October 6-7, 2023: Harvard University workshop on historical kinship, psychology, and economic prosperity.
October 30, 2023: Invited speaker at Department of Psychology, University of Michigan.
November 9-10, 2023: Human Flourishing Forum at Pontifical Academy of Sciences, Vatican.
November 30, 2023: Keynote speaker at Risk Management Symposium 2023, Saïd Business School, Oxford.
January 21-24, 2024: Invited speaker at meeting on “Creativity: innovation, transmission and motivation in animals, humans and societies” at Pontifical University of the Holy Cross, Rome.
April 8, 2024: Invited speaker at Booth School of Business, The University of Chicago.
Here’s the take home: Studying hardware won’t help you understand the capabilities of pivot tables in Excel nor Code Interpreter in ChatGPT.
Your head is filled with entire analogies, metaphors, epistemologies, and tools that you once learned and now effortlessly use for thinking. It’s how you cook, how you count, and why you think invisible germs are a good explanation for disease. But invisible spirits are not.
Studying our genes and neural hardware won’t help you understand human intelligence. Our cultural software endows us with *new* cognitive capabilities.
How does this software get written? How do we become more brilliant, creative, and improve our education systems?
Consider how we count. We went from counting 1, 2, 3, many, as some small-scale societies still count, to a full-blown number system. Numbers likely emerged as an innovation for more efficiently tracking cattle and crops – you need to know who owes you what!
This new cognitive capability used a metaphor – fingers. But there’s nothing unique about fingers & 10 is awkward (16 would be better). Cultures have counted on body parts from base 6 to 27. But to count beyond body parts, we needed a different metaphor. Something like stones.
‘Calculus’ comes from ‘pebble’ (think calcium or limestone), and was used for addition and subtraction. Stones let you think about addition or subtraction beyond how creative you can get with body parts. There are some stones, and you can throw down more or snatch some away.
Stones are great for natural numbers: 1, 2, 3, 4, 5, etc. But stones don’t make zero obvious. What does zero pebbles look like? It looks a lot like zero of everything else – it’s nothing – and ‘nothing’ is hard to imagine. Zero came a lot later. What about negatives?
The number line as a metaphor helped make zero more concrete and easily transmissible even to children. Number lines work by mapping numbers not to objects but to movement and position, but they also revealed the negative numbers, which are not otherwise intuitive!
“Negative numbers darken the very whole doctrines of the equations and make dark of the things which are in their nature excessively obvious and simple” as Francis Maseres complained in the 18th century.
Nothing about numbers is intuitive to our ape brains. But these metaphors, mental models, and cultural innovations – cultural software – literally changed our minds and gave us new capacities. They’re like software upgrades.
These kids have a Soroban abacus in their heads allowing them to swiftly add large sums: 3267 + 9853 + 6531 + 7991 + 2641 in seconds. It’s a brand new cognitive capability. New cultural software. Video here: https://twitter.com/mmuthukrishna/status/1684576156803289091?s=20
Some innovations are more general than others. For example, thanks to the invention of writing, I can convey information through straight and squiggly lines on a page. I’m doing it right now and I’m literally changing your brain.
Another lesson: Mental tools can go out of date. Mental math became less useful. My middle school teacher, warned us about not being able to +, -, x, / without a calculator (because we wouldn’t be carrying calculators in our pocket). He didn’t foresee the arrival of the iPhone.
Much of what we assume are human capabilities are actually cultural software, invented and transmitted. This can be hard to see because we all live in a bubble. Academics in Ivory Towers, coastal elites, rural small towns – all part of a big bubble.
Almost everyone you’ll ever meet went to school; can all read, write, & count; and consumes some form of television and online media.
Breaking out of this big bubble requires going back in time or to far-flung places.
If our cultural software is what makes us smart, it means that we can be deliberate in how it gets written. We can seek out new mental models, intellectually arbitrage our way to creativity & discovery, and revitalize our education systems.
If you liked this post and want to learn more about how cultural evolution can be applied to our lives, companies, and societies, please pre-order A Theory of Everyone: https://linktr.ee/theoryofeveryone. Pre-orders really help with the success of the book and Amazon pre-orders guarantee the lowest price. Thank you!
