciplinary distinctions between psychology and anthropology—and indeed, between the study of individuals and broader social sciences more generally—may have to be jettisoned. Just as many of the old disciplinary boundaries of biology ceased to make sense in light of the evolutionary synthesis, the cultural evolution of cognition may force us to think of psychology as fundamentally cultural, and culture as fundamentally psychological.

Secondly, bringing cultural variation under the rubric of evolutionary models holds the potential to complete the evolutionary synthesis, by subsuming human behaviour under the same umbrella as the rest of life, without ignoring the very real cultural variation which defines so much of what it is to be human (Muthukrishna and Henrich 2019 ). Contrary to the arguments of earlier generations of sociobiologists and their opponents, this approach does not require a ‘reduction’ of humanity to something less than human in order to explain our nature. Instead, by recognising that evolutionary models can apply to a wide variety of forms of information transmission—not simply genes alone—cultural evolution shows how the enormous variation that makes us perhaps most distinctive as a species can be understood within the same broad framework with which we have come to view the whole family tree of life. To break down existing barriers not only between psychology and the social sciences, but between the study of humans and of the rest of life, would be to fulfil the ultimate promise of evolutionary theory; but it would also be to demonstrate the profound beauty of a theory which, in the final analysis, is general enough to apply to everything from the most sublime works of human creativity to the humble constructions of an earthworm.

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Cultural Evolution

In the broadest terms, theories of evolution seek to explain why species are the ways they are. For many evolutionists, this means explaining the possession by species of characteristic adaptations. It also means explaining diversity within species. The general mark of modern theories of cultural evolution is their insistence on the significance of cultural inheritance—particularly various forms of learning from others—for both of these questions. The prima-facie case for cultural evolutionary theories is irresistible. Members of our own species are able to survive and reproduce in part because of habits, know-how and technology that are not only maintained by learning from others, they are initially generated as part of a cumulative project that builds on discoveries made by others. And our own species also contains sub-groups with different habits, know-how and technologies, which are once again generated and maintained through social learning. The question is not so much whether cultural evolution is important, but how theories of cultural evolution should be fashioned, and how they should be related to more traditional understandings of organic evolution.

1. What is Cultural Evolution?

2. natural selection and cultural inheritance, 3. historical pedigree, 5. problems with memes, 6. cultural evolution without memes, 7. the explanatory role of cultural evolutionary theories, 8. population thinking, 9. evolvability, bibliography, other internet resources, related entries.

Theories of cultural evolution need to be distinguished from theories within evolutionary psychology, even though both may involve an application of evolutionary ideas to the explanation of cultural phenomena. The evolutionary psychologist (e.g. Tooby and Cosmides 1992) tends to assume that the most important inheritance mechanism in all species—our own included—is genetic inheritance. Evolutionary psychology regards the human mind as evolving through a conventional process of natural selection acting on genetically inherited variation. For example, an evolutionary psychologist might explain the widespread taste among humans for fatty foods in terms of the importance in our species' distant past of consuming as much fat as possible on those rare occasions when the circumstances presented themselves. Such a hypothesis can also help to explain novel cultural trends: the recent increase in obesity is explained as the result of a novel environmental change—the increased availability of cheap, high-fat foods—acting in concert with a once-adaptive, now dangerous, gustatory preference.

Darwin believed, as do biologists today, that natural selection can explain the origin of many complex adaptive traits. In Darwin's original presentation of natural selection, he requires that parent organisms differ in their abilities to survive and reproduce, and that offspring resemble their parents in terms of the traits that promote or inhibit these abilities (Darwin 1859/1964). This explanatory schema is largely neutral regarding what mechanism accounts for parent-offspring resemblance. For example, offspring might learn skills from their parents, and thereby come to resemble them behaviourally. From the perspective of natural selection explanations, it does not matter why offspring resemble parents, only that they do resemble them.

Darwin's theory of natural selection explains adaptation by appealing to what we now call vertical transmission —the inheritance of parental traits by offspring. As we have seen, cultural processes such as learning might, in principle, underpin this form of inheritance. But we do not learn only from our parents—we also learn from peers, authority-figures and so forth. This is known as horizontal transmission . Once we acknowledge the possibility that learning can underpin natural selection, we also acknowledge that a theory of evolution—a theory which seeks to explain change, including adaptive change in a population—may also need to be further expanded to encompass horizontal transmission. The admittance of horizontal transmission into evolutionary theory necessitates far more radical revisions to traditional Darwinian models of evolution. This is because horizontal transmission opens up the possibility that some traits may spread through a population in spite of the fact that they reduce the fitness of the individuals who bear them.

In a classic early work of cultural evolution, Cavalli-Sforza and Feldman (1981) ask (among other things) how we can explain declining birth rates among Italian women in the nineteenth century. These women went from having around five children on average to having only two. It would be extremely implausible to argue that this occurred as result of natural selection (Sober 1991, 482). It would be implausible, for example, to argue that the fitness of women with smaller families was greater than the fitness of women with larger families. True enough, an individual's long-term fitness (measured in terms of numbers of grandchildren, or great-grandchildren) may sometimes be augmented by having a few strong offspring rather than lots of weak ones (Lack 1954). But surely Italian women could have raised more than two children to be healthy adults. Cavalli-Sforza and Feldman instead argue that the practice of having fewer children spread through Italy because women acquired the trait both from peers and from individuals from their mother's generation, through modes of cultural transmission. Forms of horizontal transmission are required to explain this transition, because if cultural transmission was always vertical, then the trait of having greater numbers of offspring would be maintained in the population by natural selection, albeit selection acting via cultural inheritance.

One might react to this with confusion: why is a body of theory needed to make these claims? Of course we acquire traits from others by learning. And of course those others from whom we learn can include peers as well as parents. In part, we can respond to this bewilderment by pointing to the virtues of clarifying the conditions required for cultural inheritance to overcome natural selection. Cavalli-Sforza and Feldman argue that if women simply acquired whichever preference for family size was the most widely adopted in their local cultural environment, then cultural inheritance would not have enough of an effect to overcome natural selection. Women must be disposed to acquire the preference for small family size even when it is present in only a small proportion of their cultural circle, if small family size is to replace large family size in the population as a whole. This is an illuminating claim, and it takes a quantitative model to show it.

This question of what benefit is to be had from setting these sorts of claims in a quantitative theory will be raised in more detail later in this article. For the moment, note that one may also ask why it should be the case that we are able to learn from non-parents at all, given the adaptive costs of such a disposition. If the tendency of Italian women to learn from their peers has led them to reduce their fitness by reducing their family size, why did natural selection allow such learning dispositions to become established in the first place? Boyd and Richerson, two other pioneers in cultural evolutionary theory, claim that the overall adaptive benefits of learning from non-parents in fact outweigh the overall adaptive costs (Richerson and Boyd 2005, Ch. 4). They give several reasons for this view. Suppose an inventive (or lucky) individual is able to discover some behaviour, or technique, which augments fitness. If other individuals in the population can copy that behaviour, then their fitness will probably be augmented, too. It will often be difficult for individuals to ascertain which behaviours in fact augment fitness, hence which behaviours should be copied. The problem, then, is how to tune a learning mechanism so that beneficial behaviours are copied, while non-beneficial behaviours are not.

Boyd and Richerson suggest that prestige bias can overcome this problem: if individuals copy techniques from those who are in prestigious positions, then this increases the chances that they will copy techniques that are, in fact, beneficial. As they put it, ‘Determining who is a success is much easier than determining how to be a success’ (Richerson and Boyd, 2005, 124). The value of prestige bias relies on the supposition that those individuals who are able to get themselves into prestigious positions have a better than average tendency to make use of fitness-enhancing techniques. This heuristic will not be failsafe: after all, not every technique a prestigious individual uses will also augment fitness, and some individuals may be accorded prestige without good cause. But the question which settles the plausibility of natural selection explaining prestige bias is not whether prestige bias will sometimes lead to the copying of maladaptive techniques; the question, rather, is whether individuals who learn from the prestigious will tend to be fitter on average than individuals who either do not learn at all, or who are equally likely to learn from any member of the population, regardless of their social status.

Richerson and Boyd (2005, 120–22) suggest that other learning heuristics may be adaptive. One of these they call conformist bias . They argue that imitation of the common type—the ‘When in Rome’ rule—is more likely than not imitating at all, and more likely than imitation of a randomly-chosen member of a population, to provide an individual with behaviours that are appropriate to novel situations. This may mean acquiring behaviours appropriate to a new biological environment: when moving into a new habitat, with unknown plants and animals, it is best to eat the foods the locals eat, for one thereby avoids poisoning. But it can also lead to the generation of socially appropriate behaviours, which will obviate ostracism or attack.

These examples show the nature of the interaction between cultural evolutionary thinking and more traditional natural selection thinking. Natural selection acting on genetic variation can establish dispositions to learn from peers in spite of the fact that under some circumstances these dispositions lead to the proliferation of maladaptive traits. Once these learning dispositions are in place, we should not assume that every trait in a population must be explained by reference to the biological fitness benefit it has conferred in the past. Evolutionary adaptationists tend to ask, of any given trait, what effect might have led natural selection to favour that trait. Even if an adaptationist stance of this sort is justifiable for learning mechanisms, this does not mean that an adaptationist stance is justifiable for learned traits.

The notion that culture itself evolves, and that Darwinian insights can be applied to understanding cultural change, is by no means new. A very early example of cultural evolutionary thinking comes from William James:

A remarkable parallel, which to my mind has never been noticed, obtains between the facts of social evolution and the mental growth of the race, on the one hand, and of zoological evolution, as expounded by Mr Darwin, on the other. (James 1880, 441)

James's paper is primarily concerned with using what he regards as a proper understanding of Darwinism to undermine the ‘so-called evolutionary philosophy of Mr Herbert Spencer’ (ibid., 422). Spencer had argued that ‘great men’ were of secondary importance in determining the course of history, on the grounds that ‘Before he can remake his society, his society must make him’ (from Spencer's Study of Sociology , quoted in ibid., 449). The great man needs to be made, and society does this. Hence ultimately it is society itself that explains social change.

