In February 2026, Behavioral Scientist partnered with the peace science organization Neuropaz to host an online event exploring the latest work and thinking at the intersection of behavioral science and peace and conflict.

The theme of Neuropaz 2026—hard truths and paths forward—reflects the many roadblocks that characterize this line of work, including funding cuts to research and aid agencies, technologies that amplify outrage, and politicians who prioritize power over peace. Neuropaz 2026 brought together leading scientists, practitioners, policymakers, and funders to face these obstacles head-on. Through open, candid conversations about what stands in our way, we hoped to illuminate new paths forward.

Below, you'll find all the recordings from the event, plus a curated selection of further reading and resources from each of the speakers.

— Evan Nesterak, Editor-in-Chief, Behavioral Scientist and Andrés Casas, Founder, Neuropaz

Neuropaz 2026 — Hard Truths and Paths Forward: Peace as a Scientific Challenge

WELCOME AND INTRODUCTION

Andrés Casas and Evan Nesterak introduce the event and provide an overview of the program. They also show a brief video about the life and work of Emile Bruneau (to whom the event is dedicated).

In recognition of the tenth anniversary of the historic peace agreement in Colombia, Neuropaz presents Juan Manuel Santos, president of Colombia from 2010-2018 and leader of the peace process, with the inaugural Neuropaz Lifetime Achievement Award. President Santos joins Andrés Casas for a conversation to reflect on the decade since the agreement and share how he continues his work toward peace through Fundación Compaz.

Further Reading & Resources from President Juan Manuel Santos:

• Santos, J. M. (2016). “Peace in Colombia: From the Impossible to the Possible.” Nobel Prize Lecture. (English | Spanish)
• Santos, J. M. (2021). The Battle for Peace: The Long Road to Ending a War with the World's Oldest Guerrilla Army. Lawrence, KS: University Press of Kansas. (English | Spanish)
• Fundación Compaz website (English | Spanish; Instagram; LinkedIn)
• Open Library of the Colombia Peace Process (English | Spanish)
• Santos, J. M. “La historia no contada del proceso de paz.” Podcast. (Link)

BUILDING PEACE IN COLOMBIA 10 YEARS AFTER THE HISTORIC PEACE AGREEMENT

In this session, Andrés Moya and Felipe De Brigard explore two lines of applied research that emerged after the 2016 Peace Agreement. The two cases are powerful illustrations of how behavioral science is being applied to help build peace and prevent conflict in Colombia and beyond.

Further Reading & Resources from Andrés Moya:

• Semillas de Apego website (Link)
• Sánchez-Ariza, J., Cuartas, J., & Moya, A. (2023). The mental health of caregivers and young children in conflict-affected settings. AEA Papers and Proceedings, 113, 336–341. (Link)
• “Rompiendo el Ciclo” | “Breaking the Cycle.” (2025). Semillas de Apego. (Link)
• Andrés Moya's LinkedIn (Link)

Further Reading & Resources from Felipe De Brigard:

• Memory & Forgiveness website (Link)
• Imagination and Modal Cognition Lab website (Link)
• Love, S. (2024). “Must you forget to forgive? A scientist tests the relationship.” Psyche. (Link)
• “Forgetting and Forgiving | Dr. Felipe De Brigard.” (2022). John Templeton Foundation. (Link)

Further Reading & Resources from Andrés Casas:

• “Pathways for Peace.” (2023). Pirata Films. (Link)
• “Evaluating How Movies Build Support for Peace: An RCT in Cinemas.” (2022). Gusano Films. (Link)
• Casas, A. & Hameiri, B. (2022). Giving peace a chance: Lessons from translational research in Colombia. Peace and Conflict: Journal of Peace Psychology, 28(3), 284–291. (Link, open access)
• “Hope.” (2019). Season 1, episode 6 of Why We Hate. Amblin Television and Jigsaw Productions. (Link)

The post RECORDING & RESOURCES—Neuropaz 2026: Hard Truths & Paths Forward appeared first on Behavioral Scientist.

Once a field that rarely strayed from the lab, behavioral economics has become a means of answering all sorts of real-world questions. When do investors take financial risks? How does discrimination evolve over time? Do AI agents inherit the biases of their creators? 

These particular questions are part of the wide-ranging research program of Alex Imas, a behavioral economist and professor at the University of Chicago who studies how people understand the choices they face. There, he has an office down the hall from Richard Thaler—once a distant role model who helped found Imas’ chosen field, now a friend and collaborator.

Behavioral economics was built on what Thaler called “anomalies,” defined as “empirical observations that are inconsistent with standard economic theory.” For instance, standard economic theory predicts that people will sell a bottle of wine for the same price they’d buy it for (the value shouldn’t change based on whether you already own it). It also predicts that the more money that’s inside a lost wallet, the less likely people will be to return it (higher incentives to keep it). 

But in both cases, people often do the opposite. They refuse to sell a cherished bottle of wine for $200 but wouldn’t dream of buying a bottle for the same price. And the more money inside a lost wallet, the more likely people are to return it.

In 1986, Thaler began documenting anomalies in a controversial and widely read column in the Journal of Economic Perspectives. That column evolved into a 1992 book titled The Winner’s Curse, and, despite the relentless skepticism of many veteran economists, the momentum continued—resulting in a Nobel Prize for Thaler and a new field called behavioral economics. Now, 33 years later, Thaler and Imas have teamed up to write a new edition of The Winner’s Curse. Their goal was to review the original claims about anomalies and update the record with what we’ve learned since.

I recently sat down with Imas to talk about the book. How do biases discovered in the lab using pens and mugs play out in housing markets and stock exchanges? Has behavioral economics been used more often to exploit people than to empower them? And a big question that seems simple but isn’t: What makes a choice difficult?

Our conversation has been edited for length and clarity.

Heather Graci: This book is a then-and-now look at behavioral economics. After examining the last 30 years of research in the field, what change stands out to you the most?

Alex Imas: The biggest change in behavioral economics has been the move [from the lab] to the field, I think. A lot of the original anomalies that were documented in the “Anomalies” columns and then put together in the original Winner’s Curse were lab experiments. There are a couple of examples of using stock market data and things like that, but primarily, things like prospect theory and the endowment effect came from students making decisions with relatively low stakes, sometimes with no stakes at all. A lot of the pushback against these sorts of studies by economists was, “We actually do not care at all about these students. We care about market participants making consequential decisions.”

The reason that behavioral economics, in our view, has become so successful in economics more broadly is because of this move to the field.