I spoke about “Mapping Psychological Terrae Incognita:Explorations Beyond WEIRD Psychology”, which primarily focused on these papers:
Muthukrishna, M., Bell, A. V., Henrich, J., Curtin, C., Gedranovich, A., McInerney, J. & Thue, B. (2020). Beyond Western, Educated, Industrial, Rich, and Democratic (WEIRD) Psychology: Measuring and Mapping Scales of Cultural and Psychological Distance. Psychological Science, 31(6), 678-701. [Download] [Supplementary] [Code] [Summary Post] [Publisher] [Twitter]
White, C. J. M., Muthukrishna, M. (equal senior) & Norenzayan, A. (2021). Worldwide evidence of cultural similarity among co-religionists within and across countries using the World Values Survey. Proceedings of the National Academy of Sciences, 118 (37) e2109650118. [Download] [Supplementary] [Publisher] [Twitter]
Muthukrishna, M., Henrich, J. & Slingerland, E. (2021). Psychology as a Historical Science. Annual Review ofPsychology, 72, 717-49. [Download] [Publisher] [Summary Post] [Twitter]
I was invited to speak at the this month, where I discussed my research on the conditional nature of human cooperation and its potential threats to our progress and advancement.
Muthukrishna, M., Henrich, J. & Slingerland, E. (2021). Psychology as a Historical Science. Annual Review ofPsychology, 72, 717-49. [Download] [Publisher] [Twitter]
Henrich, J. & Muthukrishna, M. (2021). The Origins and Psychology of Human Cooperation. Annual Review ofPsychology, 72, 207-40. [Download] [Publisher] [Twitter]
The research is also related to my forthcoming book and to a new grant, which aims to deepen our understanding of the underlying mechanisms that enable cooperation and how they can be leveraged to foster greater harmony and unity in our interconnected world.
Schimmelpfennig, R. & Muthukrishna, M. (2023). Cultural Evolutionary Behavioural Science in Public Policy. Behavioural Public Policy. [Publisher] [Download] [Twitter] [LinkedIn]
Muthukrishna, M., Bell, A. V., Henrich, J., Curtin, C., Gedranovich, A., McInerney, J. & Thue, B. (2020). Beyond Western, Educated, Industrial, Rich, and Democratic (WEIRD) Psychology: Measuring and Mapping Scales of Cultural and Psychological Distance. Psychological Science, 31(6), 678-701. [Download] [Supplementary] [Code] [Summary Post] [Publisher] [Twitter]
Henrich, J. & Muthukrishna, M. (2021). The Origins and Psychology of Human Cooperation. Annual Review ofPsychology, 72, 207-40. [Download] [Publisher] [Twitter]
Muthukrishna, M., Henrich, J. & Slingerland, E. (2021). Psychology as a Historical Science. Annual Review ofPsychology, 72, 717-49. [Download] [Publisher] [Twitter]
Muthukrishna, M., Francois, P., Pourahmadi, S., & Henrich, J. (2017). Corrupting Cooperation and How Anti-Corruption Strategies May Backfire. Nature Human Behaviour, 1(0138). [Download] [Summary Post] [Publisher]
Muthukrishna, M., Doebeli, M., Chudek, M., & Henrich, J. (2018). The Cultural Brain Hypothesis: How culture drives brain expansion, sociality, and life history. PLOS Computational Biology, 14(11): e1006504. [Download] [Supplementary] [Summary Post] [Publisher] [Twitter]
Schimmelpfennig, R., Razek, L., Schnell, E., & Muthukrishna, M. (2021). Paradox of Diversity in the Collective Brain. Philosophical Transactions of the Royal Society B: Biological Sciences. [Download] [Summary Post] [Publisher] [Twitter]
Uchiyama, R., Spicer, R. & Muthukrishna, M. (2021). Cultural Evolution of Genetic Heritability. [Target article]. Behavioral and Brain Sciences, 1-147. [Download] [Summary Post] [Publisher] [Twitter]
Muthukrishna, M., Henrich, J. & Slingerland, E. (2021). Psychology as a Historical Science. Annual Review ofPsychology, 72, 717-49. [Download] [Publisher] [Twitter]
Henrich, J. & Muthukrishna, M. (2021). The Origins and Psychology of Human Cooperation. Annual Review ofPsychology, 72, 207-40. [Download] [Publisher] [Twitter]
I presented “The Evolution of Comity: Ultimate Constraints on the Scale of Cooperation” at Duke University. The most relevant papers are:
Muthukrishna, M., Henrich, J. & Slingerland, E. (2021). Psychology as a Historical Science. Annual Review ofPsychology, 72, 717-49. [Download] [Publisher] [Twitter]
