Annual Review of Psychology

Henrich and Muthukrishna

The Origins and Psychology of Human
Cooperation
Joseph Henrich and Michael Muthukrishna

ABSTRACT
Illuminating the puzzle of our species’ ultrasociality requires not only explaining the breadth andintensity of human cooperation but also why it varies across societies, over history and among
behavioral domains (within societies). To address these patterns, we introduce an evolutionary
approach that considers how genetic and cultural evolution, as well as their interaction, may haveshaped both the reliably developing features of our minds and the well-documented differences incultural psychologies around the globe. We review the major evolutionary mechanisms that havebeen proposed to explain human cooperation, including kinship, reciprocity (partner choice),
reputation, signaling and punishment, discuss key culture-gene coevolutionary hypotheses, such as
those surrounding self-domestication and norm psychology, and consider the role of religions,rituals and marriage systems. Empirically, we bring together diverse experimental, observational, andanthropological evidence from studies of children and adults from diverse societies as well as from
non-human primates.

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The origins and nature of our species’ cooperative psychology and prosocial behavior has been amajor scientific challenge since at least the time of Darwin. Recently, however, progress on thisquestion has accelerated with the rise of a highly interdisciplinary version of evolutionary
psychology, one that takes seriously our status as the “third chimpanzee” (Diamond 2006), but also
recognizes that humans have become a uniquely cultural species. Here we chart progress in thisendeavor with the aim of directing ongoing research, clarifying key debates, and connecting
psychology to the broader scientific exploration of cooperation.
SIDE BAR: Substantial progress has been made on the problem of cooperation by integrating four converging
lines of inquiry: (1) Phylogeny: what features of cooperation or social psychology might the genus Homo haveinherited from our primate ancestors? (2) Selective processes: what evolutionary processes, considering both
genetic and cultural inheritance, are responsible for the array of cooperative psychologies observed? (3)Proximate psychology: how can we best describe the psychological mechanisms involved in cooperative
behavior at the population or species’ level? And, (4) ontogeny: how, where and when do these psychologicalmechanisms develop. These, Tinbergen’s Four Questions, have catalyzed efforts to understand humancooperation, driven much cross-cultural, developmental, and comparative research, and permitted our species
to be seated within the natural world.

The question of cooperation focuses on how and why individuals make choices that help others (oravoid hurting them) at a personal cost. Our species immense capacity for cooperation compared tomost other animals has led researchers from diverse disciplines to focus on understanding our“ultrasociality” (Campbell 1983; Richerson & Boyd 1998). Here, we too open with this central
observation, but then add to it four more stylized facts about human cooperation that sharpen the
puzzle and constrain potential explanations (Chudek & Henrich 2010):
1) Ultrasociality: Why is the scale and intensity of human cooperation so much greater thanthat found in other mammals? Humans cooperate more than other mammals at both smallscales, such as within hunter-gather bands or families, and at larger scales, such as within
ethno-linguistic populations or nation-states (Gurven et al. 2012). Any theory that purports
to explain human cooperation needs to also account for why similar and related species,
such as chimpanzees, display neither the scale nor intensity of cooperation found in humans.
All-purpose explanations that resort to features like “language” or “cognitive abilities” only
make the problem worse: language itself creates a prickly cooperative dilemma (Boyd &Mathew 2014; Lachmann & Bergstrom 2004) and more potent cognitive abilities (e.g.,memory) are at least as likely to thwart cooperation as to enhance it (McNally et al. 2012;
Muthukrishna et al. 2018).
2) Scale and intensity differences: Why does the scale and intensity of cooperation vary sodramatically among societies, from groups where the scale of cooperation is limited to small
hamlets or extended families (Johnson 2003) to modern nation-states that routinely
cooperate on the order of thousands or even millions of individuals? Explaining humancooperation requires accounting for why some societies do NOT cooperate nearly as muchas others, despite existential pressures for greater cooperation. For example, why couldn’t
villages in New Guinea scale up above 300 people when facing a world of endemic warfare
where larger communities could enjoy greater safety, security and prosperity (Tuzin 2001)?
3) Domain differences: Why do the domains of cooperation vary so much from society tosociety? Comparative ethnography shows that different social groups inhabiting the same2

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Henrich and Muthukrishna

ecology cooperate in different domains. Some cooperate only in warfare and fishing, whileothers, just downstream, cooperate only in house-building and communal rituals (Curry et al.
2019).
4) Rapid expansion: Over the last 12,000 years, how and why have some human societies
scaled up from relatively small-scale communities to vast states (Turchin 2015)? Theories ofhuman cooperation need to explain this rapid process, and why it proceeded at different
rates in different populations and on different continents.
5) Non-cooperative and maladaptive behavior: Why do the sanctioning and otherincentivizing mechanisms that support cooperation, such as those based on punishment,
reputation, and signaling, also enforce behaviors that are unrelated to cooperation, such as
ritual practices, food taboos, and clothing customs? Why do these same mechanisms
sometimes even sustain maladaptive practices, like the consumption of dead relatives(spreading prion diseases), female foot-binding, and clitoral infibulation (Durham 1991;
Mackie 1996; Vogt et al. 2017)?
Ongoing efforts to explain cooperation in our species will need to confront these puzzles. However,some psychologists may react to these explanatory challenges by seeing them as questions that resideoutside of their discipline. This occurs because much of psychology, though certainly not all of it,
operates without the kind of cumulative scientific frameworks found in other sciences(Muthukrishna & Henrich 2019). Here, by introducing readers to an evolutionary framework thatincorporates culture, we’ll show how fundamental questions about human nature, psychologicaldiversity, social structure, and child development can be addressed in a cumulative fashion. As we’llsee, the current evidence supports the view that our psychology coevolves culturally with ourinstitutions, so any account of “human cooperative psychology” requires a theory that integrates thecultural evolution of social norms and institutions.
We’ll begin by introducing an extended evolutionary synthesis that considers the emergence of our
species’ capacities for cultural learning, the rise of cultural evolution, and the ensuing interaction
between our genetic and cultural forms of inheritance. Then, using the extended synthesis as atheoretical menu of the major evolutionary mechanisms that have been proposed to explain humancooperation, we’ll review key results related to kinship, direct reciprocity, reputation, punishment,
and signaling. For each of these mechanisms, we’ll consider the potential role for both genetic andcultural evolutionary processes in light of research from both children and adults from diverse
populations, as well as from other primates. After highlighting the limitations of these mechanisms
for explaining human cooperation, we’ll consider how intergroup competition in cultural evolution
interacts with these within-group mechanisms to provide a more complete account. Next, having
illustrated various ways that cultural evolution can produce social norms, we’ll discuss threeinterrelated culture-gene coevolutionary hypotheses that consider how social norms, as a recurrent
feature of ancestral human environments, may have driven the genetic evolution of various aspectsof our social psychology. Finally, we’ll close by presenting research that anchors our cultural
evolutionary psychology in history, and analyze how the diffusion of specific institutions, including
those related to kinship and religion, have shaped our cooperative psychology.

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A CULTURAL SPECIESWhether they were stranded in Australia, Panama, the Canadian Arctic or the Gulf Coast of Texas,
numerous cases of “lost European explorers” illustrate that our huge primate brains are singularly illequipped to survive as hunter-gatherers (Boyd et al. 2011a). We don’t innately know, and usually
can’t individually figure out, how to detoxify plants, fashion tools, make clothing, start fires, or
locate water. Unlike other animals, we are entirely dependent on learning from other people for ourvery survival, even for our survival as foragers; as a species, we are addicted to culture—on acquiringa substantial portion of our phenotype by tapping into a large body of non-genetic information thathas been filtered and accumulated over generations. This process, termed cumulative cultural evolution,
creates a storehouse in the form of strategies, attentional biases, motivations, tastes and cognitive
heuristics that are necessary for us to accomplish even the basics of survival (e.g., finding food),
which most other species manage with little to no cultural input (Dean et al. 2014; Henrich 2016).
Without access to this non-genetic inheritance, we are virtually helpless. While many species rely onsocial learning to some degree, little or no cumulative cultural evolution has been found outside of
the genus Homo (Henrich & Tennie 2017; St Clair et al. 2018). How can we apply an evolutionary
approach to a species that is so heavily reliant on culture?
Beginning in the 1970s, a few evolutionary researchers began to apply the logic of natural selection
to the evolution of our capacities for culture and to think systematically about how to model culturaltransmission over generations (Boyd & Richerson 1976; Feldman & Cavalli-Sforza 1976). The
evolutionary framework that blossomed from these intellectual roots can be partitioned into three
categories of inquiry: (1) the genetic evolution of our species’ capacities for culture, (2) cultural
evolution and the emergence of institutions, and (3) the process of culture-gene coevolution.