The reason that behavioral economics, in our view, has become so successful in economics more broadly is because of this move to the field. In a lot of the updates, we show, “Look, here was the original result—here's the endowment effect with cups and pens. Now here's the endowment effect in the housing market, here's the endowment effect for traders trading options.” Situations where we know that people know what they're doing, they have a lot of money on the line, they have plenty of opportunities to learn, and these anomalies are still relevant. 

Over the last few decades, some of the ideas in the Anomalies column have begun to influence products and policies. Research on inertia and mental accounting led to programs like Save More Tomorrow, for example. I’m curious where you think that hasn’t happened, where evidence is robust, but policymaking hasn't caught up.

We have plenty of evidence where behavioral economics has been used to hurt decision-making and exploit behavioral biases, but there’s been very little policy to take that seriously. In my view, the biggest practitioners of behavioral economics have not been nudge units and policymakers; they have been private firms trying to use behavioral economics to extract more from consumers. And that is something that (a) hasn't really been addressed by policy, and (b) isn’t really in the academic work either. A lot of the academic work is still focused on, “Here’s a program to improve decision-making because of people’s biases,” rather than, “Look, people are being exploited explicitly through their behavioral biases, and we should try to curb that.”

Why do you think that is?

Companies are hard to regulate—you need a burden of proof. Economics has a welfare criterion as a burden of proof for government or policy intervention. And we need a high burden of proof for government intervention. This is a good thing.

At the same time, sometimes it’s a bit too high, where the welfare criterion is something like the Pareto criterion, where everyone needs to be better off from the policy. Maybe government intervention should be allowed if the company is worse off, but all the customers are better off, or something like that. 

So there’s a higher burden for government intervention in markets, which is both a good thing, and in this case, potentially a bad thing. Here’s an example: In 2011, Facebook wanted to expand by getting more and more users on the platform. How do you get more and more users? 

We have plenty of evidence where behavioral economics has been used to hurt decision-making and exploit behavioral biases, but there’s been very little policy to take that seriously.

It used to be the case that you added people on Facebook. You’d say, “Oh, hey, Heather, come to Facebook, be my friend.” What Facebook did in 2011 is exploit the default effect, basically. They said, “The default is all of your contacts will be uploaded when you sign up.” And you had to go through this byzantine process to make sure that didn’t happen, which nobody did, and this exploded the network. And on top of that, people weren’t just invited, they were told this person was inviting you. They were playing with the social element of behavioral economics too. And this is just the tip of the iceberg in terms of what tech companies are doing.

How do you regulate against that? It’s not clear. It’s hard to say, “Don’t use the default effect.” Why? Maybe people want to have all their friends added automatically. It becomes very difficult, but I think this is where the work needs to be.

In writing this book, you spent a lot of time reflecting on the work of the behavioral economists who came before you. In the same way that behavioral economics has changed over the last 30 years, have behavioral economists changed too? Whether in the questions they’re asking, the problems they’re concerned with, or their approach to science—what lessons could researchers today learn from them?

Behavioral economics started as an interdisciplinary field, and then it lost that for a long time. Most behavioral economists working for about 20 years were economists—there were very few psychologists contributing, with notable exceptions like Eldar Shafir and George Loewenstein (who’s very much both). I think what researchers in behavioral economics today can take from the early work is to be very open-minded and to try to be interdisciplinary.

Was there anything that you and Richard disagreed on while you were working on the book?

Not too much. But I think we disagree on the importance of this new research looking at the cognitive foundations for behavioral anomalies, things like complexity, attention, memory. I’m not even sure if we disagree necessarily, but I’m a strong proponent of this research, and I think Richard is a bit less of a strong proponent.

Why is it important to understand the cognitive foundations of behavioral anomalies?

First, because it connects us back to psychologists. But second, when you don't know the cognitive foundations of these anomalies, it’s really hard to predict them. It’s really hard to say, “When are people going to be more or less loss-averse? When are people going to be myopic, and when are they going to be super forward-looking? When are they going to be narrow-bracketing, and when are they going to be broad-bracketing?”

You have all of this variation in the anomalies—some anomalies even reverse depending on the context. Thinking about how these anomalies are generated by cognitive constraints allows you to set these things up as a maximization problem, where people are trying to maximize what they think are their outcomes. But they can’t attend to everything, they can't remember everything, so they need to do what they can with what they have. 

And what would the counterargument be?

That it potentially introduces a ton of degrees of freedom. When there are a lot of different parameters, the models do become more complicated. But the hope is that this is a journey. With better tools like machine learning and AI, we can have a lot more data on what these constraints are, so we can predict where they bind and eliminate some of these degrees of freedom.

The original edition of this book came out 33 years ago. Imagine another edition 33 years from now. Given the trajectory of the field that you’ve documented in The Winner’s Curse, what do you imagine the updates to the updates will be?

The hope is that there is no edition in 30 years—the edition is the textbooks that we have in economics, because they’ve been updated. Richard’s hope was that behavioral economics would cease to exist as a field; it would just become economics. That’s still the hope—that economics will have cognitive foundations, and we can make very good predictions, and this is all in the textbooks.

Richard’s hope was that behavioral economics would cease to exist as a field; it would just become economics. That’s still the hope.

Now, do I think that’s going to happen? I honestly don’t know. There’s a paradigm shift on the AI, machine learning, and computer science side. With the availability of new data, and the ability to analyze the new data, I don’t know what economics is going to look like in 30 years. I don’t know what behavioral economics is going to look like. Maybe economics is not going to exist as a field in the way we think about it today, and that’s nothing to say about behavioral economics.

How do people think that AI might affect behavioral economics?

There are two views on this. One view is that behavioral economics is going to be completely irrelevant. Everybody will have their own super smart AI agent, that AI agent will make super rational decisions for them, so they will not make any mistakes and there won’t be any biases to study. We won’t need behavioral economics anymore. 

I think that’s not correct. I have research coming out showing that these AI agents inherit the biases of people who control them. You can show this empirically. So I don’t think we’re going to be in a world where behavioral economics becomes irrelevant.

In my view, behavioral economics is more relevant now than it was 30 years ago. Even though the promise of tech was that it would be less relevant, we have seen the opposite.

It could be true that in 30 years, tech and AI will have led to more inequality and more exploitation of biases, where people who have resources will be able to use these tools effectively to get more resources, while people who don't know how to use these tools effectively (because they haven't been trained to do so) are in worse circumstances or are being exploited more. That’s a situation that we could end up in, and it’s really up to us where we go.

