Category Archives: Research

Corruption, Competition & Scales of Cooperation at Nuffield College, Oxford University

This week I was invited to speak at the Centre for Experimental Social Sciences (CESS), Nuffield College and Oxford University. I presented a talk on “Corruption, Competition & Scales of
Cooperation: Corruption is Rooted in our Relationships” based on my recent paper “Corrupting cooperation and how anti-corruption strategies may backfire” (more details).

I also discussed the general approach to understanding corruption using cultural evolution and the science of cooperation – corruption is one scale of cooperation undermining another. For example, nepotism is cooperation at the scale of kin, well explained by inclusive fitness, undermining cooperation at the scale of the formal institution. More on this framework can be found at ProMarket or Evonomics. Finally, I presented some work in progress based on this approach, including some work by my students.

Many thanks to Raymond Duch, Sönke Ehret, and Sonja Vogt for hosting.

The social and cultural roots of whale and dolphin brains

Last week, my paper with Kieran Fox and Susanne Shultz was published in Nature Ecology and Evolution. The paper was a multiyear project, which consisted of countless hours spent poring through marine mammal literature to create the most comprehensive database of cetacean physiology, social structure, life history, and behavior to date. We then used this database to test some of the predictions of the Social Brain and Cultural Brain Hypotheses. Some of the confirmations of these predictions are shown in Figure 3 of the paper below.

Cetaceans represent a great test for the Social Brain and Cultural Brain Hypotheses (CBH), because of how evolutionarily alien these species are, and how strange their underwater world is compared to the world we inhabit. We have previously tested the CBH predictions with primates, but their evolutionary closeness to humans means that the relationships we find may be due to our evolutionary logic or due to these features (such as large brains and high sociality) being present in a common ancestor. Thus finding these relationships in cetaceans is strong evidence for the evolutionary logic. It also sets up cetaceans as an interesting control group for understanding human evolution.

The ongoing massive media response and public interest in marine mammals and the evolutionary sciences was heartwarming. Altmetrics suggested a score of 1026, in the top 5 of articles in Nature Ecology and Evolution, receiving the most attention of recent articles and top 50 of all articles of a similar age. Highlights included several video and audio interviews, including with BBC World NewsBBC World Service Radio “Science in Action”CBC “The Broadcast” (below), and the front page of the print edition of the The Times and front page of the website of The Guardian.

Selected Media Coverage

Ars Technica

The Age

Quartz

New York Magazine

Newsweek

Scientific American

Sydney Morning Herald

The Telegraph

Today

Vice

Some unexpected places, including Cosmo 🙂

Corrupting cooperation and how anti-corruption strategies may backfire

This month, my paper with Patrick Francois, Shayan Pourahmadi, and Joe Henrich was published in Nature Human Behaviour. Manfred Malinski did a great job summarizing and contextualizing some of the key findings. The key findings were:

  1. Introducing the possibility of bribes into an institutional punishment public goods game results in reduced contributions.
  2. In an institutional punishment public goods game, stronger leaders result in more cooperation. In our modified “bribery game”, stronger leaders result in less cooperation.
  3. Anti-corruption measures including transparency and tying leaders payoffs to the success of the public good result improve contributions, except if economic potential is low and leaders are weak. Here, they can actually further reduce contributions.
  4. Culture matters. Exposure to corrupt norms via living in corrupt places increases bribes, but having an ethnic heritage that includes corrupt countries, but not having actually lived there yourself results in less bribery.

Figures 1, 2 and 3, reproduced below illustrate these results.

Raw contributions (of the ten endowed points) and 95% confidence intervals for each within-subject treatment (control, BG, BG with partial transparency or BG with full transparency) in each between-subjects structural context (strong versus weak leader and poor versus rich economic potential). These data are consistent with our theory that predicts that more powerful leaders increase contributions in the IPGG but decrease contributions in the BG.
Darker blue indicates greater public goods provisioning and darker red indicates reduced public goods provisioning. All coefficients were extracted from a single model by changing reference groups. The columns represent the reference group treatment (control versus BG), while each row shows the coefficient of each treatment compared with this reference group. The contributions were z scores, so the coefficients represent s.d. The full model is reported in the Supplementary Information. In all models, we accounted for the clustering inherent in the experimental design by including a fixed effect for the number of subjects and random effects for participants within groups. Note that in all treatments and structural contexts, the BG has lower contributions than the structurally equivalent IPGG (control). Corruption mitigation effectively increases contributions (although not to control levels) when leaders are strong or the economic potential is rich. When leaders are weak and the economic potential is poor, the apparent corruption mitigation strategy, full transparency has no effect and partial transparency further decreases contributions. *P < 0.10; **P < 0.05; ***P < 0.01; ****P < 0.001.
Odds ratios and 95% confidence intervals are shown for each behaviour (accept bribe, punish or do nothing).

