We see this divide not just in psychology labs and sports grounds but, of course, in politics too. Which brings us to the blow-up between Ukrainian President Zelenskyy and the Trump-Vance White House on Friday. Some saw an ungrateful foreign leader full of entitlement over American tax dollars who could have shown more gratitude and deference to the leaders of a country on which his country depends. Others saw understandable frustration of a fatigued wartime leader refusing to be strong-armed into a deal and tired of frivolous political games after 3 years of death and destruction from an unprovoked war.

If you’ve only seen the clips flying around social media, I encourage you to watch the full thing, maybe on 2x. There’s tensions here and there, but it’s largely positive (C-Span’s thumbnail has Zelenskyy and Trump smiling!). Things turn around 38:20.

A journalist asks:

Poland was under Russian control for decades after the second World War. When I was a kid I looked at the United States not only as a most powerful country, richest country in the world, the country that has great music, great movies, great muscle cars, but also as a force for good. Do you [sic] and now I’m talking with my friends in Poland and they they are worried that you align yourself too much with Putin. What’s your message for them?

Trump responds:

If I didn’t align myself with both of them, you’d never have a deal. You want me to say really terrible things about Putin, and then say, ‘Hi Vladimir, how are we doing on the deal?’ That doesn’t work that way. I’m not aligned with Putin, I’m not aligned with anybody, I’m aligned with the United States of America and for the good of the world. I’m aligned with the world and I wanna get this thing over with. You see the hatred he’s [Zelenskyy] got for Putin, it’s very tough for me to make a deal with that kind of hate. He’s got tremendous hatred, and I understand that, but I can tell you the other side isn’t exactly in love with him either. So, it’s not a question of alignment, I have—I’m aligned with the world. I want to get the thing set—I’m aligned with Europe, I want to see if we can get this thing done. You want me to be tough? I can be tougher than any human being you’ve ever seen, I’d be so tough, but you’re never going to get a deal that way, so that’s the way it goes. Alright, one more question…

And then Vance interjects, responding largely to the journalist:

Hey, I want to respond to this. So, look, for four years the United States of America, we had a president who stood up at press conferences and talked tough about Vladimir Putin, and then Putin invaded Ukraine and destroyed a significant chunk of the country. The path to peace and the path to prosperity is maybe engaging in diplomacy. We tried the pathway of Joe Biden, of thumping our chest and pretending that the President of the United States’ words mattered more than the President of the United States’ actions. What makes America a good country is America engaging in diplomacy. That’s what President Trump is doing.

To which Zelenskyy asks:

Can I ask you?

Vance:

Sure.

Zelenskyy:

Yeah?

Vance:

Yeah.

Zelenskyy:

Okay, he occupied our parts, big parts of Ukraine, part of East and Crimea, so he occupied it in 2014. So, during a lot of years, I’m not speaking about just Biden, but those time was … President Obama, then President Trump, then President Biden, now President Trump and, god bless, now President Trump will stop him. But during 2014, nobody stopped him. He just occupied and took. He killed people, you know?

And then it devolves into the spectacle spreading on social media and covered by every newspaper in the world.

There’s a lot to be concerned about. I want to share some thoughts.

Three years ago to the day (Feb 28, 2022), I wrote a Substack piece warning that Putin’s invasion of Ukraine risked puncturing the “Long Peace” that had defined the post-WWII world. I framed it as a realpolitik problem: from the perspective of cultural evolution, moralizing about “good guys” and “bad guys” is far less useful than analyzing power, resources, incentives, and who wins in a cultural-group selective competition. The past few days have reaffirmed an important shift in the rules of that competition. We are no longer living in a world of ideals—we are firmly back in an era of realpolitik, where pragmatism beats principle, the immediate trumps the ideal, and what can I get right now matters more than what is right.

That’s a terrifying world of tooth and claw, and as Trump put it yesterday, whether you have the cards or not. No one is playing nice. When war comes, will you and your country be ready?

Preview:

• The Budapest Memorandum and the End of Pax Americana

• Chinese Warships in Australia and ANZUS Treaty

• Border Wars Are Back

• The West’s Welfare Addiction—and the Lessons from Portugal

• Who Wins? The Playbook in Plain Sight

The Budapest Memorandum and the End of Pax Americana

Read more

The exchange between then-CEO Parag Agrawal and Musk epitomizes the clash between neurotypical and autistic cognitive styles—Agrawal emphasizing social cohesion and Musk favoring directness - “what did you get done this week?”.

The Rise of Autism Culture

Autism, once framed as a deficit in social cognition, is associated with enhanced rationality. In their 2021 review of the literature in Trends in Cognitive Science, Rosenkrantz conclude that the

“empirical evidence strongly suggests that individuals with Autism Spectrum Disorder (ASD) display enhanced rationality: judgments that are more objective and decision-making that is less biased than that of neurotypical individuals.”

Less emotional decision making, less reliance on intuition, more logic and consistency in decisions, less sunk-cost bias, less effect of how information is presented, less focus on desirable information over undesirable information, less blindly following norms, and less likely to fall for the Linda problem:

Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.

Which is more probable?

1. Linda is a bank teller.

2. Linda is a bank teller and is active in the feminist movement.

Read more

Every so often, I get emails asking how to learn the math behind cultural evolution. Here’s one I received this week, for example:

For anyone with a similar curiosity—or who knows someone interested—here’s the roadmap I recommend.

Cultural Evolution 101

Start with Culture and the Evolutionary Process by Boyd and Richerson. This classic is the foundation of cultural evolution, and its analytical models are an excellent entry point. If you’ve taken high school calculus, you’ve got the tools to begin. Math is best learned by doing: tackle a model, and when you get stuck (and don’t despair if you do), backtrack to figure out what step you’re missing. WolframAlpha, Mathematica, SymPy, and even AI assistants like ChatGPT and Claude are your friends!

For simulation-based approaches, Paul Smaldino’s new textbook, Modeling Social Behavior: Mathematical and Agent-Based Models of Social Dynamics and Cultural Evolution, is an accessible introduction.

Once you’ve gained some footing, find a published paper with a model that aligns with your interests, replicate its steps, and then tweak it—introduce a new variable, modify an assumption, or extend its logic. See how those changes alter the predictions. You might be able to publish what you find. You could also play with this tool I use in my class: https://pb101-models.herokuapp.com

Some other useful resources are Otto and Day’s A Biologist's Guide to Mathematical Modeling in Ecology and Evolution and Hanna Kokko’s Modelling for Field Biologists and Other Interesting People. Both will give you a sense for the kinds of models that are common in the evolutionary sciences and are useful for looking things up when you get stuck.

Why Build Models?

Personally, I think all psychologists, behavioral scientists, and cultural evolutionary researchers should at least learn to read the models so that they can inform your empirical work or you can modify similar enough models for your own purposes. Ideally, we move toward general models, by building on the foundational work rather than writing down things with no link or basis to past work.

If you can’t solve something analytically, try solving it numerically, or running a simulation. You may not even end up publishing or using the model, but I promise that it will sharpen your thinking by revealing the hidden assumptions and gaps in logic that our minds hide from us as we think through a problem. It’s hard to hold more than a few interacting variables in your head, so a computer or system of equations can help you overcome those limitations. A quote from a paper I wrote with my other advisor, Mark Schaller:

Remember that the choice isn’t between modeling vs not modeling—it’s between a formal mathematical model with explicit assumptions and logic vs a fuzzy mental model with hidden assumptions and unknown leaps of logic hard to fully specify in words. Or as the OG’s Rob Boyd and Pete Richerson put it:

If other scholars have recommendations I should pass onto students, please send them my way. And please share this with young scholars who might be interested.

Share

Best wishes,

Michael

As you know from my catch up post, I recently joined Electric Twin as a scientific advisor. Here’s an article I wrote on what simulation offers science in general and simulating humans could offer behavioral science. The original article is here.

Simulating Ourselves

The Promise of AI and Behavioral Science

Interventions are to the social sciences what inventions are to the physical sciences – an application of science as technology¹. But psychological and behavioural science has problems that prevent it from being sufficiently reliable, trustworthy, and immediately useful in the real world. More widespread preregistration with fewer undocumented deviations^2,3, better Bayesian statistics^4,5, or less overgeneralization from Western, educated, industrialized, rich, and democratic (WEIRD) societies^6–8 would go a long way to increasing the trustworthiness and reproducibility of psychological and behavioral research. But if I could wave a magic wand, these would not be the changes I would make. Because in the parlance of biology^9,10, these are proximate symptoms of ultimate problems—problems that lie in how we theorize, how we measure, and, ultimately, how we connect social science to the real world. 

‍

‍

1. A Problem in Theory

Many, if not most psychological and behavioural science papers, are motivated by minitheories and hypotheses based on past data or intuitions and observations from the particular, often WEIRD life experience of researchers; or verbal explanations labeled “theories” with no formal models or falsifiable predictions¹¹. Here’s an example. For the last twenty five years, behavioral scientists have agonised over whether people prefer fewer choices over many choices. A 2000 study suggested that people were more likely to purchase gourmet jams or chocolates when offered 6 choices over 24 or 30. It led to TED talks with millions of views and an industry of research around the so-called paradox of choice or choice overload^12,13. But as I explain in my paper, A problem in theory¹¹, the question of whether people prefer fewer or more choices

“is either nonsensical or underspecified… our species has had to make decisions with different numbers of choices to survive. Our decision-making strategy will be affected by the importance of the choice (for example, when faced with 30 mortgage options a consumer reaction of “I guess I don’t need to buy a house” is clearly suboptimal given the importance of the decision), by the information they have available (for example, the decisions of others), and indeed, by the number of choices.” 

Subsequent meta-analyses of all studies on the paradox of choice have failed to replicate the original jam study and suggested either no effect¹⁴ or a variety of factors that might moderate the effect¹⁵. To their credit, the key researchers wrote about these many problems in a 2022 Behavioral Scientist article, concluding with it’s complicated… 

I don’t mean to single out this particular line of research. It’s just one example of a common lack of attendance to theory in psychological and behavioural science. Another prominent example, if only because it remains popular in the business world, is the idea of growth mindset vs a fixed mindset. The difference between a growth and fixed mindset has repeatedly failed to replicate or at best shown weak effects well overblown compared to the original claims and alternative interventions. Training a growth mindset is intuitively appealing, but poorly theorised and doesn’t actually do much.‍

As Henri Poincare so eloquently put it: 

“science is built up of facts, as a house is built of stones; but an accumulation of facts is no more a science than a heap of stones is a house”¹⁶. ‍

Few papers attempt to accumulate scientific knowledge through general theories or overarching theoretical frameworks leading to a lot of stones of varying sturdiness but little attempt to build the house¹⁷. ‍

2. A Problem in Measurement

The rareness of theory built from first principles makes it challenging to “carve nature at its joints” when it comes to psychological constructs. Are self-esteem, well-being, or neuroticism truly universal constructs, or are they specific to WEIRD culture and recent generations? Take neuroticism as a personality trait, for example. Even widely studied frameworks like the Big Five personality traits—openness, conscientiousness, extraversion, agreeableness, and neuroticism—do not appear consistently across all societies. In some smaller, traditional societies, only two core traits—extraversion and conscientiousness—emerge. Moreover, the extent to which these traits split into five distinct dimensions often correlates with the complexity of a given society.^18,19.

Beyond the question of whether these psychological constructs truly exist in all cultures, reliably measuring them across cultural contexts is a challenge. This difficulty is compounded by questionable measurement practices²⁰. Self-report measures, which may be more reliable than implicit methods like response times (commonly used for implicit bias)²¹, can be poorly correlated with actual behavior. For instance, self-reported social media use correlates only modestly (r = .38) with actual logged usage²²—a serious limitation for research aiming to explore social media’s effects on mental health. Stated preferences, too, frequently diverge from the “revealed preferences” of actually observed behavior; what people say they will buy is often not what they actually buy.