Evolved Capacities for Culture
This enterprise begins by asking: how has natural selection shaped our minds and brains to allow
individuals to most effectively extract adaptive practices, know-how, strategies, preferences anddecision-heuristics from the minds and behaviors of those around us? This leads to the ‘who’, ‘what’
and ‘when’ of cultural learning (Rendell et al. 2011).
1) ‘Who’ should individuals learn from? Both experimental and field observations show that
adults, children and often even infants preferentially attend to and learn from individuals
based on cues of competence, skill, success, prestige (receiving deference or attention fromothers) and similarity to the learner on sex and ethnicity as well as other traits (Chudek et al.
2013; Harris & Corriveau 2011; Wood et al. 2013).
2) ‘What’ sorts of content should learners attend to and how should they process it (Sperber1996)? A diverse portfolio of research looks at how the content of cultural traits
differentially influences attention, memory and inferences across a wide range of domains
including artifacts, living kinds, social norms and ethnic groups (Barrett & Broesch 2012;
Casler et al. 2009; Greif et al. 2006).
3) ‘When’ should individuals rely on cultural learning over their own experience or intuitions(Aoki & Feldman 2014)? Experimental work with infants, children and adults suggests thatpeople shift to weight more heavily what they acquire from other people, even over theirown direct observations, especially as situations become more uncertain and problems get
more difficult (Morgan et al. 2012; Muthukrishna et al. 2016).
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Together, this ensemble of cultural learning abilities and biases provides a psychologically-richaccount of cultural transmission that can not only explain the generally adaptive character of cultural
evolution, but can illuminate a broad range of otherwise puzzling patterns, including phenomena
such as food taboos (Henrich & Henrich 2010) and rituals (Legare & Souza 2014).
Crucially, despite the existence of content-based ‘what’ mechanisms, human cultural learning abilities
nevertheless influence an incredibly wide range of behavioral domains. Of course, people of all ages
culturally acquire linguistic labels, pronunciations, tool uses, new technologies, social rules, foodtaboos and beliefs in invisible things like germs, gods, fairies, ghosts and vitamins (Harris 2012). But,
going deeper, cultural learning can also modify our motivations, preferences, biases and selfconfidence to more closely match those of our preferred models in ways that influence ourjudgments and decision-making (Rosenthal & Zimmerman 1978). Experimental studies reveal that
exposure to the choices of others shape our preferences for particular foods (Birch 1987), songs(Berns et al. 2010), overconfidence (Cheng et al. 2017), and mates (Zaki et al. 2011). Most importantfor our goals here, cultural learning influences costly behaviors, including charitable giving (Rushton1975), blood donations (Rushton & Campbell 1977), cooperative contributions (Fowler & Christakis
2010; Gächter et al. 2012), fairness (Blake et al. 2016), helping (Eisenberg & Mussen 1989), patience(Garvert et al. 2015), aggression (Bandura 1977), standards for self- and other-rewarding (Bandura &Kupers 1964), and a willingness to engage in third-party punishment (Salali et al. 2015).

Cultural evolution: the emergence of social norms and institutions
To understand how culture shapes our behavior and psychology, and ultimately our genetic
evolution, we need to go beyond the psychological foundations of our reliably developing capacities
for learning from others to consider what happens as individuals adaptively learn from those aroundthem and interact repeatedly over generations. Using this approach, theorists have constructed
mathematical models, rooted in what’s known about learning, to examine the cultural evolution oftechnology (Creanza et al. 2017), social stratification (Henrich & Boyd 2008), honor cultures(McElreath 2003) and the formation of symbolically-marked ethnic groups (McElreath et al. 2003).
Here, drawing on the largest segment of this literature, we focus on the emergence of costly social
norms, where individuals engage in costly behaviors that are monitored and incentivized in some
way by their groups or communities. Interestingly, many of these models were originally constructedto study the evolution of large-scale cooperation, but it turned out that the mechanisms involved—
related to reputation, punishment and/or signaling—can sustain any equally costly action regardlessof whether it delivers benefits to anyone. So, to understand cooperation and human social life moregenerally, evolutionary models of cooperation have told us to focus on the more general category ofsocial norms. Institutions, by this account, are simply packages of social norms that interlock togovern some domain of life, such as marriage or exchange. Formal institutions arise when decisionmakers, often aided by writing, clarify and standardize the informal institutions generated by social
norms.
In the next section, we’ll explore the evolution of cooperation by considering the major mechanismsthat have been propose for explaining human cooperation including those that can sustain costly
social norms via cultural learning. To lay the groundwork for this, we’ll first consider the origins ofsocial norms at a more abstract level: once individuals possessed sufficiently sophisticated cognitive
abilities to reliably culturally learn both (1) how to behave in particular contexts and (2) the
standards for judging others in these contexts, stable patterns of costly behavior will emerge and can5Annual Review of Psychology

Henrich and Muthukrishna

be sustained for long periods within a community. These patterns are social norms because anyone
who deviates—doesn’t pay the cost—will be sanctioned or otherwise incentivized to conform insome way. Below, we’ll review the empirical evidence suggesting that the cultural evolution of social
norms in human societies over tens or even hundreds of thousands of years has shaped the genetic
evolution of our minds, including by producing a norm psychology. However, for now, let’s begin byconsidering evidence indicating that social norms do indeed matter for individually costly behaviors
and that this is associated with variation across societies.
Deploying a simple dictator game (see Table 1’s description) among both children (n = 833) andadults (n = 255) across eight populations (Figure 1), ranging from hunter-gatherers in Tanzania tourbanites in Berlin, House et. al. assessed people’s willingness to share equally with an anonymous
partner. Participants had to pick one of two possible allocations: (A) two rewards for themselves or
nothing for their partner (2/0), or (B) one reward for themselves and one for their partner (1/1).
Before making their decision, participants were randomly assigned to see one of three videos inwhich an adult from their community states either that (1) the 1/1 option is ‘good’ and the 2/0 ‘bad’(Fair condition), (2) the 2/0 is ‘good’ and the 1/1 ‘bad’ (Selfish), or (3) both are ok (Neutral). Aftermaking their choices, many participants were then shown both the Fair and Selfish videos and asked
which makes the ‘more correct’ statement about the decision—a normative assessment.
For adults across these populations, Figure 1a shows the estimated average frequencies of costly 1/1
choices (the horizontal bars) and ‘more correct’ judgments of the Fair videos (triangular points).
Unsurprisingly, adults were more likely to make 1/1 choices in places where this was judged as‘more correct.’ Analyses revealed that adult’s choices are influenced by both their own preferences
regarding which choice is more correct and the normative judgments of their communities. Thesefindings indicate a role for normative concerns in adults’ decisions.
Turning to the developmental data, Figure 1b models the age trajectories of the 1/1 choices. In allsocieties, as they get older, children moved toward both the behaviors and normative judgments of
the adults in their societies, leading to quite different trajectories in different societies. This suggestthat norms not only influence adult behavior but shape child development. Notably, divergent
developmental trajectories for costly social behaviors, like those shown here, are not uncommon incross-cultural studies of child development (Blake et al. 2015a; House et al. 2013b; Rochat et al.
2009). What is unusual is for developmental studies of WEIRD children to provide anything like a
good approximation for Homo sapiens.
In contrast to this population-level variation, the data also reveal a universal pattern in response tonorms. First, across populations, children responded to the video statements (Fair, Selfish andNeutral) by moving their behavior in the normatively prescribed direction. This confirms thatchildren respond to norms in this context. Second, as children moved through middle childhood,their behavior was better predicted by the normative preferences expressed by the adults in theircommunities. These patterns are consistent with theories proposing that middle childhood (aroundages 6-11), a unique period of human life history, evolved to support cultural learning and,specifically, the acquisition of social norms (Henrich 2016).

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AdultsChildrenFigure 1. Binary Dictator Game results from 8 diverse populations. (A) For the adults, this figure shows the means(horizontal bars, predicted from a model) and confidence intervals (vertical lines) for making the 1/1 (fair) allocations
across populations. The triangles mark the frequencies of adults’ normative responses indicating that the 1/1 choice is“more correct.” (B) For children, the developmental trajectories of frequencies of the 1/1 allocations (predicted from amodel) for each population. The dots on the right mark the adult averages. Populations: the Shuar are Amazonian slashand-burn horticulturalists in Ecuador; the Hadza are Tanzanian hunter-gatherers; Tanna is an island in Vanuatu where apopulation of hunter-horticulturalist was sampled. Pune is a city in Maharashtra, India. In Argentina, the Wichí aresedentarized hunter-gatherers and La Plata is a provincial capital. These plots are modified from House et. al. (2019).

Overall, a synthesis of evidence from across the social sciences suggests that human social behavior
is heavily influenced by both cultural and genetic transmission—a dual inheritance system. To tackle
this complexity, we need to think about how cultural and genetic evolutionary processes might eachcontribute to human cooperation and how these inheritance systems might interact in a process ofculture-gene coevolution. Importantly, while cultural and genetic transmission differ in importantways, we can flesh out an approach to cooperation by considering how each can solve, or fail tosolve, different kinds of cooperative dilemmas.