James argues that the central key to Darwin's natural selection mechanism is to decouple the causes of variation from the causes of selection (see Lewens, 2006, Ch. 2). Variations are produced by unknown causes, and the environment selects among them. Variations themselves (for James's Darwin) are inexplicable. The same is true of great men: ‘The causes of production of great men lie in a sphere wholly inaccessible to the social philosopher. He must simply accept geniuses as data, just as Darwin accepts his spontaneous variations’ (James, 1880, 445). Great men, like spontaneous variations, are essential and inexplicable elements of the evolutionary process. Just as Darwin's theory credits environment and variation with distinctive, yet vital, roles, so both great men and the social environment are important for the explanation of social change:

This social evolution is a resultant of the interaction of two wholly distinct factors: the individual, deriving his peculiar gifts from the play of physiological and infra-social forces, but bearing all the power of initiative and origination in his own hands; and second, the social environment, with its power of adopting or rejecting both him and his gifts. Both factors are essential to change. (Ibid., 448)

There are problems associated with any effort to trace the pedigree of cultural evolutionary theories back to Darwin himself. One of the reasons for this is that cultural evolutionary theories often define themselves in opposition to those which claim that genetic inheritance is the only significant inheritance mechanism. Clearly one cannot cast Darwin as a cultural evolutionist in this manner, for he had no notion of genetic inheritance to oppose. Having said this, Darwin did believe that what was learned in one generation could be inherited in later generations. But far from distinguishing cultural inheritance from organic inheritance, Darwin thought that all inheritance should be explained by the transmission of ‘gemmules’. These were understood to be particles produced throughout the body, of a character specific to the body part that produces them. Darwin believed that gemmules then travelled to the gonads, where they were transmitted to offspring in the sex cells. Darwin claimed that gemmules were produced throughout the body in order to explain the inheritance of acquired characteristics. So in one sense Darwin is in alignment with modern cultural evolutionists—he believed that characteristics learned during the life of a parent could be transmitted to offspring. But in another sense Darwin is opposed to modern cultural evolutionists, for rather than distinguishing between different interacting inheritance systems (e.g. cultural and genetic inheritance), Darwin tends to use the transmission of gemmules to explain the inheritance of all types of trait.

There are other respects in which one might choose to regard Darwin as a proto-cultural evolutionist. Darwin sometimes integrates discussion of technological evolution into his broader discussions of natural selection. In the Descent of Man , Darwin pauses to discuss technical innovation, arguing that successful innovations will usually be imitated, thereby increasing the success of a group as a whole, increasing the size of that group, and consequently increasing the chances of inventive members being born into it (Darwin 1877/2004). This explanation combines natural selection's central concern with reproductive output, and cultural evolution's central concern with imitation. Darwin writes:

…if some one man in a tribe, more sagacious than the others, invented a new snare or weapon…the plainest self-interest, without the assistance of much reasoning power, would prompt the other members to imitate him; and all would thus profit…If the new invention were an important one, the tribe would increase in number, spread, and supplant other tribes…In a tribe thus rendered more numerous there would always be a rather greater chance of the birth of other superior and inventive members. (Darwin 1877/2004, 154)

Finally, Darwin endorses the view, widely favoured these days, that natural selection need not act on organisms. Rather, natural selection is substrate-neutral . A natural selection process can occur whenever certain abstract conditions—these days often expressed as differential reproduction with inheritance—are met. Darwin explicitly endorses the view that natural selection can act on entities other than organisms in the context of language change, a cultural phenomenon. This position is briefly explored in the Origin of Species , and further expanded in the Descent of Man . In this work, he endorses the opinion of Max Müller:

A struggle for life is constantly going on amongst the words and grammatical forms in each language. The better, the shorter, the easier forms are constantly gaining the upper hand, and they owe their success to their own inherent value. (Darwin 1877/2004, 113)

Darwin asserts that this is no mere analogy: ‘The survival or preservation of certain favoured words in the struggle for existence is natural selection.’ This claim—that cultural entities of various sorts can undergo natural selection processes in their own right—is not a necessary feature of a theory of cultural evolution. Cultural evolutionary theory in general requires only a systematic effort to model the effects of cultural inheritance, and one might decide that thinking in terms of natural selection acting on units of culture is not the best way of doing this. We will investigate these issues in more detail later in this article.

We have already mentioned Herbert Spencer, and Spencer is sometimes regarded as a key early advocate of efforts to apply evolutionary thinking to human culture (e.g. Jablonka and Lamb 2005, 21–22). As early as 1855, in his Principles of Psychology , Spencer proposed a form of evolutionary epistemology, arguing for a third way between empiricism's emphasis on the necessity of experience for knowledge, and rationalism's insistence on the importance of a priori knowledge. Spencer reasoned that if the experiences of past generations were imprinted on human minds, then it would be true both that some forms of knowledge in current generations were a priori , and also that this knowledge had its origins in experience, albeit the experience of our ancestors. Darwin himself had made a brief note along similar lines in his M notebook: ‘Plato…says in Phaedo that our ‘ necessary ideas ’ arise from the preexistence of the soul, are not derivable from experience.—read monkeys for preexistence.’ (Barrett et al 1987, 551) Much later in the twentieth century, Konrad Lorenz would argue for a similar set of views in his efforts to see the Kantian a priori through the lens of evolutionary biology (Lorenz 1941).

Although Spencer is sometimes credited with initiating the application of evolutionary thinking to culture, Spencer's contributions in this domain and others are often regarded as scientifically worthless (although see Jablonka and Lamb 2005, 372-3 for an exception). Ernst Mayr, for example, claimed that ‘It would be quite justifiable to ignore Spencer totally in a history of biological ideas because his positive contributions were nil’ (Mayr 1982, 386). Spencer is usually treated harshly for his adherence to the importance of ‘use-inheritance’, according to which habits initially learned are eventually inherited automatically in offspring. This form of inheritance would be classed by many as ‘Lamarckian’, in contrast to the ‘Darwinian’ forms of inheritance that are typically placed in the foreground in presentations of modern evolutionary theory.

Some recent modern theorists have argued that Lamarckian inheritance should not be dismissed out of hand (e.g. Jablonka and Lamb 1995). Whatever we think of this move, the tendency to praise Darwin while damning Spencer often overlooks the fact that Darwin, too, believed in the biological significance of use-inheritance, and it figured strongly in his own views of cultural evolution. Spencer is also criticised for his ‘social Darwinist’ beliefs, but Darwin, too, was a social Darwinist of sorts, and held evolutionary views regarding race, social degeneration and other such topics that most would dismiss today (see Lewens 2006, chapter eight). As we have seen, Darwin's theory of pangenesis was developed partly in order to explain what he took to be the phenomena of use-inheritance, and a general account of use-inheritance played an important role in Darwin's cultural evolutionary account of human moral progress. Indeed, at one point in the Descent of Man , Darwin quotes Spencer at length and with approval:

Our great philosopher, Herbert Spencer, has recently explained his views on the moral sense. He says, “I believe that the experiences of utility organised and consolidated through all past generations of the human race, have been producing corresponding modifications, which, by continued transmission and accumulation, have become in us certain faculties of moral intuition—certain emotions corresponding to right and wrong conduct, which have no apparent basis in the individual experience of utility.” (Darwin 1877/2004, 148)

Serious efforts to construct cultural evolutionary theories can be traced to the work of Lumsden and Wilson (1981), Cavalli-Sforza and Feldman (1981), and Boyd and Richerson (1985). All of these authors have attempted, in one way or another, to produce formal models that can integrate the effects of cultural inheritance into more standard biological models of evolution. We have already looked at some of the claims of these theorists, but before looking at their work in more detail, let us look at the theory of memetics. This theory, originally put forward by Richard Dawkins (1976), is perhaps the best known attempts to apply evolutionary thinking to culture. It seeks to draw a very strong analogy between evolution at the cultural level, and biological evolution. Memetics begins with an abstract characterisation of selection as a process requiring entities that reproduce, such that parents resemble offspring. Memetics takes the view, popularised by Dawkins, that entities which have the ability to make faithful copies of themselves—so-called ‘replicators’—are required to explain this trans-generational resemblance. In standard biological models of evolution it is assumed that genes are the relevant replicators. Genes make copies of themselves, and this ability explains why offspring organisms resemble their parents. If culture is to evolve, it therefore becomes necessary to find some form of cultural replicator that explains cultural inheritance. Memes play this role. Dawkins gives a list of some exemplary memes: ‘tunes, ideas, catch-phrases, clothes fashions, ways of making pots or of building arches.’ Note that while it is sometimes assumed that all memes are ideas (and vice versa) Dawkins's list includes other types of thing, such as ways of making pots, which are techniques.

Dawkins's claim is that ideas, for example, can be conceptualised as entities that hop from mind to mind, making copies of themselves as they go. On the face of things, this seems an attractive proposition. Just as genes make copies of themselves at different rates according to their effects on the organisms that bear them and on their local environments, so ideas make copies of themselves at different rates according to their effects on the organisms that bear them and on their local environments. In a community of scientists, for example, different hypotheses are entertained, and some come to be believed more widely than others. A hypothesis that begins in the mind of one or two scientists thereby spreads, until it is widely held in the research community. Another hypothesis quickly dies. We can perhaps characterise the features that make some hypotheses likely to spread, and others likely to perish. ‘Fit’ hypotheses may have predictive power, or simplicity, or they may integrate well with existing bodies of theory. Note that what this example shows is that taking the meme's-eye perspective does not literally show that we are being manipulated by selfish cultural replicators. One can describe scientific change as a struggle between selfish memes, but one can also describe just the same process in terms of scientists choosing to accept, or to reject, theories by reference to familiar criteria of explanatory power, theoretical elegance and so forth. It is only an incidental feature of the metaphor of memetic selfishness that appears to deprive humans of control over which ideas they do, and do not, accept.

There are various problems associated with memetic views, most of which focus on limitations of the gene/meme analogy. These worries are sometimes raised by theorists from the social sciences who are hostile to evolutionary theories of culture. But they are also raised quite frequently by cultural evolutionists who argue that the meme concept is not the right way to ground a theory of cultural evolution. Here are some of the most frequently encountered arguments against the meme concept (the remainder of this section draws on Lewens, 2006, Ch. 7):

Cultural units are not replicators : Replicators, remember, are supposed to be units that make copies of themselves. Some critics of the meme concept argue that there is no known mechanism that could explain how memes are copied. But this is a mistake. An idea can be copied simply through observation and inference: agent B can observe the behaviour of agent A , infer that A holds some belief X , and thereby come to hold the same belief as A . Ideas can also be copied using linguistic communication. Agent A might be convinced of belief X , she tells B about it, and B comes to believe X too. In both cases one can say that belief X makes a copy of itself, albeit via language, inference, and so forth. A more pressing worry for memetics is that imitation is usually too error-prone to underpin replication. If I make a cake on the basis of a secret family recipe, you eat the cake, and you then attempt to make another one, then the chances are that the recipe you hit upon will not, in fact, be exactly the same as the one I used, even if you are able to make a similar-tasting cake. Another significant worry for memetics is that when the same ideas do spread through a population, it is rarely because they are literally copied from each other. Sperber argues that cultural reproduction is rarely meme-like, but instead makes use of what he calls ‘attractors’—culturally shared patterns of thought, which enable representations to spread through a population without literal copying. Sticking with the cake example, perhaps you eat a slice of my Victoria sponge, you like it, and you decide to make one for yourself. Perhaps the recipe you use is very similar to mine. But you have not figured out by tasting my cake which ingredients needed to go in and in what order. Rather, you already knew how to make a Victoria sponge. Eating my cake simply triggered the use of a recipe that was already in your repertoire. In this case, the recipe has appeared in my head, and because of this it has appeared in yours, but not because your recipe is a copy of mine. Sperber argues that memetics is mistaken because most cases of the spread of ideas are like this:

… most cultural items are ‘re-produced’ in the sense that they are produced again and again—with, of course, a causal link between all these productions—but are not reproduced in the sense of being copied from one another…Hence they are not memes, even when they are close ‘copies’ of one another (in a loose sense of ‘copy’, of course). (Sperber 2000, 164–65)

Both of these concerns raise serious problems for the generality of memetics: not all ideas are replicators, hence not all ideas are memes. Whether this shows the meme concept to be useless depends on whether there are insights to be had by distinguishing cultural inheritance that is meme-like from cultural inheritance that is not (Sterelny 2006).