Henrich, J. & Muthukrishna, M. (2021). The Origins and Psychology of Human Cooperation. Annual Review ofPsychology, 72, 207-40. [Download] [Publisher] [Twitter]
I was invited by the Behavioural Insights Team (BIT) to speak about cultural behavioral science and its applications in public policy. I explain how, by using cultural evolution as a theory of human behavior can help some of the challenges in behavioral science in terms of long term change and contextual factors that affect whether an intervention will work. This figure from a key paper presents the history of behavioral science that has led to cultural evolutionary behavioral science as an obvious next step.
The most relevant papers are:
Muthukrishna, M. (2019). Cultural Evolutionary Public Policy. Nature Human Behaviour, 4, 12-13. [Download] [Publisher]
Schimmelpfennig, R. & Muthukrishna, M. (2023). Cultural Evolutionary Behavioural Science in Public Policy. Behavioural Public Policy. [Publisher] [Download] [Twitter] [LinkedIn]
My talk begins with an overview of the problem plaguing behavioral economics – the lack of a theoretical foundation that can guide policy interventions. I introduce cultural evolution as a possible solution to bridge the theoretical gap. By using cultural evolution as a theory of human behavior, improvements can be made in policy efficiency. For example, studying how social norms change and evolve over time will provide a foundation for implementing effective policy interventions in multicultural societies. Some of this history is captured in this figure from the paper:
Considering the historical path dependence of norms provides crucial in understanding why certain populations hold certain beliefs, like vaccine hesitancy and a distrust in healthcare systems. Identifying how people acquire cultural norms, and narrowing down the ultimate causes for behavior (through cultural distance tools like world.culturalytics.com) could provide insights into designing interventions that work.
Understanding cultural evolution and behavioral science can help reanalyze the literature on public policy, providing insights into why some approaches are successful while others are not. I explain how studying universal cognitive capabilities will provide a deeper understanding of norm change, and thus, improve policy design.
Muthukrishna, M., Doebeli, M., Chudek, M., & Henrich, J. (2018). The Cultural Brain Hypothesis: How culture drives brain expansion, sociality, and life history. PLOS Computational Biology, 14(11): e1006504. [Download] [Supplementary] [Summary Post] [Publisher] [Twitter]
Schimmelpfennig, R., Razek, L., Schnell, E., & Muthukrishna, M. (2021). Paradox of Diversity in the Collective Brain. Philosophical Transactions of the Royal Society B: Biological Sciences. [Download] [Summary Post] [Publisher] [Twitter]
I was invited to speak at the Royal Navy workshop, hosted by the Royal United Services Institute, UK’s leading defense and security think tank.
I discussed the evolution of innovation through collective intelligence, drawing examples from history and archaeology. I also highlighted how modern societies can benefit from past civilizations’ collective intelligence to promote progress and innovation. The papers most relevant to this talk are:
Schimmelpfennig, R., Razek, L., Schnell, E., & Muthukrishna, M. (2021). Paradox of Diversity in the Collective Brain. Philosophical Transactions of the Royal Society B: Biological Sciences. [Download] [Summary Post] [Publisher] [Twitter]
Muthukrishna, M., Bell, A. V., Henrich, J., Curtin, C., Gedranovich, A., McInerney, J. & Thue, B. (2020). Beyond Western, Educated, Industrial, Rich, and Democratic (WEIRD) Psychology: Measuring and Mapping Scales of Cultural and Psychological Distance. Psychological Science, 31(6), 678-701. [Download] [Supplementary] [Code] [Summary Post] [Publisher] [Twitter]
It was an insightful exploration of the role of collective intelligence in human innovation in a defense context. My thanks to RUSI for inviting me and organizing the event.