Selected Media Coverage

Ars Technica

Stigler Center, University of Chicago Booth School of Business

Evonomics

Folha de S.Paulo (Brazil; Interview)

The Statesman (India)

DennikN (Slovakia)


You can find a bit more context in the article below, also published on Evonomics and Stigler Center, University of Chicago blog:

Corruption is Rooted in Our Relationships

There is nothing natural1 about democracy. There is nothing natural about living in communities with complete strangers. There is nothing natural about large-scale anonymous cooperation. Yet, this morning, I bought a coffee from Starbucks with no fear of being poisoned or cheated. I caught a train on London’s underground packed with people I’ve never met before and will probably never meet again. If we were commuting chimps in a space that small, it would have been a scene out of the latest Planet of the Apes by the time we reached Holborn station. We’ll return to this mystery in a moment.

There is something very natural about prioritizing your family over other people. There is something very natural about helping your friends and others in your social circle. And there is something very natural about returning favors given to you. These are all smaller scales of cooperation that we share with other animals and that are well described by the math of evolutionary biology. The trouble is that these smaller scales of cooperation can undermine the larger-scale cooperation of modern states. Although corruption is often thought of as a falling from grace, a challenge to the normal functioning state—it’s in the etymology of the word—it’s perhaps better understood as the flip side of cooperation. One scale of cooperation, typically the one that’s smaller and easier to sustain, undermines another.

When a leader gives his daughter a government contract, it’s nepotism. But it’s also cooperation at the level of the family, well explained by inclusive fitness2, undermining cooperation at the level of the state. When a manager gives her friend a job, it’s cronyism. But it’s also cooperation at the level of friends, well explained by reciprocal altruism3, undermining the meritocracy. Bribery is a cooperative act between two people, and so on. It’s no surprise that family-oriented cultures like India and China are also high on corruption, particularly nepotism. Even in the Western world, it’s no surprise that Australia, a country of mates, might be susceptible to cronyism. Or that breaking down kin networks predicts lower corruption and more successful democracies (Akbari, Bahrami-Rad & Kimbrough, 2017; Schulz, 2017). Part of the problem is that these smaller scales of cooperation are easier to sustain and explain than the kind of large-scale anonymous cooperation that we in the Western world have grown accustomed to.

So how is it that some states prevent these smaller scales of cooperation from undermining large-scale anonymous cooperation? The typical answer is that more successful nations have better institutions. All that’s required is the right set of rules to make society function. But even on the face of it, this answer seems incomplete. If it were true, Liberia, who borrowed more than its flag from the United States, ought to be much more successful than it is4. Instead, these institutions are supported by invisible cultural pillars without which the institutions would fail. For example, without a belief in rule of law—that the law applies to all and cannot be changed on the whim of the leader—it doesn’t matter what the constitution or legal code says, no one is listening. Without a long time horizon, decisions are judged on how well they serve our immediate needs making larger-scale projects, like reducing the effects of Climate Change, harder to justify5. Similarly, institutions often lack the punitive power to actually punish perpetrators. For example, most people in the US and UK pay their taxes, even though in reality the IRS and Her Majesty’s Revenue and Customs lack the power to prosecute widespread non-compliance; your probability of getting caught is low. The tax compliant majority may never discover that they can cheat or how to get away with it (Chetty, et al. 2013) and they may not actively seek this information as long as the probability of getting caught is non-zero, the system seems fair, and it seems like everyone else is complying. Or in other words, it’s a combination of norms and institutions. But, it gets tricky—institutions are themselves hardened or codified norms6 and the norms themselves evolve in response to the present environment and due to path-dependence of previous environments, past decisions, and the places migrants come from. Modern groups vary on individualism (Talhelm, et al., 2014) and even sexist attitudes (Alesina, et al., 2013) based on their ancestors’ farming practices7. The science of cultural evolution describes the evolution of these norms and introduces the possibility of out-of-equilibria behavior (people behaving in ways that do not benefit them individually) for long enough for institutions to try to stabilize the new equilibria. For a summary of cultural evolution, see Joseph Henrich’s excellent book and for an even shorter summary see this chapter). How do we begin to understand these processes?