Surveys and psychological studies often rely on self-reported data, yet these self-reports are typically a blend of perception and reality, wishful thinking, social desirability, unstable on-the-spot responses, or even random noise. People are often unreliable narrators, reflecting not only what they think they want but also what they believe they should want and how they wish to be seen by others.

3. A Problem in Generalizability and Application

The lack of robust theory and reliable measurement hinders the ability to generalize and apply findings in psychological and behavioral science. Without a strong theoretical foundation, it’s difficult to distinguish results that are unusual and interesting from results that are unusual and probably wrong. It’s difficult to know how to apply them to real-world situations¹—or to know which cultures, demographics, or time periods they might be relevant to^1,23,24. And without reliable measurement, mapping findings onto statistical models becomes problematic^25,26. Behavioral scientists often claim that “context matters” for the effectiveness of nudges and interventions, but without a sound theoretical framework and reliable measurement, we have no way of understanding how or why context matters. And if the science doesn’t work in the real world then in reality, it doesn’t work at all.‍

AI as a solution

These are all problems where breakthroughs in AI, and in particular, Large Language Models (LLMs), will be transformational. LLMs give us new ways to rethink the field—its theories, its methods, and its applicability to diverse populations—in ways that were unimaginable just 3 years ago.

Trained on vast, open-ended datasets—like online discourse, literature, historical records, and social media—LLMs capture an almost incomprehensibly broad range of human interactions and cultural norms across societies and over time. These are stored in a latent space that can be explored by researchers to refine, test, falsify and confirm pre-existing theories, or expose yet-to-be-considered emergent patterns, revealing associations that are invisible through traditional means to motivate new theories. Imagine trying to theorize about empathy without access to diverse contexts: an LLM trained on massive, multilingual data sets can reveal how empathy manifests in different social contexts, offering insights that no controlled experiment could easily capture.

The holy grail of psychological and behavioral science is building generalizable theories to explain and predict human behavior and cultural change tested with accurate measurement from a diversity of real world contexts that can then reliably inform policies and interventions. We’re not there yet. While we have developed useful insights that have guided a range of interventions^27–30 and now understand the rules underlying human behaviour and cultural change as a “theory of human behaviour” or “theory of everyone” as I refer to it in a recent book³¹, we have not yet created universally predictive models. Human behavior is  immensely complicated, complex, and highly variable. But human behaviour is not random. There are patterns and predictors of those behaviors. Each of us is a product of millions of years of genetic evolution, thousands of years of cultural evolution, and a short lifetime of personal experiences. But those experiences come from a society shaped by evolutionary constraints. Thus, just as we can’t predict the path of a single gas particle we may struggle to predict the behaviour of an individual human. But just as we have no problem describing gas laws of many particles, we may be able to predict the behavior of cultural clusters and specific societies and subpopulations^7,32. Key to this is effective measurement.

But psychological and behavioral measurement typically requires costly, time-consuming methods that frequently fall short, especially when data collection relies on self-reports from narrow, usually WEIRD samples. Real world interventions are even more expensive and time consuming. LLMs allow us to confront these issues with a scale, precision, and context that behavioral science has been missing. ‍

Imagine a tool capable of predicting how diverse groups of people might respond to a policy, product, intervention or message–free from the biases and overgeneralization from small samples typical of most surveys. Such a tool wouldn’t just simulate typical responses; it would allow researchers to investigate the behaviors of specific subpopulations that were previously unreachable or difficult to study—from diehard QAnon followers talking to each other on dark corners of the Internet to Gen Alpha who were born into a fully digital world. 

A report from McCrindle—who coined the term “GenAlpha” —suggest that this generation spends more time on screens than any generation before. But other reports suggest, the majority of GenAlpha go outside or reduce technology use to manage their mental health. Perhaps both trends are true, as Gen Alpha adapts to an increasingly digital world. At the moment, we don’t know, but their digital footprints reveal where they’re going.

Large Language Models (LLMs) trained on vast online interactions can now be used to create digital twins and synthetic populations, making it possible to quickly “poll” specific groups or simulate complex interactions that would otherwise be time-consuming, costly, and challenging to study. These models allow researchers to conduct high-throughput behavioral and cultural experiments, varying parameters and exploring outcomes with synthetic populations. Insights gained from these simulated experiments can then be tested in the more costly and intensive real world, when it truly matters. This approach holds the potential to revolutionize behavioral science.

Here are some promising paths forward.

Theory: Expanding Theory-Building Through Emergent Data Patterns

Traditional theory-building in psychological and behavioral science often begins with a hypothesis drawn from a combination of existing literature, expert opinion, and intuition. Researchers then test this hypothesis in controlled lab settings, hoping that their findings will generalize beyond the sample. This approach, however, has its limitations—especially when models grounded in first principles and set within a cohesive theoretical framework are possible³³. Even when theories are backed by replicated experiments, they often struggle to predict complex behaviors outside of narrow lab settings, constrained by the cultural and demographic specifics of the sample and the inherent limits of verbal theorizing.

LLMs offer a fundamentally different approach. They allow us to start with synthetic data or to rapidly iterate through the scientific cycle of theorizing, hypothesizing, testing, and refining the theory or measurement. There are challenges, of course: off-the-shelf LLMs primarily reflect behaviors of individuals from WEIRD samples^34,35. Yet in theory, these models also capture a diversity of behaviors from various subpopulations within WEIRD societies and other global contexts. For communities that spend little time online or languages that are underrepresented on the web, accurate simulation may be a bigger challenge, but current LLMs hold within their latent space the 60-70% of the world who are now online ready for researchers to explore.

At Electric Twin and in my lab, we’ve made progress in simulating subpopulations and getting LLMs to behave like specific cultures and as we continue to make progress, the scientific cycle of theory-hypothesis-testing-refinement becomes fluid, faster, and more dynamic. Eventually researchers will be able to test and refine concepts in real-time with synthetic populations that reflect real-world diversity.

Measurement, Generalizability, and Intervention: Precision and Scale Through Synthetic Populations

Social scientists often rely on methods like surveys or focus groups, which are labor-intensive and prone to biases from the interviewer, dominant individuals, group composition, or even anchoring on what was said first. Moreover, people might not answer honestly on sensitive topics and they may be influenced by the mere presence of an interviewer. 

The synthetic populations that Electric Twin’s technologies make possible address these limitations by creating virtual respondents who mirror the diversity within WEIRD populations (hopefully beyond soon!). These are commercial tools, but the science behind them will allow researchers to quickly gauge responses to questions, framing, or messaging across a wide range of synthetic yet lifelike participants. I’m particularly excited by some new experiments in interactions between different synthetic participants. Thanks to falling costs in LLMs, we can now simulate focus groups and larger, more diverse populations.

There has been an explosion of papers replicating psychological and behavioral experiments with LLMs^36–42 and exciting perspectives and reviews mapping out the possibilities of human-machine behavior, culture, and cultural evolution^43,44. Building on these and overcoming the caveats expressed by the authors, LLMs become predictive tools, simulating how various demographic groups might react under specific circumstances. 

Imagine a public health campaign testing its messaging on key virtual subpopulations before rolling it out to the public. Each subpopulation, modeled as a synthetic population, could reveal which framing is most effective and which issues resonate most with different communities. There are also commercial applications for businesses understanding customers, employees, shareholders, or new markets. For example, being able to test ahead of time could have helped Walmart avoid its multibillion failure in Germany, widely attributed to a cultural mismatch. Or Australian DIY giant Bunnings failure in the UK:

“If there are two golden rules for internationalising retailers, they are: 1. Develop as comprehensive an understanding of the new market as possible, by whatever means. 2. Be prepared to modify your domestic business model to meet the dictates of the new market, however drastically. In acquiring Homebase, Wesfarmers has spectacularly failed to heed either of these two golden rules… Bunnings is a highly successful business in its native Australia. The UK is not Australia.”

Beyond the obvious commercial applications, behavioral scientists can deploy these simulated populations to test behavioural interventions or public policies, using the model's feedback to refine approaches before investing in real-world trials to confirm those predictions.

But the implications go deeper. The latent space modeled by LLMs is a frontier for behavioral scientists to explore and discover patterns too complex for traditional analysis. We can validate current psychological constructs to see if they make sense beyond what a handful of human raters might think and even discover new constructs. We can also begin to develop theories that account for the diversity of humanity across the globe, of different age groups, and within specific subpopulations, overcoming what we might call the “average person problem”.

I’m a big fan of data driven decision, but it suffers from the “average person problem”: findings are typically about the median or mean of population and therefore represent a generalized average individual. In the real world, the “average person” is ironically rare or perhaps doesn’t exist; each of us falls somewhere along a broad spectrum of behaviors and preferences. We are continually moving points on a copula—the joint probabilities of a multivariate distribution. Or to put it another way, the average human being would be a Christian Indian or Chinese man named Muhammad with an annual salary of $15,000. You get the point – the median and mean person isn’t that meaningful. 

AI allows for insights tailored to specific segments, not just a non-existent average person. This personalized, segmented approach could mean a healthcare system where advice isn’t generic but informed by what works for people with specific profiles and histories. It could mean better cross-national teams in the military. It could help finance firms predict different sectors and markets.

Learning Through Simulation: Lessons from Other Sciences

Simulations have long been the key to scientific and technological advancement. Scientists and engineers studying the steam engine eventually led to thermodynamics, not the other way round. Even after the invention of the airplane, safely and efficiently training pilots required Ed Link to invent the flight simulator⁴⁵. Training in the air was costly and time consuming, not to mention dangerous. Ed Link’s “Link Trainer” mechanical flight simulator brought the cost down by almost 2 orders of magnitude. Flight training became faster, safer, and cheaper thanks to the ability to work out best practices and experiment with new practices without risk and at low cost. 

Until now, behavioral scientists have lacked comparable tools for simulating human thought and social processes. But now that we can simulate human behavior, we should expect similar breakthroughs that simulation offered other sciences and technology—in our case for the growing theory of everyone, and for policy, and interventions. 

Imagine being able to simulate the evolution of social norms in different cultural groups or predict how cultural values might shift in response to global events. As prices fall, these kinds of simulations become feasible, turning cultural evolution into an applied science with predictive power that was previously out of reach. ‍

For all of these reasons, I’m incredibly excited to join Electric Twin as a scientific advisor. It’s an exciting time to be able to bring my two careers in software engineering and natural language processing, and psychological and behavioral science to shape this new era at their intersection. And of course, all that we’ve learned about ethical behavioral science interventions also apply to this new frontier for behavioural science^46–49. There are new possibilities, new challenges, and huge potential.

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46. Sunstein, C. R. The Ethics of Influence: Government in the Age of Behavioral Science. (Cambridge University Press, New York, 2016).

47. Jachimowicz, J., Matz, S. & Polonski, V. The Behavioral Scientist’s Ethics Checklist. (2014).

48. Michie, S., van Stralen, M. M. & West, R. The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science 6, 42 (2011).

49. Lades, L. K. & Delaney, L. Nudge FORGOOD. Behavioural Public Policy 6, 75–94 (2020).

The first book cover from a foreign translation has been released and it’s very Korean! I love it.

Here’s the full jacket. If any one can read Korean and spots any mistakes, please let me know!

Best wishes,

Michael

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And here are my new papers and new book plans:

New Papers

Schnell, E., & Muthukrishna, M. (2024). Indirect Reciprocity Undermines Indirect Reciprocity Destabilizing Large-Scale Cooperation. Proceedings of the National Academy of Sciences.