Robin Schimmelpfenning and I discussed our co-authored paper on the paradox of diversity in the collective brain. We discuss how the rate of innovation depends on sociality, information fidelity, and cultural trait diversity. While cultural trait diversity offers the largest potential for empowering innovation, it also brings with it potential coordination and cooperation challenges. Diversity, in other words, is both a source of innovation and divisive; a double-edge sword. We then propose using cultural evolvability as a framework for resolving this paradox.
Our presentation was followed by a discussion session and subsequently a panel discussion chaired by Dominic Abrams.
Here’s the take home: diversity empowers innovation through recombination but also by definition divides us. We call this the paradox of diversity. A principled way to resolve this tension is by considering cultural evolvability.
We discuss implications for entrepreneurship, polarization & a nuanced take on diversity. This framework can also guide researchers and practitioners in how to reap the benefits of diversity by reducing costs.
Let’s start with innovation: A folk understanding of innovation is that it’s driven by a talented few – the giants upon whose shoulders we stand. But that view is inconsistent with theoretical and empirical work in cultural evolution.
Instead, innovations emerge as ideas flow through our social networks, requiring a specific innovator no more than your thoughts require a specific neuron. See: Innovation in the Collective Brain
People are often unaware of how little they actually deeply understand about the world – what’s referred to as the “knowledge illusion” or “illusion of explanatory depth”. The Knowledge Illusion is a good pop book on the topic.
The world is not only complicated, but more complicated than our psychology allows us to believe. Innovations emerge through incremental improvements through partial causal models and large leaps through serendipity & recombination.
Three key levers that affect innovation are sociality (size & interconnectedness of a population), transmission fidelity (how well you can transmit information between people), & cultural trait diversity. Diversity has the most potential benefit and the largest challenges.
Let me say a bit about each. 1. Sociality: in general +ve relationship, because large pops had to solve the coordination problem to become large. Interconnectivity has an optimal point. Small work groups can be easily overconnected; large pops should be more connected.
2. Transmission fidelity: under selection as cultural complexity increases. Hunter-gatherers not much explicit instruction.Industrial revolution eventually led to schools to download a minimum common cultural package – reading, writing, arithmetic, algorithms for thinking.
Today we have the printing press, radio, TV, Internet, social media, and Zoom. But there’s still too much to learn. Unless you get a PhD, 21C students don’t learn mathematics developed after 1900; scientific training is longer, & major contributions are made at an older age.
We spend longer learning, delaying kids. Theres limit to pop size & transmission; how long you can delay being productive. Another path is to divide up info & labor-specialize. Get smart at 1 thing & stupid at everything else: cultural trait diversity
Different kinds of diversity & different ways to measure it. Our focus is on cultural trait diversity-beliefs, behaviors, assumptions, values, technologies, & other transmissible traits. e.g. languages, processing techniques, technical skills, family structure & occupation.
In the public discourse, diversity often refers to ancestry or physical characteristics-skin color, ethnic origin, religion, sex, gender, sexual orientation, or ability. These may correlate with cultural trait diversity, but correlation may weaken over generations.
For example, Americans with different ancestries may possess similar WEIRD psychology (part of why I have an issue with the WEIRD=White take, though that’s a separate issue). I’ll use diversity from herein, but that’s what we mean.
Diversity can be distributed in different ways: Diversity between pops culturally evolves as pops adapt to local differences, influencing future generations through historical path dependence created by past conditions or founder populations. See also: Psychology as a Historical Science
Diversity within populations evolves as information and labor are divided as discussed above.
Within-population diversity includes disciplinary differences, such as the sciences and humanities, industry specialisations, guilds, and firms. Diversity can be structured as ‘cultural clusters’ by ethnicity, class, wealth, occupation, politics, religion, or incidental geographic layout. Cultural clusters may intersect, such as in ethnic occupation specialization-lots of examples. See also: Beyond WEIRD Psychology: Measuring and Mapping Scales of Cultural and Psychological Distance
Diversity may also exist within certain individuals—multicultural individuals, ‘third culture kids’ (like Barack Obama), interdisciplinary researchers, and so on.