The real world is messy and before we start running randomized control trials or preparing case studies, it’s useful to model the basic dynamics of cooperation using a simpler form that gets at the core elements of the challenge. One commonly used model is called the “Public Goods Game”. The gist of the game is that I give you, and say 9 others, $10. Whatever you put into a pool (the public good), I’ll multiply by say 3, but then I’ll divide the money equally regardless of contribution. This is similar to paying your taxes for public goods that we all benefit from, like roads, clean water, or environmental protections. The dilemma is this: the best move is for everyone to put all their money in the pool. Then they’ll all go home with $30. But it’s in my best interests to put nothing in the pool and let everyone else put their money in. If I put in nothing and they put in $10 each, I’ll go home with almost $40 ($10*9*3people / 10 = $37). What happens when we play this game?

Well, if we play it in a WEIRD8 nation, where prosocial norms tend to be higher, people put about half their money in, but as they gradually realize they can make more by putting in less, contributions dwindle to zero. One way to sustain contributions is to introduce peer punishment—allow people to spend some portion of their money to punish other people. This is similar to the kind of punishment we might see in a small village. I know who you are or at least I know your parents or people you know. If you steal my crops, I’ll punish you myself or ruin your reputation. In the game, if we introduce the possibility of peer punishment, contributions rise again. The problem is that this doesn’t scale well. As the number of people grows, we get second-order free-riding—people prefer to let someone else pay the cost of punishment. When someone cuts a queue, you grumble—someone ought to tell that person off! Someone other than me… And you can also get counter-punishment—revenge for being punished. The best solution seems to be to create a punishment institution. Pick one person as a “Leader” and allow them to extract taxes that can be used to punish free-riders. This works really well and scales up nicely. It’s similar to a functioning police force and judiciary in WEIRD nations. In fact, the models suggest that the more power you give to the leader, the more cooperation they can sustain. Aha! Problem solved. Not quite. Models like these are very useful for distilling the core of a phenomenon, they can miss things. Recall where we started—smaller-scales of cooperation can undermine the larger-scale.

In our recently published paper, we wanted to show just how easy it was to break that well-functioning institution. We did it by introducing the possibility of another very simple form of cooperation—you scratch my back, I’ll scratch yours—bribery. And then we wanted to show the power of invisible cultural pillars by measuring people’s cultural background and by trying to fix corruption using common anti-corruption strategies. We wanted to show that these strategies, including transparency, don’t work in all contexts and can even backfire.

Our “Bribery Game” was the usual institutional punishment public goods game with the punishing leader, but with one additional choice—players could not only keep money for themselves or contribute to the public pool, they could also contribute to the leader. And the leader could not only punish or not punish, they could instead accept that contribution. What happened? On average, we saw contributions fall by 25% compared to the game without bribery as an option. More than double what the pound has fallen against the USD since Brexit (~12%9). Fine, bribery is costly. The World Bank estimates $1 trillion is paid in bribes alone; in Kenya, 8 out of 10 interactions with public officials involves a bribe, and as Manfred Milinski points out in his summary of our paper, most of humanity—6 billion people—live in nations with high levels of corruption. Our model also reveals that unlike the typical institutional punishment public goods game, where stronger institutions mean that more cooperation can be sustained, when bribery is an option, stronger institutions mean more bribery. A small bribe multiplied by the number of players will make you a lot richer than your share of the public good! So can we fix it?