Schnell, E., & Muthukrishna, M. (2024). Reply to Gross et al.: Indirect reciprocity undermines large-scale cooperation under realistic conditions. Proceedings of the National Academy of Sciences.

Schimmelpfennig, R., Spicer, R., White, C., Gervais, W. M., Norenzayan, A., Heine, S., Henrich J., & Muthukrishna, M. (2024). The Moderating Role of Culture in the Generalizability of Psychological Phenomena. Advances in Methods and Practices in Psychological Science.

Schimmelpfennig, R., Spicer, R., White, C., Gervais, W. M., Norenzayan, A., Heine, S., Henrich, J., & Muthukrishna, M. (2024). Methodological Concerns Underlying a Lack of Evidence for Cultural Heterogeneity in the Replication of Psychological Effects. Communications Psychology

Basava, K., Bendixen, T., Leonhard, A., George, N. L., Vanhersecke, Z., Omotosho, J., Mather, J. & Muthukrishna, M. (2024). Coleoid Cephalopods Demonstrate Asocial Path to the Evolution of Big Brains. bioRxiv

Basava, K., Bendixen, T., Birk Sorensen, A. L., George, N. L., Vanhersecke, Z., Omotosho, J., Mather, J. & Muthukrishna, M. (2024). A phylogeny of extant coleoid cephalopods with brain data. bioRxiv

Kroupin, I., Davis, H. E., Lopes, A. J. P., Konkle, T., & Muthukrishna, M. Major urban-rural differences in perception of a visual illusion. Psycharxiv

Long story short, it’s been a productive, but busy year, and I hope you’ll forgive me for the long silence. More soon, I promise!

Best wishes,

Michael

My new book

The working title of my new book is:

Read more

I owe you an apology for the long silence. This past year has been a whirlwind for me—though a good one. My wife, Stephanie (who’s now sharing her insights on cultural evolution and family life over at Evolving Together), has a reasonable rule given that we have three young children: I shouldn’t travel more than a few days for one trip per month. But there’s been so much interest in applications of A Theory of Everyone that I’ve repeatedly been breaking that rule. Thanks to her support, I’ve given talks from Argentina to Zanzibar. In the last 4 weeks alone, I flew back and forth between Malaysia, Sydney, and South Africa.

I’ll post the transcripts and summaries from each of these talks soon. But in this post, I want to catch you up on what’s been happening. Let me start by previewing what I’ll be writing about on Substack in my next few posts, followed by a rundown of my 5 favorite talks from this year. I’ll also mention some new ventures—exciting companies, new papers, and a startup city in Zanzibar. Finally, for paid readers, I’ll reveal the tentative title and description for my next book.

Evolving Together

Parenting experiences in different countries, science that guides us, and the adventures we've had through our travels. Join us as we explore the world, blending research, culture, and life lessons to raise resilient, curious kids in a changing world.

By Salgado Muthukrishna Family

Upcoming posts

1. The Clash of Cognitive Cultures: The relationship between autism, geek culture, and tech startups. Or how Elon Musk seems to avoid a cognitive cultural shift that usually happens when companies expand.

2. How Fish Discos and Other Overburdening Regulation Have Held Back British Growth: Some wild stories on why building in the UK is such a hassle—featuring some of the reasons behind the long delayed Hinkley Point C nuclear reactor. By the way, I'm thrilled with the nuclear renaissance we seem to be going through. I also highly recommend this report on why Britain has stagnated from the Works in Progress guys.

3. The Path to Land Value Taxes: This one is taking me longer to flesh out.

4. What AI Needs to Learn about Human Intelligence: Including why the ARC (Abstraction and Reasoning Corpus) is a WEIRD benchmark.

5. A bunch of articles from the cutting room floor that expand on parts of my last book—stay tuned.

5 of my favorite talks this year

1. First up, Malaysia’s Khazanah Megatrends Forum (KMF). Khazanah, the sovereign wealth fund, steers Malaysia’s strategic future, and KMF is where they present that vision and invite policitians, policy makers, CEOs, and in Malaysia, Asia Pacific, and around the world to discuss elements of that vision. This year’s vision was informed by “A Theory of Everyone”, so I was the keynote speaker. It was probably my favorite talk of the year, just because it was gratifying to see my research directly put into practice. I met the Prime Minister, Finance Minister, and head of the sovereign fund.
   
   The theme this year was “Pursuit of Potatoes”, inspired by how the introduction of sweet potatoes cut China’s peasant revolts by 2/3. Malaysia’s goal is to find similar adjacent possibilities today to satisfy modern people’s hunger, which is no longer just about food. As I said, I’ll write more about this soon, but in the meantime, you can read their concept paper here and watch the talk here.

   

2. My second favorite event was again policy focused at the Global Solutions Summit (GSS), where I spoke on “Paradigm Shift: Reorienting Economics and Economic Policy”. The GSS is the Global Solutions Initiative’s (GSI) annual meeting. The GSI is a Berlin-based think tank network established in 2017 under Germany’s G20 presidency. It pulls in academics, think talks, and policy makers to develop policy responses to major global problems addressed by the G20, the G7 and other global governance fora. Bill Gates keynoted this year—you can watch his talk here.

   GSI’s founding and current president is Dennis Snower. One of the things I’m most excited about for 2025, is that Dennis and I are planning on establishing a “Human Flourishing Institute” at the LSE. Again, I’ll write more on this later, but it’s basically applying cultural evolution as a framework to understand human progress and propose policies and interventions to ensure progress continues. Here’s a sneak peak from the current version of our proposal:

   

   We’re looking at funding for various activies, so if you’re into progress studies or know people who are, please reach out!

3. My third favorite event was at the Vatican’s Pontifical Academy of Sciences where I was on a panel and part of various discussions on AI, religion, and the future of humanity as part of Humanity 2.0. The Pontifical Academy of Sciences is the pope’s advisory committee on science whose members have included Werner Heisenberg and Stephen Hawking (you don’t need to be Catholic). It was honestly a bit surreal to present cultural evolution to the cardinals! Here’s a short clip of what I had to say (while maintaining my tie-less commitment):

4. My fourth favorite event was in Zanzibar, where I spoke at Zanzalu, hosted in the new city of Fumba. As you may know from reading my book or following my work, I’ve long been an advocate of startup cities as a path to economic growth. Most new cities fail or end up as tax havens. I believe Fumba has a real shot at being the Hong Kong of East Africa - an engine of development for the whole region and an example of how to successfully build a startup city. Why do I have faith in Fumba? There are several reasons:
   (a) the involvement of the Charter Cities Insitute (CCI) (where I’m a Fellow)—home of the New Cities Map (and dataset on what works and doesn’t) and authors of the Governance Handbook. Kurtis Lockhart, Director of CCI, has stepped down to get more involved in building Fumba.
   (b) educational investment to create a Harvard-MIT combo. IIT Madras have set up the first IIT international campus there—IIT is the MIT / Stanford of India, supplying much of the US tech sector with high quality engineers. The African School of Economics (ASE) has also set up a campus. ASE was founded by Princeton economist, Leonard Wantchekon, who himself has an utterly fascinating life history. And the Africa Urban Lab (AUL), which Kurtis will direct. That ticks the education box, attracting foreign students and ensuring sufficiently high skills for the new city. But as I know from my work on education in Namibia, skills without a market aren’t all that useful, so I’m glad about:
   (c) presence of large tech businesses like Wasoko (who I consider the real Amazon of Africa), which alongside historic links to Oman, ticks the ability to attract investment and high skilled industries; and
   (d) several other reasons that deserve a more full throated follow up post. But I’ll summarize it the way I summarized it to Hon. Mudrik Soraga, Zanzibar’s minister in charge of investment:
   (1) create the conditions for people to make money,
   (2) make sure that the money splashes around and helps local people and the surrounding region and we don’t just have a gated community that locals resent and have no buy-in, and then
   (3) get out of the way and let the magic happen.
   
   You can watch one of the three talks I gave while I was there:

   As a costly signal of my belief in the future of Fumba and Zanzibar, I’ll be AUL’s Research Lead for Cities, Culture, and Technology.

   

5. Number 5 was much harder to pick as there were several candidates. I hope it wasn’t a recency bias or how nice it was to return to Australia after so long, but it was probably my Wallace Wurth lecture at UNSW to help the university celebrate it’s 75th anniversary. Rather than a traditional lecture, it was a free-ranging, fireside chat that courted controversy, hosted by evolutionary biologist Rob Brooks. The chat and transcript are available here or on Spotify. I’ll post a cleaned up transcript soon - we tackled diversity, immigration, degrowth, and more.

   

New Projects

Besides the groundwork for the Human Flourishing Institute and Africa Urban Lab, I’ve been busy with a few other projects:

1. I founded the London School of Artificial Intelligence (LSAI). AI is reshaping education and the workplace, and while companies, governments, and universities are adapting, kids are being left behind. At the LSE, I’ve helped set the AI in education policy (devolved to departments - in case you’re curious, it’s highly permissive - we’re training people for the world as it is, not as it was), have been involved in discussions with large tech companies on behalf of LSE, and talk to companies about AI, behavioral science, and culture through my consultancy work. All of these sectors have solid plans for exploring and integrating AI. The missing piece is children’s education. There’s a lot of work in this space from AI tutors like Khan Academy’s Khanmigo to an explosion of GPT wrappers for education (TeachmateAI is a popular one in the UK) to thoughtful frameworks for integrating LLMs in education (for example, this thoughtful paper from Google “Towards Responsible Development of Generative AI for Education”). LSAI’s approach is different.
   
   We put the power of AI directly in children’s hands (or via their teachers), teaching them to safely and effectively use AI with our child-safe guardrailed models and then showing them how to use these skills to prompt their own personalized tutor that behaves exactly how they prefer to learn - faster, slower, more detailed, funnier, more emojis, or with examples from cars, kittens, football, or whatever they’re interested in. Our pilot data has been astonishing in terms of the speed children learn once they use their personalized AI tutors. We’ve signed a contract to get it into schools with the goal of running an RCT on curriculum and non-curriculum (e.g. critical thinking, epistemic vigilance) outcomes and are looking for an African site to run the same RCT in a developing context. We have some candidates for Africa, but feel free to reach out if you’re interested in collaborating.
   
   From my book, you know my concerns about the ability of education systems to keep pace with social and technological change or even help studens achieve their full potential. A teacher teaching a class of 25-30 students or more can’t give sufficient attention to the top to let them go as fast and far as they can or the bottom who need a lot more support. Instead, either the middle, bottom or top are prioritized depending on the incentives of the country, constraints of the curriculum, amount of funding, and proclivities of the teacher. Imagine if all kids had a personal AI tutor who got to know them and could support them at their level? I’m excited about the possibilities.

2. Also in the AI sphere, I joined Electric Twin as scientific advisor. Electric Twin is founded by Ben Warner and Alex Cooper. Ben was the Chief Advisor to the British Prime Minister on Digital and Data and founded the Prime Minister’s office data science team. He’s a bit like the Nate Silver you’ve never heard of with a better track record, most recently predicting in October last year that if Trump ran against Harris, the most likely outcome was a 311-227. He was off by 1. Alex was a commanding officer of the SAS (UK special forces) and government advisor with a background in simulation for conflict and crisis. We’re combining their approaches with cultural evolution and behavioral science, powered by AI, with the goal of creating a more predictive model of human behavior.
   
   Some of the most exciting research is now outside of academia and this particular collaboration means doing science with applications always in mind. I wrote more about what AI offers cultural evolution and behavioural science here (I’ll repost on substack soon).

3. I joined Besample as an advisor and investor. Besample is an online platform that lets you easily sample beyond Western, educated, industrialized, rich, and democratic (WEIRD) countries. My role is more mentorship than massive involvement - the field needs a platform that makes cross-cultural research easier and I want to help them succeed.