Cultural units do not form lineages : A closely-related criticism of memetics draws on the fact that while in genetic replication we can trace a new copy of a gene back to a single parent, ideas are rarely copied from a single source in a way that allows us to trace clear lineages (Boyd and Richerson 2000). Memeticists are fond of analysing religious belief in terms of the spread of memes. But while religious beliefs may well spread through populations of humans, it seems unlikely that we will always be able to trace token instances of faith back to one source. Of course, on some occasions religious believers may indeed be converted by a single evangelist. On other occasions individuals acquire belief in God through exposure to several believers in their local community. In these circumstances, belief in God is not caused by one identifiable earlier token of the same type. Within the realm of biological evolution, an understanding of Mendel's laws has been important in explaining some aspects of evolutionary dynamics. Mendel's laws rely on an understanding of genes as discrete, transmitted units. But if token ideas can appear in an individual in virtue of that individual's exposure to several sources or just one, then this makes it unlikely that anything close to Mendel's laws will be discovered within cultural evolution. This suggests a practical limitation on enquiry that may result from this disanalogy between ideas and genes.

Culture cannot be atomised into discrete units : Ideas stand in logical relations to each other. Whether an individual is able to acquire some belief, for example, depends on their related conceptual competencies. It is impossible to believe in the theory of relativity without understanding it, and one cannot understand it without holding many additional beliefs relating to physics. The same is true for non-technical beliefs. Depending on which religion one is talking about, belief in God is likely to be related to various other beliefs concerning forgiveness, retribution, love and so forth. This has led some critics to argue that it is a mistake to take a view of culture which atomises it into discrete units, assigning replicative power to them individually. The anthropologist Adam Kuper complains that ‘Unlike genes, cultural traits are not particulate. An idea about God cannot be separated from other ideas with which it is indissolubly linked in a particular religion’ (Kuper 2000, 180). Memeticists are likely to respond by saying that although ideas are inter-linked, this does not undermine the meme-gene analogy. For there is a sense in which genes, too, need to be studied in a context that takes other genes into account. A DNA sequence can have different effects in different organisms, depending on the network of relations it enters into with other genetic and developmental resources. Just as the significance of belief in God can vary with social context, with the result that it can make little sense to think of ‘belief in God’ as a meme, so the function of some DNA sequence can vary with organic context, with the result that it makes little sense to identify some sequence type as a gene for the purposes of evolutionary analysis.

These criticisms focus on whether there truly are memes. But there are also criticisms of the usefulness of the meme concept, regardless of whether memes exist. As was already indicated above, one might worry that memetics merely offers a cosmetic re-packaging of a familiar set of stories about cultural change. Perhaps science is composed of replicating entities struggling against various selection pressures, but what insight does this offer us, if in the end it presents us with nothing more than an alternative idiom in which to describe the various factors that affect the evaluation of scientific hypotheses? Perhaps clothes fashions are memes, but even if that is the case, one still needs to explain what makes one clothing meme fitter than another, and the fear is that once spelled out this will quickly boil down to a well-known appeal to consumer psychology.

The most serious and most respected efforts to apply evolutionary thinking to culture begin from a different starting point to memetics. Meme theorists tend to begin with a general characterisation of evolution by natural selection, namely as a process that requires differential competition between replicators. Hence the meme theorist looks for some strict analogue to the gene in the cultural realm, which can play the replicator role. Dawkins implies that it is only because humans are subject to colonisation by replicators other than genes that human evolution escapes ‘the tyranny of the gene’. On this view, memes are required in order to free our species from a form of biological determinism.

The alternative to this view begins with the observation that cultural inheritance is important, and it seeks to integrate cultural inheritance into traditional evolutionary models. But this general motivation leaves open the issue of whether cultural evolution requires the existence of cultural replicators. Clearly one can accept many of the criticisms of the meme concept, and still attempt to model the effects of cultural inheritance. Rather than seek to show that there are cultural replicators, one can instead seek to build models that allow for error-prone learning, and that acknowledge that an individual's beliefs are often the result of exposure to many sources, rather than copying from just one source. The interest of cultural evolutionary models in this tradition is sometimes simply to show how cultural change of various sorts—not necessarily adaptive cultural change—can subsequently affect genetic evolution, and vice versa. This is the general goal of models of gene-culture co-evolution. But cultural evolutionary models also aim to assess the role of cultural inheritance in the construction of adaptation.

One might think that even if cultural change does not require cultural replicators, at least adaptive cultural change does. The general Darwin scheme for explaining adaptation demands reliable inheritance—it demands that once a fitness-augmenting mutation arises, it can be retained in future generations. If cultural learning is error-prone, or if individuals acquire cultural traits by taking an average of many different models, then one might think that if some individual is able to discover a fitness-enhancing behaviour, that trait will be lost to future generations either because it is mis-copied, or because it is combined with other less adaptive traits to produce an averaged mish-mash of a behaviour.

All of these inferences have been challenged by recent cultural evolutionary theory. Cultural evolutionists agree that at the level of the population, cumulative evolution requires that fitness-enhancing cultural traits are preserved in the offspring generation. However, they deny that this requires faithful transmission between individuals. A formal model from Henrich and Boyd (2002) shows how so-called ‘conformist bias’ can overcome the effects of error-prone learning to produce reliable inheritance at the population level. ‘Conformist bias’ is the tendency of individuals to adopt what they believe is the most common representation in a population. Henrich and Boyd cite evidence that conformist bias is a real phenomenon. Henrich and Boyd's theoretical model assumes that individuals are poor at inferring the representations of others. Even so, they argue that when we look to the population level, conformist bias helps to correct the effects of such errors, producing a population-wide distribution of representations in the offspring generation that is close to the population-wide distribution of representations in the parent generation. Henrich and Boyd explain the reason for this. In general, error-prone transmission has a tendency to produce a mixture of different representations. In a population that already contains several different representations at significant frequencies, the effect of error on a population-wide distribution of representations is therefore low. In a population in which one representation is common, the effects of error are much more significant. But if we add conformist bias, we increase the chances of a commonly held representation remaining commonly held in future generations, even with error-prone imitation.

Boyd and Henrich acknowledge that this does not make population-level distributions perfectly reliably inherited. But this does not show that cumulative evolution acting on cultural inheritance is impossible. At the genetic level, highly faithful copying processes allow even very small selective forces to preserve adaptive variation. Less faithful copying demands stronger selective forces if adaptive variation is not to be lost. Boyd and Henrich are confident that selective forces in the cultural realm are stronger than selective forces in the genetic realm. The moral, once again, is that it is important not to focus too closely on genetic evolution as a model for cultural evolution.

Boyd and Henrich also argue that a cultural evolutionary theory can accommodate Sperber's claim that cultural reproduction rarely works through genuine copying. Even if there is a small number of ‘attractors’—ways of thinking that we are particularly likely to adopt given some external stimulus—it does not follow that evolutionary models have no role to play. Most obviously, one can still argue that various selective forces affect which of a number of attractors comes to predominate in a population. Returning once again to the cake example, there may be attractors corresponding to Victoria Sponges, Ginger Cakes and Banana Bread. Even so, one can seek to understand why at a given moment in time more Victoria Sponges are being produced than Ginger Cakes, and the framework of cultural evolutionary theory, which looks to the factors that make individuals likely to be used as models for imitation, and the factors that make representations (recipes, in this case) likely to be emulated once a model is picked, can be used to explain this differential success without strict copying.

Memetics tends to be driven by a desire to see cultural analogies to genetic evolution. Cultural evolutionary models in the manner of Boyd and Henrich are driven instead by a desire to find ways of understanding how cultural inheritance affects evolutionary processes. These sorts of cultural evolutionary models do not assume that cultural inheritance works in the same way as genetic inheritance. Indeed, they are free to model cultural inheritance in ways that depart quite markedly from genetic inheritance. Yet they remain recognisably evolutionary in style, primarily because they seek to explain the changes in trait frequencies in a population over time. They do this by making broad assumptions about how individuals acquire cultural traits—for example, they may assume that an individual's representations are the product of learning from a variety of peers, or that they arise from attending particularly to authority figures—and by assessing how such rules will play out at the population level. Moreover, these rules for cultural acquisition are not merely conjectured, they are given experimental support. Cultural evolutionary theorists seek to document the effects of various empirically confirmed forms of bias, such as conformist bias and prestige bias. Just as Darwin's own theory of evolution by natural selection remained largely conjectural until supplemented by empirical work showing how inheritance worked, and by statistical work focusing on the population-level consequences of inheritance, selection, mutation and other forces, so cultural evolutionary theory has gained its insights from a similar combination of empirical and mathematical approaches.

At the beginning of this entry it was claimed that the case for cultural evolution was irresistible. No one can deny that cultural inheritance is an important factor in explaining how our species has changed over time. Cultural inheritance is not merely a process that acts in parallel to genetic evolution, it is intertwined with genetic evolution. Cultural changes bring about alterations to the environment, which in turn affect both how genes act in development, and what selection pressures act on genes. In spite of all this, one might still worry that it is a mistake to understand the importance of culture using the tools of evolutionary theory. This is because one may be sceptical of the existence of a theory that is both general enough to cover all forms of cultural change, and informative enough to be enlightening.

There is no doubt that it is often important to remind overly-enthusiastic orthodox Darwinians of the importance of culture. For example, it seems that the increased incidence of lactose-tolerance among human populations has arisen as a consequence of a cultural innovation—namely dairy farming. The relatively recent appearance of this genetically-controlled adaptation demonstrates that human physiological nature is something that continues to change, and it also demonstrates the causal impact of culture on genes (Richerson and Boyd 2005, 191–92). Such examples by themselves show the rashness of any view that claims either that human nature has remained fixed since the Stone Age, or that genes are somehow in the evolutionary driving seat. Yet none of this shows that we can develop a general, informative theory of cultural evolution. One might fear that in the end cultural change, and the influence of cultural change on other aspects of the human species, are best understood through a series of individual narratives. Our brief examination of memetics illustrated this concern. We gain no real explanatory insight if we are told that ideas spread through populations, some more successfully than others. We want to know what makes some ideas fitter than others. And it is not clear that there will be any general rules that can help us to answer this question. In the biological realm we need detailed accounts of local environmental circumstances, species-specific physiology and anatomy, and so forth, to tell us what makes one organic variant fitter than another. Similarly, in the cultural realm we will need to look at local psychological dispositions to explain why some ideas are more likely to spread than others. So any explanatory value to be had from memetics is parasitic on conventional work done in psychology. And if individual preferences are subject to change over time, then there may be no general and informative theory of cultural evolution to be had; rather, we will have to settle for local explanations that look to shifting preferences. Rather than provide a new scientific framework for an understanding of culture, memetics will tend to degenerate into conventional narrative cultural history.