Box 1. Key concepts and standard behavioral experiments
Term
Explanation
This is a two-player experimental interaction in which the pair is provided
with an endowment—usually a sum of money (or other valuable items) andeach participant is assigned to either the “proposer” or “receiver” role. The
Ultimatum Game
proposer must allocate the money between the two, from zero to the fullendowment. The receiver can either “accept” or “reject” the proposed
division. If they accept, the money is divided as per the proposal. If the7Annual Review of Psychology

Dictator Game

Third Party
Punishment
Game

Public Goods
Game

Free-rider
Problem

2nd and higher
order free-rider
problems

Equilibrium
Selection
ProblemHenrich and Muthukrishna

respondent rejects the division, both players receive nothing. An income
maximizing receiver should always accept any positive allocation, so a similar
proposer should allocate the smallest non-zero amount. In its canonical form,
this interaction occurs only once and the players are anonymous.
This two-player experimental interaction parallels the Ultimatum Game
except that the receiver is passive and so cannot accept or reject the allocationfrom the proposer, who is now called the “dictator.” An income maximizing
dictator allocates zero.
This is an interaction among three players, A, B and C. A and B play a
Dictator Game in which A allocates any part of the endowment to B, thepassive recipient. Player C is given an endowment equal to half of thatprovided to players A and B. Player C can pay any amount of this endowment
to take money away from player A. Whatever amount C decides to pay ismultiplied (often by 3) then subtracted from player A. In the simpler version
highlighted below, player C could only choose between paying $10 (20% of
their stake) to take $30 away from player A or do nothing (pay zero). Income
maximizing player Cs will never pay anything and so income maximizing
player As will allocate nothing to B.
In this interaction, n participants are each given a monetary endowment,
which they can anonymously contribute any portion of to a public good.
After all n players have had a chance to contribute, the public good ismultiplied by a factor between 1 and n and then divided equally among all
players irrespective of contributions. Income maximizers should always
contribute zero.
The free-rider problem describes the situation in cooperative dilemmas inwhich players derive a benefit from the contributions of other players without
paying a sufficient cost. E.g., a player in the public goods game above who
contributes zero to the public good “free rides” on the contributions of otherplayers.
One solution to the free-rider problem is to allow people to punish free-riders
at some cost to themselves. However, this creates a second-order free-rider
problem, whereby players derive a benefit from other players’ willingness topunish free-riders, but do not themselves pay the cost of punishment.
Punishing these second order free-riders creates a third-order free-rider
problem and so on.
This problem arises when evolutionary processes produce more than one
dynamically stable behavioral outcome under the same conditions—e.g., bothfull cooperation and full defection might be simultaneously stable. Eachstable state may have a different ‘basin of attraction’ that depends on thefrequencies of different strategies or behaviors in the population. Suchsituations may call for additional explanatory processes, such as intergroupcompetition, to explain how or why rare or difficult to access equilibrium
states proliferate.

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EVOLUTIONARY MECHANISMS OF COOPERATION
Sustaining individually costly behaviors, including those that deliver benefits to others, requires someform of non-random interaction. Those who pay the costs of cooperation must either accrue
benefits or avoid other costs, such as penalties or other sanctions. This logic holds for both genetic
and cultural transmission, though in some cases certain biases in human cultural learning may permit
cooperative solutions that are not accessible to natural selection acting on genes (Henrich 2009;
Henrich & Boyd 2001: see Endnote #6). Even in these cases, however, one must ultimately explain
how and why the relevant learning biases evolved and remain stable. Consider the simplest case (e.g.
food sharing): if more cooperative individuals preferentially bestow benefits (food) on othercooperators (others who share food), a degree of cooperation can be sustained, depending on thecosts of helping (c), the benefits delivered by helping (b), and the strength of the non-random
association or preferential delivery (b, how much shared food is deliver to other sharers vs. nonsharers). Cooperation is sustained when 𝛽 ∙ 𝑏 > 𝑐. More complex mechanisms for cooperation
involve sanctioning those who don’t pay the required costs, such as through preferential exclusion,
ostracism, exploitation or punishment (Bhui et al. 2019). Here, we’ll briefly review the theory andevidence for some of the most fundamental mechanisms, considering each from both a genetic andcultural evolutionary point of view.

Kin-based altruism
Evolutionary biologists have long recognized that if individuals preferentially aid close genealogicalrelatives, a degree of cooperation can be sustained (Hamilton 1964). Cooperation can be stabilizedwith higher costs or lower benefits to relatives, the closer the genealogical relationship (r, thecoefficient of relatedness). Here the non-random association is created by favoring relatives: 𝑟 ∙ 𝑏 >𝑐. By directing help in accordance with r, individuals can preferentially deliver benefits to othercooperators. The more closely related a receiver, the higher the likelihood that they too haveacquired cooperative traits via inheritance from a recent common ancestor. To take a genes eye
view, genes that can identify and preferentially favor copies of themselves will spread at the expenseof those that do not.
Phylogenetically, based on both field and laboratory studies with non-human primates, there’s goodreason to believe that humans have inherited a genetically evolved psychology for this kin-based
altruism from our common ancestors. Ample evidence from both monkeys and non-human apes
reveals the impact of genealogical relatedness on social patterning (who hangs out with whom) andcooperative interactions, including coalitional support, status-pursuit, and grooming (Langergraber2012; Sandel et al. 2019; Surbeck et al. 2011). Kinship also likely plays a role in the food sharing
observed in some species (Jaeggi & Gurven 2013), though in contrast to the ubiquitous food sharing
across human societies, there’s remarkably little voluntary food sharing in non-human primates.
Like in other primates, kinship explains many of the costliest forms of human cooperation, ranging
from child investment to organ donations. Kin-based cooperation is clearly a human universal(Curry et al. 2019). Researchers have documented the powerful effects of genealogical relatedness
for social interactions and cooperation both across diverse societies and back into historical time
(Alvard 2009; Barrett et al. 2002). Important domains of cooperation include food sharing, childcare(Kramer 2010), adoption (Silk 1987), alliance formation (Dunbar et al. 1995), residence choice (Hillet al. 2011), crisis aid and much more.
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SIDE BAR (above): Paralleling our primate cousins, humans also estimate their relatedness to others using
associations with their mothers, time spent together growing up, and phenotypic matching (Bressan &Kramer 2015), including both facial and olfactory similarities. Cues of kinship based on facial similarity
can even increase cooperation in a public goods game (Krupp et al. 2008).

In principle, the logic of kin-based altruism can apply to cultural as well as genetic inheritance. In
genetic models, knowing someone’s genealogical relatedness provides the probability that you share
altruism genes by recent common descent—if you have the altruism gene than there’s a 50% chance(roughly and on-average) that your brother has a copy too. In theory, nothing about this logic
excludes cultural evolution from exploiting the same trick (Allison 1997). In practice, however,
because cultural transmission is fundamentally different from genetic inheritance, there’s little reason
to suspect much of a role for cultural relatedness on cooperation—where individuals would
preferentially help those with the same ‘cultural ancestors’ (e.g., the same role models). The issue isthat individuals often learn from many models drawn from a large pool. This means that the cultural
relatedness between any pair goes down dramatically as the pool of potential models expands. Basedon data from a small Fijian community of roughly 200 people, cultural relatedness based oncommon descent (i.e., learning from the same models) is 0.053 or less. This value implies that thecosts of helpful actions, c, would have to generate benefits, b, that were 19 times greater than thecosts. This is not promising, given that such a small, isolated island population provides perhaps thebest possible conditions for cultural relatedness to favor cooperation (Boyd et al. 2011b).
Nevertheless, theorists have identified one set of conditions in which cultural kinship can favorcooperation: in groups dominated by a single highly prestigious individual, where individuals look tothis person for cues of how to behave, cooperative actions can invade and remain common(Henrich et al. 2015). Predictions derived from this model find support in both experiments andfield observations (Gächter & Renner 2018; Henrich 2016).

Direct Reciprocity
As with kinship, theorists have also long considered how psychological mechanisms rooted inreciprocity—also termed reciprocal altruism—might generate a sufficient degree of non-randomness
in the distribution of costs and benefits to create conditions favorable to cooperation (Axelrod &Hamilton 1981; Trivers 1971). Reciprocity-based strategies, which often incorporate a tit-for-tat
logic (“you scratch my back, I’ll scratch yours”) can operate through mechanisms based on partner
choice, partner fidelity, or both. Under partner choice, individuals build relationships with those who
deliver benefits to them (Barclay 2011; Hruschka & Henrich 2006). Those who fail to provide
sufficient benefits to a partner don’t get to keep them. By contrast, under partner fidelity, individuals
attempt to persuade recalcitrant partners into greater cooperation by withholding benefits (Schino &Aureli 2017).
Phylogenetically, the evidence for reciprocity is mixed. Both experimental and observational datafrom non-human apes suggests a role for partner choice in some forms of cooperation (Engelmann& Herrmann 2016; Schino & Aureli 2010; Schweinfurth & Call 2019). In contrast, evidence for thecontingent cooperation necessary to support reciprocity based on partner fidelity is quite limited(Brosnan et al. 2009; Melis et al. 2016). Though clever experimentalist have managed to design tasks
that permit chimpanzees to respond contingently to help received (Engelmann et al. 2015; Melis et10Annual Review of Psychology