Diversity is therefore both the product of cultural evolution and fuel for the engine of further innovation
But without common understanding & common goals, the flow of ideas in social networks is stymied, preventing recombination, and reducing innovation. Consider communication without a common language or collaborations between scientists & humanities, or different scientists.
We formalize this paradox of diversity trade off in the paper and will be exploring solutions in future work. Check out the paper for details. We argue cultural evolvability is the right way to think about this.
Evolvability refers to not how well adapted a population is to current circumstances, but its ability to evolve to future circumstances. Variation or diversity, and the forces that create and stabilise that diversity are key factors that create evolvability.
Cultural evolvability is a balance between diversity and selection, exploring and exploiting, sampling and specialising, convergent and divergent thinking, stability and change, efficiency and flexibility.
Lots of related work on diversity and selection, explore-exploit or sampling-specialising trade-off in development, search for global solutions & avoidance of saddle points within machine learning.
As an aside, ML insights: in a sufficiently high dimensional space there are effectively no local optima, only saddle points with some dimension that allows escape. Biological & cultural systems have large dimensionality, there are no true evolutionary stable equilibria.
Next, we review the diversity literature through the lens of cultural evolvability. First we lay out 9 challenges to interpreting the literature. It’s a literature that would be benefit from theory. See also: A Problem in Theory
Diversity overlaps with challenging aspects of psychology, norms & institutions: racism, prejudice, xenophobia, sexism, discrimination, power differences, social and economic inequalities.
Love to write about at some point, here we focus on coordination challenges, which influence and are influenced by these problematic features of the world. Our goal is to review the overall patterns in the literature and make sense of these in light of cultural evolvability.
Here’s some of the mixed lit: Within countries, diversity is often approximated by birthplace diversity, professional diversity, ethnic diversity or linguistic diversity. Research looking at the relationship between diversity and economic growth suggests: positive effect of birthplace diversity, but -ve effect of ethnic and linguistic diversity. Research asking questions about immigrants in general often ignore the heterogeneity – cultural distance and education (effectively the cultural traits in your head) matter. Even more mixed in firms. Overall, educational diversity and deep level diversity seem to be positive for innovation within a firm. Mixed effects within teams – we think the paradox of diversity can disentangle.
We derive some insights:
1. Cultural evolvability means tolerance for diversity. Currently less adaptive traits may be more adaptive when the environment changes. Different environments lead to different evolvability strategies. In materially insecure societies, not following a successful “Tiger Mom” strategy—working hard to secure scarce educational opportunities and subsequent employment opportunities—has a much larger cost. But this leads to incremental over radical innovation.
2. Cultural evolvability means under-optimization & inequality. Cultural evolvability necessarily means inequality in outcomes, because not all will have the optimal strategy for the current environment.
Firms face a tradeoff: strategies that favour efficiency & strategies that favour flexibility. Consistent, strong cultures perform well in stable markets, but poorly during times of change. Under-optimizing and allowing for flexibility increases a firm’s evolvability.
Not all firms can bear the cost of under-optimising in the short term-high risk, high value approaches better suited to larger firms or larger countries. Read about Satya Nadella and Microsoft:
Compromize strategies: skunkworks, ecosystems of different firms trying different strategies (e.g. Silicon Valley), or countries composed of different states or regions trying different approaches (e.g. what Justice Brandeis described as “laboratories of democracy”).
In shared multi-agent reinforcement learning, diversity increases performance through exploration and individualized behaviors. Evolvability means many approaches will be suboptimal or even fail, but the successful approaches can be spread and benefit the group as a whole.
Indeed one of the benefits of access to multiple cultures in pluralistic, multicultural societies is the ability to create new approaches by learning, borrowing, copying from each other and other cultures. We should do more of that.