The usual answer is transparency. There are also some interesting approaches, like tying a leader’s salary to the country’s GDP—the Singaporean model10. So what happened when we introduced these strategies? Well, when the public goods multiplier was high (economic potential—potential to make money using legitimate means—was high) or the institution had power to punish, then contributions went up. Not to levels without bribery as an option, but higher. But in poor contexts with weak punishing institutions, transparency had no effect or backfired. As did the Singaporean model11. Why? Consider what transparency does. It tells us what people are doing. But as psychological and cultural evolutionary research reveals, this solves a common knowledge problem and reveals the descriptive norm—what people are doing. For it to have any hope of changing behavior, we need a prescriptive or proscriptive norm against corruption. Without this, transparency just reinforces that everyone is accepting bribes and you’d be a fool not to. People who have lived in corrupt countries will have felt this frustration first hand. There’s a sense that it’s not about bad apples—the society is broken in ways that are sometimes difficult to articulate. But societal norms are not arbitrary. They are adapted to the local environment and influenced by historical contexts. In our experiment, the parameters created the environment. If there really is no easy way to legitimately make money and the state doesn’t have the power to punish free-riders, then bribery really is the right option. So even among Canadians, admittedly some of the nicest people in the world, in these in-game parameters, corruption was difficult to eradicate. When the country is poor and the state has no power, transparency doesn’t tell you not to pay a bribe, it solves a different problem—it tells you the price of the bribe. Not “should I pay”, but “how much”?

There were some other nuances to the experiment that deserve follow up. If we had played the game in Cameroon instead of Canada, we suspect baseline bribery would have been higher. Indeed, people with direct exposure to corruption norms encouraged more corruption in the game controlling for ethnic background. And those with an ethnic background that included more corrupt countries, but without direct exposure were actually better cooperators than the 3rd generation+ Canadians. These results may reveal some of the effects of migration and historical path dependence. Of course, great caution is required in applying these results to the messiness of the real world. We hope to further investigate these cultural patterns in future work. The experiment also reveals that corruption may be quite high in developed countries, but its costs aren’t as easily felt. Leaders in richer nations like the United States may accept “bribes” in  the form of lobbying or campaign funding and these may indeed be costly for the efficiency of the economy, but it may be the difference between a city building 25 or 20 schools. In a poor country similar corruption may be the difference between a city building 3 or 1 school. Five is more than 3, but 3 is three times more than 1. In a rich nation, the cost of corruption may be larger in absolute value, but in a poorer nation, it may be larger in relative value and felt more acutely.

The take home is that cooperation and corruption are two sides of the same coin; different scales of cooperation competing. This approach gives us a powerful theoretical and empirical toolkit for developing a framework for understanding corruption, why some states succeed and others fail, why some oscillate, and the triggers that may lead to failed states succeeding and successful states failing. Our cultural evolutionary biases lead us to look for whom to learn from and perhaps whom to avoid. They lead us to blame individuals for corruption. But just as atrocities are the acts of many humans cooperating toward an evil end, corruption is a feature of a society not individuals. Indeed, corruption is arguably easier to understand than my fearless acceptance of my anonymous barista’s coffee. Our tendency to favor those who share copies of our genes—a tendency all animals share—lead to both love of family and nepotism. Putting our buddies before others is as ancient as our species, but it creates inefficiencies in a meritocracy. Innovations are often the result of applying well-established approaches in one area to the problems of another. We hope the science of cooperation and cultural evolution will give us new tools in combating corruption.


1 Putting aside what it means for something to be natural for our species, suffice to say these are recent inventions in our evolutionary history, by no means culturally universal, and not shared by our closest cousins.

2 Genes that identify and favor copies of themselves will spread.

3 Helping those who help you.

4 The United Nations Human Development Index ranks the United States 10th in the world. Liberia is 177th.

5 Temporal discounting the degree to which we value the future less than the present. Our tendency to value the present over the future is one reason we don’t yet have Moon or Mars colonies, but the degree to which we do this varies from society to society.

6 Written laws can serve a signaling and coordination function; rather than having to interpret norms from the environment. When previously contentious norms are sufficiently well established, you may do well to codify them in law (legislating before they are established might mean more punishment—consider the history of prohibition in the United States).

7 Not that agriculture is the main reason for these cultural differences!

8 Western Educated Industrialized Rich Democratic

9 This doesn’t upset me at all 😐.

10 Singapore’s leaders are the highest paid in the world, but the nation also has one of the lowest corruption rates in the world—lower than the Netherlands, Canada, Germany, UK, Australia, and United States [source].

11 Note, there are some conceptual issues that make interpretation of the Singaporean treatment ambiguous. We discuss this in the supplementary. We’ll have to further explore this in a future study. Such is science.