As long as these AI agents are being made by for-profit companies, I can't imagine that there will ever be a guarantee that these super-smart, perfectly rational AI agents are aligned with your best interests.

Is there more money to solve problems or exploit problems?

Exactly. At the end of the book, you throw down a gauntlet to other behavioral economists. You write: 

“We pose only one problem, and it comes with a warning: It may end up being too difficult to make real progress. Furthermore, there is a self-referential aspect to it. What we’re interested in is this: What makes a choice difficult?” 

Why is this an important question for researchers to ask? How might the field, and the world, change if we answer it? 

We are trying to pose the question: Why do these anomalies emerge in the first place? A lot of these anomalies—and this was going back to Herb Simon—are a response to complexity. So we’re trying to understand what decisions people find complex. 

This is the first-order question to predict when anomalies are going to be larger, when they’re going to be mitigated, when people are going to look like they’re acting rationally, when they're going to look super messed up and super noisy and super anomalous and super biased. This is all about being able to map a situation as being complex or not.

This is a first-order question, and we really don’t have an understanding. We’re starting to. Ryan Oprea, Ben Enke, and a bunch of other behavioral economists are actively working on this question—I don’t want to say that nobody’s working on this. It’s just the early days.

Do we answer this question by looking at where people get things wrong?

That’s part of it. But it’s also very difficult to translate that to notions of complexity. There are notions of complexity in computer science, which is the number of bits that are required to process information. But think about a common problem where people get it wrong systematically, the bat and ball problem: “A bat and a ball cost $1.10 in total and the bat costs $1 more than the ball. How much does the ball cost?”

This is a very simple problem in terms of computer science, but people get it wrong. They answer quickly that the ball costs 10 cents, but it doesn’t. It costs 5 cents. And they do this because of something that they're finding complicated, or because they don't realize that the problem is more complicated than it is. People are very confident in their answers, and if you give them high incentives, they will tell you the same answer, they will not ask for help. 

There are a lot of seemingly simple problems that people get wrong, so the notion of complexity needs to capture that.

The post Behavioral Economics, Then and Now: A Conversation With Alex Imas appeared first on Behavioral Scientist.

An empty to-do list is a familiar fantasy. We yearn for the mundane to be complete (inbox empty, laundry clean, plumber called) so that we can turn our attention to the sublime. The novel we’d write, the marathon we’d run, the vacation we’d take if we just had the time. 

“The day is never coming when all of the other stuff will be ‘out of the way,’ so you can turn at last to building a life of meaning and accomplishment that hums with vitality,” writes Oliver Burkeman in his book, Meditations for Mortals. “For finite humans, the time for that has to be now.”

That means choosing carefully how we want to use our limited time and letting the rest fade away.

It’s easier to agree with Burkeman than to internalize his advice, particularly as tech companies make increasingly compelling promises to revolutionize human productivity. If ChatGPT drafts our emails and takes notes on our meetings and summarizes every book on our bookshelves so we can read them in 6 minutes instead of 6 hours, maybe we can do it all!

But this is the very siren call that Burkeman has warned us about, luring us into what he calls the “efficiency trap.” Being more productive at work doesn’t mean more novel writing and marathon running. It usually just means more work.

We recently invited Burkeman to our Behavioral Design Podcast to discuss the true and false promises of AI for productivity. We discuss the efficiency trap, what readers lose when writers are no longer people, and how to balance the importance of planning for tomorrow with the delight of showing up today. 

An excerpt from our conversation is below. The conversation has been edited for brevity and clarity. You can listen to the full episode here.

Aline Holzwarth: It's not hyperbole to say that your work has truly, deeply changed my life. But there's a cliché version of your work that most people would be familiar with, which is the idea that life is short, make the most of it. And it's tempting to reduce it to that. 

Part of what makes your work so special, for me at least, is making concrete the 4,000 weeks of life that we have. This idea that we are struggling to stay “on top of everything.” It's not just hard, it’s impossible. Once you embrace that impossibility of it, there's something freeing. 

Oliver Burkeman: One response you can have when you start thinking about how short life is, “Well then I better treat it like it's incredibly precious and cram as much as I can into every available space. And if I waste a moment of it, that's terrible, and I've got to be doing extraordinary, impressive things with my work time and my leisure time.”

And that's not only not the point I want to make, it’s almost the whole thing that I'm setting myself up against. If you think more deeply about our limitations, our finitude, and you let it sink under your skin a bit, it's relaxing and empowering at once. 

It isn't this rather stressful feeling of, “Oh no. That means I've got to be hypervigilant in every moment.” It's good news. It's de-stressing news, but it's also empowering and action-oriented news. The other pole that I’m trying to avoid here is that there's no point in doing anything. Just sit on the couch and eat potato chips. 

I don't think it's either of those. I hope, because this is what I would like life to be like, that it can be peaceful and also active.

Aline Holzwarth: One of the primary things that you hear when a new AI tool comes out is: “It will 10x, 20x your productivity. Here are the extreme efficiency gains that you're going to see from using this tool.”

Tell me all the things that are wrong with that.

Oliver Burkeman: I don't see any reason why extra efficiency that comes from AI is an exception to the rule about what happens when you get more efficient and optimized that I've written about quite extensively now. All things being equal, if you free up time and bandwidth in your work through efficiency savings, then the result is that new capacity fills with even more things to do. 

The classic example is if you get much quicker at answering email, you reply to more people more quickly, and then they reply to you, and then you have to reply to those replies. And before you know it, you're doing more email with your life.

Aline Holzwarth: One interesting related case is AI medical scribes. When a doctor and a patient are in a room, the AI scribe is taking notes on their conversation and then can generate an automated summary and assist the physician in writing up their notes for that patient later.

Right now, physician burnout is a huge problem. Easing up on documentation seems like a purely good thing. However, rather than using this freed up time for the physician to enjoy their evenings at home without working, or have more quality time with a patient, instead they’re seeing more patients.

Oliver Burkeman: Yeah. And this is not a new thing with AI. It’s the idea that the reward for good time management is more work. This is sardonically known as the Kaiser Reward among the people in the Kaiser Permanente health care system because if you see more patients more effectively, the reward will be that you have to see even more patients. And that's ultimately capitalism. The natural movement of the economic system that we exist in is not going to say to those doctors, “You take it easy with your spare time. And if we need more patients to be seen, we'll hire some more doctors.” That's not what would happen.