4. At the LSE, from 2017-2022, I was part of an initially 3 person team that launched the BSc undergraduate program (read more here). It’s one of the things I’m most proud of. The degree is now #1 in the UK. I still teach on the program, but have now turned my eye to the PhD program as the new PhD Director.

5. I’m working with the UNDP on a couple of different projects, including helping establish a new human development index and researching how AI is changing education.

6. Finally, BBC Radio 4 greenlit a new series that I’ll be hosting on the rise of “big theory, big idea books” and what they can really tell us, bringing them into discussion with one another. A bit like this article on TOTTEE books. Some of you may recall an episode of analysis that I hosted on BBC Radio 4 in 2021.

7. Finally, here are my papers this year, which for all the above reasons I’ve been kept busy, I also haven’t publicized on social media yet!

As usual, I’ve run out of space… follow up email incoming.

Best wishes,

Michael

Elon Musk’s tweet about money inspired me to dust off some writing I had previously cut from my book, A Theory of Everyone. In an early draft, I had gone into extensive detail about what money fundamentally is and how it functions in society. However, early readers felt this section distracted from the core thesis of the book.

I thought Musk’s provocative tweet provided a great excuse to resurrect and share some writing on:

• What exactly is the money supply?

• What does it mean to print money?

• How is new money created?

• And what does it mean to have money that isn’t spent such as through wealth passed beyond a lifetime, a Swiss bank account, or a 401k pension?

• What is GDP and what is inflation?

Readers of my book will recognize how this fits into the discussions on economic growth and taxes, in particular land value taxes. Let me know what you think in the comments below or on social media.

What is money?

Seas shells, beads, gold, coins, paper notes, and electronic balance sheets have at various points been used as money. But none of these are money in themselves. People are rarely interested in pieces of paper or numbers on a bank website for their own sake. What matters is what money can buy you. Zimbabwe’s one hundred trillion dollar bill is a nice collectors item, but not valuable if all you can buy is a loaf of bread.

What people care about is what they can do with money—buy food, houses, heating, healthcare, clothes, and entertainment. In other words—how much they can consume the goods and services that others produce. This is the difference between the nominal value of money and its real value.

In a world before money, we might keep a stock of the carrots we grew or swords we smithed to swap for bread from the baker or beer from the brewer. But because it’s difficult to store carrots for too long or store the services that a physician provides for later bartering, we instead store a claim on future goods and services. We keep track of these claims by swapping IOU notes. That claim is money. Having more money means you have access to a larger share of available goods and services. The relative supply of carrots and market demand for those carrots determines its value and what it’s worth in IOU notes.

We collectively agree that paper with a head of state or historical figure, beads, gold, or Bitcoin have some value, typically because they’re fixed in amount or we can control how much there is. Ultimately, the total supply of these proxies for the space of the possible (read my book or perhaps listen to this podcast around 36:43) represent a share of the currently available goods and services—what we can make and what we can do.

Much like shares in a business, the amount of money you have is your share in a country’s productive capacities. This is most apparent when you retire.

What is a pension, 401k, superannuation?

While you are working, you save up money for your retirement. Retirement is the time when you stop producing contributions to our collective goods and services and become exclusively a consumer of food, healthcare, housing, and gifts for your grandkids. We keep track of your post-production consumption ability using a pension, 401k, superannuation, or whatever other financial vehicles your society uses to accumulate retirement “savings”.

These savings are a promised stake of future production, but this promise, regardless of the size of your pension, is contingent on there actually being sufficient production; sufficient goods and services for you to consume. This in turn is a function of energy, technology, number of people, and their skills and education.

This is the problem that many countries face with falling birth rates and the relatively large population of Baby Boomers who are about to retire. Japan is a portend of things to come in many developed countries. Around 2011, Japanese newspapers reported that adult diapers had for the first time begun outselling baby diapers. There isn’t enough production to meet their consumption without undermining the ability of the working generation to consume for themselves or significantly decreasing their quality of life.

Without enough of the share of money for young people to buy a house and feel secure, the next generation struggle to settle down and start a family, reducing the birth rates and further reducing future production and future innovation. This transition to lower birth rates is sometimes called the demographic transition. Almost all countries go through it as they get wealthier.

So what do you do? Japan tried robots as a solution, but the technology didn't advance fast enough for this to be viable. Japan had no choice but to use a solution they had long avoided - immigration.

Japan is a highly homogeneous country with a general sentiment to keep it that way. But by 2019, the writing was on the wall. In April 2019, the new Immigration Control and Refugee Recognition Act came into effect. The conservative government led by then Prime Minister Shinzo Abe sold it as a necessary step for meeting the demands of business owners who needed selective, skilled migration. Immigrant numbers quickly hit record highs. Immigrants still represent just 2.5% of the population, but that percentage will continue to rise and new laws are in the works to continue the expansion, for example, streamlining the path for high skilled immigrants. Japan hasn’t figured out what model of multiculturalism it wants to use, a decision that will affect the future of Japanese society.

This is the solution most countries use - bring in new people. To solve this challenge of insufficient production, most countries expand their working population through immigration, but of course this requires other countries to have above replacement birth rates and has all of the challenges we discussed in Chapter 7. In an economically effective immigration system, countries compete for the most productive immigrants, who create more wealth than they consume through the social welfare state.

Without such a replacement in production, the price of everything goes up as each dollar represent less goods and services. What matters is how many loaves of bread or other essentials your $100,000, one million, or one trillion dollars buys you. What matters is that there’s enough to go around for both the working and retired to have a reasonable division of consumption. If there isn’t enough, either pensioners take a hit to their quality of life or young people can’t secure jobs that meet their needs, buy houses and start families. This leads to inequality between the young and old and discourages the desire to have children.

It’s a vicious feedback loop.

How do we measure how much there is to go around? Usually, through GDP.

What is GDP?

Gross domestic product or GDP is a measure of the production of goods and services in a country over some time period. It’s typically measured in money for want of a better metric, but it is ultimately an attempt to measure the space of the possible that our excess energy creates.

The United States has the highest GDP in the world, followed by China, Japan, Germany, India, United Kingdom, France, and Canada. But this is GDP overall. You can increase GDP by simply having more people. Each person adds more goods and services and so grows overall GDP. But often what we really care about is GDP per person.

GDP per capita is how much GDP there is per person (though of course this is unevenly distributed). This is sometimes adjusted for purchasing power—how much the same goods and services cost in each country—the difference between the nominal value and real value. The highest GDP per capita countries are typically those with highly productive, educated populations leading to innovation (such as the United States), or where there is access to large energy reserves (such as Saudi Arabia). Of the major high GDP world economies, the United States is still in the top 20 for GDP / person at $60k / person, but all other countries on the list above fall much lower. Small countries who are innovative, energy-rich, or both can have higher production per person than larger economies.

Some countries are wealthier than others. Some people, contribute more than the average. This is why high skilled immigration that meets missing needs in a country is so valuable.

So “money” isn’t real. It’s a claim on a share of current production. Like shares in a company, money is shares in a country. How money is invested is an allocation over our current supply of energy, people, and resources. That allocation can be productive—a brilliant new start up—or unproductive—speculatively holding onto a property with no development for hope of increased value.

So how is money created?

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How is money created?

Printing money rarely refers to the literal printing of bills. Instead, money is created through debt. When a bank makes a loan, it is creating money out of thin air.

The bank is often loaning money it doesn’t actually have the cash reserves to make. It’s all fine as long as not everyone tries to get their money out (this is referred to as a bank run) and enough loans are being paid back with interest.

That interest represents new money. When a business takes a loan, it uses that money to do something useful and create value, such as through goods and services. It then uses the profits to pay the bank back. When the money is used for wealth creation—the expansion of the space of the possible—rather than wealth appropriation—rent-seeking, then printing money has no effect on inflation. The interest rate where the creation of new money does not affect inflation is the neutral rate of interest. Money is created because the space of the possible expanded. Here, not creating new money would be deflationary - the value of the bills, notes, and coins would go up, incentivizing people to hold onto the money itself, which in turn would slow the economy. By corrolary, inflation encourages efficient allocation (investment) of money as we shall discuss in a moment.

Once upon a time, money was backed by reserves of gold. In theory, you could take your paper and coins and exchange it for some amount of gold. But even then, there was typically more money than gold reserves. Nonetheless the supply of gold restricted the supply of money, leading to booms and busts. The end of the Bretton Woods agreement in the early 1970s ended the need to keep gold reserves, avoiding booms and busts and allowing a central bank to control the money supply.

Before we continue, there are at least three important points worth emphasizing here. The first is that how you make money is essential—unproductive uses are simply claims with no growth in the space of the possible. Second, as discussed earlier, pensioners aren’t the only ones making an unproductive claim on our goods and services. So too are those who are using money accumulated over generations (this is a contentious point that I’ve removed from this post to not distract you, but you can read more about it in my book or maybe I’ll write a future post on it). And finally, we must be able to trust those who control the money supply both in how much money is created and in terms of how money enters the economy based on loans to ensure that inflation is kept under control. Those who can spend money before inflation effectively have more real money—the Cantillon effect.

Inflation is a powerful force that means that the current supply buys less and less goods and services over time. This is often a good thing.

What is inflation and what does it mean to invest?

The money supply must expand with the space of the possible. Because money is created through loans, interest rates are set to match the rate of expansion. If expansion is slow, rates should be low. If expansion is fast, the rate should be high. This neutral interest rate is difficult to figure out for multiple reasons, including that the rate that is set by a central bank has a delay in the effect on the economy. So central banks are constantly making their best guess and turning the knob higher and lower to try to get it right.

When done right, inflation is low enough and loans are made judiciously enough that people are incentivized to invest their money in ways that grow the space of the possible rather than simply driving up house prices or other forms of wealth appropriation.

In contrast, in a world of deflation, money itself becomes worth storing and so people are incentivized to not spend but hold onto their cash. This grinds the economy to a halt. Even money kept under a mattress will be worth more in the future. This in turn discourages investment and economic activity in favor of mattress hoarding. Much like what happens with gold.

In contrast, a little bit of inflation means that money stored under a mattress gradually becomes worthless, approaching the value of the paper it’s printed on. This means that people are incentivized to keep their money in circulation. In fact, they are incentivized to allocate their money in ways that grow faster than inflation. Some people do this directly by starting businesses or investing in the businesses of others such as through the stock market. Others hand it off to someone else to do that investment for them such as through mutual funds.

Putting your money into an interest-bearing bank account shifts the allocation decision to the bank. The bank then pays you an agreed return and takes the difference in how much money they actually made in loans (or their choice of investments). This incentivizes banks to lend money to people who are more likely to pay it back. Similarly, a bond hands your money over to those issuing the bond, allowing them to invest on your behalf and returning a share to you. Putting your money into a fund leaves the job to a fund manager. Putting it into a passive ETF like Vanguard allocates it into a large basket of companies, effectively putting it into the growth of the economy as a whole. Holding particular currencies is a bet on the particular country’s ability to produce future goods and services at a rate higher than others. Investment in specific companies is a bet that they will make more money than others. And so on. The goal is always to pick investments that are equal to or greater than inflation to maintain current wealth.

The trouble begins when true economic growth begins to slow—when the space of possible slows its expansion or even begins to shrink.

One of the most important, but least discussed ratios is government debt to GDP ratio—where the government is spending more than the country is producing. That is the government expenditure represents a large percentage of the country’s production. When this expenditure is not expanding the space of the possible—investment in research and development or means of acquiring more oil—but instead in costs that don’t grow the space of the possible, the country increases its risk of default.