There are two broad sets of responses to this line of argument, each of which picks up on a different explanatory element of mainstream evolutionary theory. Boyd and Richerson argue that informative insights arise out of cultural evolution's ‘population thinking’ (Richerson and Boyd 2005). Sterelny (2001, 2003, 2006, forthcoming) argues that illuminating insights regarding the general conditions required for evolvability also apply in the cultural realm. We will look at each line of defence in turn.

‘Population thinking’ means many things to many people. For Boyd and Richerson it denotes any effort to abstract from a characterisation of individual psychological profiles, in a way that allows an exploration of the consequences of these individual-level dispositions for population-level properties. We have already seen an example of this sort of population thinking in action. It is far from obvious that conformist bias among individuals can enable population-level inheritance in spite of individual-level errors in copying. To show that these properties of individual psychology (conformist bias and error-prone learning) combine to yield population-level inheritance requires some abstract mathematical modelling. And the establishment of this population-level consequence is important, for it enables the investigator to revise the constraints one might naively think must bear on cultural inheritance if cumulative cultural evolution is to occur.

In a useful article, Elliott Sober (1991) suggests that theories of cultural evolution may have limited value for the work of social scientists, on the grounds that social scientists are primarily interested in explaining what makes individuals likely to adopt one idea, rather than another. They want to know, for example, why nineteenth-century Italian women decided that they would rather have two children than five, not what the population-level consequences of their decisions might be. Richerson and Boyd respond by saying that Sober's argument assumes, erroneously, that ‘we are all good intuitive population thinkers’ (Richerson and Boyd 2005, 97). In Sober's original article he points out that population thinking might save cultural evolutionary models from vacuity in just this way:

So the question about the usefulness of these models of cultural evolution to the day-to-day research of social scientists comes to this: Are social scientists good at intuitive population thinking? If they are, then their explanations will not be undermined by precise models of cultural evolution. If they are not, then social scientists should correct their explanations (and the intuitions on which they rely) by studying these models. (Sober 1991, 492)

Many of Richerson and Boyd's models are enlightening. As we have seen, it takes work to show that cumulative cultural adaptation does not require replication. Note, however, in favour of Sober's scepticism, that the most interesting cultural evolutionary models are often those which show the general circumstances under which it is possible for cultural inheritance to be effective in producing adaptation. Boyd and Richerson's claim in favour of the importance of prestige bias is primarily an effort to show how natural selection might have favoured cultural learning. Sober's concern is with whether models such as these will also affect ‘the day-to-day research of social scientists’, who are not so interested in establishing such general conditions for cumulative cultural evolution, but who are instead interested in understanding particular episodes of social and cultural change. Even here, Richerson and Boyd's population thinking may have some bite. They seek, for example, to explain the disappearance of important technologies on Tasmania. Drawing on the work of Joseph Henrich, they suggest (Richerson and Boyd 2005, 138) that the maintenance of technologies and the associated behaviours required to produce and operate them may require a population that is large enough for the rate of innovation to offset the degradation that results from error-prone imitation. If Boyd and Richerson are right about this episode in the history of Tasmania, then we may be able to explain the differences in the abilities of the Tasmanians, compared with other peoples, to maintain a set of technologies, merely by citing population size, rather than other forms of social or cultural difference.

A second, related, way to vindicate models of cultural evolution looks to the question of the general features of inheritance systems that make for evolvability in a lineage. This project has been pioneered in recent years by Kim Sterelny (e.g. 2001, 2003, 2006, forthcoming). Once again, let us illustrate the general nature of these issues by beginning in the organic realm. Darwin's theory is intended to explain adaptation. The basic conditions for natural selection do not, in spite of appearances, suffice for the appearance of functional traits. A system in which offspring resemble parents with respect to fitness-enhancing traits may not develop complex adaptations. The environment needs to cooperate: if selective pressures change very quickly then there will be no sustained environmental demands of the sort that might build complex adaptations over time. Development also matters. If ontogeny is set up in such a way that changes to any one trait tend to be accompanied by changes to all other traits, then the chances are that cumulative adaptation will be particularly hard to come by. For even in those cases where a mutation contributes positively to the function of one trait, the chances are that it will contribute negatively to overall fitness in virtue of its disruption of the functioning of other traits. Development also needs to make a wide range of variation available. If it is highly constrained, so that only a small number of forms are possible, then selection is not presented with a broad enough range of raw materials from which to fashion complex traits. It also seems that cumulative adaptation relies on the suppression of ‘outlaws’ (Sterelny 2001). Group selection, for example, is often held to be an ineffective agent of group-level adaptation, on the grounds that it is vulnerable to ‘subversion from within’. This occurs when individual organisms go it alone, sabotaging complex features of group organisation in favour of their own fitness. Individual-level selection, in contrast, can build individual-level adaptations. This is because, by and large, genes in a given human organism share a ‘common fate’—they do not behave as though they were in direct competition, struggling for representation in future generations. When genes genuinely ‘go it alone’, for example by sabotaging meiosis so that some have greater chances of appearing in future generations than others, then the overall integrity of the organism can be compromised, and individual-level adaptation is undermined.

By applying these sorts of considerations to the cultural realm we can attempt to understand the likely costs and benefits associated with various different forms of cultural inheritance (vertical, horizontal, meme-like and so forth). We can also perhaps come to an understanding of the different evolutionary forces that might bring these different forms of cultural inheritance into existence. And, in turn, these insights may facilitate comparative work that seeks to document the general conditions that are required for a species to make use of cultural inheritance in order to build complex adaptations such as tools. This way of thinking offers the promise, for example, of explaining why few, if any, non-human species are able to build progressively more and more complex cultural features in a cumulative manner (Richerson and Boyd 2005, 107). The exploration of the significance of these conditions in the cultural realm is contentious, partly because the conditions for evolvability themselves are disputed. Boyd and Henrich's work brings out the fact that although population-level inheritance is important for adaptation, parent-offspring resemblance is not, in fact, necessary. Questions relating to evolvability are also tied up with difficult issues relating to the units-of-selection debate. As we have seen, natural selection at a higher level of organisation may be required to generate mechanisms that suppress the ability of disruptive ‘outlaws’ to go it alone at lower levels of organisation. Does something like this occur in the cultural realm? Does selection on human groups act so as to limit the ability of individual humans to go it alone? In what ways might cultural inheritance be involved in these processes? These questions are complex, both in terms of how they should be posed and how they should be answered. But some of the most interesting work in cultural evolutionary theory may come from efforts to answer them.

Issues relating to evolvability are sometimes framed in terms of systems of information transfer. On this view, if offspring are to resemble parents, developmental information must be transmitted from one generation to the next. The question is what forms of information transmission system do this job. This mode of framing the issue is contentious, for it is not always clear how we are to understand the concept of information, and what it means for some causal contributor to development to count as an information-bearer, rather than some other kind of developmental participant, such as an information-reader, say, or a background condition for information transfer (see Oyama 2000 and Griffiths 2001 for discussion of these issues). This general way of thinking about inheritance has, however, been influential in characterising so-called ‘major evolutionary transitions’ (Maynard Smith and Szathmary 1995). Key transitions in the history of life are said to include the development of DNA-based inheritance, the emergence of chromosomes, the advent of the ‘genetic code’, and events such as the arrival of sociality and language. Maynard Smith and Szathmary propose that we can think of these events as modifications to the mechanisms of inter-generational information transmission.

Jablonka and Lamb (2005) argue that thinking in terms of information transmission systems also allows us to point out salient differences in the forms of social transmission underlying cultural evolution. They claim that only some forms of social transmission make use of a system of symbols . Consider, for example, that to say that some birds inherit their song by social transmission is not to say that birdsong is a symbolic system. Humans, on the other hand, trade in publicly-accessible symbols. Moreover, repositories of symbols, most obviously in the form of libraries and computer databases, are vital inheritance systems for humans, allowing the preservation and accumulation of knowledge across generations. Note, also, that there are different types of symbol system. In some cases the relationship between a symbol and what is represented is arbitrary. The is the case for a word like ‘man’, which does not look or sound like a human male. In other cases of iconic symbolism, the relationship is one of resemblance: a sign for the gents' toilet looks like a man.

Jablonka and Lamb use the characteristic differences between typical modes of social inheritance in animals and humans to illuminate the impact our own symbolic transmission systems have on human cultural evolution (see also Deacon 1997). Although they argue that there can be non-linguistic symbolic systems (2005, 224), language exemplifies nicely the way in which systems of symbols contain elements that can be recombined in countless ways to yield a vast array of different meaningful messages. Repositories of symbolically stored information, such as books, can also be searched, annotated, edited and so forth, in ways that add to their power and versatility. This manner of thinking opens up a number of challenging issues. The question of the degree to which symbolic systems resemble other inheritance systems is an illuminating one. Consider, by way of example, Stegmann's (2004) discussion of the sense in which the genetic code is ‘arbitrary’. One quickly realises that any attempt to say precisely what makes some inheritance system a symbolic system, and any attempt to differentiate between types of symbolic systems (linguistic, non-linguistic and so forth), will be exceptionally philosophically demanding.

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[Please contact the author with suggestions.]

-->Darwin, Charles --> | Darwinism | epistemology: evolutionary | evolution | heritability | James, William | natural selection | psychology: evolutionary | replication | Spencer, Herbert

Acknowledgments

The editors would like to thank Christopher von Bülow for carefully reading this entry and identifying a good number of typographical errors for correction.

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Article Contents

Clarifying the confusion, bottom-up or top-down, what is cultural evolution anyway.

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Alberto J C Micheletti, Eva Brandl, Ruth Mace, What is cultural evolution anyway?, Behavioral Ecology , Volume 33, Issue 4, July/August 2022, Pages 667–669, https://doi.org/10.1093/beheco/arac011

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The term cultural evolution has become popular in the evolutionary human sciences, but it is often unclear what is meant by it. This is generating confusion and misconceptions that are hindering progress in the field. These include the claim that behavioral ecology disregards culture. We argue that these misunderstandings are caused by the unhelpful use of term cultural evolution to identify both a phenomenon—culture changing through time—and a theory to explain it—the potential role of cultural transmission biases in driving this change. We illustrate this point by considering recently published influential studies and opinion pieces. If we are to avoid confusion, the term cultural evolution is best reserved to identify the phenomenon of cultural change. This helps clarify that human behavioral ecologists do not disregard culture, but instead have studied its evolution from the very beginning. Different approaches to the study of human behavior can coexist and complement each other in the framework offered by Tinbergen’s four evolutionary questions. Clarifying key terms is crucial to achieve this synthesis.

Cultural evolution is becoming a blanket term for any kind of human behavioral evolution. However, we believe that this is leading to confusion because the term “cultural evolution” is being used to indicate both a phenomenon—culture changing through time—and an approach to study it—the focus on cultural inheritance and the potential role of transmission biases in shaping culture. This confusing use of the term is widespread in the literature and in informal discussion (we may even have been guilty of this ourselves). For example, Schulz et al. (2019 : 1) state that “cultural evolution often favoured some form of cousin marriage.” Are they referring to cultural evolution as opposed to genetic evolution? Cousin marriage is surely a culturally transmitted behavior, so this comparison appears irrelevant here. Or, by cultural evolution, do they mean the action of transmission biases? Or are they referring to the whole phenomenon of cultural change? If so, how can culture changing per se “favour” a particular outcome? Innovation, migration, or cultural drift may lead to this outcome, but only some form of selection, genetic, cultural or perhaps both, may “favour” a given outcome.