Henrich and Muthukrishnaal. 2008; Schmelz et al. 2017), consistently getting chimpanzee pairs to initiate and sustain tit-for-tat
style reciprocity in plausibly real-world conditions remains largely elusive (Warneken 2018). Taken
together, the diverse body of mixed results regarding reciprocity in other primates suggests thathumans have probably inherited some capacity for reciprocity based on partner choice from ourshared ancestors but likely only the rudiments of partner fidelity.
In contrast to kinship, our dual inheritance system complicates matters because cultural transmission
is particularly well suited to produce, or enhance, both the partner choice and partner fidelity formsof reciprocity. There are at least three reasons for this. First, human cultural learning often involves
the copying of other people’s goals, actions, and strategies. If individual A helps individual B, and B
copies A’s action, then tit-for-tat style reciprocity is off and running. Similarly, if A is a tit-for-tat
strategist and B copies A’s strategy or even learns some rough version of it, then A and B canpotentially begin reciprocal helping with compatible strategies. This tendency to copy an opponent’s
or partner’s strategy is empirically well documented, even in interactions where it reduces payoffs(Belot et al. 2013; Naber et al. 2013). Second, theoretical work on reciprocity demonstrates that a
strategy’s success depends heavily on the other strategies active in a particular population(Lorberbaum et al. 2002; van Veelen et al. 2012). This fact makes it difficult for genetic evolution topre-program one, or even an ensemble, of successful strategies. Cultural learning can tackle thisdilemma by allowing individuals to rapidly adapt their behavior to the local distribution of strategies
and thereby prevent the collapse of cooperation. Finally, theoretical work shows that adding social
norms, which allow third parties to adjudicate disagreements about who did what during
unsuccessful interactions, can dramatically increase the range of conditions under which direct
reciprocity can emerge (Mathew et al. 2013). For all three of these reasons, culture may have createdconditions favorable to the emergence of reciprocity-based cooperation, and the interaction between
culture and genes may create conditions favorable to a reliably developing reciprocity psychology.
Empirical support for this culture-gene coevolutionary view emerges from several patterns.
Reciprocity-based cooperation in some form is clearly a universal (Fiske 1991; Jaeggi & Gurven2013). Yet, while evidence from WEIRD toddlers suggests an early emergence for the rudiments ofpartner choice (Kuhlmeier et al. 2014; Olson & Spelke 2008), the development of actual reciprocitybased cooperation and contingent helping doesn’t robustly emerge until middle childhood(Chernyak et al. 2019; House et al. 2013a; Warneken 2018)—just when children begin to adhere tocostly norms (Smith et al. 2013).
The relatively late and gradual development of positive reciprocity contrasts with negative
reciprocity (“an eye for an eye and a tooth for a tooth”). Using a computer-based experimental
setup, Chernyak et. al. (2019) tested children from ages 4 to 9 in Boston by permitting them tointeract with four other children who were represented by avatars. In one condition, participantsreceived a sticker from one of the avatars (positive reciprocity) while in another condition one of thefour avatars took one of the child’s stickers (negative reciprocity). When given the option of whether
to do anything, Figure 2A shows that children who had been given a sticker only graduallydeveloped the inclination to reciprocate a sticker to this partner. This rising inclination towards
contingent reciprocity is matched by a parallel rise in normative endorsements of reciprocity
regarding what one “should” do in this context. By contrast, revenge or negative direct reciprocity
appears already fully developed in four-year old children (in this population) and remains strong
throughout middle childhood. This is noteworthy because experiments with chimpanzees readily
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reveal negative direct reciprocity (Jensen et al. 2007), but neither positive reciprocity (Melis et al.
2016) nor third-party punishment (Riedl et al. 2012).

Figure 2. Developmental trajectories for (A) positive and negative direct reciprocity for children in Boston; and (B) positive
reciprocity in a repeated Dictator Game with two options, a 1/1 split or 2/0, in Fiji and Los Angeles. Adapted from
Cherneyek et. al. (2019) and House (2017).

Importantly, Chernyek et. al. also demonstrate that children’s adoption of positive reciprocity during
middle childhood is facilitated by hearing stories about characters who performed and endorsed
reciprocal actions. This suggests that reciprocity may depend, or at least be enhanced by, theacquisition of reciprocity norms. Tellingly, other work suggests that the more sophisticated strategies
for positive reciprocity dreamt up by theorists to contend with more complex social environments
don’t even begin to show themselves until after age 10, at least in WEIRD children (Blake et al.
2015b).
While developmental trajectories for positive reciprocity like that shown in Figure 2A reliably
emerge in WEIRD populations (House 2017; House et al. 2012), Figure 2B permits us to compare
age trajectories for the same experiment in Los Angeles and Fiji. In this experiment, children tookturns selecting one of two options (A) 2 for the actor and 2 for their partner or (B) 3 for the actor
and 0 for the other person. Children in both populations develop contingent responding, whichindicates that reciprocity reliably develops in diverse social context. Yet, these populations showrather divergent trajectories—in Fiji, the older kids increasingly select the 3/0 option, so playing titfor-tat drives cooperation down, not up.

SIDE BAR: In contrast to most societies over human history, where people experienced tight kin groups(Schulz et al. 2019) and low relational mobility (Thomson et al. 2018), WEIRD people often live in large
populations and possess great freedom to pick and change their friends, spouses, neighbors, communities andbusiness partners. As a consequence, by the time WEIRD people reach adulthood, they often maintain aportfolio of long-term friendships, where short-term tit-for-tat accounting is eschewed in favor of aggregatedemotional indices, and numerous shorter-term acquaintances, where tit-for-tat style accounting is relevant(Silk 2003). In other societies, including some foragers, conditions are less favorable to partner choice and thismechanism is less important (Smith et al. 2018).

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In closing this section, we note that both theoretical work and empirical evidence suggest thatkinship and reciprocity can interact synergistically to increase cooperation in small, tightly knit
communities (Axelrod & Hamilton 1981). However, these mechanisms cannot tackle the fivechallenges of human cooperation; neither mechanism ‘scales-up’ effectively, especially in a species
like humans. Genealogical relatedness cannot readily account for our (1) ultrasociality (relatedness is
not sufficiently higher than other primates), (2) domain differences in cooperation (except based onb/c), (3) rapid expansion (r declines dramatically as groups expand), and (4) non-cooperative or
maladaptive behavior (kin-altruism requires b > 0). When kin-based altruism does appear to play abroader role, it’s usually supported by cooperative norms, such as those that prescribe prosociality
towards in-laws, extended family members and stepchildren (McNamara & Henrich 2017). Similarly,
while reciprocity can sustain cooperation in dyads, it tends to collapse in larger groups (Boyd &Richerson 1988). Reciprocity also cannot explain differences in the domains of cooperation (except
via variation in b/c) or the existence of maladaptive behavior (reciprocity also requires b >0). In fact,
the intensive cooperation in smaller groups (like clans and villages), which can be enhanced bykinship and reciprocity, actually impedes the emergence of cooperation at higher levels—among
many clans or ethnic groups (Schulz et al. 2019). What we call corruption, cronyism and nepotism isreally just cooperation at a smaller scale, often among relatives, friends and reciprocal partners, at theexpense of cooperation at a larger scale (Muthukrishna 2017; Muthukrishna et al. 2017).

Origins of institutions: reputation, punishment and signaling
People in some populations readily give blood, recycle, help the poor, report crime and volunteer for
war. To explain cooperation at larger scales, where genealogical relatedness and the potential forreciprocity necessarily declines, evolutionary theorists have developed models that can sustain
cooperation based on mechanisms involving punishment, reputation and signaling as well as
combinations of these mechanisms. In typical models of diffuse punishment, for example,
individuals who fail to cooperate, say in contributing to a public good, will be punished (at a cost) by
any punishers in the group. This can sustain cooperation by penalizing defections, but it creates
what’s called the second-order free rider problem (Box 1). Who will punish those who benefit by evading
the costs of punishing free riders? One solution is to randomly designate a single punisher—if
there’s only one punisher, the free rider problem vanishes (Boyd & Richerson 1992; O’Gorman et al.
2009).
Alternatively, diffuse or third party punishment may be a signal of otherwise hidden inclinations
toward cooperativeness or trustworthiness (Gintis et al. 2001; Jordan et al. 2016). Here, individuals
signal their social-behavioral qualities by punishing non-cooperators (or any norm violator), which
both sustains costly norms (including larger scale cooperation) and promotes beneficial futureinteractions for the signaler.
Yet a third solution proposes that, after a norm violation, punishers signal their intent to punish andthen punish if enough others also signal their punitive intentions (Boyd et al. 2010). If too few
individuals signal, punishers withhold their sanctions. By coordinating punishment and compelling
defectors into cooperation, this system can guarantee that punishers tend to end up in cooperative
groups, and when they don’t, they can still avoid the costs of punishment.
SIDE BAR: Another solution to cooperation on larger scales arises from the details of cultural transmission.
Although human learning relies heavily on the success or payoffs in evaluating alternative strategies or behaviors,
when payoffs are pretty similar (and thus uncertain) people also tend to preferentially copy the most common
13 conformist transmission. The strength ofstrategy (Muthukrishna et al. 2016)—a learning strategy called
conformist transmission is probably too weak to favor cooperation over defection when cooperation is common,but it could potentially favor punishing over not punishing, especially in a world where most people comply withthe local cooperative norms so punishers rarely have to punish (Guzman et al. 2007; Henrich & Boyd 2001).