When the economy slows, the interest rates is lowered, but at some point, interest rates are so low, but production is still low and then we are in trouble. The value of money erodes because less is being created. We can now see that ultimately, less is being created because energy return on investment (EROI) and cheap energy availability is falling, the energy ceiling is collapsing, and we’re running out of ways to innovate new efficiencies (again, you’ll have to read my book to understand this point).

There are a few important lessons here. Not all investments are equally good for growing the space of the possible. A bad use of money includes buying a property and sitting on it with no increase in value in the hope it will go up in the future. We’ll revisit this in a moment – it’s part of why land taxes and other taxes on unproductive money are critical. A bad system is one that incentivizes people to use money in unproductive ways.

I’ll stop here, but here’s some recommended reading:

1. My colleague Nick Barr, a world-leading expert on pensions has a fun and accessible paper on “Pension Design and the Failed Economics of Squirrels”

2. A rabbit hole of monetary and social change that happened after 1971:

   https://wtfhappenedin1971.com

3. The history of money is described well by Niall Ferguson in The Ascent of Money: A Financial History of the World, Penguin, 2008; and by Jeffrey E. Garten in Three Days at Camp David: How a Secret Meeting in 1971 Transformed the Global Economy, Amberley, 2021.

4. The history of key economic ideas see Lawrence H. White, The Clash of Economic Ideas: The Great Policy Debates and Experiments of the Last Hundred Years, Cambridge University Press, 2012; and for recent events, two books by Ben Bernanke, 21st Century Monetary Policy: The Federal Reserve from the Great Inflation to COVID-19 and The Courage to Act: A Memoir of a Crisis and Its Aftermath, both W. W. Norton, 2022 and 2015 respectively.

5. And of course, A Theory of Everyone puts the pieces together in a larger context of evolution, growth, and progress.

Best wishes,

Michael

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See the previous post on Unleashing Productivity in 12 Steps: A Practical Guide. Continued 10. Wellness Matters: Have A Reset Device

In addition to the irregular Reset, you also need to find the rhythm of your work-life balance. It can be at different scales for different people. For some, every day should the classic 8:8:8 balance - 8 hours of work, 8 hours of play, 8 hours of sleep. For others, grinding 10-12 hour days through the week and resting on the weekend is sufficient. When I was an engineer, we worked through the night if needed to meet a deadline and rested later. Others grind through their 20s and 30s, hoping to rest in their 40s and 50s, like anti-aging guru Bryan Johnson who after founding Braintree, acquiring Venmo, and selling Braintree-Venmo to Paypal for $800M, is now spending his money trying to reverse the clock caused by an unhealthy grind that got him there in the first place. Others enjoy the grind for it’s own sake for as long as they can. The grind itself is play or well aligned with their Ultimate Why.

Source: https://www.flickr.com/photos/193463161@N07/

Everyone is different, and you can have work-life balance at different scales and different times. But the ultimate message is to know yourself and look after yourself. Never underestimate the power of good sleep, exercise, and nutrition. A healthy body and mind are the foundations of sustained productivity. Overall output suffers if you burn out. For me, I have fixed commitments due to my children - I have the morning and evening shift and my wife has the day time. But being an academic means that I can work in bursts and rest as needed. My wife and I try not to compromise on 7-8 hours of sleep a night. Sleep consolidates learning, allows ideas to ruminate, and repairs our bodies.

11. Reflect and Adapt

During grad school, a survey went out to see how much people were working. Some grad students and most pre-tenure faculty were working the most - 60-80 hours a week. But how true were those numbers? A few of us agreed to install RescueTime to track how much we actually worked and what we actually did. The results were universally eye opening.

No one was working as much as they thought and most of us were wasting a lot of time on everything from email to social media. If you don’t mind the privacy concerns, RescueTime is an excellent way to check your assumptions. It also allowed us to improve our productivity by seeing how much time we actually worked and how much time we wasted. Once we were at the level of productivity we wanted to be and it was consistent, most of us uninstalled RescueTime. Reflection and adaptation had taken place.

Reinforcement learning is everywhere. All systems rely on feedback and change to learn, adapt, and improve. If you don’t have a system for tracking and learning what you’re doing, what worked, what didn’t, you cannot adapt your approach accordingly. You can’t learn and you can’t improve.

12. Celebrate Small Wins

The danger in not having irregular, but somewhat frequent rewards in the absence of sufficient intrinsic motivation is that you chase external rewards that are unconnected to your goals. The likes and retweets. Video games untied to reality, but that give you a sense of achievement. Video games and social media disconnected from your goals, are supernormal stimuli. They are what pure sugar candy is to nutritious fruits and vegetables. They are rewards without the actual achievement.

In academia and entrepreneurship, rewards are few and far between. The corporate world often has built in rewards to keep employees motivated but the more you work for yourself or have a system of uncertain rewards (like research), the more you need intrinsic motivation or to create your own reward system. I encourage the lab to celebrate all the small wins - submitting the paper or grant, the conference acceptance, the first draft done.

Writing this email took longer than I planned for, which I guess is a bonus lesson. Your schedule must have enough flexibility or it becomes brittle and breaks easily with unexpected shocks and the impossibility of perfect planning.

As promised, a little bonus for paid subscribers is below: the apps, platforms and technologies I use to manage my projects.

Wishing you all a productive 2024!

Best wishes,

Michael

What technologies and apps do I use?

Some of this is specific to my job as an academic, but here you go:

Writing

I write primarily in Word and LaTeX, with Google Docs if collaborators insist. All my citations are managed in Zotero.

It’s hard to spot errors in your writing, so I often use Text to Speech to proof read (Accessibility built into Mac, and TTS Reader when I was on Windows). It’s easier to hear the errors when spoken out loud.

I also wrote a lot of A Theory of Everyone with Speech to Text and continue to do so from time to time: Mac built in Dictation and Dragon Naturally Speaking (Windows). This made the prose more casual reflecting how I speak and was a good break from typing.

Communication

Superhuman (affiliate link) has been a game changer for processing my emails. There’s an academic discount. I use it for both Gmail and Outlook.

I communicate with my lab through Slack.

Project Prioritization

Read more

Happy New Year - I hope you all had a restful break! Classes begin next week and so I’ve been tying up loose ends on projects and deciding what I want to focus on this year. I thought I’d write my first substack of the year on my personal approach to productivity - apologies for the Buzzfeed headline!

Those who know my research and broader work, know that I do a lot of different things:

1. I research and write papers (and recently a book) on a wide array of topics that relate to genetic and cultural evolution, from corruption to whales and dolphins to religion to culture and behavioral science to genetics to historical psychology, cooperation, measuring culture, measuring parsimony in statistical models and the occasional collaboration on Frontal Traumatic Brain Injury in Rats or spontaneous dynamic hierarchical organization in a non-uniform life-like cellular automata.

2. I develop software, including the Database of Religious History and Culturalytik

3. I speak, consult, and run training for governments and companies

4. I teach undergrads, summer schools, Masters, and direct a PhD program

5. I do science communication from documentaries to radio shows and various interviews

6. I raise 3 children

7. I try to be a good husband, colleague, friend, and human being

It’s a lot, so I have to be careful in how I invest my time. Investment is exactly how I think about productivity - I think about the expected value return on how I spend each year, each month, each day, and each half hour.

In A Theory of Everyone, I wrote:

We all face a trade-off in how much time to allocate to work, to our families, to our friends, and to ourselves. In tackling this trade-off, I personally am obsessed with efficiency. I’ve spent years figuring out how to maximize my use of the twenty-four hours I have each day, the fifty-two weeks I have each year, and the eighty or so years the average Western male gets for a lifetime. This obsession includes how to efficiently distribute my cognition in a way that prevents my to-do lists and project prioritization tools from getting in the way of focused deep work; how to hack my psychological limitations by doing things like leaving work unfinished at the end of a day to make it easier to restart the next (an application of the Zeigarnik effect); accepting that while it is inevitable that I will procrastinate, it is not inevitable what I will procrastinate on – I can procrastinate by working on low priority things that do actually need to get done – productive procrastination; and even how much time to spend on optimization itself and how much free time I need to ensure there’s space for spontaneity.

My obsession even extends to how to efficiently be a better parent to my three children, efficiently be a better partner to my spouse, and how to efficiently relax.

To quickly relax my mind, I find sensory deprivation tanks a cheat code to meditation. I float with ear plugs in a pitch-black tank filled with body temperature Epsom-salted water, like a personal perfectly warm Dead Sea. After a few minutes, my mind wanders and then starts to self-organize. My anxiety and stress dissolve in the water.

To quickly relax my body, I stress it. Apart from lifting weights, one of my favorite ways to stress my body is by profusely sweating in a German Aufguss sauna ceremony. For ten to twelve long minutes, gloriously scented, steamy air heated to at least 85°C (185°F) is whirled and beaten around the room and at participants by a skilled Aufguss sauna master. Blood rushes to your brain and body. Stress-free participants stagger out of this communal ritual to a short warm shower followed by an icy cold 4 to 5°C (40°F) dip.

But here’s the rub. No matter what weird psychology or ceremonies I use, there is a limit to my efficiency. At the end of the day, I still have only twenty-four hours, of which continued efficiency requires eight dedicated to sleep – efficient sleep of course, optimized for letting ideas ruminate. Imagine how much more you or I could do if we had more than twenty-four hours.

There are ways to get more than twenty-four hours.

The book goes on to discuss the role of energy, technologies, and cooperating with others. Here I want to take a step back and show you how I think about productivity overall.

Back to the Buzzfeed headline.

Unleashing Productivity in 12 Steps: A Practical Guide

1. Understand Your Ultimate Why

Productivity isn’t just about doing more; it’s about doing what matters. So you need to start by asking yourself: “What am I trying to achieve?” This could be career-oriented, personal development, or even leisure goals. Knowing your ‘why’ helps prioritize tasks and keeps you motivated.

My dog, Suzie, died when I was 8. Since then, I’ve been captivated by the inevitability of death, or at least my own mortality. My grandfather died when I was 16 and one of my best friends, who I’d known since Grade 6 died when I was 17. We were in our last year of high school. These were stark reminders that our time is finite. We’re all going to face that final curtain one day, so I find motivation in asking: “What do I want to achieve before that moment?”

Steve Jobs was similarly motivated:

Source: https://www.goodreads.com/quotes/427317-remembering-that-i-ll-be-dead-soon-is-the-most-important

It’s about running the tape backwards from the end - envisioning what you’d like to have accomplished when you’re looking back on your life. This perspective isn't morbid; it’s a powerful motivator that’s always in the back of my mind. It should urge us to make meaningful choices and to spend our time on earth in ways that resonate with our deepest values and aspirations. In this light, productivity transcends efficiency; it’s a race against time to imprint our existence with purpose. For me that purpose is to try to leave the world a better place than I found it.

Biologists talk about ultimate vs proximate explanations:

• Ultimate explanations delve into behavior or traits in terms of evolutionary advantage. We like sweetness more than bitterness because fruits and vegetables at their peak of calories and vitamins have more fructose and animals that can detect and prefer this moment do better than those that don’t. Bitterness is associated with poisons.

• Proximate explanations, on the other hand, focus on the mechanisms and processes. When we eat sweet foods, the mesolimbic system - the brain’s reward system - is triggered. But that’s because evolution has made it sensitive to sugars - it could have been triggered by bitterness if that had a better evolutionary advantage. The ultimate and proximate go hand in hand.

My Ultimate Why is that I see problems in the world and on the horizon that I want to figure out how to fix.