“Trusting and the Law” conference at the Lorentz Center, Leiden, Netherlands

I gave a keynote presentation at the Lorentz Center conference on “Trusting and the Law“. This was my first legal conference. The audience included judges, lawyers, and legal scholars. I presented a talk on “Economic Psychology and the Science of Cultural Evolution”, where I discussed some of the “invisible cultural pillars” that uphold legal institutions. It was fascinating to discuss differences in the approach to “evidence” in science and the law.

lorentz-trust-law

Cultural Transmission and Social Norms Workshop” at the School of Economics, The University of East Anglia, UK.

I was invited to present my work on innovation and cultural evolution at the “Cultural Transmission and Social Norms Workshop” hosted by the School of Economics at The University of East Anglia, UK. I presented “Innovation in the Collective Brain: The Transmission and Evolution of Norms and Culture”, beginning with an introduction to cultural evolution for the audience of primarily economists. I then discussed innovation as a product of our “collective brains“.

This research is summarized in this news post and in the original paper.

Muthukrishna, M. & Henrich, J. (2016). Innovation in the Collective Brain. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1690).  [Telegraph] [Scientific American] [Video] [Evonomics] [LSE Business Review] [Summary Post] [Download] [Data]

2016 CGS/ProQuest Distinguished Dissertation Award in the Social Sciences

On Thursday, I was at the Council of Graduate Schools (CGS) Annual Meeting in Washington, DC to receive this year’s CGS/ProQuest Distinguished Dissertation Award in the Social Sciences. The award ceremony was held in the Regency Ballroom of the beautiful Omni Shoreham. The press release with more details can be found here: http://www.proquest.com/about/news/2016/Winners-of-2016-CGS-ProQuest-Distinguished-Dissertation-Awards.html.

It was an unexpected honor, but also validation of my research agenda and approach to science. My acceptance speech was a brief summary of my dissertation and Dual Inheritance Theory and Cultural Evolution more generally.

Media

UBC Alumni profile: Michael Muthukrishna’s quest to understand the human puzzle

LSE Q&A with Dr Michael Muthukrishna, Assistant Professor of Economic Psychology

Center for Advanced Study in the Behavioral Sciences at Stanford University, CA

I spent the weekend at a productive interdisciplinary workshop on “Religion, Ritual, Conflict, and Cooperation: Archaeological and Historical Approaches” at the Center for Advanced Study in the Behavioral Sciences (CASBS) at Stanford University. CASBS is located on the top of one of the beautiful hills around Stanford.

We discussed the challenges and successes in inferring religious belief and practice from the archeological and historical record  and new theoretical models and tools for exploring religious history, including the Database of Religious History (DRH).

Other attendees included:

David Carballo (Boston University)
Chris Carleton (Simon Fraser University)
Jesse Chapman (Stanford University)
Mark Csikszentmihalyi (UC Berkeley)
Megan Daniels (Stanford University)
Russell Gray (Director, Max Planck Institute for the History and the Sciences)
Conn Herriott (University of Jerusalem)
Ian Hodder (Stanford University)
Joseph Manning (Yale University)
Jessica McCutcheon (University of British Columbia)
Frances Morphy (Australian National University)
Howard Morphy (Australian National University)
Ian Morris (Stanford University)
Ara Norenzayan (University of British Columbia)
Beate Pongratz-Leisten (NYU)
Neil Price (Uppsala)
Benjamin Purzycki (University of British Columbia)
Ben Raffield (Simon Fraser University)
Katrinka Reinhart (Stanford University)
Celia Schultz (University of Michigan)
Edward Slingerland (University of British Columbia)
Charles Stanish (UCLA)
Brenton Sullivan (Colgate College)
Edward Swenson (University of Toronto)
Robban Toleno (University of British Columbia)
Robyn Walsh (University of Miami)
Joseph Watts (University of Auckland)

Innovation in the Collective Brain

Last week, my paper with Joe Henrich on “Innovation in the Collective Brain” was published in Philosophical Transactions of the Royal Society B: Biological SciencesI explain some of the key points in the video below:

To very briefly summarize, innovation is often assumed to be an individual endeavor driven by geniuses and then passed on to the masses. Consider Thomas Edison and the lightbulb or Gutenberg and the printing press. We argue that rather than a result of far-sighted geniuses, innovations are an emergent property of our species’ cultural learning abilities, applied within our societies and social networks. Our societies and social networks act as collective brains.