3. Cultural evolvability helps explain levels of entrepreneurship. Cultural evolvability requires doing something different. Most new businesses fail & the willingness to take a risk depends on personal and population-level costs and benefits.
A. personal cost of deviation: many deviations will result in lower payoffs than following the majority trait. If it were obvious how to do better, most of the population would already use the better strategy. Tolerating diversity in traits, thus, means tolerating failure.
Reducing cost of failure increases entrepreneurship: bankruptcy laws, social safety nets, rich parents – a child with parents in the top 1% income distribution is 10 times more likely to be an inventor than a child born below the median, controlling for measures of ability.
B. population-level benefit of deviation. In a large economy with a large customer base comes large rewards for large innovations – the few winners can win bigger. Amazon can make more money in the United States than in Australia. Here’s a great video of Bezos describing his vision back in 1997:
C. Who pays the cost and who benefits from the innovation at a population-level, a function of the scale of cooperation. Even if at an individual-level the benefits of entrepreneurship don’t outweigh costs, they may do so at a population-level.
Silicon Valley offers an example. For every Apple & Amazon, there are 1000s of start-ups that have failed – most start-ups fail & the overwhelming majority never receive funding (114) – ‘unicorns’ are called unicorns for a reason. But the few successes pay for the failures.
4. Cultural evolvability prevents polarization & cultural speciation. Harshly punishing minor deviations increases extremism. If you’re harshly punished anyway, may as well take the extreme position. If there weretolerance for diverse view points, I’d moderate my position.
Model and results also inform debates on freedom of speech, predicting large sanctions for small deviations may encourage a divided society. By corrolary tolerating multicultural diversity of opinions and cultural traits may prevent polarization.
Cultural clustering complicates everything, but I’ll let you read about that in the paper. It gets into colonialism, resource competition, and intergroup violence. 50/ To conclude, diversity has been central to the success of all life. Until around 1.2 billion years ago the source of that diversity was mutation – genetic innovation through serendipity and incremental improvement alone. Single cells reproducing by simple replication.
Sex unlocked the recombinatorial power of diversity, increasing evolvability and the speed of evolution. So too with culture, but there are many barriers to cultural traits meeting and recombining.
We live in an increasingly connected & multicultural world. Migration is a constant feature of the human story, but since the Age of Mass Migration, more people from more culturally distant societies live side by side. Their countries of origin must coordinate as never before.
So much human potential is lost through unequal access to information and adaptive cultural traits. The goal of any society or org should be to reap the benefits of diversity and minimize the costs, thereby maximizing human potential. We discuss several strategies.
Humans are a deeply cooperative species. Our greatest achievements and our worst atrocities are both cooperative acts. In a more diverse world, the challenge is greater, so too are the potential gains.
I was a keynote speaker at the Cooperative AI workshop at the 2021 Conference on Neural Information Processing Systems (NeurIPS). I spoke about “Cultural Evolution and Human Cooperation” and its relevance for understanding problems in cooperative AI. Some of the topics covered included, an introduction to dual inheritance theory and cultural evolution, how and why humans cooperate, and why human cooperation varies in scale, intensity, and domain across societies.
🚨New paper (& #RStats pckg) in Psych Methods: “Parsimony in Model Selection: Tools for Assessing Fit Propensity” w/ Carl Falk
Say you have 2 theories rep in 2 stats models. Which theory/model is correct? The one that best fits the data right?
Ok, but what about parsimony?
Occams razor: given 2 equally fitting models, all else being equal pick the simpler / more parsimonious, model. But how do you quantify parsimony?
Some researchers equate parsimony with degrees of freedom, but as we show you can have fewer parameters, but less parsimony.
Another way to think about it is what Kris Preacher called “fit propensity”. Some models may fit the given data better not because they represent a more correct theory, but because they would fit any data better. Even nonsensical data. It’s the opposite of parsimony.
Incidentally, the context for this research is the theory crisis and the importance of formalizing theory, even if in a statistical model. More here:
New paper: we argue that the replication crisis is rooted in more than methodological malpractice and statistical shenanigans. It’s also a result of a lack of a cumulative theoretical framework: & (nature.com/articles/s4156…)(muth.io/theory-nhb)
Back to fit propensity.