I can’t address these problems alone and certainly not implement them, but I can spend my time focusing on the things I think others are missing and think about how they might be implemented. It’s the subject of Part 2 of A Theory of Everyone. In an introduction to my HBES plenary a couple of years ago, Joe Henrich described this approach as “mission driven”:

Your Proximate Why works best when connected to your Ultimate Why. You need to think about your reward systems and make sure they’re connected to your goals. One heuristic is to think about the moments when you were in a state of Flow. You were immersed in whatever you were doing to the exclusion of all else. You felt happy. You felt like you in the right place. Ask yourself, “Who was I in those moments?”.

There’s a meme about how inside you there are two wolves - one good and one bad. The wolf that wins is the one you feed.

Source: https://knowyourmeme.com/memes/inside-you-there-are-two-wolves

Maybe. But, there are also different people. There are different Yous that also need to be fed.

For me, there’s at least 5 Me’s:

1. The Parent who finds pleasure in teaching and learning from his kids and has joy and flow in many of those moments (but also frustration and boredom - the days are long but the years are short!).

2. The Public Speaker who is energized by spreading knowledge from a stage to as many people as possible in an engaging way as possible.

3. The Scientist who hits flow in careful analysis to uncover truths that others have yet to see.

4. The Mentor who finds satisfaction in helping other people achieve their potential and become their best selves.

5. The Engineer who takes pride in building things that last.

There are more Me’s - The Husband, The Friend, The Entrepreneur, The Volunteer, and so on. To be a happy and contented person, you must get to know and feed the different You’s and which ones generate the most Flow and satisfaction. An ideal life in one structured to maximize these moments.

2. Follow Your Biorhythms

My wife, Steph, is not a morning person. I’m not a morning person either, but she’s barely a person in the morning and becomes human around 12pm and a productive person around 5pm as I start to fade into uselessness. I’m alert by 7am and peak between 11am and 2pm. It works well for us that we’re different.

Each of us is different. You’ll see advice about getting up at 5am or how mornings set the tone for the day. I’m with those who think we give too much power to morning people with their 9am meetings. But there are more birds than just larks and night owls.

One important aspect of productivity is getting to know your own unique biorhythms - when you’re typically energized and you’re typically flagging. Adjust what you do during those times.

Schedule the focused, deep, important, difficult work when you’re at your best. Reply emails, deal with bureaucracy and administration, cook and clean, process your receipts and take your boring meetings when you’re not.

3. Prioritize Ruthlessly - Scientists vs Engineers

My advisor, Joe Henrich, and I share a background in engineering. It was only after I met Joe that I noticed a fundamental difference in how scientists and engineers are trained to think. Scientists often delve deeply into a specific segment of the world, aiming to understand it in its minutest details. This approach is essential for deep, nuanced understanding. Good engineers think differently; they can’t afford the luxury of such narrow focus. Instead, they must maintain an understanding of how their specific part integrates into a larger system. This trains an ability to zoom in and out of a problem, grasping both the minutiae and the overarching system.

Applying this principle to productivity, not all tasks are created equal. Some tasks are embedded in the details, while others are instrumental in setting the broader context. The better you get at zooming in and out to see the map and each specific turn, the more effective you’ll be in getting to where you want to go.

Tasks also vary in their importance, urgency, and amount of work required. To prioritize my tasks, I use a variation of the Eisenhower Matrix that also includes amount of work:

Source: https://asana.com/resources/eisenhower-matrix

Each project is assessed for its anticipated impact (high, medium, low) and required effort (high, medium, low). I then sort tasks by highest impact and least effort. In addition, I try to focus on projects and tasks that align most closely with my goals, and set aside or delegate less aligned or less critical tasks. My full set up and list of tools and apps is in a follow up email for paid subscribers.

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4. Become a List Processing Machine

David Allen, in his productivity philosophy, refers to the concept of having a ‘mind like water’, drawing inspiration from Bruce Lee’s famous metaphor. David defines this as, “A mental and emotional state in which your head is clear, able to create and respond freely, unencumbered with distractions and split focus”.

The key to achieving this state is externalization.

Externalization means taking what’s in your mind – your tasks, ideas, plans, and worries – and putting them somewhere outside of yourself. This could be in a journal, a digital tool, or any system that works for you.

By externalizing, you free up mental space. Instead of trying to remember everything or getting overwhelmed by a mental to-do list, you allow your mind to focus fully on the present task. This not only improves concentration but also reduces stress and mental clutter.

In practice, externalization might involve daily, weekly, monthly and yearly planning sessions where you lay out your tasks and objectives. I’ve just finished externalizing my yearly focus for 2024.

It also means having a system for capturing ideas and thoughts as they come, so they’re not lost, but also not distracting you. It could even be storing the idea in other people’s heads or knowing who to turn to if you want to know something.

I’m not rigid about the Getting Things Done philosophy or the system; it’s more about having a reliable place to store information and know where a task is at, how important, urgent, and amount of work it needs, how it connects to the bigger picture, who’s responsible, and what needs to happen next, so your mind isn’t trying to juggle it all. It’s about creating a workflow that allows you to engage with your tasks one at a time, fully and without distraction, much like water that flows over and around obstacles.

5. Collaborate, Cooperate, and Focus on Your Comparative Advantage

Mathematician and physicist, Stanisław Ulam (who was unfortunately left out of the recent Oppenheimer movie!) once asked economist Paul Samuelson (who won the second ever Nobel Prize in economics) to name one idea in economics that was both universally true and not obvious. Samuelson went away and came back with the principle of comparative advantage.

Comparative advantage means focusing on what you can do better or more easily than others. It’s about playing to your strengths and acknowledging that you can’t be the best at everything. When you apply this idea to your work, it encourages you to invest time in areas where you excel, while being open to collaboration and cooperation in other areas. This is where the true power of teamwork and technology, like AI, comes into play. Collaborating with others allows you to complement each other’s skills, creating a synergy where the whole is greater than the sum of its parts. It’s about finding those win-win situations where everyone’s unique strengths are utilized effectively. These days, AI tools can be used to handle repetitive tasks or increase your abilities in your weaknesses or make you more creative in your strengths.

Long story short, don’t try to do everything yourself and find ways to bring others on board for mutual benefit. Remember also that there’s no limit to the amount of good you can do it you’re willing to let others take the credit.

6. Embrace the Power of 'No'

Time is finite. Every ‘yes’ to something trivial is a ‘no’ to something vital. Learn to tell the difference and decline requests that don’t align with your priorities - your Ultimate and Proximate Whys. Here’s where an investment mindset is helpful.

Beyond task management, I take an investor-like approach to risk management. My focus is usually divided among three types of projects:

1. ‘boring but important’ ones that ensure stability and progress of my goals

2. ‘exciting and probable’ ones that keep me engaged and have likely success, and

3. ‘moonshot’ projects that, while risky, could potentially lead to groundbreaking discoveries and achievements.

This diversified portfolio approach in project selection not only balances risk but also keeps the work dynamic and fulfilling.

Saying no also means saying no to existing projects where the expected return has changed with new information. Triage ruthlessly and don’t fall for the sunk cost bias - persisting with a project simply because you’ve already invested a lot of time and energy. Dump projects that are going nowhere or not going where you expected and pick another item from your list.

As a psychological hack, I put these projects into an “on hold” pile to be revisited later. Of course, I never do.

7. Hack Your Psychology: Productive Procrastination, Pomodoro Technique, Ziegarnick and other Hacks

My friend, Dave Liu graduated as one of the top engineers in his cohort, went on to do a PhD in biomedical engineering, got bored, went back to med school, became a consultant anesthesiologist, and now works as a critical-care clinician-scientist-engineer. One of the secrets he imparted on me was productive procrastination.

It’s hard not to procrastinate, but you can choose what to procrastinate on. Have a list of things that are boring but need to done - emails, receipts, merging duplicates in Zotero, or organizing your to do list - and procrastinate on those to keep moving forward.

Productive procrastination is an example of hacking your own psychology to protect you from your own foibles. I could write another book on other hacks, which range from:

• doing boring work in focused Pomodoro bursts (25 minute sprints with a 5 minute break)

• Locking yourself out of social media

• Leaving work unfinished at the end of a session or day to make it easier to start the next day

• Do something small to get started. I like Rob Henderson’s example

8. Minimize Distractions

My meetings used to be everywhere - spread throughout my day and throughout my week. I was working in the gaps between meetings. I was quick to respond to emails. Often, I was replying those emails in those gaps! In other words, I was letting other people create my calendar. It’s easier to take control of your schedule as you become more senior, but throughout your career, you must schedule time for deep work.

Chunk your meetings during specific times, with rare exceptions. Turn off non-essential notifications. Reply emails and messages in bursts (productive procrastination!). Schedule time for deep work where you focus on those important, difficult tasks that move you closer to your goals. It’s very easy to be busy but not get anything done.

Sometimes, the biggest productivity boost is simply not being interrupted.

9. Keep Learning and Don’t Forget

Keep a commonplace book, such as a Zettlekasten to capture your knowledge. Externalize the connections between the things you know. Writing an expansive book like A Theory of Everyone would have been nearly impossible without a system that could capture the details of what I’ve learned. My specific setup is in another email.

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The general point is to have a way to give you access to a mix of what everyone is talking about and what only a few people are talking about. For the former, there’s the news and social media. For the latter, there’s reading outside your discipline, being part of forums and communities of people not in your social circle, learning from those you vehemently disagree with, small conferences and workshops, and scouring the less visited parts of the Internet or Wikipedia and diving deeper through textbooks and specialist handbooks.

Stay curious and keep learning with the caveat that you should watch your input to output ratio and making sure you’re not just a collector of knowledge, but a person who exploits that knowledge, producing insights and content, putting that knowledge to good use.

10. Wellness Matters: Have A Reset Device

Japanese have onsens, Koreans have jjimjilbang, Germans and Scandinavians have saunas, aufguss and löyly ceremonies.

In the Anglo world, we typically socialize with alcohol. Whatever it is, it’s useful to have your Reset Device. Your Reset Device is what brings you back to baseline, energized to keep going. Depending on your budget, interests, and where you live, it could be walks through the forest, a swim in the ocean, time with friends, a hot tub, massage, or sauna.

I’ve hit the email limit! Continued here:

Rob Henderson has been tweeting excerpts from A Theory of Everyone. Ab excerpt about the origins and evolution of the broad American smile seemed to get a lot of attention.

Full excerpt

America’s melting pot is helped by a long-running history of migration from so many different places. This has shaped many aspects of American culture. For example, this long history of migration has made America a deeply expressive culture which puts a lot of emphasis on explicit emotional expression – thinking about how you feel and the feelings of others. Many Americans may take this focus on expressing emotions for granted, but it is not universal.

Americans are known for their broad smiles, obvious displays of anger, and other clear emotional expressions. These features are common in countries with long histories of migration. When your neighbor doesn’t speak your language or share your culture, emotions serve as a common ground for communication. Clear expression is critical to being clearly understood.

In 1990, after the fall of the Soviet Union, the iconic American burger chain McDonald’s opened its first restaurants in Russia. One of the first challenges was teaching Russian workers to smile as part of that authentic McDonald’s experience. Both workers and customers initially found this difficult. In Russia people who smile when something isn’t funny are considered crazy. But with sufficient training, workers – and customers – accepted the new smiling norm. They came to understand that people smiling without a joke might be crazy or they might just be American. They came to accept a local norm at McDonald’s. Of course, this didn’t change the overall culture. Prior to hosting the 2018 FIFA World Cup, Russia once again ran smile training sessions for service workers lest tourists from countries with long histories of migration leave with the impression that Russians are unfriendly.

Within cross-cultural psychological research, the emotiveness of Americans is often contrasted with other, more monocultural countries like Japan, known for its more muted emotional expression. Japan’s homogeneous culture allows for any Japanese person to know what any other Japanese person feels from the context alone. A Japanese person would immediately recognize a shameful situation or one that would provoke anger without anyone displaying emotions. Outsiders, on the other hand, may be oblivious to contextual cues, not realizing when they’ve offended their hosts.