Innovations, large or small, do not require heroic geniuses any more than your thoughts hinge on a particular neuron.

The paper outlines how many human brains, which evolved primarily for the acquisition of culture, together beget a collective brain. Within these collective brains, the three main sources of innovation are:

  1. serendipity
  2. recombination, and
  3. incremental improvement.

We argue that rates of innovation are heavily influenced by:

  1. sociality
  2. transmission fidelity, and
  3. cultural variance.

We discuss some of the forces that affect these factors. These factors can also shape each other. For example, we provide preliminary evidence that transmission efficiency is affected by sociality—languages with more speakers are more efficient.

F3.large

We argue that collective brains can make each of their constituent cultural brains more innovative. This perspective sheds light on traits, such as IQ, that have been implicated in innovation. A collective brain perspective can help us understand otherwise puzzling findings in the IQ literature, including group differences, heritability differences, and the dramatic increase in IQ test scores over time.

Selected Media Coverage

The Telegraph

Scientific American

Society for Personality and Social Psychology (SPSP) Conference in San Diego, California (2016)

I chaired a symposium on  “Understanding Religions: Integrating experimental, ethnographic and historical approaches” at the Society for Personality and Social Psychology (SPSP) conference in San Diego, CA.

Joe Henrich began by introducing the broader research agenda, describing the two puzzles of (1) the rise of societal complexity and large-scale cooperation and (2) the emergence and spread of particular religious elements, such as big, powerful, moralizing gods and ritual behavior.

Coren Apicella presented recent evidence of high levels of rule bending in the Hadza, a a minimally religious foraging population.

I then introduced the Database of Religious History and presented some preliminary analyses, showing the relationship between ritual and cooperative behavior. I also updated the audience on data collection and some of the directions we’re going in (such as measuring cultural distance–more soon!).

Finally, Ted Slingerland gave an overview of what the humanities can offer the psychology of religion, with an entertaining presentation of how a lack of deep understanding of history and culture can lead to misinterpretations (such as claims that Chinese don’t have religious beliefs, nor mind-body dualism).

Other highlights of the conference included a debate between Leda Cosmides and Joe Henrich (moderated by Jon Haidt) on “Big Questions in Evolutionary Science and What They Mean for Social-Personality Psychology” and a debate between Jon Haidt and Kurt Gray on “Purity and Harm in the American Culture War: A Debate on the Structure of Morality“.

12622384_10153314723986570_4145041329231477361_o (1)

Leda, Jon, and Joe answering questions after the debate. Photo credit: Cristine Legare

Cultural Evolution – Chapter in Handbook of Evolutionary Psychology, 2nd Edition

Maciek Chudek, Joe Henrich, and I wrote an introduction to Cultural Evolution in the most recent Handbook of Evolutionary Psychology, 2nd edition – edited by David Buss.

The chapter provides a brief overview of the science of cultural evolution, including its psychological foundations and implications. We discuss how humans evolved a second-line of inheritance, crossing the threshold into a world of cumulative culture. We begin by asking how culture can evolve, dispelling the mythical requirement of discrete genes and exact replication.

Evolutionary adaptation has three basic requirements: (1) individuals vary, (2) this variability is heritable (information transmission occurs), and (3) some variants are more likely to survive and spread than others. Genes have these characteristics so they evolve and adaptive. Culture also meets all three requirements, but in different ways. Like bacterial genes, cultural information spreads horizontally and need not be limited to parental transmission to offspring.

We discuss the evolution of our capacity for culture, asking how and when capacities for culture will evolve (when is cultural learning genetically adaptive).

The answer: culture is adaptive when asocial learning is hard and environments fluctuate a lot, but not too much.

We also outline the evolution of some of our social learning biases (a large part of the third requirement of an evolutionary system):

  1. Who we learn from (e.g. skilled, successful, and prestigious models; conformist transmission)
  2. What moderates these choices (e.g. self-similarity, age, sex, ethnicity; Credibility Enhancing Displays, CREDs).
  3. Some examples in the real world, such as the social spread of suicides (Werther effect) and literally learning better from same-sex and same-race instructors.
  4. Content biases on what to learn: e.g.  animals and plants, dangers, fire, reputation, social norms, and social groupings.

Cultural evolution shapes the beliefs and behaviors of groups so that they come adapted to the local environment (including culture) over time, shaping preferences and psychology.