If you think more deeply about our limitations, our finitude, and you let it sink under your skin a bit, it's relaxing and empowering at once.

Aline Holzwarth: What do you think about the possible future where machines are doing all of the work, and we can just lie on the beach? 

It's possible that many could do that now, but we choose to work. We enjoy working. Part of me fears that the rise of these AI tools that are creating all of these efficiencies is making the work that we enjoy less enjoyable. So that you become this task master of sorts, where all you're doing is setting goals for this tool rather than doing the fun critical thinking.

Oliver Burkeman: It's a great point. This is the AI version of a time-honored complaint that if you get good at a job, you get kicked upwards into being a manager of that job instead of doing the job that you came to do because you found it so interesting.

I have a friend who voluntarily demoted himself in a high school setting away from a management position because he realized that what he wants to do is teach. And I can easily see that if we all become managers of AI and nothing more—there may be people for whom that is a passion, but there'll be lots of us for whom it isn't.

Samuel Salzer: I've called this automation drift. I’ve come across a writer and a coder recently who have experienced it. The writer has these amazing AI tools, allowing them to turn this lengthy process of writing into just sparking the idea and letting the AI write it automatically. And they find themselves like, “Wow, I can write so much more.”

But what they do now is not writing anymore. They're just editing. And what they used to enjoy doing was the writing part and they hated editing. Thanks to these tools, they basically ended up outsourcing the writing part to the AI and became an editor doing the stuff they hate.

And then the coder, same story. They enjoy the coding task. They enjoy writing code, and now they just have to debug. Their output may have increased, but it has drifted to this point where they’re doing the least enjoyable part of the task.

Oliver Burkeman: An issue that comes up a lot in these arguments—just on social media and online spaces, I don't mean in more specialist contexts—somebody will say, “AI could never write a novel as beautiful as a novel by Jane Austen.”

And then someone else will come in and say, “You are so naive, it's ridiculous. Of course it will. It's days away. Maybe it's already done it.” 

Sam Altman will come in with his AI-generated short story, and I'm sitting there feeling like, am I going crazy here?

Because what gives a novel written by a human its value is not that it reached a certain level of technical skill that an AI could surely emulate, but that it was written by a conscious, emoting sensibility. I am a conscious emoting sensibility too. In that connection, there is a relationship which there can't be unless you want to argue that the LLMs are sentient, which is a pretty radical position to take. Even then you'd have to argue that they were sentient in some vaguely similar-to-us way. 

Samuel Salzer: The counterargument is that if it passes the Turing test, if you read the story and you find it meaningful, and then you realize it was written by AI, it doesn't really matter. You had that experience, you had the same emotion. 

Oliver Burkeman: I can't pretend that I care if I find out that the technical instructions for how to use my new dishwasher were written in that way. 

But at the end of this call, if I was to discover that in fact you had not really been there, but it had been a brilliant cloned simulation based on scouring all of your previous output and podcasts and things, the idea that nothing would have been lost there is completely crazy to me.

It would make no sense to say, “Well, I had an interesting time and I had some thought-provoking ideas.” That's not enough. What's happening is that some people are connecting to each other, and on some level it's based on my assumption that what's going on inside your mind is broadly similar to the kind of thing that's going on inside my mind. Rather than that there's nobody there.

Samuel Salzer: I had that experience when I realized that many nonfiction books are ghostwritten.

Oliver Burkeman: I think there is a real art to good ghostwriting, which is probably more than just ghostwriting. It's interviewing and drawing out subjects. I forget the ghostwriter’s name, but Life by Keith Richards is a brilliant book. And that's only because a real skill is used—and probably the biggest skill is this relational one, right? Hours of chatting with Keith Richards.

But there are contexts where once you discover that's what happened, you feel scammed. I've definitely had journalistic colleagues who've interviewed celebrities on the occasion of the publication of their autobiography where it's unclear if the celebrity has read their own autobiography. I'll name no names. 

What gives a novel written by a human its value is not that it reached a certain level of technical skill that an AI could surely emulate, but that it was written by a conscious, emoting sensibility.

So something there is lost because again, and I feel more convinced of this with every passing month, the thing that matters is always relational in some way. If something is ghostwritten in a way that feels like it violates a relationship, then something is deeply lost.

It isn't a problem in contexts where there's no expectation. You don't feel that you're in a relationship with another sensibility in the dishwasher instructions. The fact that this could get a lot worse with AI doesn't mean it's not already a problem.

Aline Holzwarth: Another pre-AI problem that’s exacerbated by AI is information overload. How should we deal with this version of AI intruding in our lives?

Oliver Burkeman: I've written about this funny moment in the earlier history of the social internet, when some leading people sincerely believed that this problem would go away because the filters would get better and better. So you'd take all these haystacks and you'd have a way of picking out the needles that really mattered to you, and you could forget all the rest.

But Nicholas Carr, the technology critic, points out that the exact opposite has happened. If the supply of incoming information is effectively infinite, then when filters get better, all that happens is this massive amount of pure distilled stuff that you want. The problem, he puts it, is not finding a needle in a haystack, but how to deal with haystack-sized piles of needles. 

One has to gradually reorient from thinking of these supplies of information as things you're supposed to get through, get on top of, deal with, and think of them as things that we exist in the midst of and get to pick from.

So the metaphor I use is seeing your "To Read" piles as a river rather than a bucket. Not something that fills up and then it's your job to empty it, but something that flows past you. You pluck things from it that seem interesting, and you let the others go.

The metaphor I use is seeing your "To Read" piles as a river rather than a bucket . . . You pluck things from it that seem interesting, and you let the others go.

I sometimes think that I'm saving things for later in order to never return to them as well. I’m sitting there writing something, and then I read a post somewhere, and it triggers something in my mind. So I click save for later, and it’s like noticing a thought in meditation. You can let go of it. It’s gone. And then I can get on with the thing I was doing. 

Samuel Salzer: AI brings this promise of distilling the most important part of something into its essence. We've lived through some of this pre-AI with these services that say, “Read a book? Who has time for that? Here's a 10-minute summary.” Selling this idea that you can absorb all of the benefit from that book in 10 minutes. And that is obviously, I think you would agree, a false promise. 

Aline Holzwarth: As someone who hoards research articles, sometimes the point of the research article is not to get the incredible enjoyment of reading the research article, but just to know the findings so that you can apply them to your work. So that's where I'm torn on these summarizer tools because I do very much see an upside in distilling a research article to its main points.