Fit propensity is often ignored in model selection. Perhaps because the answer to “how do we assess fit propensity?” has been “not easily”. In this paper, we fix that.
We offer a toolkit and 5 step process for researchers to assess parsimony of SEMs using an R package (ockhamSEM).
Basic idea: generate random data (or constrained random data, e.g. only positive) as covariances and see which model fits better in this universe of nonsense.
So in the opening model, using standard model selection approach, you might conclude that 2A is a better model than 1A. 2A has a lower AIC so it’s the best theory for generating the data, etc. But you’d have ignored that 2A lacks parsimony.
2A fits a wider range of data better – even nonsense.
Between these models, you might think the factor model is a better fit than the simplex model, but it lacks parsimony – much more so if the data are all positive covariances!
To show some of the complexities of considering parsimony, we investigate the factor structure of the Rosenberg Self-Esteem Scale.
Spoiler: fit indices interact with fit propensity.
Some quick background: Religions bind people into communities with moral norms about what is right, good, & true. Ever notice that major world religions seem to have some broad stroke similarities like big families and being nice to neighbors? Why is that?
One hypothesis is that having those helped those religions grow in the competition with other religions. Not all religions in history share these features. The Shakers, for example, an offshoot of the Quakers, practiced celibacy not just for a priestly class, but for all.
The Shakers are no longer with us.
But religions also have plenty of differences, not only in explicitly religious beliefs, but in broader cultural values that affect national culture. Jesuits and Mainline Protestants, for example, historically increased levels of education. And Protestant values may help explain America’s traditionalism, individualism, and moralization of work. Religions are also shaped by national culture, taking on regional forms. Here’s a buff, Korean Jesus:
Some have also argued that “religion” is mostly a label or an identity, swamped by national culture – think nominal Christians. Or religion may just predict overtly religious beliefs, rituals, and moral attitudes.
For the cultural-group selection theory to work, major world religions should be “super-ethnic” identities, binding people beyond their ethnicity or national borders. That is, those who share a religion living in different countries should be more similar to those who don’t share the religion.
Using a new method for measuring cultural distance called the Cultural Fixation Index (CFst; read more about it here: https://michael.muthukrishna.com/beyond-weird-psychology-measuring-and-mapping-scales-of-cultural-and-psychological-distance/), we looked at cultural distance between major world religions in the World Values Survey. What did we find?
CFst are large enough to have competition between distinct cultural-groups of cultural traits, even if you remove overtly religious beliefs. The “People of the Book”—Christian, Muslim, and Jewish people—share cultural similarities. Christians are about as culturally similar to both Jewish and Muslim people as Americans are to Canadians or Australians are to Brits. But just as the United States is similarly geographically distant from Uruguay and Ukraine, but Uruguay and Ukraine are not geographically close to each other, Jewish and Muslim people are a similar cultural distance as people in the United States and the Philippines.
You can take a look at national cultural distance with this app: https://world.culturalytics.com/
As a side note, Buddhists are interesting. They look like Hindus, as fellow Dharmist, but also like Christians, Muslims & Jews.
But of course, these broad generalizations hide a lot of cultural clustering within countries. Fellow citizens more culturally similar than co-religionists in a different country, but foreigners who share a religion are more similar than those of a different religion. And that similarity is stronger if they’re highly religious.
And this broad generalization was true in our data even for places we didn’t expect. Like Muslims in India and Pakistan.
All of this holds true controlling for religious freedom, geographic, linguistic, & genetic distance.
We also looked at the interaction between national and religious culture, showing that non-American Christians are most similar to Americans. America is still a very Christian country and Christianity might therefore be considered the WEIRDest religion (using America as a proxy for WEIRD). That’s consistent with Joe Henrich’s hypothesis for the role of Christianity in creating WEIRD psychological and cultural traits: https://en.wikipedia.org/wiki/The_WEIRDest_People_in_the_World
And finally, non-Americans with no religious denomination are also similar to Americans without religious traits. That’s consistent with other work showing that the US looks like other secular, developed nations except when it comes to traditional religious values.