Turning to the population-level, we explain why sociality influences cultural complexity (larger, more interconnected populations have more terms and technologies), how cultural evolution can lead to maladaptive behavior, and how intergroup competition can help eliminate these maladaptive behaviors, briefly discussing the viability of cultural-group selection.

Finally, we discuss how genes can adapt to culture: culture-gene coevolution and how this process may have led to the rapid expansion of the human brain.

The When and Who of Social Learning and Conformist Transmission

Tom Morgan, Joe Henrich and I recently published a paper on the “The When and Who of Social Learning and Conformist Transmission” in Evolution and Human Behavior.

Conformist transmission is a type of frequency dependent social learning
strategy in which individuals are disproportionately inclined to copy the most common trait in their sample of the population (e.g. individuals have a 90% probability of copying a trait that 60% of people possess). The bias is particularly important, because it tends to homogenize behavior within groups increasing between group differences relative to within group differences.

Our three key findings across two experiments were:

  1. Substantial amounts of conformist transmission. We found substantial reliance on conformist biased social learning, with only 3% and 9% (or 15%) showing no bias in Experiments 1 and 2, respectively.
  2. Increased social learning and stronger conformist bias as the number of options increased. Both the amount of social learning and the strength of conformist biases increased as the number of options increased (i.e. 60% of people wearing black shirts is more persuasive in a world of black, red, blue, yellow, and white shirt colors than in a world of only black shirts and white shirts). These results mean that all prior experiments have underestimated reliance on social learning and the strength of conformist transmission, since all use only 2 options.
  3. IQ predicts both social learning and the strength of the conformist bias. IQ predicts less social learning, but has a U-shaped relationship to the strength of the conformist bias. These results suggest that higher IQ individuals are strategically using social learning (using it less, but with a stronger conformist bias when they choose to use other information).

For a list and discussion of all key findings, see the Discussion section of the paper.

Selected Media Coverage

CBC Radio “The 180” Interview

Global TV News Interview

Fast Company

Database of Religious History at Department of Statistics, University of British Columbia, Canada

I was invited to present the Database of Religious History at the Department of Statistics Seminar Series. Nancy Heckman,  Head of the Statistics Department, watched our award winning video on the database and was interested in possible connections with researchers in statistics. I presented some of the technical design aspects of the database as well as our statistical approach to analyzing the data.

Afterwards, I had lunch with several members of the department, including Nancy Heckman, Ruben Zamar, Cindy Greenwood, and Davor Cubranic, as well as with Andrew Trites, Director of the Marine Mammal Research Unit and North Pacific Universities Marine Mammal Research Consortium and Fisheries Centre Co-Director. I hope that collaborations with the Department of Statistics will allow us to find new ways to share and analyze our rapidly growing data.

Cultural Brain Hypothesis at Arizona State University, Arizona

This week I visited Arizona State University, Arizona. Rob Boyd and Joan Silk invited me to present my research on the Cultural Brain Hypothesis at the Evolution of Social Complexity Colloquium Series, sponsored by the School of Human Evolution and Social Change, the Institute of Human Origins and the Consortium for Biosocial Complex Systems.

The Cultural Brain Hypothesis (in prep; co-authored with Maciek Chudek and Joe Henrich) describes the evolution of large brains and parsimoniously explains several empirical relationships between brain size, group size, social learning, mating structures, culture, and the juvenile period. The model also describes the selection pressures that may have led humans into the realm of cumulative cultural evolution, further driving up the human brain size.

The School of Human Evolution and Social Change and the Institute of Human Origins has an exceptional group of human evolutionary researchers. While at Arizona State University, I caught up with Rob BoydJoan SilkKim HillSarah MathewCharles Perreault, Michelle Kline, and Matt Gervais.

Cultural Brain Hypothesis, Cultural Evolution & Human Social Networks at Stanford University, California

This week I visited Stanford University, California. Jamie Holland Jones invited me to present my research on human evolution, cultural evolution, and social networks at the Stanford Anthropology Colloquium Series. I presented three related projects:

The Cultural Brain Hypothesis (in prep; co-authored with Maciek Chudek and Joe Henrich), describes the evolution of large brains and parsimoniously explains several empirical relationships between brain size, group size, social learning, mating structures, culture, and the juvenile period. The model also describes the selection pressures that may have led humans into the realm of cumulative cultural evolution, further driving up the human brain size.