The downside is that the AI summarizer tool doesn't think critically about the methods or the findings. It doesn't have the ability at this point to tell you whether you should trust the research or not. So you're not so much deprived of the enjoyment of reading it, but maybe the ability to critically analyze it.

Oliver Burkeman: Cal Newport, whose work I admire a lot, used to say that they are ideal as a way to reach a judgment about which book to then go and read in full without tricking yourself that you're going to know all about it. 

There are so many problems with the idea that you should give up reading books because of summarization services. And I'm not even getting to the point of how any books are going to be produced for these summarization services if nobody's buying the books. Separate question.

For a lot of reading, the reading is the point. When I read a book, certain things stick with me, and maybe I use them to trigger other ideas. Maybe I'll quote the book in a subsequent piece of my own writing. I'm the one who gets to say what is salient and important. A summary of what AI thinks the author thinks is most important is beside the point.

Even pre-AI note taking, unless you're studying a specific work for a university exam, when it’s your job to regurgitate what's in the book, your job is not to try and make a summary of what the author was trying to deal with. It's to figure out which bits trigger interesting sparks in your own mind. 

I'm sure I could think of a hundred books I've read where I can tell you, “Alright. The thing I really remember from that is X,” but who knows if the author thought that was a particularly important thing.

I've had that experience myself as an author. People say, “I think the thing that really stuck with me from your book was X.” And then I have to think. I did put that in, didn't I? It just wasn't particularly salient to me.

Aline Holzwarth: In Meditations for Mortals, you write:

“If there’s a single truth at the heart of the imperfectionist outlook, it’s the one to which we turn as we begin this final week: that this, here and now, is real life. This is it. This portion of your limited time, the part before you’ve managed to get on top of everything, or dealt with your procrastination problem, or graduated or found a partner or retired; and before the survival of democracy or the climate have been secured: this part matters just as much as any other and arguably even more than any other, since the past is gone and the future hasn’t occurred yet, so right now is the only time that really exists.

"If instead you take the other approach—if you see all of this as leading up to some future point when real life will begin, or when you can finally start enjoying yourself, or feeling good about yourself—then you’ll end up treating your actual life as something to ‘get through,’ until one day it’ll be over, without the meaningful part ever having arrived. We have to show up as fully as possible here, in the swim of things as they are.”

As a behavioral scientist, this is extremely interesting and fraught with complexity. So much of my time is spent trying to help people help their future selves. Saving for retirement, exercising now to be healthy later. I feel this real tension between whether I should be designing for people's future selves or current selves. 

Oliver Burkeman: I'm not sure I have a neat answer to resolve all this. Obviously on some level, it's both. I think there are a lot of ways in which a lot of us spend too much time thinking about our future selves, and I don't think that undermines the idea that there are ways in which we definitely should, and which people like you should be helping us to do so.

I think an awful lot of the project of trying to change your life, trying to get better habits and all this, is motivated by this idea that there's something both deeply wrong with you now, but also deeply provisional about life now. And the important thing is to “get there.” Of course when you get there, you're having the same thought about a new future point. So you're never actually showing up to life. 

We need to make sure that the ways in which we are taking care of our future selves don't become forms of punishment that vacate meaning from our present selves. 

I think being aware of that risk is totally compatible with using some of your present moments for judicious future planning. That can entail being a bit more indulgent and gentler toward your present self, making sure that your present self is actually having a good time. 

I hate any kind of response to this fascinating conundrum that ends in a compromise, but one simple response to this is to say that we need to do both of these things. We need to make sure that the ways in which we are taking care of our future selves don't become forms of punishment that vacate meaning from our present selves. 

Aline Holzwarth: Some of the more effective ways to get people to do the things now that will benefit them later are to make them more meaningful or fun. To take your exercise example, joining a rock climbing gym with some other dad buddies of yours, that could be something that achieves both of those goals.

Oliver Burkeman: So there we go. Beyond the compromise into the synthesis. These things are the same thing in some way. And I feel like that is the case incredibly frequently with this sort of puzzle.

Samuel Salzer: A few years back, I went to visit my dad, and I noticed that he was doing the dishes and didn't have a dishwasher. I was like, “Dad, have you ever thought about getting a dishwasher? It's convenient. It will save you time. You don't have to worry about doing the dishes anymore by hand.”

And he said, “I enjoy doing the dishes.”

I said, “You enjoy doing the dishes?”

And he said, “Yeah. A long time ago, I realized that I'll have to do the dishes so often in my life. My future is going to have so much dish washing on a daily basis that I’m gonna find a way to enjoy it.”

So he found a way to make dish washing his daily meditative practice, and I thought that was very inspiring. Maybe the dishwasher is overrated sometimes.

Aline Holzwarth: What is your most controversial opinion about AI?

Oliver Burkeman: A lot of very grand predictions about AI, either very optimistic utopian ones, or especially the really doomer predictions, are best understood as coping mechanisms that the person holding them is engaging in to deal with the fact that they don't have a clue what's coming. The controversial opinion there is that anybody who says with a strong degree of certainty that they know what this future is going to look like is dealing with their own emotions, but they don't have any reason for that certainty.

The post AI, Productivity, and Human Finitude: A Conversation With Oliver Burkeman appeared first on Behavioral Scientist.

We are happy to announce a new online conversation series, “Frontiers,” where we’ll host live conversations with people who are pushing the boundaries of behavioral science. The series will showcase behavioral science perspectives on contemporary issues, cutting edge approaches to applying behavioral science, and conversations on the craft of behavioral science and design.

The Frontiers Conversation Series will run throughout 2025 (about once per month) and will feature:

• Rory Sutherland (May) — Don’t Forget the Human

• Cass Sunstein (May) — Exploring Behavioral Science and Current Events

• Siri Chilazi (June) — Pursuing Workplace Equality in an Era of Backlash

• Daron Acemoglu (July) — Technology on the Rise, Institutions on the Brink

• Emily Oster (September) — Parenting Better Through Data

• Piyush Tantia (October) — How Can We Continue to Innovate in Behavioral Design?

• Kristen Berman (November) — Silicon Valley Vibe Check: What’s the Latest at the Intersection of AI and Behavioral Design?

Conversations will be led by Behavioral Scientist Editor-in-Chief, Evan Nesterak, and will run for 45-60 minutes. Below, you’ll find a full preview for each conversation, as well as the complete schedule (dates, times, duration).