This emotional control has further downstream effects – for example, eyes are often used as a focus of emotional expression more than the mouth in many similarly homogeneous East Asian cultures. By corollary, Asian immigrants to America are often surprised by explicit ‘I love yous’ and the ‘Thank you; you’re welcome’ routine, even for family members. In places like India or China, being so explicit with family members would be considered odd or even insulting. Implicit communication can be more efficient when everyone shares the same norms and understanding, but it makes it difficult for newcomers who have to discover hidden norms and rules through faux pas – ideally someone else’s. A broad policy of explicit communication may be uncomfortable and unfamiliar initially, but is part of what helps newcomers assimilate more easily.

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For those who want to read more, this is one of the seminal papers: https://www.pnas.org/doi/full/10.1073/pnas.1413661112

Long story short, America and other countries of the New World tend to be more emotionally expressive and as a result care a lot about feelings. Children are trained to think a lot about their own feelings, how to express those feelings, and how their actions affect other people’s feelings. This emphasis is not universal. A European student of mine had a moment of revelation upon reading the chapter,

Generative AI Models

The American smile and emphasis on emotion spreads through American movies, books, and TV shows, but hints can also be found in AI. This Twitter thread and Medium post documents the prevalence of the American smile in generative AI models:

Best wishes,

Michael

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Hello from Michigan! In other news, A Theory of Everyone has finally arrived in the US and Canada! You can watch the LSE launch video here.

A lot of podcasts and media were timed around the US launch. Some of the podcasts that came out in the last couple of days include:

• Sean Carroll’s Mindscape

• Razib Khan

• Converging Dialogues

• Erika Taught Me

Much more to come over the next few weeks. I’ll keep you posted.

In the Field with Michael Muthukrishna E08

The 8th episode of In The Field with Michael Muthukrishna was shot at the Lincoln Memorial in Washington DC. It echoes some of the conclusions to the book. That we don’t have to accept the world as it is. That change is possible. That the world was made by people no smarter than us, and thanks to rising IQ scores, probably less smart. And that a committed, well-connected, and well-resourced group powered by an easily understood idea is capable of moving us to a different equilibrium.

Transcript

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A Theory of Everyone must be a terrific book (in the original sense of terrifying). It’s new release date in the US and Canada is next Tuesday, October 31, ready for Halloween. I suppose it does deal with some scary topics like tax, inequality, and energy scarcity (but with solutions and pathways).

The 7th episode of In The Field with Michael Muthukrishna was shot at George Washington’s house in Philadelphia, which was lost for decades, accidentally demolished, and at one point used as a public toilet. When the significance of the remaining walls was realized, it was turned into a commemoration. But a commemoration not to George Washington, but to George and Martha Washington's nine personal slaves.

Since 2010, this site has stood as a reminder that we, the people, did not always include all people. And while all men are created equal, might have seemed self-evident, it was not self-evident that it should include women or those with the wrong skin color. As I describe in my book, the words in the Constitution may have helped trigger progress through consistency and runaway cultural evolution, but they were far from practiced at the time that they were written.

• George Washington had over 600 slaves

• James Madison had over 100 slaves

• James Monroe around 75 slaves

• Andrew Jackson around 200 slaves

• John Tyler had 29 slaves

• James K Polk had 56 slaves

• and Zachary Taylor had around 300 slaves

John Adams and his son John Quincy Adams are notable for being the only two presidents and the first dozen to not have any slaves!

I think it's appropriate that this commemoration be situated in the birthplace of America between the Constitution Center and Liberty Bell, between a center that celebrates all the promise of the powerful words of the US Constitution and a broken bell that nonetheless remains a symbol of all those who yearn for freedom. This is part of modern America's ongoing reckoning with all aspects of its past.

But resolving the ongoing results of this past, of tackling the challenges of diversity, governance in the 21st century, inequality and maximizing the performance and potential of as many people as possible is essential to reaching the next era of abundance.

Transcript

I'm standing at the site of George Washington's presidential house in Philadelphia. The president lived here from 1790 to 1797 after the American capital was moved from New York City to Philly. The location and the significance of the house was lost over history, and at one point it was used as a public toilet. In 1951, the remaining original walls were destroyed without anyone realizing the significance of those bricks.

In the early 2000s, when this history was rediscovered, people were naturally outraged and a commemoration was planned. But a commemoration not to George Washington, but to George and Martha Washington's nine slaves whose names are listed on this wall. Christopher Sheels, George's personal slave. Their coach, Hercules. Hercules’s son, Richmond. The slaves who looked after the horses Giles and Paris. Martha’s personal slaves Oney Judge and Moll, the house slave Austin and the coach driver Joe Richardson. Videos play on repeat, reminding people that Washington not only had slaves, but in his own words, feared that they would talk to the free blacks in Philadelphia, writing that the idea of freedom might be too great a temptation for them to resist. By Pennsylvania law, the slaves would be free once they lived in Philadelphia for six months. And so Washington made sure that they were taken out of the state before that time limit was set, making sure that the clock reset. Washington in fact wrote to Secretary Tobias Lear if, upon good advice, it is found expedient to send them back to Virginia, I wish to have it accomplished under the pretext that they may deceive both them and the public. When George Washington died, he freed his slaves on his death, but his wife, Martha, did not. Instead, their grandchildren “inherited” her slaves and their children. The US capital would eventually move from Philadelphia to Washington, D.C., in part as a compromise with the Southern slave owning states to not have the nation's capital in an abolitionist state.

Since 2010, this site has stood as a reminder that we, the people, did not always include all people. And while all men are created equal, might have seemed self-evident, it was not self-evident that it should include women or those with the wrong skin color. As I describe in my book, the words in the Constitution may have helped trigger progress through consistency and runaway cultural evolution, but they were far from practiced at the time that they were written.

George Washington had over 600 slaves. I doubt he knew all their names. James Madison had over 100 slaves. James Monroe around 75 slaves. Andrew Jackson around 200 slaves. John Tyler had 29 slaves. James K Polk at 56 slaves, and Zachary Taylor had around 300 slaves. John Adams and his son John Quincy Adams are notable for being the only two presidents and the first dozen to not have any slaves.

I think it's appropriate that this commemoration be situated in the birthplace of America between the Constitution Center and Liberty Bell, between a center that celebrates all the promise of the powerful words of the US Constitution and a broken bell that nonetheless remains a symbol of all those who yearn for freedom. This is part of modern America's ongoing reckoning with all aspects of its past.

But resolving the ongoing results of this past, of tackling the challenges of diversity, governance in the 21st century, inequality and maximizing the performance and potential of as many people as possible is essential to reaching the next era of abundance.

Best wishes,

Michael

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During the pandemic, we all moved our lectures online. I teach introductory psychology - Foundations of Psychological Science - which covers all the typical material you’d get in PSYCH101, but rather than teach disconnected ideas, the course places psychology within a theoretical framework showing all the connections. The program is now the #1 in the country and since we’re back in person, I’ve thought I’d make the course available on Udemy.

If anyone is interested (or knows someone who is), it’s free with one of these links (they expire, but I can make more):

https://www.udemy.com/course/foundations-of-psychological-science/?couponCode=C7DB9213E52490FC2BFF

Another link if the one above doesn’t work: https://www.udemy.com/course/foundations-of-psychological-science/?couponCode=834F8ACF8E6A929ACB71

Course content

This course provides an introduction to human cognition and behaviour, addressing foundational topics in psychological science. These foundational topics include key concepts such as evolution, genetics, neuroscience, human evolutionary biology and anthropology, and specific topics, such as perception, memory, heuristics and biases, decision-making, child development, psychopathology, personality and individual differences, emotion, attraction and sexuality, cross-cultural differences, social relations, stereotypes and prejudice, norms and attitudes, social learning, social influence and persuasion, and group processes.

The course will offer an integrated perspective on these topics, investigating the evolution and variation in human psychology over time, across cultures, and over the lifespan. The course will introduce the history of the study of humans and human psychology, offering students the historical context to trends in research. By the end of the course, students will have a broad knowledge of key topics in psychology and related disciplines. Students will be prepared for more in-depth investigations of more advanced topics in later courses.

Students will also understand how psychology relates to and informs other disciplines concerned with humans and human behaviour. The course is therefore suitable to students enrolled in other programmes who wish to enrich their understanding by drawing on the psychological sciences.

Best wishes,

Michael

How did Japan create successful car companies and award winning whiskeys and what does that have to do with multiculturalism and immigration? In this 6th episode of In the Field with Michael Muthukrishna, shot in Japan, we answer these questions.

Multiculturalism and immigration are of course hot topics. In Britain, Home Secretary Suella Braverman’s speech earlier this month on what she saw as the failures of multiculturalism prompted both cheering and criticism. Japan’s openness to immigration, particularly since the new immigration act in 2019 reveals why immigrants are a necessary lifeblood for many countries. Especially for Japan, because of its long life expectance and low birth rates - in 2011 Japanese newspapers reported that adult diapers had for the first time begun outselling baby diapers.

👉 You can pre-order my book here: http://atheoryofeveryone.com

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Transcript:

In the mid-twentieth century, Japanese brands were synonymous with poor quality. The car industry is a good example. Early Japanese cars were actually poor quality knockoffs of European and American models. Some were hybrids of different designs that Japanese car manufacturers could get their hands on. But eventually, copying turned to recombination and the kind of incremental improvement that the Japanese are now famous for: Kaizen, the Toyota Way, and other ways of continuously incrementally improving led Toyota, Nissan, Mazda, and other Japanese car companies to become the reliable brands that they are today.

Whiskey is another example, but it wasn't just technology or alcohol. Japan is also an economically successful integration of Western-style non-family corporations and liberal democratic institutions tailored to their history and culture. Countries that are high on collectivism and the importance of family tend to suffer from corruption, particularly nepotism. But Japan, despite holding Confucian ideals of filial piety and hierarchy, doesn't. It has low levels of corruption, around the same as Canada or Australia, like South Korea, Hong Kong, or Singapore.

Japan is a new cultural recombination and proof that there are paths out of suboptimal, corrupt, poor equilibrium that still retain cultural diversity. Multiculturalism is a word that means different things to different people. Some good, some bad. It can be a divisive topic for those brave enough to talk about it at a dinner party. In my book, I talk about different ideal models of multiculturalism: The “No hyphen” full integrationist model typified by France, non-integrationist mosaic or salad bowl models typified by Canada, and the melting pot model that creates a new blended culture associated with America. I also introduce the Umbrella Model that combines the best of these to resolve the paradox of diversity, grabbing the bull by both its horns, so to speak, reaping diversity’s benefits while minimizing its costs.

Japan, where I am now, right now, will soon have to resolve this paradox. The challenge that Japan faces is a portend of things to come in many developed countries. Japan has one of the longest life expectancies and lowest birthrates, well below replacement. Around 2011, Japanese newspapers reported that adult diapers had for the first time begun outselling baby diapers. The country was running out of people to do the work, to keep society running, and to keep the space of the possible large enough for everyone. Some suggested robots as a solution, but the technology didn't advance fast enough for this to be a viable solution. Japan had no choice but to use a solution that it had long avoided - immigration.

Japan is a highly homogeneous country, with a general sentiment to keep it that way if they could. But by 2019, the writing was on the wall. In April 2019, the new Immigration Control and Refugee Recognition Act came into effect. The Conservative government, led by then-Prime Minister Shinzo Abe, sold it as a necessary step o meet the demands of business owners, Japan has leaned into selective skilled migration. Immigrant numbers have quickly hit record highs as a result. Although immigrants still represent just two and a half percent of the population, this percentage is expected to continue rising.