A second example reveals how this ambiguity can lead to confusion that is hindering progress in the field. A study by Barsbai et al. (2021) shows that human behaviors tightly fit local environmental conditions, following very similar patterns to those shown by mammals and birds living in the same area. In a commentary to the study ( Hill and Boyd 2021 ), the wording appears to present cultural evolution and adaptation to local ecology as alternative explanations for the diversity and distribution of these traits. They state: “Hence, the study appears to validate the basic premise of the evolutionary perspective called ‘human behavioural ecology’. However, it is a mistake to conclude from this that culture is unimportant” ( Hill and Boyd 2021 : 236). This seems to suggest that human behavioral ecology ignores culture. Yet, Barsbai et al. (2021) do not deny that the foraging, reproductive, and social behaviors they examine are culturally transmitted, at least in humans. Neither do they assume that cultural history plays little to no role in shaping the observed patterns, as seems to be implied by Hill and Boyd (2021 : 236) when they state: “ecological factors explain much variation in human behaviour, but so too does cultural history.” Cultural phylogeny may indeed play a role and, for this reason, the authors control for it in their analyses ( Barsbai et al. 2021 ).

Barsbai et al. (2021) simply show that a variety of human behaviors—almost certainly culturally transmitted—fit local ecology in the same way as behaviors that are probably mostly genetically controlled in birds and mammals. Therefore, their analysis suggests that these cultural traits have been shaped by inclusive fitness interests. In line with a behavioral ecological approach, they are agnostic as to the mechanism leading to this fit. It is possible that it came about through one or more specific biases in cultural transmission or, more generally, because humans are flexible learners that make conscious, strategic choices about what to adopt, sensitive to pay-offs ( Burton-Chellew and West 2021 ). Although it is tempting to contrast adaptation to local ecology and “culture” or “cultural evolution” as two competing forces shaping the change of behavior through time, such a contrast is impossible. As Boyd has acknowledged elsewhere ( Boyd 2018 ), adaptation to local ecology is an outcome of the process of cultural evolution, whereby cultural selection has favored a set of cultural variants because they are adaptive in a specific environment. Therefore, the tools of behavioral ecology are always going to be needed to understand cultural evolution.

Evolutionary biologists, too, have sometimes used language suggesting this unhelpful dichotomy between adaptation and culture. For example, Burton-Chellew and West (2013 : 1043) ask “Will culture be more important for certain classes of traits such as those less linked to fitness?” We suspect that these authors were meaning to suggest that fitness-insensitive cultural transmission mechanisms can sometimes result in non-adaptive outcomes (especially when a trait is less fitness relevant). However, the way they presented their argument can be potentially misleading. Behaviours can be culturally transmitted, and many human behaviors are, and yet they can still be shaped, at least to some extent, by the inclusive fitness interests of their bearer.

As testified by the examples above, using the same term to identify both a phenomenon and a theory to explain it is unhelpful. It becomes unclear whether one is referring to an explanandum —what we are trying to explain—or an explanans —the set of statements we use to explain it ( Hempel and Oppenheim 1948 ). This hinders discussion between researchers employing different approaches, as one may write about cultural evolution as explanans and the other might read it as explanandum. It leads to the false dichotomy between culture and adaptation to ecology that we have discussed above.

For these reasons, we believe that the term cultural evolution is best reserved for the phenomenon, not implying any one approach or theory. Just as the phenomenon of organic evolution and Darwin’s theory about it are distinct ( Brady 1985 ), so are cultural evolution and our explanations for it. Another term should be used to refer to approaches centered on cultural transmission (e.g., “cultural transmission approaches” or “social learning approaches”).

In this way, it also becomes clear that behavioral ecology does not disregard culture. Behavioral ecologists aim to explain whether and how behaviors serve an adaptive function ( Nettle et al. 2013 ), and most human behaviors are at least partially influenced by transmitted culture. Thus, much of human behavioral ecology studies the cultural evolution of human behaviors. It does so either by exploring the ecological incentives that shape the adoption of specific cultural traits, or by considering culture as part of the environment that determines cost-benefit scenarios faced by individuals ( Mace 2014 ).

Cultural behaviors can be studied from a range of different perspectives. In the 1980s, three evolutionary approaches to human behavior emerged: evolutionary psychology (which focuses on cognitive adaptations that underly behavior; Tooby and Cosmides 1990 ), human behavioral ecology ( Nettle et al. 2013 ), and a third one focusing on cultural transmission (often confusingly referred to as “cultural evolution”). Tinbergen’s (1963) four questions about behavioral evolution—mechanism, ontogeny, function, phylogeny—still offer a useful framework for organizing this research. They are valid regardless of whether a behavior is genetically controlled, culturally inherited or a bit of both—and they are complementary. Rather than being mutually exclusive, these three evolutionary approaches simply tackle human behavior, including cultural traits, at different levels of explanation ( Figure 1 ). Suggesting a dichotomy between culture and adaptation to local ecology, though perhaps intuitively appealing, is misleading: it generates confusion between function and ontogeny.

Human behavioral ecology, evolutionary psychology, and the cultural transmission approaches (shown in green, blue, and brown, respectively) ask different evolutionary questions about human behaviors. Notice that some might extend the domain of interest of the cultural transmission approaches to include mechanism, and others might extend evolutionary psychology to cover ontogeny, depending on what definition of psychological mechanism is adopted.

Models of cultural transmission derived from population genetics seek to predict the distribution of cultural phenotypes bottom-up, from transmission processes such as conformity bias. This does not mean that these models and related hypotheses disregard adaptation. In fact, major theorists have proposed that transmission biases have been selected for because they facilitate the spread of adaptive solutions via social learning ( Boyd and Richerson 1985 ; Boyd 2018 ). However, the emphasis on transmission dynamics means that when addressing cultural phenotypes, mechanistic explanations are favored. In contrast, behavioral ecologists seek to predict the distribution of cultural traits top-down, from the adaptive problems they are designed to solve. In many cases, the top-down approach might generate results more readily than the bottom-up approach. With social learning processes showing few general rules (as multiple mechanisms are likely to be acting at the same time), predicting cultural diversity from the mechanisms of social transmission is going to be very hard.

Models informed by inclusive fitness, and their test in the field, are key to help us understand cultural diversity; they build a clearer picture of the diversity of human behavior than cultural learning approaches alone can do. Cultural transmission dynamics can sometimes prevent the realization of inclusive fitness interests; more empirical research is needed to establish when this is indeed the case (the demographic transition from high to low fertility is one candidate; Colleran 2016 ). Yet, contrary to some suggestions, this does not mean that fitness-based models are inadequate or that only transmission dynamics should be prioritized as a matter of course.

This work was supported by a European Research Council Advanced Grant (grant number 834597) to R.M. and A.J.C.M.

We thank Hanzhi Zhang, Sarah Peacey, Mark Dyble, members of the Human Evolutionary Ecology Group at UCL Anthropology, and two anonymous reviewers for useful comments on an earlier version of this manuscript. All authors developed the idea and wrote the manuscript.

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To arrive at the edge of the world's knowledge, seek out the most complex and sophisticated minds, put them in a room together, and have them ask each other the questions they are asking themselves.

Biological and Cultural Evolution

In the near future, we will be in possession of genetic engineering technology which allows us to move genes precisely and massively from one species to another. Careless or commercially driven use of this technology could make the concept of species meaningless, mixing up populations and mating systems so that much of the individuality of species would be lost. Cultural evolution gave us the power to do this. To preserve our wildlife as nature evolved it, the machinery of biological evolution must be protected from the homogenizing effects of cultural evolution.

Unfortunately, the first of our two tasks, the nurture of a brotherhood of man, has been made possible only by the dominant role of cultural evolution in recent centuries. The cultural evolution that damages and endangers natural diversity is the same force that drives human brotherhood through the mutual understanding of diverse societies. Wells's vision of human history as an accumulation of cultures, Dawkins's vision of memes bringing us together by sharing our arts and sciences, Pääbo's vision of our cousins in the cave sharing our language and our genes, show us how cultural evolution has made us what we are. Cultural evolution will be the main force driving our future.

FREEMAN DYSON is an emeritus professor of physics at the Institute for Advanced Study in Princeton. In addition to fundamental contributions ranging from number theory to quantum electrodynamics, he has worked on nuclear reactors, solid-state physics, ferromagnetism, astrophysics, and biology, looking for problems where elegant mathematics could be usefully applied. His books include  Disturbing the Universe ,  Weapons and Hope ,  Infinite in All Directions ,  Maker of Patterns,  and  Origins of Life .  Freeman Dyson's  Edge  Bio Page  

BIOLOGICAL AND CULTURAL EVOLUTION: SIX CHARACTERS IN SEARCH OF AN AUTHOR

In the Pirandello play, "Six Characters in Search of an Author", the six characters come on stage, one after another, each of them pushing the story in a different unexpected direction. I use Pirandello's title as a metaphor for the pioneers in our understanding of the concept of evolution over the last two centuries. Here are my six characters with their six themes.

1. Charles Darwin (1809-1882): The Diversity Paradox. 2. Motoo Kimura (1924-1994): Smaller Populations Evolve Faster. 3. Ursula Goodenough (1943- ): Nature Plays a High-Risk Game. 4. Herbert Wells (1866-1946): Varieties of Human Experience. 5. Richard Dawkins (1941- ): Genes and Memes. 6. Svante Pääbo (1955- ): Cousins in the Cave.

The story that they are telling is of a grand transition that occurred about fifty thousand years ago, when the driving force of evolution changed from biology to culture, and the direction changed from diversification to unification of species. The understanding of this story can perhaps help us to deal more wisely with our responsibilities as stewards of our planet.

1. Charles Darwin (1809-1882). The Diversity Paradox.

In the Pirandello play, "Six Characters in Search of an Author", the six characters are actors who arrive at a theater to begin rehearsing a play. The theater manager apologetically informs them that there has been a misunderstanding and he has no play for them to rehearse. He begs the actors to go home. But the leading actor refuses to leave and starts improvising a play, making up the story as he goes along. One by one, the other actors join in, each of them pushing the story in a different unexpected direction. At the end of the performance, all the actors are fully engaged, and together they bring the story to a dramatic climax. I have borrowed Pirandello's title, and used his characters, as a metaphor for the pioneers in our understanding of the concept of evolution over the last two centuries.

Until recently, evolution was considered to be a biological process, driven by the slowly acting forces of speciation and extinction. Speciation is the birth of new species by splitting of an existing species into genetically isolated populations that do not interbreed. Extinction is the disappearance of a species that dies out without leaving descendants. Our first character, Charles Darwin, published his great work,  The Origin of Species , in 1859. He demonstrated, with a wealth of evidence, from observations of species in the wild and from the effects of selective breeding of plants and animals, that natural selection is [a] powerful force driving evolution. His book made a stronger statement, that natural selection is [the] cause of evolution. The difference between [a] and [the] was hardly noticed by the readers of his book. His theory triumphed and became for a hundred years the view of evolution accepted by almost all biologists and by the majority of educated people.