The conversation series is open to Supporters of Behavioral Scientist—readers with a paid monthly or annual subscription—or those who purchase a ticket. This financial support helps power our work as an independent, nonprofit magazine. Find out more about becoming a Supporter or purchasing a ticket here.

Those who register for the series can attend live and will receive recordings after the event. And Supporters get a plus one—you can give one colleague, friend, or family member full access to the event.

We hope you’ll join the conversation series. If you have any questions feel free to get in touch by emailing us at editor@behavioralscientist.org. 

See you there,

Evan Nesterak, Editor-in-Chief

2025 Frontiers Conversation Series

Don’t Forget the Human
with Rory Sutherland on Wednesday, May 7

Rory Sutherland, Ogilvy’s witty adman, has a knack for noticing what others miss. And what frequently gets missed is the human side of a problem. Too often, Rory points out, we fall into the trap of turning everything into a “high school math problem,” where we think only of budgets, deadlines, and KPIs. We forget the human on the end of our product or service and think only of what we can track in a spreadsheet. 

“I always ask the question,” Rory writes, “What would’ve happened if you hadn’t given the brief for High Speed 2 [a rail project in the UK] to a load of engineering firms who immediately focused on speed, time, distance, capacity? What if you’d given the brief to Disney instead? They would’ve said, ‘First of all, we’re going to rewrite the question. The right question for High Speed 2 is: How do we make the train journey between London and Manchester so enjoyable that people feel stupid going by car?’ That’s the right question.”

What do we gain by putting humans back into the equation? And how can we do it better? That’s what we’ll speak with Rory about. Questions made all the more urgent as the rise of AI threatens to push humans even further out of consideration.

Rory Sutherland is the vice chairman of Ogilvy in the U.K, where he established their behavioral science group in 2012. He is the author of Alchemy, a TED speaker, the creator of Nudgestock, an annual event at the intersection of behavioral science and marketing, and an accidental sensation on TikTok.

Read Rory Sutherland’s articles on Behavioral Scientist.

Exploring Behavioral Science and Current Events
with Cass Sunstein on Wednesday, May 28

Cass Sunstein is one of the few people who might be able to make sense of what’s going on right now. Sunstein is a professor at Harvard Law School with experience working in two government administrations, and an author who has written about policy tradeoffs, campus free speech, and how to make the government simpler.

Rapid changes in U.S. government policy, particularly in higher education and scientific research, have created a new landscape that businesses, nonprofits, and universities need to navigate. Cass’s work can help people do just that.

We’ll ask him about the issues that he sees as the most pressing, what he perceives as temporary versus permanent shifts, and his advice for those charting their course through these changes.

Cass Sunstein is a legal scholar and professor at Harvard University. He served in government under the Obama and Biden administrations. He is the author of a number of books, including Nudge (with Richard Thaler) and Noise (with Daniel Kahneman and Olivier Sibony). He also edited Can It Happen Here?, a volume exploring the possibility of authoritarianism in the United States, and authored Simpler, which describes the ways the government can function more effectively based on his experience working in the Obama administration. He writes the newsletter, Cass’s Substack.

Read Cass Sunstein’s articles on Behavioral Scientist.

Pursuing Workplace Equality in an Era of Backlash
with Siri Chilazi on Wendesday, June 18

“Very few people are against fairness but we don’t all agree on what it entails,” write Siri Chilazi and Iris Bohnet in their 2025 book Make Work Fair. “Whatever you may think about the current DEI approaches being used in organizations, or however you feel about the current tenor of the public debate around DEI, we hope you share our ultimate goal: making work fair for everyone.”

Siri and Iris’s goal in Make Work Fair is neither to preach nor judge but to show “how we can (re)design work for real results based on the best evidence social science has to offer.”

In June, we’ll speak with Siri about this (re)design process and the science behind it. Siri helps lead the Women and Public Policy Program at Harvard Kennedy School and works with organizations ranging from Fortune 500 companies to start-ups on equality. In her role, she has seen the good, bad, ugly, and transformative. Companies that are all talk and no action. Others that continue to rely on their “gut” when it comes to hiring and promotions. And organizations that have fully integrated equality into their structure. 

When it comes to workplace equality, how can we distinguish what looks good from what actually works? Siri will fill us in.

Siri Chilazi is a senior researcher at the Women and Public Policy Program at Harvard Kennedy School, where she advances gender equality in the workplace through research and research translation. She frequently collaborates with organizations ranging from start-ups to Fortune 500 companies and leading professional service firms. She is the author of Make Work Fair (with Iris Bohnet), which offers an evidence-based approach to achieving workplace equality.

Read Siri Chilazi’s articles on Behavioral Scientist, including an excerpt from Make Work Fair.

Technology on the Rise, Institutions on the Brink
with Daron Acemoglu on Thursday, July 17

With Daron Acemoglu, we’ll get the big picture on where our world is headed. The 2024 winner of the Nobel Prize in economics has spent his career understanding how our social institutions and technology shape the world we live in today and the one we’ll live in tomorrow. 

As governments, universities, and other social institutions around the world face threats from inside and out, AI grows ever more capable, and the companies that control AI become ever more powerful. With institutions on the brink and technology on the rise, what are the greatest risks and opportunities for individuals and societies today?

When it comes to AI, Acemoglu fears we might “squander the AI opportunity.”

“The risk is . . . that AI will undermine our ability to learn, experiment, share knowledge, and derive meaning from our activities,” Acemoglu writes. “AI will greatly diminish us if it ceaselessly eliminates tasks and jobs; overcentralizes information and discourages human inquiry and experiential learning; empowers a few companies to rule over our lives; and creates a two-tier society with vast inequalities and status differences. It may even destroy democracy and human civilization as we know it. I fear this is the direction we are heading in. But nothing is preordained.”

Daron Acemoglu is a professor of economics at MIT and the 2024 winner of the Nobel Prize in economics. At MIT, he codirects MIT’s Shaping the Future of Work Initiative. He is the author of a number of books, including Why Nations Fail (with James Robinson) and Power and Progress (with Simon Johnson). 

View Daron Acemoglu’s Nobel Prize lecture.

Parenting Better Through Data
with Emily Oster on Wednesday, September 10

The world of parenting is a noisy one (and not just because of the kids). Conflicting and confident advice on the news, social media, podcasts, articles, and books can breed uncertainty at a time when the stakes feel their highest. Not to mention the parade of (often unsolicited) advice from friends, relatives, and other parents.