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Large-scale cooperation can also be sustained by reputational systems, often termed indirect reciprocity,that ties different kinds of social interactions together via a shared account of who is in goodstanding. In the classic model (Panchanathan & Boyd 2004), individuals experience two kinds ofinteractions: a public good involving many individuals and a dyadic mutual aid interaction.
Individuals decide whether to help based on their partner’s contribution to the public good (e.g., pay
taxes). If a player defects in the public good, their partner can abstain from helping them in the
dyadic interaction when they are in the role of ‘donor’ (without themselves getting a bad reputation
for it). Essentially, the withdrawal of help in the dyadic helping game, which benefits the person
freed from the burden of helping, is used to sanction non-contributors to the public good. The
model has been confirmed in laboratory experiments (Hauser et al. 2016), though the strength of
this reputation-based cooperation is dependent on being able to track and verify the accuracy of
reputations and may be undermined by reputations in different domains and at different scales.
Collectively, these and other similar models reveal two additional important features about themechanisms that can sustain larger-scale cooperation. First, although all these models were initiallybuilt to examine cooperation, the mathematics reveal that—unlike kinship and direct reciprocity—
they can sustain any equally costly behavior regardless of whether any benefit is delivered to thecommunity. That is, they are models about how to sustain social norms, of which cooperative social
norms are a subclass. Second, they contain what theorists call multiple stable equilibria. This means that
under the identical conditions—same costs, benefits, group size, error rates, etc.—there are stablestates for the populations in which no costly norms exist (e.g., all defection) and states in whichcostly norms are adhered to and cooperation can be maintained. In the language of complex
systems, there are basins of attraction for both defection and costly norms that depend on thefrequency of different behaviors or strategies in the population.
Are these models of large-scale cooperation best understood as products of genetic or cultural
evolution? Unlike kinship and reciprocity, both the assumptions employed in these models and theresults derived strongly suggest that they are most plausibly understood as cultural processes. And,even if they are viewed as genetic evolutionary processes, most rely on one or more key cultural
products, like shared behavioral standards (what counts as ‘cooperation’ or qualifies as ‘good
standing’), recognized social roles (who is a ‘donor’), institutions (for punishment) and the ability todisseminate reputational information (Bhui et al. 2019; Leimar & Hammerstein 2001). Empirically,
reputations are formed and shared primarily via cultural transmission since most people don’t
directly observe most social interactions—so, most reputation-based models presuppose
sophisticated cultural learning abilities. The interpretation of these models as cultural evolutionary
processes is underlined by the fact that none of them have been successfully applied to explain
cooperation in non-humans. Consistent with this, experimental work indicates that non-humanprimates show no concern for their reputations (Engelmann et al. 2012), no inclination to cooperatewith strangers (de Waal et al. 2008), little tendency to engage in costly third-party punishment (Riedlet al. 2012). In contrast, by middle childhood, human children are concerned about their reputations,
responsive to the normative demands of their societies (Figure 1B), and willing to engage in third14

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party punishment of social norm violations in societies that rely on diffuse punishment (Jordan et al.
2014; McAuliffe et al. 2015).
In light of this evidence, these various evolutionary models should be thought of as hypotheses
about the kinds of institutions that cultural evolution may have generated to sustain cooperation,
both in larger groups and among ephemeral interactants. Facing different ecological, economic andsocial circumstances, we should expect cultural evolution to have rigged up distinct combinations ofthese cooperation-sustaining mechanisms, variously deploying elements of punishment, reputationand signaling (among others) in different ways in different societies and in different behavioral
domains (e.g., food sharing, warfare, house building). In some societies, reputational incentives
variously favor tipping at restaurants, obeying parking regulations, circumcising daughters, giving
blood, sharing meat with campmates, raiding other communities and being equitable in monetary
exchanges with strangers or anonymous others. In WEIRD societies, individuals can signal theirtrustworthiness by punishing uncooperative strangers (Jordan et al. 2016) while in other societies
this would be seen and responded to as an anti-social attack (Enke 2019; Henrich forthcoming;
Herrmann et al. 2008). Thus, larger scale human cooperation likely relies on a mosaic of cultural
evolutionary mechanisms that variously harness reputation, signaling and punishment, among others(see below), in a diversity of creative and unexpected configurations.
To illustrate this, consider how village-level cooperation is sustained in the South Pacific (Henrich &Henrich 2014). In a subsistence-oriented Fijian community, a system involving negative indirect
reciprocity—tolerance of those who exploit those with a poor reputation—maintains a wide rangeof social norms including those related to helping in community projects, contributing to village
feasts (food sharing) and constructing one’s house in a prescribed orientation. If someone violates
one of these social norms, both they and their extended family fall into bad standing. If thisreputation gets bad enough, after repeated violations, it’s as if their reputational shield falls and theirfellow villagers can exploit them with impunity. For example, a family who violated community-wide
norms by working on Sundays had some of their cooking pots and crops stolen (while they wereaway in another village) and one of their agricultural fields was torched at night. Normally, had theseacts been performed against someone in good standing, villagers would have pulled together, shared
information and tracked down the thief-arsonist. But, when victims are in bad standing, villagers justshrug and let it pass. In this institution, norm violators are sanctioned but the punishments areneither costly nor altruistic. To the contrary, punishers get both material (e.g. food and pots) andsocial benefits. Most people don’t punish and those who do are widely believed to be rivals of thepunished, who hold long simmering jealousies or grudges. No one thinks these punishers are
admirable, trustworthy, or great future partners. Instead, people are willing to tolerate such antisocial actions when the victim is in bad standing. It’s only ‘bad’ to do ‘bad’ things to ‘good’ people.
Those who do bad things to bad people are tolerable. A cultural evolutionary model inspired by thisfield data reveals the effectiveness and relative simplicity of this mechanism for sustaining large-scalecooperation (Bhui et al. 2019).
This Fijian mechanism for costly norms contrasts with the use of diffuse punishment found in
WEIRD societies. In these peculiar populations, experimental research indicates that people arewilling to pay costs to punish strangers and anonymous others for norm violations (Fehr &Fischbacher 2003; Fehr & Gachter 2002). Evolutionary models in which diffuse punishment
sustains cooperation were developed early, which isn’t surprising since they reflect WEIRD
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intuitions about punishment. To see this experimentally, let’s begin by focusing on the differences in
how Fijian villagers and Americans punish in the Ultimatum and Third-Party Punishment Games(Box 1). In the Ultimatum Game, over 70% of non-student Americans reject offers of 10% of the
stake, the lowest possible offers that could be punished with a cost. In Fiji, 85% of participants
refused to punish this offer amount. In the Third-Party Punishment Game, over half of American
university students (no data for non-students) were willing to punish the lowest possible offer whileamong Fijian villagers that fraction drops to one-third. Post-game interviews indicate that while
participants in both places felt that people should give half of the stake, Fijians but not Americans
felt that unilaterally rejecting or punishing would not be appropriate. In post-game interviews,
Fijians don’t even report a desire to punish (Henrich & Henrich 2014). Despite their unwillingness
to punish low offers, Fijians make relatively high offers on both experiments. The lack ofpunishment in these experiments doesn’t reflect a lack of sanctioning for norm violations in village
life but rather a lack of fit between people’s cultural psychology, which is adapted to their local
institutions (underpinned by negative indirect reciprocity), and the experimental design.
Cultural evolutionary theory suggests that small communities like those in Fiji require different
mechanisms to sustain cooperation than those necessary in large, anonymous societies. In tightlyknit communities, diffuse punishment of any kind risks the (1) threat of counter-punishment—
revenge—against either the punisher or their family and (2) damaging a long-term relationship fromwhich one cannot easily escape—Fijians are tied to their land by customary rights. By contrast, inlarge anonymous societies with high levels of residential mobility and formal policing institutions forserious harms, these issues are mitigated and, as discussed, diffuse punishment can be sustained by avariety of mechanisms. Confirming these theoretical predictions in a global sample from 33
communities from 15 subsistence-oriented societies that included foragers, farmers and pastoralists,
costly punishment by both second and third parties in one-shot anonymous interactions varies
dramatically with the size of the community. To measure people’s willingness to punish, their
Minimum Acceptable Offer (MAO) was calculated for both the Ultimatum and Third-Party
Punishment Games. MAO is the lowest offer amount that a person won’t punish. As Figure 3
shows, populations with larger communities express a much greater willingness to engage in the
costly punishment of those who violate fairness norms in both the UG and TPG (Henrich et al.
2010). Smaller communities mostly do not punish while in large communities a majority or pluralityof people will punish anything but an equal offer. Unsurprisingly, communities with more third
party punishment also make more equal offers to anonymous members of their community in
Dictator Games (Henrich et al. 2006).