Darwin himself was well aware that nature contains many mysteries that his theory does not easily explain. There is a mismatch between the real world, with its amazing richness of diverse species, many of them obviously burdened with superfluous flowers and feathers, and the theoretical world of Darwinian evolution in which only the fittest should survive. Naively, we should expect Darwinian evolution to result in a world with a much smaller number of species, each selected by superior fitness to be a winner in the game of survival. All through his life, Darwin was puzzled by the abundance of weird and wonderful species that look like losers but still survive. I call this abundance the diversity paradox.   If only the fittest survive, we should expect to find a few hundred superbly fit species adapted to live in various habitats. Darwin looked at the real world and found an extravagant display of species, with a great diversity of superficial differences. He saw elaborate structures that are expensive to maintain. The theory of evolution by natural selection should tend to keep creatures plain and simple, but nature appears to prefer structures that are elegant and complicated.

Darwin understood that sexual reproduction is a powerful cause of diversity of species. For a sexual species to exist and survive, it is advantageous to have distinctive ornamentation of one sex, usually the male, and a strong preference of the other sex for a mate with that particular ornament. The mating system causes the population possessing it to be genetically isolated from other related populations. The mating system becomes a genetic barrier, creating a new species and maintaining its identity. A species like the bird of paradise with an elaborate mating system may derive enough advantage from the uniqueness of the system to pay for the cost of the feathers. Another cause of diversity of species is symbiosis, enabling two or more species to help each other to survive or to reproduce. A conspicuous example of symbiosis leading to diversity is the simultaneous evolution of flowering plants and insects. Another example is the coral reef and the reef-fish. Darwin concluded that sexual selection and symbiotic coevolution would explain the overall diversity of natural ecologies. But he had no hard evidence to justify this conclusion. We now know that he was mistaken. Another cause of diversity, of which he had no conception, also plays a dominant role in natural evolution.

Darwin knew nothing of genes. He was unaware of the work of Gregor Mendel, the Austrian monk who worked in his monastery garden and did experiments on the inheritance of pod-color in peas. Mendel discovered that heritable traits such as pod-color are inherited in discrete packages which he called genes. Any act of sexual reproduction of two parents with different genes results in offspring with a random distribution of the parental genes. Heredity in any population is a random process, resulting in a redistribution of genes between parents and offspring. The numbers of genes of various types are maintained on the average from generation to generation, but the numbers in each individual offspring are random. Mendel made this discovery and published it in the journal of the Brünn Natural History Society, only seven years after Darwin published  The Origin of Species . Mendel had read Darwin's book, but Darwin never read Mendel's paper. In 1866, the year when Mendel's paper was published, Darwin did a very similar experiment, using snap-dragons instead of peas, and testing the inheritance of flower-shape instead of pod-color. Like Mendel, he bred three generations of plants, and observed the ratio of normal-shaped to star-shaped flowers in the third generation. Unlike Mendel, he had no understanding of the mathematics of statistical variations. He used only 125 third-generation plants and obtained a value of 2.4 for the ratio of normal to star-shaped offspring. This result did not suggest any clear picture of the way flower-shapes are inherited. He stopped the experiment and explored the question no further. Darwin did not understand that he would need a much larger sample to obtain a statistically significant result. Mendel understood statistics. His sample was sixty-four times larger than Darwin's, so that his statistical uncertainty was eight times smaller. He used 8023 plants.

Mendel's essential insight was to see that sexual reproduction is a system for introducing randomness into inheritance. In peas as in humans, inheritance is carried by genes that are handed down from parents to offspring. His simple theory of inheritance carried by genes predicted a ratio of three between green and yellow pods in the third generation. He found a ratio of 3.01 with the big sample. This gave him confidence that the theory was correct. His experiment required immense patience, continuing for eight years with meticulous attention to detail. Every plant was carefully isolated to prevent any intruding bee from causing an unintended fertilization. A monastery garden was an ideal place for such experiments. Unfortunately, his experiments ended when his monastic order promoted him to the rank of abbot. Obedient to his vows, he ceased to be an explorer and became an administrator. His life-work lay hidden in an obscure German-language journal in Brünn, the city that later became Brno and is now in the Czech Republic.

The idea of genes remained generally unknown to biologists until twenty years after Darwin's death. Darwin imagined various ways of mixing inherited traits of parents and distributing them to offspring, but he never imagined genes. Without the concept of genes, it was impossible for him to calculate correctly the rates of speciation and extinction in any natural population. He never attempted such calculations. If he had made such calculations with a model of inheritance based on mixing, he would have got drastically wrong answers. He had the good sense not to make such calculations without a verified model of inheritance. Without experimental knowledge of the statistics of inheritance, he had no way to guess reliably how effective natural selection could be in creating new species and exterminating old ones.

2. Motoo Kimura (1924-1994). Smaller Populations Evolve Faster.

At this point in the play, our second character enters, Motoo Kimura, author of the book, The Neutral Theory of Molecular Evolution , published in 1983, more than a hundred years after Darwin's masterpiece. Kimura was a Japanese geneticist who came as a student to work with Sewall Wright at the University of Wisconsin. Sewall Wright was one of the biologists who explored the evolutionary implications of Mendel's discovery after Mendel's paper was rediscovered in 1900. I was lucky to meet Sewall Wright accidentally at the faculty club at the University of Wisconsin in 1987. I was visiting the University and went to the faculty club for lunch. Sitting alone at a small table was a lively old man who turned out to be Sewall Wright, then 98 years old but still in full possession of his wits. He gave me a first-hand account of how he read Mendel's paper and decided to devote his life to understanding the consequences of Mendel's ideas. Wright understood that the inheritance of genes would cause a fundamental randomness in all evolutionary processes. The phenomenon of randomness in evolution was called Genetic Drift. Kimura came to Wisconsin to learn about Genetic Drift, and then returned to Japan. He built Genetic Drift into a mathematical theory which he called the Neutral Theory of Molecular Evolution.

After the discovery of the structure of DNA molecules by Crick and Watson in 1953, Kimura knew that genes are molecules, carrying genetic information in a simple code. His theory applied only to evolution driven by the statistical inheritance of molecules. He called it the Neutral Theory because it introduced Genetic Drift as a driving force of evolution independent of natural selection. I never met Kimura, but he was still alive when I began to study his work, and I was delighted to receive a personal message of encouragement from him before he died in 1994.

Kimura did not prove that Darwin's theory was wrong. He proved that Darwin's theory was incomplete. Darwin missed Genetic Drift, which was sometimes important and sometimes unimportant. The evolutionary effects of natural selection are generally independent of the size of the evolving population, while the effects of genetic drift depend strongly on population size. Other things being equal, the rate of genetic drift is proportional to the inverse square-root of population size. The inverse square-root is a simple consequence of the statistics of independent random variables. The average of any N independent random variables varies at a rate inversely proportional to the square-root of N. For any firmly established species with a population measured in millions or billions, genetic drift is negligible, natural selection is dominant, and the Darwin theory is accurately valid. For a newly emerging or endangered species with population measured in tens or hundreds, genetic drift dominates, selection is relatively unimportant, and the Kimura theory is valid. The random jumps of genes in a small population produce evolutionary change much faster than the gentle push of natural selection. Kimura understood that genetic drift is the main driving force in the quick jumps when species are created or extinguished.

Kimura's theory explains the diversity paradox that puzzled Darwin. Why are we surrounded by such an astonishing diversity of birds and insects and microbes? From the point of view of Darwin, a small number of dominant species would have been sufficient. Kimura explains the mystery by invoking the power of genetic drift, which becomes suddenly rapid and effective just when it is needed, when small populations can vary fast enough to become genetically isolated and form new species.

Genetic drift in local enclaves gives to every large established species the power to diversify into a family of new species. At the ragged edges of small populations, where random jumps prevail, speciation is driven by Kimura's neutral theory. Darwin's theory is still true away from the edges, where selection has time to operate on big populations.

3. Ursula Goodenough (1943- ). Nature Plays a High-Risk Game.

After Kimura, our third character enters the play. She is Ursula Goodenough, a biologist born in 1943 and still active at Washington University in St. Louis. Like Darwin and unlike Kimura, she is an observer and experimenter. She gave us another important insight into the mystery of diversity. She analyzed published reports on the rate of random genetic mutation in genes of various kinds in many different sexually reproducing species, from algae to mammals. She and others noted that in a large number of species there are two families of genes that have mutation-rates much higher than average genes. The two families both have specialized functions. One family is genes involved with the immune system. There is an obvious reason for immune-function genes to mutate rapidly, since they must respond rapidly with production of fresh antibodies to detect and kill invading microbes.

The other rapidly mutating family of genes is involved with sexual mating systems. Goodenough observed a systematic tendency for genes active in the rituals of mating to mutate fast. The reason for this accelerated variation of mating genes is not obvious. Nature is forcing genetic drift to move faster in mating systems than in other bodily functions. If this is generally true, as Goodenough observes, it means that genetic drift in mating systems must have a special importance as a driving force of evolution. She proposes a general theory to explain the facts. In the big picture of life evolving over billions of years, established species with large populations evolve slowly and have a mainly conservative effect on the balance of Nature. The big jumps in evolution occur when established species become extinct and new species with small populations diversify. The big jumps, made by new species, are driven by genetic drift of small populations. For small populations to form new species, they must become genetically isolated. Rapid change of mating systems is a quick road to genetic isolation. Goodenough concludes that the rapid mutation of mating-system genes is Nature's way of achieving big jumps in large-scale evolution. Rapidly evolving mating systems gave us the diversity of species that astonished Darwin. Twenty years ago, Goodenough wrote a paper with the title, "Rapid Evolution of Sex-related Genes", describing her observations and conclusions. I consider this paper a great piece of work, a classic contribution to science, comparable with the books of Darwin and Kimura.

The picture of Nature revealed by Kimura and Goodenough is new and striking. Nature loves to gamble. Nature thrives by taking risks. She scrambles mating-system genes so as to increase the risk that individual parents will fail to find mates. The increase of risk of sterility of individuals is a part of Nature's plan. She imposes the increased risk on the whole population, so that a rare event will occur with greater probability, when a pair of lucky parents, whose names might happen to be Adam and Eve, are born with matching mating-system mutations. That rare event gives a pair of parents a chance to give birth to a new species. Nature knows how to play the odds. By putting her thumb on the mating-system mutation scale, she increases the risk of sterility of all parents, and increases the chance that a lucky pair will start a new species. Nature knows that, in the long run, established species are expendable and new species are essential. That is why Nature is ruthless to the individual parent and generous to the emerging species. Risk-taking is the key to long-term survival and is also the mother of diversity.