What’s a parent to do? How should they navigate questions about vaccines, breast-feeding, sleep-training, birth control, IVF, and more?

Amid all the noise, Emily Oster has emerged as a clear signal. Through her books and her website ParentData, she offers data-backed guidance while conceiving, during pregnancy, for the earliest years, when kids start school, and beyond. 

What’s most helpful about Emily’s approach is that her goal isn’t to tell people what to do, it’s to help them understand the data so they can make decisions for their families. In an age when Momfluencers sell silver bullets on the one hand, and parents feel like they're being lectured at on the other, Emily’s work stands out. She seeks to inform, not sell, dictate, or guilt-trip. 

At the start of the new school year, we’ll link up with Emily to find out what questions parents are asking, what the data has to say, and what she’s learned about helping parents make the decisions that are right for them.

Emily Oster is a professor of economics at Brown University. She is also the founder and CEO of ParentData, a data-driven guide to pregnancy, parenting, and family life. Her books also bring data-informed decision-making to pregnancy and raising a family and include Expecting Better, Cribsheet, The Family Firm, and The Unexpected (with Nathan Fox).

Read an interview with Emily Oster on Behavioral Scientist.

How Can We Continue to Innovate in Behavioral Design?
with Piyush Tantia on Wednesday, October 8

“Could we put good behavioral design in the hands of ordinary people, empowering them to improve their lives, homes, workplaces, and more?” Piyush Tantia asks in a recent article. 

In the article, Piyush explored whether we could create a “pattern language” for behavioral design—identifying a set of “context patterns [that] allow us to apply a behavioral lens to broader problems and entire systems more easily.”

For 15 years, Piyush has been at the forefront of behavioral design. The founding executive director of ideas42 and its chief innovation officer, he led the development of their behavioral design process, which has been applied to hundreds of projects reaching millions of people—projects dealing with financial inclusion, charitable giving, global health, higher education, the justice system, and more. 

With Piyush, we’ll explore how we can continue to innovate in behavioral design, including the possibility of creating a pattern language, how to develop a context mindset, and why he thinks the way we teach behavioral design needs an upgrade. 

Piyush Tantia is the former founding executive director and chief innovation officer at ideas42. For fifteen years he helped ideas42 develop into a leading applied behavioral science organization. He has been a visiting lecturer at the Princeton School of Public and International Affairs and frequently lectures at Harvard, Wharton, and Columbia. He was also a 2021-2022 fellow at the Center for Advanced Study in the Behavioral Sciences at Stanford University, where he explored the intersection of behavioral science and design.

Read Piyush Tantia’s articles on Behavioral Scientist.

Silicon Valley Vibe Check: What’s the Latest at the Intersection of AI and Behavioral Design?
with Kristen Berman on Wednesday, November 14 (previously Wednesday, November 5)

When I need a vibe check for what’s happening at the intersection of technology and behavioral science, I talk to Kristen Berman. For a decade and a half, Kristen, the CEO and founder of Irrational Labs has been integrating behavioral insights into some of the most familiar tech products from companies like Google, Microsoft, Lyft, Bumble, and LinkedIn. 

I spoke with Kristen earlier this year to get a better sense for what’s happening at the frontlines of AI and behavioral science in Silicon Valley. We’ve scheduled a follow-up for this November. We’ll chat about the latest advances in AI, how companies are integrating it into their products and workplaces, and the ways users and employees are responding.

We’ll also chat about the instances where hype is moving faster than the actual tech, what she’s most skeptical about, how AI is impacting behavioral design, and her predictions for the year ahead.

Kristen Berman is the CEO and founder of Irrational Labs, a behavioral design firm based in the Bay Area that helps companies build behavioral insights into their products and services. She is also the author of the Product Teardowns newsletter and host of The Irrational Mind podcast. Before all of this, she worked on financial inclusion at Common Cents Lab and helped Google start its behavioral science team. She also brings behavioral design into her personal life, starting the communal living environment Radish in Oakland, CA.

Read our interview with Kristen Berman and her articles on Behavioral Scientist.

Full Event Series Details

Dates (2025):

• Wednesday, May 7: Rory Sutherland — Don’t Forget the Human
  
• Wednesday, May 28: Cass Sunstein — Exploring Behavioral Science and Current Events
  
• Wednesday, June 18: Siri Chilazi — Pursuing Workplace Equality in an Era of Backlash
  
• Thursday, July 17: Daron Acemoglu — Technology on the Rise, Institutions on the Brink
  
• Wednesday, September 10: Emily Oster — Parenting Better Through Data
  
• Wednesday, October 8: Piyush Tantia — How Can We Continue to Innovate in Behavioral Design?
  
• NEW DATE: Friday, November 14: Kristen Berman — Silicon Valley Vibe Check: What’s the Latest at the Intersection of AI and Behavioral Design? (previous date was Wednesday, November 5)

Start time (for all events except Daron Acemoglu): 9:00 am San Francisco | 12:00 pm New York | 5:00 pm London | 6:00 pm Berlin

Start time for Daron Acemoglu (July 17): 12:00 pm San Francisco | 3:00 pm New York | 8:00 pm London | 9:00 pm Berlin

Duration (for all events): 45 - 60 minutes

Register: Reserve your spot here 

The post Event: 2025 Frontiers Conversation Series appeared first on Behavioral Scientist.

Income inequality has been increasing steadily in the United States since the late 1970s. In 1978, American CEOs were paid 31 times as much as the typical worker. As of 2023, that number has skyrocketed to 290 times as much.

What is behind this still widening gap? One prevailing narrative is that Americans just don’t care much about inequality—that in the land of hard work and opportunity, we all rest easy knowing that everyone is getting their just deserts. 

Leslie McCall has spent her career studying public opinion about inequality and its implications for policy and politics. McCall is a professor of sociology and political science at the City University of New York and associate director of the Stone Center on Socio-Economic Inequality. She investigates perceptions of inequality in the United States and around the world using data from long-running surveys like the General Social Survey and the International Social Survey Programme. 

Through her research, McCall has found that the narrative of American indifference to inequality misses the mark. Her work reveals a paradox that she details in her book, The Undeserving Rich. “Americans can at times express little enthusiasm for welfare state policies,” she writes, “and still yearn for a more equitable society.” In other words, Americans tend to think that income inequality is a problem, but many aren’t sure they want the government to be the one to solve it. 

Amid a backdrop of mass government layoffs and the increasingly visible influence of billionaires in American politics, I reached out to McCall to better understand how we ended up here. We discuss Americans' perceptions of inequality and wealth, who they think should do something about it, and how all of this plays out in today’s political scene. 