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Figure 3. Predicted frequencies of MAOs across possible offer amounts for both the (A) Ultimatum Game and (B) Third
Party Punishment Game. Predicted values are based on a fitted model that includes community size along with a host ofcontrol variables including demographics, schooling, income, wealth, household size and market integration. Adaptedfrom Henrich et. al. (2010)

This line of theorizing, and now a substantial body of evidence including developmental and crosscultural findings, suggests that when researcher study “human cooperation” in WEIRD societies
using behavioral game experiments or similar methods, they are really studying the cultural
psychology that has emerged in association with the spread of a particular constellation of
institutions.
There is, however, an underappreciated issue with all models of large-scale cooperation discussed
above: the problem of equilibrium selection (Box 1). As noted, these evolutionary models can sustain
many different stable norms under identical conditions. A few of these norms are cooperative, inthat they generate social benefits, but most are neutral or even socially costly (hurting others or thegroup as a whole). This raises a question: what kind of process can filter out the more cooperative
norms from the multitude of other norms. Without some additional mechanism to ‘select’ thecooperative norms from all the other stable norms, cooperation would be quite rare. Theorists haveproposed three kinds of equilibrium selection mechanisms to address the challenge. First, coalitions
or powerful leaders might push social norms that favor their own interests. The wealthy, forexample, might use their political power over generations to push norms (or laws) to benefit
themselves; for example, favoring low taxes or polygynous marriage (Singh et al. 2017). Second,some social norms are more stable than others to perturbations, so stochastic fluctuations andshocks—epidemics, floods, hurricanes, etc.—will tend to favor the most stable norms (Young
1998). Finally, competition among groups with different norms—different stable equilibria—will
tend to favor the spread of group-beneficial norms (Boyd & Richerson 1990). These three processes
can be integrated into models of multi-level cultural evolution.

Multi-level cultural evolution and cooperation
Thinking about individuals as nested into overlapping hierarchies of social groups has provided a
powerful way to think about the pyramid of conflicts of interest between individuals within smallergroups and among smaller groups within larger populations. Competition among groups at lower
levels can favor cooperation at higher levels. Nuclear families that manage to expand into clans beatindependent nuclear families. Clans that bind themselves into tribes, through either age-sets orsegmentary lineages, tend to beat lone clans. But, at each level, the interests of lower level groups
can be at odds with larger formations—clans within tribes compete for grazing lands, access towater holes and wives. This means stronger cooperation and greater solidarity at lower levels can be
detrimental to cooperation at higher levels (Maynard Smith & Szathmáry 1999; Muthukrishna 2017).
Within multi-level structures, it’s intergroup competition that drives the cultural evolution ofcooperative norms, which can operate directly (e.g., norms for bravery in warfare) or indirectly byshaping the social networks or organization of groups (Henrich forthcoming). Importantly,intergroup competition need not take the form of violent conflict, though this has certainly been asalient form over human history (Bowles 2006; Wrangham & Glowacki 2012). Researchers are
studying (at least) four additional forms of intergroup competition (Henrich 2016; Richerson et al.
2016):
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1) Prestige-biased group transmission: Individuals and communities preferentially attend toand learn from more successful or prestigious groups. This causes social norms and beliefs
to diffuse from more successful groups, firms or other communities to less successful ones
and can drive the spread of more competitive institutions. When countries preferentiallycopy the U.S. Constitution in setting up new governments, that’s prestige-biased grouptransmission (Rockmore et al. 2018).
2) Differential reproduction: Norms can influence the rate at which individuals have children.
Since children tend to share the norms of their community, any norms that increase birthrates or slow death rates will tend to spread. Some world religions, for example, have spread
more rapidly due to their pronatalist norms: the Mormon polygyny and large families in the
19th century facilitated the rapid expansion of this religion (Daynes 2001).
3) Differential migration: Whenever possible, people will migrate from less prosperous
communities to more prosperous ones. Since migrants, and especially their descendants,
typically adopt the local social norms, beliefs and customs (Mesoudi et al. 2016), differential
migration propels the spread of norms and institutions that generate prosperity and security,
as more successful communities grow at the expense of less successful ones.
4) Differential group survival without conflict: In hostile environments, only groups withinstitutions that promote extensive cooperation and sharing can survive. Groups without
such either retreat to more plentiful environments or significantly decline during droughts,
hurricanes, eruptions or other shocks. The right norms and institutions allow groups tothrive in ecological niches where other groups cannot. Some groups can succeed and others
fail without the groups ever meeting each other.
To test these ideas empirically, researchers have drawn on multi-level models of the evolution ofcooperation (Richerson et al. 2016; Zefferman & Mathew 2015). These models predict thatintergroup competition can sustain greater cooperation when interacting groups maintain stabledifferences in social norms. Specifically, cooperation can be sustained when 𝑅 ∙ 𝑏 > 𝑐, where R =CFST/(1-CFST) and CFST is the ratio of the cultural variation between groups to the total culturalvariation (Muthukrishna et al. forthcoming). CFST measures how much of the individual differencesin cooperative norms are structured into groups (for genetic evolution, this is FST). Working amongfour rural Kenyan populations, Handley and Mathew (2020) collected data from 759 individuals in
different patrilineal clans across four tribes (ethno-linguistic groups). To assess variation in social
norms, individuals were interviewed about 49 different practices. To assess cooperative inclinations
and their parochial boundaries, participants responded to 16 vignettes in which a main actor either
helped or didn’t exploit a target individual. Instead of the abstract structures of economic games, thevignettes involved common circumstances from daily life in East Africa—cattle raiding, sharing
water, grazing, lying, etc. The social identity of the target in the vignettes varied from a fellow clan
member to someone from one of the other three tribal populations. As predicted by formal theory,
Figure 4 shows that cooperative inclinations (the percentage of cooperative responses to thevignettes) rise as CFST between groups declines. The data predict that most of the fiercest intergroupcompetition should occur between tribes. Consistent with this, detailed studies among one of these
populations, the Turkana, suggest the moral circle ends at the tribal border: its bad to raid other
Turkana, even if they are physically and socially distant, but not to raid surrounding non-Turkana(Mathew & Boyd 2011). Sometimes it’s even perceived as ‘good’ to harm other tribes.
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A wide range of other research supports an important role for intergroup competition. This work
includes laboratory studies that illustrate how introducing intergroup competition drives up
cooperation in public goods games (Bornstein & Benyossef 1994; Saaksvuori et al. 2011),
longitudinal field studies among hunter-gatherers that suggest how competitive interactions among
bands may sustain food sharing (Smith et al. 2018), and natural experiments that demonstrate how
increasing inter-firm competition can drive up impersonal trust and cooperation among strangers(Francois et al. 2018).

Figure 4. The structure of cultural variation creates conditions in which intergroup competition can favor greater parochialcooperation. The plot shows the relationship between CFST estimates based on 48 social norms and 16 vignettes withdifferent targets at different social distances that assess the extent of cooperation. Adapted from Handley and Mathew(2020).

Some evolutionary psychologists have consistently expressed concerns about any theory ofcooperation that considers a role for intergroup competition or what is often termed “group
selection” (Krasnow et al. 2016; Pinker 2012). This skepticism, unfortunately, largely arises from two
related misunderstandings. First, such critiques often fail to appreciate that cultural evolutionarymodels involving intergroup competition usually solve the free-rider problem using purely withingroup selective processes (not ‘group selection’), such as those built on reputation, punishment andsignaling (Boyd & Richerson 1990; Henrich 2004). Intergroup competition is only employed totackle the equilibrium selection problem, not the free-rider problem (Box 1). So, it makes no sense
to write as if explanations based on signaling, reputation and punishment are alternatives to thosethat include intergroup competition. Instead, those who put forth explanations rooted in these
within-group mechanisms must explain how they address the equilibrium selection problem.
Opposing equilibrium selection mechanisms like intergroup competition in favor of free-rider
suppression mechanisms like signaling makes about as much sense as arguing for spark plugs overcarburetors in explaining how combustion engines function.
A related, misunderstanding arises from importing theoretical insights about the operation ofintergroup competition from models of genetic evolution. The well-established differences between
cultural and genetic transmission mean that cultural evolution is more likely to create conditions
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Henrich and Muthukrishnafavorable to intergroup competition. Cultural evolution is fast, noisy and non-vertical compared togenetic evolution (Boyd et al. 2011b; Perreault 2012). Intergroup competition requires groupdifferences; the impact of intergroup competition on genetic evolution is diminished when there isgene flow or migration between groups; these deplete the variation between groups and drives down
FST values. If defectors migrate into groups of mostly cooperators, they can prosper and produce
offspring who are also defectors. However, under cultural transmission, the children of immigrants
are often culturally distinct from their parents (due to powerful non-vertical transmission) andculturally indistinguishable from the non-migrants around them. The children of immigrants, forexample, will typically speak the local language without their parents accent, a cue of where they’re
acquiring their culture (Cohen 2012). If we, for example, compare the CFST values calculated by
Mathew and Handley to the genetic equivalents for other African tribal populations, we find that
Kenyan CFST’s are around 0.1-0.2 whereas genetic FST’s are around 0.002—a difference of twoorders of magnitude in the potential role for intergroup competition. Qualitatively, this same patternholds at the level of countries (Bell et al. 2009; Muthukrishna et al. forthcoming; Richerson et al.
2016).
The above account suggests that individuals, and their genes, would have increasingly foundthemselves in groups governed by social norms, including many cooperative norms, maintained bymechanisms related to reputation, signaling and punishment. Might this have influenced our species
ongoing genetic evolution?