4. Herbert Wells (1866-1946). Varieties of Human Experience.

With three characters on stage, it would appear that our play is coming close to an end. Then a fourth character bursts in, jumping back a hundred years into the past and telling a different story. His name is Herbert Wells, born in 1866, educated as a biologist but using his knowledge to give us a fresh view of evolution. The first three characters thought of evolution as a biological process, governed by the rules of inheritance from parent to offspring. Wells knew that biological evolution is only half of a bigger story. The other half of the story is cultural evolution, the story of changes in the life of our planet caused by the spread of ideas rather than by the spread of genes. Cultural evolution had its beginnings as soon as animals with brains evolved, using their brains to store information and using patterns of behavior to share information with their offspring. Social species of insects and mammals were molded by cultural as well as biological evolution. But cultural evolution only became dominant when a single species invented spoken language. Spoken language is incomparably nimbler than the language of the genes.

Wells saw that we happen to live soon after a massive shift in the history of the planet, caused by the emergence of our own species. The shift was completed about ten thousand years ago, when we invented agriculture and started to domesticate animals. Before the shift, evolution was mostly biological. After the shift, evolution was mostly cultural. Biological evolution is usually slow, when big populations endure for thousands or millions of generations before changes become noticeable. Cultural evolution can be a thousand times faster, with major changes occurring in two or three generations. It has taken about two hundred thousand years for our species to evolve biologically from its origin in Africa until today. It has taken only about two hundred years of cultural evolution to convert us from farmers to city-dwellers, and to convert a large part of North America from forest to farmland.

Besides his expert knowledge of biology, Wells had a deep interest in the lives of ordinary human beings, with their destiny governed by ancient human emotions of love and hate, fear and greed. He began his professional life as a novelist, telling stories and bringing his characters vividly to life. His view of the human condition can be seen more clearly in his novels than in his biology. One of his novels is  Tono-Bungay , written in 1912. The narrator is George Ponderevo, a young and capable crook who is at home in the chaotic world of twentieth century capitalism. The chief character is uncle Teddy Ponderevo, an amiable swindler who invented the wonder-drug Tono-Bungay, guaranteed to cure all diseases and to bring us health and happiness. George knows how to keep the cash flowing, with raucous advertising campaigns and sales of shares in fraudulent companies.

For a while, the Tono-Bungay bubble makes them rich. Then the bubble bursts, and they are hunted criminals. Uncle Teddy dies in the crash of a home-made air-ship. George escapes in a private war-ship that he happens to own. The last chapter is entitled, "Night and the Open Sea", with George's ship swiftly and silently slicing through the dark waves. Wells is writing with a premonition of the horrors of World War One, which broke out two years later, destroying millions of people who would sacrifice their lives to the tribal gods of Empire and Country. The owners of war-ships would survive to find new victims.

Another novel,  The Time Machine , is concerned directly with evolution. The Time Traveler finds himself in the future, eight hundred thousand years from now. Wells draws one of the bleakest pictures of the future ever imagined. Humans have evolved downhill into two degenerate species, predators and prey, with diminished bodies and minds. The predators are the Morlocks, living like rats in the cellars of ruined buildings. The prey are the Eloi, living aimless lives on the surface in beautiful surroundings, tended like cattle by the Morlocks as a source of meat. The Time Traveler befriends an Eloi girl who gives him two flowers to take home with him. The story ends with the Time Traveler vanished on another trip into the future, leaving behind the two withered flowers. The flowers are our proof that, even after the spark of reason has been extinguished, friendship and gratitude can live on in the human soul.

Wells's biggest work is  Outline of History , published in 1920, a picture of cultural evolution as the main theme of history since the emergence of our species. He begins with a proud claim: "This Outline of History is an attempt to tell, truly and clearly, in one continuous narrative, the whole story of life and mankind so far as it is known today." The next fifty pages describe biological evolution up to the rise of two human species, Modern Man and Neanderthal Man. A famous picture by the illustrator John Horrabin shows Wells's literary rival George Bernard Shaw as a Neanderthal emerging from a cave, with the caption, "Our Neanderthal Ancestor, Not a Neanderthal Man but a Parallel Species". The recent discovery of a substantial fraction of Neanderthal genes in modern Europeans shows that Wells's joke came close to the truth. After the Neanderthals come the cave-painters in France and Spain. Cultural evolution has begun and dominates the story from that time onward.

Half-way through the history comes the birth of the great world religions, Buddhism, Judaism, Christianity, Islam. Wells tells the story of these religions with a sympathetic eye, recognizing their crucial importance to cultural evolution in the last two thousand years. He gives an evocative account of the life and death of Jesus, with a memorable Horrabin illustration, three crosses on the hill of Golgotha in evening twilight. The caption reads; "The darkness closed upon the hill; the distant city set about its preparation for the Passover; scarcely anyone but that knot of mourners on the way to their homes troubled whether Jesus of Nazareth was still dying or already dead". From Golgotha the story continues with empires rising and falling, wars and pestilences raging, wealth and industry growing, and always quietly in the background the great religions with their holy books preserving the words of the prophets, raising the hopes of powerless people with visions of a better world.

The history ends with the catastrophe of World War One, and with the abortive attempt, still in progress while Wells was writing, to establish a League of Nations with effective power to keep the world at peace. Here is the message of the Outline of History as Wells saw it. "Life begins perpetually. Gathered together at last under the leadership of man, the student-teacher of the universe, unified, disciplined, armed with the secret powers of the atom and with knowledge as yet beyond dreaming, life, forever dying to be born afresh, forever young and eager, will presently stand upon this earth as upon a footstool, and stretch out its realm amid the stars."

As a result of cultural evolution, a single species now dominates the ecology of our planet, and cultural evolution will dominate the future of life so long as any species with a living culture survives. When we look ahead to imagine possible futures for our descendants, cultural evolution must be our dominant concern. But biological evolution has not stopped and will not stop. As cultural evolution races ahead like a hare, biological evolution will continue its slow tortoise crawl to shape our destiny.

We have detailed knowledge of our cultural evolution only for the last few thousand years in Europe and Asia from which written records survive. I am ignorant of Chinese history and literature, so I discuss only the Western part of the story. In Western culture we see a series of creative events occurring in small urban communities: Jerusalem around 1000 BC inventing monotheistic religion, Athens around 500 BC inventing philosophy and drama and democratic government, Florence around 1500 AD inventing modern art and science, Manchester around 1750 AD inventing modern industry. In each case, a small population produced a star-burst of pioneers who permanently changed our way of thinking. Genius erupted in groups as well as in individuals. It seems likely that these bursts of creative change were driven by a combination of cultural with biological evolution. Cultural evolution was constantly spreading ideas and skills from one community to another, stirring up conservative societies with imported novelties. At the same time, biological evolution acting on small genetically isolated populations was causing genetic drift, so that the average intellectual endowment of isolated communities was rising and falling by random chance.

Over the last few thousand years, genetic drift caused occasional star-bursts to occur, when small populations rose to outstandingly high levels of average ability. The combination of imported new ideas with peaks of genetic drift would enable local communities to change the world.

The big uncertainty in this picture of genetic drift as a driving force of human progress is the genetic isolation of small communities. We have no reliable information about the mating habits of the populations in Jerusalem and Athens and Florence and Manchester during the centuries before they became creative. They were to some extent isolated geographically, but they were also divided into tribes and hereditary classes that were isolated socially. Class prejudice and snobbery were probably the most powerful causes of genetic isolation, and these are not measurable quantities. The contribution of genetic drift to cultural evolution remains a speculative hypothesis.

When we look to the future of evolution, it is convenient to divide the future into near and far. The near future is the next century, for which we can make some reliable predictions. The far future is everything beyond the next century. In the near future, we can be sure that genetic drift is fading rapidly as a driving force of change. All over the world, humans are moving from villages into big cities where genetic drift is negligible. In the populations that are still geographically isolated, humans are becoming less socially isolated by barriers of race and class. It is unlikely that any small town in the next century can be another Athens or another Jerusalem. Wells ended his Outline with a glimpse of the far future, where nothing is certain, and all predictions are guesswork. In the far future, it is likely that humans and other forms of life will be spread out to great distances in the universe, as Wells imagined. If our destiny takes us to the stars, our descendants will again be genetically isolated, and genetic drift will resume its ancient power to mold life into new patterns of diversity.

Before we can embark on grand voyages to the stars, we must navigate the mundane hazards of the twenty-first century. The most important achievement of the twenty-first century is likely to be the emergence of China as a rich country and a world power. The rise of China is a return to the political patterns of the past, when China was a great empire ruled by a conservative Confucian bureaucracy. The intervening five hundred years, when China was left behind and impoverished by aggressively expanding Western cultures, were an unfortunate departure from the older stable equilibrium. The rise of China in this century will be a restoration of traditionally organized society after centuries of turmoil. The big problem for Western societies will be to learn how to coexist peacefully with the new Celestial Kingdom. Fortunately, we will have the powerful force of cultural evolution erasing differences between East and West. Cultural evolution must battle against the divisive forces of nation and race and political ideology.

The strongest driving force of cultural evolution today is science. Science is the international enterprise that brings us together most powerfully in a common purpose, requiring us to share ideas and tools, economic resources and material benefits. The task of East and West in this century will be to work together as friends in science and technology, while respecting our differences in politics and culture.

When we look to the future beyond one or two centuries, expansion of the domain of life into the universe will be inevitable and also desirable for many reasons. Inevitable because biotechnology and space technology will provide the means for life to make the big jump. Desirable because the cultural evolution of creative new societies requires more elbow-room than a single planet can provide. Creative new societies need room to take risks and make mistakes, far enough away to be effectively isolated from their neighbors. Life must spread far afield to continue the processes of genetic drift and diversification of species that drove evolution in the past. The restless wandering that pulled our species out of Africa to explore the Earth will continue to pull us beyond the Earth, as far as our technology can reach.

5. Richard Dawkins (1941- ). Genes and Memes.

Wells has been monopolizing the stage for far too long, and it is time for our fifth actor, Richard Dawkins, to have his turn. Wells was at heart a novelist, portraying history as a story of human beings with ideas and emotions as well as neurons and genes. Dawkins is a biologist who began his career with a study of animal behavior, only later transferring his attention to humans. Dawkins published his great work,  The Selfish Gene , in 1976, He is interested in general patterns of behavior rather than in individual humans. His book portrays human society as a mechanical system of agents with behavior governed by genes, similar to a collection of machines with behavior governed by computer programs. The selfish gene is a device with a single purpose, to achieve its own survival and replication. It is not concerned with our welfare or with our human needs. Dawkins caused a revolution in our thinking about genes with his insight that the selfish behavior of genes can explain the unselfish behavior of humans. His book is a classic because he makes a convincing case for a paradoxical conclusion, that selfish genes can orchestrate the evolution of cooperation, generosity and self-sacrifice in humans. He succeeds brilliantly in reducing our high moral principles and our ethical beliefs to the action of unthinking and uncaring molecules of DNA.

In the final chapter of his book, Dawkins turns his attention away from biological evolution to cultural evolution and introduces another innovation to our thinking about human behavior. The new idea is the meme, the cultural analog to the gene. A meme is a unit of cultural behavior, just as a gene is a unit of biological behavior. Examples of memes are ideas, customs, slogans, fashions in dress or in hair-style, skills, tools, laws, religious beliefs and political institutions. Memes spread through human populations by social contact far more rapidly than genes spread by sexual contact. Just as our behavior at the individual level is controlled by selfish genes, our behavior at the social level is controlled by selfish memes.