Sociality Influences Cultural Complexity (2014; co-authored with Ben Shulman, Vlad Vasilescu, and Joe Henrich) on the relationship between sociality and cultural complexity.

Cultural Dispositions, Social Networks, and the Dynamics of Social Influence: Implications for Public Opinion and Cultural Change (under review; co-authored with Mark Schaller) describes a mechanism through which realistic human social network structures can emerge and the implications of these mechanisms for cross-cultural differences in cultural transmission and innovation.

Human Behavior and Evolution Society Conference in Natal, Brazil

I attended the 26th Human Behavior and Evolution Society (HBES) Conference in Natal, Brazil. I gave a talk on the Cultural Brain Hypothesis and the Cumulative Cultural Brain Hypothesis.

The paper (in prep), co-authored with Maciek Chudek and Joe Henrich, describes an evolutionary model of the evolution of brains and parsimoniously explains several empirical relationships between brain size, group size, social learning, mating structures, culture, and the juvenile period. The model also describes the selection pressures that may have led humans into the realm of cumulative cultural evolution, further driving up the human brain size.

Cultural Brain Hypothesis and Cumulative Cultural Brain Hypothesis at St Andrews, Scotland

This week I visited the University of St Andrews, Scotland. Kevin Laland invited me to present my paper (in prep) on the Cultural Brain Hypothesis and the Cumulative Cultural Brain Hypothesis. The paper, co-authored with Maciek Chudek and Joe Henrich, describes an evolutionary model of the evolution of brains and parsimoniously explains several empirical relationships between brain size, group size, social learning, mating structures, culture, and the juvenile period. The model also describes the selection pressures that may have led humans into the realm of cumulative cultural evolution, further driving up the human brain size. I presented the research to Kevin’s lab and to Andy Whiten’s lab. I will also be presenting the paper early next month at the 26th Annual Meeting of Human Behavior and Evolution Society (HBES) in Natal, Brazil.

While at St Andrew’s, I met with Andy Whiten, Luke Rendell, Kate Cross, Ana NavarreteDaniel Cownden, Daniel van der Post, Cara Evans, James Ounsley, Andrew Whalen, Lewis Dean, and Murillo Pagnotta, among others. Kevin is currently on sabbatical at the University of Cambridge.

Digital Humanities Conference in Lausanne, Switzerland

I attended the Digital Humanities 2014 conference in Lausanne, Switzerland. Ted Slingerland, Brenton Sullivan, and I presented “A Large Database Approach to Cultural History”. We presented the goals, approach, design, challenges, and progress of the Database of Religious History.

As Technical Director of the project, I focused on the technical aspects. You can read more about our efforts to publicize the database here and here.

Cultural Evolution and How Sociality Influences Cultural Complexity at University of Queensland, Australia

This week I visited my alma mater, The University of Queensland, Australia. Mark Nielsen and Thomas Suddendorf (both of whom I was lucky enough to take classes with as an undergraduate) invited me to present my paper on how “Sociality Influences Cultural Complexity” and my chapter on Cultural Evolution. The chapter, coauthored with Maciek Chudek and Joe Henrich, will be appearing in the new Handbook of Evolutionary Psychology. I presented the research to the Evolutionary Psychology group, which I took great pleasure in, being a member of the group as an undergraduate.

While at Queensland, I also caught up with my Honours supervisor, Penny Sanderson, and my former colleagues, Morgan Tear and Matt Thompson.

Society for Personality and Social Psychology (SPSP) Conference in Austin, Texas

I attended the 15th Annual Meeting of The Society for Personality and Social Psychology (SPSP) in Austin, Texas. I presented a model at the Dynamical Systems and Computational Modeling in Social Psychology preconference. The model uses two principles of human decision making to produce the three key properties of human social networks – high clustering (a friend of a friend is likely your friend), low characteristic path length (“6 degrees of separation”), and a positively skewed degree distribution (most people have a few friends, but a few people have many friends).

My collaborator and advisor, Mark Schaller, presented a related model at a symposium on “The Role of Interpersonal Processes in Group Phenomena and Cultural Development”. The model presented some preliminary research using the model I presented to better understand the dynamics of social influence within social networks.