COEVOLUTIONARY PSYCHOLOGY
Cultural evolution and its products interact with genetic evolution to produce culture-genecoevolution. This process, considered to be largely theoretical speculation until only a few decadesago, has now emerged as central to understanding the selection pressures on hundreds of specificgenes in our species (Laland et al. 2010). By generating increasingly complex tools (e.g. spearthrowers), food processing techniques (e.g., cooking), languages (e.g., larger vocabularies) andinstitutions (e.g., clans) over hundreds of thousands of years, cumulative cultural evolution has
shaped the environments faced by our genes and thereby drove the genetic evolution of the uniquely
human aspects of our bodies and minds. For example, our small stomachs, short colons and weak
jaw muscles, compared to other primates, were only favored once fire and cooking had spreadculturally in our species (Wrangham 2009). Our bodies rely on cooked food, yet we have no innate
ability to cook or create fires. Thus, an increasing number of researchers argue that a proper
evolutionary approach to human behavior and psychology requires considering the interaction ofgenes and culture over hundreds of thousands of years (Muthukrishna et al. 2018; Street et al. 2017).
The emergence of social norms, as a feature of our ancestral environments, may have createdselection pressures on genes for various aspects of our species’ psychology, which would have inturn strengthened the power of social norms (Henrich 2016).

SELF-DOMESTICATION AND NORM PSYCHOLOGY
Social norms can create powerful selection pressures on genes. As explained above, cultural
evolution will often favor social norms that suppress aggression toward fellow group members andinhibit theft, rape, and other harms. Over evolutionary time, these processes would have also often
assembled norms that supported food sharing, mutual aid, communal defense, and cooperative20

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hunting. Norm violators would have initially been sanctioned in various ways, perhaps by losing
skilled hunting partners, attractive mates, and valuable allies. When such sanctions fall short, modern
hunter-gatherers readily escalate to ostracism, beatings, and even executions. Dominant individuals,
or others unable to suppress aggressive reactions, would have been particularly likely to be executedin acts of coordinated punishment (Wrangham 2019). Such normative sanctions would have createdgenetic selection pressures favoring reduced reactive aggression, stronger self-control, and greater
docility. These norms, by selecting for less reactive aggression, longer developmental windows forlearning and greater self-control, may have favored a set of corresponding morphological changes
including more juvenile faces and reduced brow ridges (Hare 2017).
To more effectively navigate a social world organized by norms, researchers have also proposed that
our species has evolved a norm psychology that facilitates the rapid acquisition of, and adherence to,social norms (Chudek & Henrich 2010). Unlike other animals, we rapidly develop a norm ontology,
approaching the social world as if its governed by rules even if we don’t yet know those rules, andreadily recognize that rule violations could (and should) have negative consequences. Testing thishypothesis, Rakoczy, Schmidt and colleagues have shown that young (German) children
automatically assume that what they’ve seen others do in some novel situation is the ‘correct way’ of
doing it (Rakoczy & Schmidt 2013; Schmidt & Tomasello 2012). Young children will try to precisely
copy the protocol involved in some action; but, even if they themselves fail, they’ll often react
negatively and seek to ‘correct’ those who use alternative methods. Children do this regardless ofwhether they have been told that there’s a ‘right way’ to do something or whether they’ve seen
anyone get corrected. This work, along with earlier research, suggests that children reliably develop
an automatic tendency to infer the existence of social norms and correct norm-violators in a broad
range of activities, from how to use toys to the proper size of charitable donations (Mischel &Liebert 1966).
As part of this norm psychology, evidence suggests that humans have evolved to—at least
partially—internalize norms as context-specific motivations or frugal heuristics for navigating daily
life (Henrich et al. 2005). This internalization may have evolved for several reasons, including tominimize cognitive effort and/or to mitigate the decision-making challenges of considering
reputational payoffs or penalties that only arrive in the future (and are thus discounted) compared to
immediate payoffs from not complying with a costly norm now (Richerson & Henrich 2012). To
empirically investigate norm internalization, researchers have integrated a range of experimental
techniques, including decision-making under time pressure and brain imaging. When placed undertime pressure, which limits the analysis of costs and benefits, people become more likely to behave
in normative ways (Rand 2016; Yamagishi et al. 2017). If the relevant norms are prosocial, people
become more cooperative and fair-minded. Complementing this with the tools from neuroscience,research suggests that both complying with costly social norms and punishing norm violators (alsoat a personal cost) activates reward circuits, suggesting that complying and enforcing norms canbecome goals in themselves (Buckholtz et al. 2008; Buckholtz & Marois 2012).

Interdependent fitness and fusion
Because cultural evolution has created institutions that share costs, mitigate risks and diffuse benefits
across groups, evolutionary theorists have hypothesized that culture may have created genetic
selection pressures for an interdependence psychology (Henrich forthcoming; Tomasello et al. 2012). To
understand this, consider the food sharing norms that have developed among all known populations
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of mobile foragers. Imagine a small band of 5 male hunters, their wives and two children per couple(20 people in total). The hunting is challenging, so the men succeed on only 5% of all days. Thismeans that each nuclear family will go without meat for a month during one out of every fivemonths, on average. However, if they share their kills, the band will almost never go a monthwithout game (less than 0.05% of months). Interestingly, now that they are sharing, the survival ofeach individual is intertwined with everyone else. If Joe the hunter dies, the chances that Mike andhis family go a month without meat rises by a factor of four. Even worse, Joe’s absence increases the
chances that one of the other hunters or their spouse will die in the coming years—poor nutrition
leads to sickness, etc. If another hunter dies, or leaves the band because his spouse passes, eachremaining person’s chances of going a month without meat increases further as does the chances ofsomeone else falling ill or dying. From an evolutionary point of view, social norms like those thatcreate broad food sharing mean that an individual’s fitness—their ability to survive and reproduce—is interwoven with the fitness of everyone else in the band. This entangles even band members who
don’t directly contribute to each other’s welfare: if Mike’s spouse nurses him back to health whenhe’s ill, and Mike shares his kills with Joe and his family, then Joe needs to worry about Mike’s
spouse. Though food sharing norms represent one well-studied case of interdependence, the same
point applies to other norms, such as those related to common defense. In fact, the threat posed byviolent intergroup conflict may be the most important domain of interdependence and thus
existential threats against one’s group may represent a particularly important cue of interdependence(Navarrete & Fessler 2005)—giving rise to what has been called coalitional psychology.
The upshot is that social norms can create communities in which every individual’s health andsurvival depends on almost everyone else. Psychologically, this hypothesis proposes that natural
selection has shaped people’s minds to assess their degree of interdependence with others, and touse these assessments to motivate greater affiliation, personal concern, and support (Bowles et al.
2004). Cues of greater interdependence likely include eating together, sharing social ties,
collaborating on joint projects, and co-experiencing traumatic events. And, though people continue
to assess their degree of interdependence throughout their lives, many of these cues operate mostpowerfully on children, adolescents and young adults, when they are forming their life-long social
networks (Bauer et al. 2014).
This interdependent psychology may create what psychologists have called identity fusion, a
psychological package characterized by deep emotional bonds that create life-long, familial-likerelationships (Swann & Buhrmester 2015). Shared trauma is a powerful cue of interdependence andthus interdependence psychology may explain the increase in prosociality observed among people
who have been affected by war or violent attacks (Bauer et al. 2014, 2016; Buhrmester et al. 2015),
and may even create the ‘band of brothers’ phenomena found in military units (Whitehouse et al.
2014). Interestingly, cultural evolution has figured out how to regularly evoke these psychologicaleffects through terrifying rites of passage (Whitehouse & Lanman 2014) and potentially throughritualized forms of economic exchange that artificially create a greater sense of interdependence(Durkheim 1933).

CULTURAL EVOLUTIONARY PSYCHOLOGY
The approach we’ve presented here generates a range of psychological hypotheses, including
predictions about both reliably developing aspects of human nature and patterns of variation among
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populations. Perhaps most important, this framework also provides a rich array of concepts andtools for generating additional hypotheses. Collections of social norms form institutions, which inturn create incentives that our minds adapt to, both during development (Figures 1 and 2) and overcultural evolutionary time as more successful motivations, strategies, heuristics, world views,
socialization practices and decision-making biases proliferate. Better institutions, infused withcustom-fit psychologies, can spread by the various processes discussed above to increase the scale
and intensity of cooperation. This means that to understand contemporary psychological variation,
we need to examine how societies have scaled up (and fallen apart) in different places and overmillennia.