Dawkins's vision of human society, as the visible face of an invisible network of selfish genes and memes, is to a large extent true. His book gave us a new understanding of the evolution of morality and religion. Like Darwin's view of nature, Dawkins’s vision may be incomplete. It is reasonable to accept his view of genes and memes as powerful agents of human behavior, but to reject his view that they explain everything.

6. Svante Pääbo (1955- ). Cousins in the Cave.

Our sixth and last actor, Svante Pääbo, born in 1955 and now a world leader in the study of human genomes, comes to the stage with startling news. After long struggles, his team of paleontologists and chemists have developed the technology for sequencing ancient DNA degraded and contaminated with modern DNA. They have succeeded in sequencing accurately the genomes of our Neanderthal cousins who lived in Europe about fifty thousand years ago. They also sequenced genomes of our own species who lived in Europe around the same time, and genomes of a third species, called Denisovans because they were found in Denisova cave in Siberia. He published the story of the sequencing and the surprising results in his book,  Neanderthal Man: In Search of Lost Genomes , in 2014.

When he compared the ancient genomes of the three species with modern human genomes, he saw abundant evidence of mixing. Modern humans originating in Europe and Asia carry several percent of Neanderthal genes. Modern humans in Papua New Guinea carry several percent of Denisovan genes. The ancient genomes come from times when the severe climate of the last ice age prevailed in Europe and Northern Asia. Humans and their cousins were precariously surviving in caves, where they probably sat huddled around the cave-fire to keep warm, cooking dinners and telling stories. It now appears that the three species frequently sat around the cave-fires together rather than separately. They mated and raised families together. Our species had the larger share of the populations and supplied most of the genes to the mixed offspring. But the Neanderthals and Denisovans never became extinct. They simply merged their genomes with ours. They survive as a part of our genetic inheritance.

The discoveries of Svante Pääbo show that as early as fifty thousand years ago the transition from biological to cultural evolution was already far advanced. Biological evolution, as demonstrated by Kimura and Goodenough, accelerated the birth of new species by favoring the genetic isolation of small populations. Cultural evolution had the opposite effect, erasing differences between related species and bringing them together. Cultural evolution happens when cousins learn each other's languages and share stories around the cave-fire. As a consequence of cultural evolution, biological differences become less important and cousins learn to live together in peace. Sharing of memes brings species together and sharing of genes is the unintended consequence.

In the long-range history of life, the transition from biological to cultural evolution was an event of transcendent importance. We became aware of its importance only recently, as a result of the discoveries of Svante Pääbo and his colleagues. The transition caused a reversal of the direction of evolution from diversification to unification, from the proliferation of diverging species to the union of species into a brotherhood of man. We see a small-scale example of this transition in the recent history of racism. Until recently, racism was a force of nature favoring the diversification of species. Humans traditionally hated and despised people of a different skin color. The natural evolutionary consequence would have been the division of our species into three new species, one pink, one black and one yellow. Only in the last few centuries, a strong reaction against racism has emerged, inter-racial marriage has become respectable, and the cultural unification of our species has pushed us toward biological unification. This is a small step in the long history of the transition of human societies from incessant warfare to brotherhood.

With our six actors all on stage, the play begins and my story ends. As an epilogue to the performance, I add some brief remarks about the practical lessons that we may learn from the story. Our species faces two great tasks in the next few centuries. Our first task is to make human brotherhood effective and permanent. Our second task is to preserve and enhance the rich diversity of Nature in the world around us. Our new understanding of biological and cultural evolution may help us to see more clearly what we have to do.

Nature's tool for the creation and support of a rich diversity of wildlife is the species produced in abundance by the rapid genetic drift of small populations according to Kimura, and in even greater abundance by the rapid mutation of mating-system genes according to Goodenough. In the near future, we will be in possession of genetic engineering technology which allows us to move genes precisely and massively from one species to another. Careless or commercially driven use of this technology could make the concept of species meaningless, mixing up populations and mating systems so that much of the individuality of species would be lost. Cultural evolution gave us the power to do this. To preserve our wildlife as nature evolved it, the machinery of biological evolution must be protected from the homogenizing effects of cultural evolution.

Unfortunately, the first of our two tasks, the nurture of a brotherhood of man, has been made possible only by the dominant role of cultural evolution in recent centuries. The cultural evolution that damages and endangers natural diversity is the same force that drives human brotherhood through the mutual understanding of diverse societies. Wells's vision of human history as an accumulation of cultures, Dawkins's vision of memes bringing us together by sharing our arts and sciences, Pääbo's vision of our cousins in the cave sharing our language and our genes, show us how cultural evolution has made us what we are. Cultural evolution will be the main force driving our future.

Our double task is now to preserve and foster both biological evolution as Nature designed it and cultural evolution as we invented it, trying to achieve the benefits of both, and exercising a wise restraint to limit the damage when they come into conflict. With biological evolution, we should continue playing the risky game that nature taught us to play. With cultural evolution, we should use our unique gifts of language and art and science to understand each other, and finally achieve a human society that is manageable if not always peaceful, with wildlife that is endlessly creative if not always permanent.

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• Introduction

Definition and scope

Distinction between physical anthropology and cultural anthropology.

• Evolutionism
• Marxism and the collectors
• Boas and the culture history school
• Mauss and the “sociological” school
• The “grand diffusionists”
• Functionalism and structuralism
• Cultural psychology
• Neo-Marxism and neo-evolutionism
• The new research and fieldwork
• Non-Western cultural anthropologists
• Applied studies

cultural anthropology

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• Discover Anthropology - Social and Cultural Anthropology
• Table Of Contents

cultural anthropology , a major division of anthropology that deals with the study of culture in all of its aspects and that uses the methods, concepts, and data of archaeology , ethnography and ethnology, folklore, and linguistics in its descriptions and analyses of the diverse peoples of the world.

Etymologically, anthropology is the science of humans. In fact, however, it is only one of the sciences of humans, bringing together those disciplines the common aims of which are to describe human beings and explain them on the basis of the biological and cultural characteristics of the populations among which they are distributed and to emphasize, through time, the differences and variations of these populations. The concept of race, on the one hand, and that of culture , on the other, have received special attention; and although their meaning is still subject to debate, these terms are doubtless the most common of those in the anthropologist’s vocabulary.

Anthropology, which is concerned with the study of human differences, was born after the Age of Discovery had opened up societies that had remained outside the technological civilization of the modern West. In fact, the field of research was at first restricted to those societies that had been given one unsatisfactory label after another: “savage,” “primitive,” “tribal,” “traditional,” or even “preliterate,” “prehistorical,” and so on. What such societies had in common, above all, was being the most “different” or the most foreign to the anthropologist; and in the early phases of anthropology, the anthropologists were always European or North American. The distance between the researcher and the object of his study has been a characteristic of anthropological research; it has been said of the anthropologist that he was the “astronomer of the sciences of man.”

Anthropologists today study more than just primitive societies. Their research extends not only to village communities within modern societies but also to cities, even to industrial enterprises. Nevertheless, anthropology’s first field of research, and the one that perhaps remains the most important, shaped its specific point of view with regard to the other sciences of man and defined its theme. If, in particular, it is concerned with generalizing about patterns of human behaviour seen in all their dimensions and with achieving a total description of social and cultural phenomena, this is because anthropology has observed small-scale societies, which are simpler or at least more homogeneous than modern societies and which change at a slower pace. Thus they are easier to see whole.

What has just been said refers especially to the branch of anthropology concerned with the cultural characteristics of man. Anthropology has, in fact, gradually divided itself into two major spheres: the study of man’s biological characteristics and the study of his cultural characteristics. The reasons for this split are manifold, one being the rejection of the initial mistakes regarding correlations between race and culture. More generally speaking, the vast field of 19th-century anthropology was subdivided into a series of increasingly specialized disciplines, using their own methods and techniques, that were given different labels according to national traditions.

Thus two large disciplines—physical anthropology and cultural anthropology—and such related disciplines as prehistory and linguistics now cover the program that originally was set up for a single study of anthropology. The two fields are largely autonomous , having their own relations with disciplines outside anthropology; and it is unlikely that any researchers today work simultaneously in the fields of physical and cultural anthropology. The generalist has become rare. On the other hand, the fields have not been cut off from one another. Specialists in the two fields still cooperate in specific genetic or demographic problems and other matters.

Prehistoric archaeology and linguistics also have notable links with cultural anthropology. In posing the problem of the evolution of mankind in an inductive way, archaeology contributed to the creation of the first concepts of anthropology, and archaeology is still indispensable in uncovering the past of societies under observation. In many areas, when it is a question of interpreting the use of rudimentary tools or of certain elementary religious phenomena, prehistory and cultural anthropology are mutually helpful. “Primitive” societies that have not yet reached the metal age are still in existence.

Relations between linguistics and cultural anthropology are numerous. On a purely practical level the cultural anthropologist has to serve a linguistic apprenticeship. He cannot do without a knowledge of the language of the people he is studying, and often he has had to make the first survey of it. One of his essential tasks, moreover, has been to collect the various forms of oral expression, including myths , folk tales, proverbs, and so forth. On the theoretical level, cultural anthropology has often used concepts developed in the field of linguistics: in studying society as a system of communication, in defining the notion of structure, and in analyzing the way in which man organizes and classifies his whole experience of the world.

Cultural anthropology maintains relations with a great number of other sciences. It has been said of sociology , for instance, that it was almost the twin sister of anthropology. The two are presumably differentiated by their field of study (modern societies versus traditional societies). But the contrast is forced. These two social sciences often meet. Thus, the study of colonial societies borrows as much from sociology as from cultural anthropology. And it has already been remarked how cultural anthropology intervenes more and more frequently in urban and industrial fields classically the domain of sociology.

There have also been fruitful exchanges with other disciplines quite distinct from cultural anthropology. In political science the discussion of the concept of the state and of its origin has been nourished by cultural anthropology. Economists, too, have depended on cultural anthropology to see concepts in a more comparative light and even to challenge the very notion of an “economic man” (suspiciously similar to the 19th-century capitalist revered by the classical economists). Cultural anthropology has brought to psychology new bases on which to reflect on concepts of personality and the formation of personality. It has permitted psychology to develop a system of cross-cultural psychiatry, or so-called ethnopsychiatry . Conversely, the psychological sciences, particularly psychoanalysis, have offered cultural anthropology new hypotheses for an interpretation of the concept of culture.

The link with history has long been a vital one because cultural anthropology was originally based on an evolutionist point of view and because it has striven to reconstruct the cultural history of societies about which, for lack of written documents, no historical record could be determined. Cultural anthropology has more recently suggested to historians new techniques of research based on the analysis and criticism of oral tradition . And so “ ethnohistory ” is beginning to emerge. Finally, cultural anthropology has close links with human geography . Both of them place great importance on man either as he uses space or acts to transform the natural environment . It is not without significance that some early anthropologists were originally geographers.