New laws are currently being developed to further this expansion. For example, there's a focus on streamlining the path for high-skilled immigrants to join Japanese society. However, Japan hasn't yet figured out what model of multiculturalism it wants to adopt.

As I explain in my book, the future of Japanese society will depend on the policy choices it makes today.

Best wishes,

Michael

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The first 3 winners of a signed copy of A Theory of Everyone have been shipped! The winners were:

1. Greg Coleman (UK)

2. Isabela Granic (Canada)

3. Mikael Shields (UK)

I’ll be drawing another 3-4 winners next week and 3-4 more the week after. After release, I may continue the competition.

A Theory of Everyone made an appearance in last weekend’s Sunday Times: There’s a reason for all these wars: minerals will decide who rules next

Drowning in cheap oil, we in the West forgot a simple truth: empires are built on energy

Lots of relevant sections from the book, like:

In An Inconvenient Truth, Al Gore uses the analogy of a frog placed in tepid water whose temperature is slowly raised to illustrate our disregard for the warning signs of a changing climate. It’s a clever analogy that’s almost certainly wrong. Animals have powerful instincts to survive in precarious and danger-filled environments. As soon as the frog becomes uncomfortable, it will leap out. In the same way, humans are not oblivious to the changes happening around them. We can feel in our bones that something is wrong. Like the frog, we are trying to make the leap.

The problem is not that we don’t notice the warning signs, it’s that we don’t know what to do about it. As excess energy falls, life becomes harder. As the challenges posed by climate change, such as large influxes of people, weaken our institutions, our societies fracture and it becomes difficult for us to realize the role of energy, innovation, cooperation, and evolution, and how together these laws have created us and our civilizations. The resulting anger and frustration can make our societies ungovernable, and we may lose faith in the fairness of our systems and in each other.

As energy return on investment and energy availability continue to fall to precipitous levels, our civilizations are quickly losing the excess energy necessary to overcome the danger we find ourselves in. This may sound dire, but history has shown us that every major civilization has been crushed by a falling energy ceiling – as their space of the possible shrunk, they were defeated by forces both outside and within.

For paid subscribers, here’s the introduction to Part 2:

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Many of you have probably heard the term WEIRD people. By WEIRD people, I don't mean people who do strange things, I mean people from Western, educated, industrialized, rich, and democratic societies like Canada, the United States, Britain, or Australia.

Here's a question for you: What do WEIRD people, Daniel Kahneman, Amos Tversky, and Chinese food have to do with one another? The answer lies in a basement food court at the entrance to the University of British Columbia's campus.

It was here that Kahneman and Tversky allegedly hatched their plans to study human rationality, creating the field of behavioral economics. And it was here that three UBC professors, Joe Henrich, Ara Norenzayan, and Steve Heine, started a conversation that led to what became known as the WEIRD People paper, which has, at the time I recorded this video, been cited over 10,000 times.

In their paper, Joe, Ara, and Steve made the compelling case that for decades, psychologists thought that by studying their students, they were learning about human behavior, but we were instead learning more than we really ought to or needed to know about first-year psychology undergrads in the universities of Western, educated, industrialized, rich, and democratic countries. These undergrads weren’t representative of the rest of the population, let alone the 88% of the world who didn’t live in WEIRD countries.

This is the fifth installment of #IntheFieldwithMM, which was shot in a slightly unconventional spot: the basement of the University of British Columbia’s foodcourt, where the food is tasty, good value and, apparently perfect for researchers to cook up brilliant field-changing new ideas.

Best wishes,

Michael

I’ll keep this brief. Two of the Substack signed book winners have gotten back to me and given me permission to mention their names. One is in the UK and the other is in Canada. Just waiting on the third and then I’ll announce. There are 7 more copies between now and soon after the US and Canadian version come out on Oct 31.

The fourth video of my series #IntheFieldwithMM is on innovation strategies-shot in Silicon Valley and Japan. I discuss different cultures of innovation using Silicon Valley and Japan as examples.

Is Silicon Valley a bastion of success or a graveyard of failure?

Is the Asian Tiger parent strategy better for creating innovators?

How do you create an innovation culture?

As always, the book is available here: http://atheoryofeveryone.com

If you’ve read it already, please leave a review on Amazon and/or Goodreads and feel free to reach out with your comments. I love reading reader reactions. Here’s a message I got this morning about Chapter 9 on inequality and inheritance taxes:

Over the coming weeks, I’m thinking of releasing some book off-cuts on things like what money is in the context of energy and how it relates to inflation. What do you think?

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Best wishes,

Michael

The book launch went very well. The auditorium was full and there were over 500 people online, with at least another 500 who have watched the launch since then. If you were one of them, thank you so much.

You can watch the launch here:

As you’ll see, Matthew Syed was a wonderful discussant, Liam Delaney did a great job chairing the event, and the audience had wonderfully insightful questions. Many people bought signed copies afterward.

Speaking of signed books, we’ve just drawn the first 3 signed copies from the list of paid subscribers. If you are a paid subscriber, please check your spam to see if you were one of them. Honestly, writing to tell people that they’ve won something online feels spammy and I’m worried spam detectors may feel the same way! It came from my lse.ac.uk email address.

Next week, we’ll be drawing 3 more winners from the list of paid subscribers at the time. After that, we’ll draw more closer to the US book release on Oct 31 and perhaps more in the coming months, giving away 10-20 copies.

Please note that we’re shipping the signed copies for all founding lab members and all random draw winners anywhere in the world.

If the winners give me permission, I’ll name them.

Best wishes,

Michael

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Just 2 days to go for A Theory of Everyone is released! I invite you all to join me for the book launch at LSE next Thursday Sept 28th, in person or online: https://www.lse.ac.uk/Events/2023/09/202309281830/theory

It’s a fireside chat with Matthew Syed, so plenty of time for questions, discussion, & book signing (if you’re there in person).

The book has also been spotted in the wild! Technically bookstores shouldn’t be selling it, but apparently a few have them on display. A photo sent by a friend:

Ok, onto the video. This is the 3rd video of my series #IntheFieldwithMM where I discuss more details about Estonia’s education system. It ties into my Guardian article from yesterday on rethinking education to make future generations smarter: https://www.theguardian.com/books/2023/sep/25/the-big-idea-how-do-we-make-future-generations-smarter

In this video, I explain how Estonia embodies so many elements of a collective brain approach to innovation and evolution. Estonia recognized that the future performance of their country depended on the future performance of their people. Their Tiger Leap revolution of their education system trained teachers in technology and connected them into a collective brain, encouraged students to learn through the Internet, seek out information from around the world and added Algorithms as a fourth pillar alongside the traditional Rs of Reading, Writing, and Arithmetic. Estonia's students now have the highest scores in mathematics, reading, and science in the Western world.

Plagiarism is bad, except when it comes to policy, where we should be plagiarizing a lot more than we do.

There's a lot we can learn from each other.

Obviously, this is all discussed in much more detail in the book.

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Best wishes,

Michael

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Full transcript

I'm in the country with the most unicorn companies per capita in the world. The building right behind me is the headquarters of the ridesharing company Bolt, giving Uber a run for its money. And the one behind it is the offices of TransferWise, now Wise. These are two of ten unicorns founded right here in Estonia.

In "A Theory of Everyone," I discuss how Estonia embodies so many elements of a collective brain approach to innovation and cultural evolution, including most of what I call the COMPASS Innovation Framework.

In 1991, after the Soviet period, half the country didn't have a telephone. But just 10 years later, by 2001, all schools were Internet connected and all students had access to a computer. The OECD's PISA tests students around the world in mathematics, reading, and science. Estonia is now at the top of the PISA tables in all, outperforming the rest of Europe, the UK, the US, Canada, and Australia. Only a handful of East Asian countries surpass them. They do it while spending less per pupil than the UK, US, Canada, and Australia, and far less than the OECD average.

Estonia recognized that the future performance of their country depended on the future performance of their people. The Estonian policy success story is a blueprint for innovation that other countries, corporations, and people can learn from.

In 1996, they founded the Tiigrihüpe - Tiger Leap Foundation - an independent foundation to revolutionize their education system and bring Estonia to the forefront of technology. It was through Tiger Leap that Estonia was connected to the rest of the world's collective brain, that curricula were changed, encouraging and teaching students how to learn through the Internet. Their teachers were trained in technology and were encouraged to seek out the best ideas from around the world and from each other.

The platform "School Life" was launched to create a teacher collective brain where teachers could share ideas, resources, and course materials. Plagiarism is bad, particularly in school, except when it comes to policy where we should be plagiarizing what works elsewhere a lot more than we do.

How did Estonia do it? Rather than top-down governmental control, Estonia has radical decentralization where municipalities and schools have autonomy, not unlike a startup city approach that I also talk about in my book. In Estonia, trust is placed in teachers' hands, but smart collaborations and sharing of best practices are facilitated, supported, and incentivized. It's a distributed solution to the paradox of diversity that helps Estonian educators actively seek out the best practices from around the country, other schools, communities, and municipalities, and from around the world, bubbling the best solutions to the top and sharing them widely.

Teachers are incentivized to share their knowledge, and the best are given opportunities to travel and learn from other teachers and education systems elsewhere. When the first generation of Tiger Leap kids entered university and then the public and private sector, Estonia was transformed forever.

Estonia has continued to improve and innovate, offering opportunities for radical revolutions not only in education but in governance, health care, and every other aspect of Estonian society. In 2012, it was the first country to start teaching programming and algorithms in elementary, in primary school to six year olds.

Reading, writing, arithmetic and algorithms. Students are even taught concepts from computer science, such as SCRUM and Agile methodologies for their everyday lives. In 2013, it was the first country to implement a radical approach to math education spearheaded by Conrad Wolfram, brother of Prodigy, mathematician and physicist Stephen Wolfram of Wolfram Alpha and Mathematica. As Conrad puts it, we currently learn mathematics in the order that it was invented. Start with the Greeks. Learn Pythagoras theorem for some reason, and then eventually algebra and calculus. But there's no reason to go through math in historical order. No reason that that should be the most efficient or effective way to teach mathematics. Because math isn't about adding and subtracting or remembering rules for calculating partial derivatives. It's about thinking. It's about logic and reasoning, only sometimes with numbers.

What does it mean, for example, to take a derivative? What does it mean to calculate an integral? When and why are these useful? Many Western education systems have let down students, particularly when it comes to math education, leading many to believe that either they're not good at it or that it's not useful. Today, Estonia recognizes that the rise of A.I. changes everything. That education itself needs another radical rethink. And so they're exploring new possibilities, such as swapping homework and schoolwork. That is, students learning through online interactive materials from the best teachers at home. And then they do their homework activities. The actual activity of learning by doing at school with teachers not as deliveries of material, but facilitators helping students learn how to learn and learn where they can find what they need online, and how best to practice.

In a world of computers on desks and smartphones in pockets and soon A.I. agents facilitating thinking and learning everywhere. The most valuable skills are no longer memorizing reams of knowledge, historical facts, or scientific formula. The most valuable skills are learning where to find this knowledge and how to use it. Kids need to learn how to learn. Kids need to learn how to seek out the most valuable information past a cacophony of noise in a world filled with information. They need to learn how to focus in a distracting world, and we all need to learn how to put what we already know to use as soon as possible for the highest return with the most real world relevance. Estonia is setting itself up to continue punching above its weight. This country, like all countries, has its problems, but it embodies a central message in A Theory of Everyone. We're often not good at designing efficient institutions and figuring out the best solutions, but we do know how to design efficiently evolving institutions that find those solutions and bubble them to the top. We can create a start up like ecosystem for all aspects of our lives that prevent us from being trapped by history and by decision made long ago.

Estonia's success so far is a lesson to us all.