Kin-based Institutions
The most primordial of human institutions are those surrounding kinship, and in most societies over
our species’ history, these institutions have dominated social, political, and economic life (Murdock
1949). Formed by constellations of norms, kin-based institutions regulate and influence (1) thetreatment of a large extended network of relatives, including distant cousins and affines (e.g.
obligations to in-laws), (2) who marries whom (e.g. preferences for cousin marriage), (3) the
inheritance of social identity (e.g. what’s my clan?), (5) post-marital residence (where should newly
married couples live?) and (4) economic interdependence (e.g. corporately-owned clan lands). Suchnorms are particularly powerful and highly stable because they anchor on, harness and extend,
aspects of our evolved psychology, including those related to kin altruism, incest aversion,
reciprocity and pair-bonding (Henrich 2016; McNamara & Henrich 2017), and thereby effectively
shape the social networks within which people operate. After the origins of agriculture, fierce
intergroup competition to control territory favored increasingly intensive forms of kinship that tiedpeople together in tight interdependent webs that supported high-levels of parochial cooperationand solidarity toward one’s kin group—toward one’s clan, kindred or tribe (Henrich forthcoming;
Zeng et al. 2018). With the rise of states, kin-based institutions were weakened, but kinshipnevertheless remained the most important institution in people’s lives.
While galvanizing cooperation at lower levels, powerful kin-based institutions inhibit the formationof voluntary associations, modern firms, and effective secular governing institutions. That is, they
impede cooperation at higher levels and among strangers. This suggests that people from
populations that have been dominated by intensive kin-based institutions will behave lesscooperatively towards strangers, anonymous others, and impersonal organizations (e.g. thegovernment). Two papers have recently tested these ideas using both global and regional datasets(Enke 2019; Schulz et al. 2019). Globally, using national-level data, Schulz et. al. show that
populations with less intensive kin-based institutions contribute more to strangers in public goods
games, engage less in anti-social (revenge based) punishment, and make more voluntary blood
donations to anonymous strangers. Using first- and second-generation immigrants from different
places both Enke and Schulz et. al confirm these same patterns using various survey-based measuresof trust and fairness towards strangers. These analyses compare individuals living in the same
country, but with different cultural origins.
If intensive kin-based institutions do indeed have substantial impacts on trust, fairness andcooperation toward strangers and anonymous others then this psychological question is transformed
into a historical question: how can we account for the variation in the intensity of kin-based
institutions around the world?
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Ritual and Religion
Social scientists have long proposed a link between cooperation and religion (Wilson 2002; Wright2009). Even in the smallest scale societies, ethnographers have argued that participation incommunal rituals strengthens group solidarity and improved social harmony (Whitehouse &Lanman 2014). Recently, experimentalists have begun to put these ethnographic speculations to thetest through both systematic field studies and laboratory experiments (Fischer & Xygalatas 2014).
For example, laboratory studies have shown that synchronous movement (e.g., dancing or marching)fosters greater solidarity and more cooperation (Lang et al. 2017; Launay et al. 2016). Consistent
with the multi-level account above, both ethnographic and historical studies reveal how intergroupcompetition has driven the diffusion of effective rituals and devotions (McNeill 1995; Sosis &Bressler 2003). This suggests that deep in our evolutionary history, intergroup competition wasfavoring social norms and rituals that increased cooperation.
However, because they evolved to bond face-to-face communities, group rituals would not havehelped, and may have even hindered, the scaling up of cooperation to larger ‘imagined communities’
in which thousands of individuals interact, exchange and cooperate. To facilitate this degree of
scaling up, researchers have argued that cultural evolution, by anchoring on our species’ innate
capacities to entertain the existence of supernatural agents, favored the emergence of increasingly
powerful and morally concerned deities (or supernatural forces) who monitor and punish noncooperative or anti-social actions, such as murder, theft or adultery (Norenzayan et al. 2016). Overtime, beliefs about these beings evolved to increase their effectiveness: gods expanded their range ofmoral concerns (e.g., honesty towards strangers), ability to monitor norm-violators (e.g. mindreading abilities, omniscience), and power to punish (e.g. controlling the afterlife). Here, consistent
with models of social norms based on costly sanctioning, gods are turned into super-punishers who
can confer costs in this life and the next.
A key psychological test of this hypothesis is whether people who believe in more powerful,
moralizing gods are indeed more inclined to cooperate with co-religionists. Establishing thiscorrelation, researchers have shown that individuals from diverse religious traditions who reportstronger beliefs in more powerful moralizing gods are more fair-minded in experiments withanonymous (and distant) co-religionists (Lang et al. 2019) and more supportive of public goods(Atkinson & Bourrat 2011). To examine whether supernatural agents can indeed cause people tobehave more cooperatively, many studies have shown that when unconsciously primed withthoughts of god (Shariff et al. 2016), and specifically thoughts of supernatural punishment (Yilmaz& Bahçekapili 2016), believers become more fair-minded, cooperative and honest with strangers.
Together with historical and cross-cultural data supporting the claim that gods became increasingly
morally-concerned, powerful and punishing over historical time (Botero et al. 2014), thispsychological evidence suggests that religions may have evolved culturally in ways that alter people’s
psychology and thereby permit the scaling up of societies.
Keeping in mind, as noted above, that cooperation at lower levels can undermine higher-level
cooperation, it’s worth considering how religions have shaped families. Beginning a few thousand
years ago, some religions began to tinker with kin-based institutions. During late antiquity, forexample, the branch of Christianity that evolved into the Roman Catholic Church developed a set ofprohibitions and prescriptions about marriage and family that dissolved the intensive kin-based
institutions of Europe, leaving primarily monogamous nuclear families by the end of the Middle
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Ages. Thus, the relatively weak European kin-based institutions that can explain a large swath of the
global variation in cooperation with strangers may be a consequence of the medieval Church’s
relentless and centuries long attack on traditional families (on cousin marriage, polygamy, clans, etc.).
Strikingly, the duration that populations around the globe have spent under the Medieval Catholic
Church predicts 17 different features of psychology, including individualism, conformity, analytic
thinking, universal moral principles, impersonal honesty, and cooperation with strangers. The
psychological impacts of these religiously-motivated changes in the family may have opened thedoor to the proliferation of impersonal markets, voluntary communities (charter towns), residential
mobility and representative governments (Schulz et al. 2019).

CONCLUSION
We began by introducing five key features of human cooperation that challenge any evolutionary
account. To address these, we presented an extended evolutionary synthesis, which considers both
genetic and cultural evolution as well as their interaction, and evaluated leading hypotheses in lightof the available evidence. Now, let’s return to our opening challenges.
(1) Ultrasociality: Genetic evolutionary mechanisms, such as kin-based altruism and direct
reciprocity, may be adequate to explain cooperation in other animals; but, to tackle humanultrasociality requires understanding our second system of inheritance—culture—and theways that it has altered our psychology, suppressed our reactive aggression, enhanced ourcapacity for internalizing norms and driven our genetic evolution in several ways. Culture iswhat has domesticated our species.
(2) Differences in the domains of cooperation: The domains of cooperation vary acrosssocieties because, at least in part, social norms vary (e.g., for raiding, food sharing, etc.). If a
population has no social norms prescribing recycling, tithing, or tipping, people don’t
generally engage in such costly behaviors.
(3) Differences in the scale and intensity of cooperation across populations: The scale andintensity of human cooperation varies dramatically across societies because it’s heavily
influenced by cultural evolution, driven by the effect of intergroup competition on
institutions. The intensity of intergroup competition has varied dramatically acrosspopulations for a variety of ecological, climatic, geographic and historical reasons (Turchin2015). This competition has selected for more prosocial norms and institutions and in somecases, such as the Catholic Church’s weakening of kin-bonds (Schulz et al. 2019), operated
by undermining the effectiveness of lower-scale mechanisms.
(4) Rapid rise in the scale of cooperation: Beginning about 12,000 years ago, the origins ofintensive food production and the stabilization of global climates dramatically increased theintensity of intergroup competition and began driving cultural evolution to scale up
cooperation (Turchin 2015). This process continues to this day.
(5) Non-cooperative and maladaptive behaviors sustained by the same mechanisms ascooperation: Because cultural evolutionary mechanisms related to punishment, signaling
and reputation can stabilize any costly norm, even norms that are costly for both theindividual and group (e.g., female infibulation), non-cooperative and maladaptive behavior
can persist for long periods. Intergroup competition provides a process that filters out
group-damaging norms, but it can be slow and incomplete, especially when many suchnorms are tightly intertwined with other important cooperative norms.
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Understanding the origins and psychology of human cooperation is an exciting and rapidly
developing enterprise. Those interested in engaging with this grand question should consider threeelements of this endeavor: (1) theoretical frameworks, (2) diverse methods, and (3) history. To thefirst, the extended evolutionary framework we described comes with a rich body of theories andhypotheses as well as tools for developing new theories, about both human nature and cultural
psychology. We encourage psychologists to take the formal theory seriously and learn to read theprimary literature (McElreath & Boyd 2007). Second, the nature of human cooperation demands
cross-cultural, comparative and developmental approaches that integrate experiments, observation,and ethnography. Haphazard cross-country cyber sampling is less efficient than systematic tests withpopulations based on theoretical predictions. Finally, the evidence makes it clear that as norms
evolve over time, so does our psychology; historical differences can tell us a lot about contemporary
psychological patterns. This means that researchers need to think about psychology from a historicalperspective and begin to devise ways to bring history and psychology together (Muthukrishna et al.
2020).

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