Our conversation has been edited for length and clarity.  

Heather Graci: You’ve found that Americans are more critical of the wealthy and less tolerant of income inequality than many believe. How would you characterize American perceptions of the wealthy?

Leslie McCall: The narrative is that people respect and envy the rich, and they want to be rich. So they don't want to support any policies that would reduce the rewards of those who are rich, because they think they're going to be rich. That's clearly not the case. I couldn't find any data that supported the notion that people think that they're going to be rich someday. 

One question that has been asked repeatedly over time is how much people think executives make, and then how much they think executives should make. You can see the difference between those two, and it's really, really dramatic. The amount that people think that executives should make is about one-quarter to one-third less than what they think they make. Then you have to remember that what people think that executives make is a major underestimate of what they actually do make. 

Even though Americans underestimate how much inequality there is, they still want dramatically less of it.

A lot of people will say, “Well, Americans just don't understand how much inequality there is. They underestimate how extreme the income distribution is.” That's absolutely true. But the problem with that argument is that even though they underestimate how much inequality there is, they still want dramatically less of it. I read a lot of research papers whose main point is, “Wow, look how much people underestimate how much inequality there is,” instead of emphasizing the fact that people want such a low level of inequality.

You've traced American perceptions of income inequality relative to its reality over many years, and you find that these two variables don't always move together. Although income inequality has increased pretty consistently over time, our concern about it has fluctuated. Why don't our perceptions of income inequality track with its actual level?

If people feel the economy is growing in general, that their wages are increasing, that their standard of living is increasing, and that they're not struggling financially, then it's not the inequality per se that matters. 

This is different from just simply the business cycle. It's not just going up and down with the unemployment rate. It's actually the extent to which people feel that when we're in a recession or recovering from a recession, everyone should be in that situation of struggling to get by. But if the rich are doing super well and the stock market is going bonkers, then that is seen as unfair. So you want to see, "Well, the economy's growing, great. But are people's incomes growing too?"

And when the economy’s growing but people’s incomes are not, that's when we're going to be upset about inequality?

Yes. The broader concept of opportunity that I use is this idea of plentiful jobs with good wages, and people's perceptions of whether or not that exists. I do think the reason why people care about inequality is because they ultimately care about economic opportunity.

Who do people think should be responsible for reducing inequality?

Oftentimes, the reason people think that the U.S. doesn't have more government redistribution is because, “Oh, Americans don't like redistribution.” Mainly, that comes from responses to questions about welfare. It is true that over time, Americans are much less likely to say that they support something called “welfare.” If you say “assistance to the poor,” then support increases.

Welfare was really demonized and racialized. It comes from a very racist standpoint that welfare is only for people of color and Black Americans in particular, and this whole set of stereotypes that were created intentionally by Republicans and conservatives to try to delegitimize those kinds of policies and reduce the welfare state.

If you ask Americans in general about the government and its role in reducing inequality, relative to other countries, a much lower share of people in the U.S. will say that they think the government has primary responsibility or should have primary responsibility for reducing inequality. That's clear. 

But I contend that the question itself is problematic, because if you ask about specific policies, there's very high support for those, even in cross-national terms, for what we call social insurance policies like social security, unemployment insurance, health insurance. There's virtually as much support in the U.S. for those as there is in other countries, even though it’s the government providing those policies.

We developed another question to get at the inverse of the idea that Americans don't like the government and don't want the government to reduce inequality, which is that individuals are responsible for their own economic fate. So yes, Americans pick the government less than people in other countries do, but does that mean that all the people who don't pick the government think individuals are responsible? 

So we asked [which group should be most responsible for reducing inequality] among a list of people and institutions: there was government, but then also private companies, low-income individuals themselves, high-income individuals themselves, and unions. And then there was an option for people to say that they didn't think inequality needed to be reduced. 

For working-class and middle-class people in high-income countries, their status is declining, and a lot of that is the result of these global processes that have to do with global trade, automation, and the power of finance.

We found that the category the most people selected was the government. It wasn't very high, but it was the modal category, like 38 percent. And then the next highest was private companies, 29 percent. If you add up private companies, unions, and high-income individuals themselves, then all of those together are higher than the government.

What that means is a very small minority believes that either income inequality doesn't need to be reduced or that low-income individuals are responsible. That really challenges the whole idea that America is this individualistic country, that it's all about individual responsibility. We don't find that. 

I'm interested and also kind of perplexed by this sentiment that private companies could be responsible for reducing income inequality because I don't understand why they would be incentivized to do so. Do you know why so many Americans look to private companies rather than the government?

I think what it means is that people want a fairer economy. I think it means that people want companies to create better jobs with higher wages, with benefits, with less inequality between the CEO and the rest of the workers because that's what we are used to in this country. In this country, we're used to our economic health and our economic gains being very much tied to working for a growing and successful large corporation. That's the way we think about economic success. So we want these corporations to do the right thing, basically. Now, whether people think that they're going to do those things … 

When we look at their policy preferences, people who select major companies and people who select government want virtually the same thing in terms of policy. They want worker representation on corporate boards. They want higher minimum wages. They want caps on executive pay. They want unions. They want higher taxes on the rich. They're actually very similar when it comes to their policy preferences. It's just that for whatever reason, when they see a question that has the government in it as a general actor abstracted from a specific policy like social security, then they back off.

If we make this a little bit more concrete, a lot of Trump's rhetoric, especially in 2016, was kind of anti-corporate rhetoric, right? Because remember he said, “I'm going to make sure that those companies don't send those jobs to Mexico. If you elect me, then your plant isn't going to close.” They saw him as someone who could be very influential and very tough on businesses that were laying off workers. You see a little bit more of that now, not exactly the same way, but it's more focused on other countries that seem to be taking industry and jobs away from the U.S. So that's where the tariffs come into play. 

I think essentially people are saying, “We want corporations to be better.” I don't think they're saying, “We think they are going to be better.”

Is the mismatch between all of these things—perceptions of income equality, the reality of income equality, and what to do about it—uniquely American? Or do we see this elsewhere in the world?

Almost everyone says there's too much inequality. Virtually no one in any country says that inequality doesn't need to be reduced.

Even if there isn't as high a level of inequality as there is in the U.S., or if it hasn't been increasing as much as it is in the U.S., most people feel like there's too much inequality. They live in these capitalistic societies that have all been subjected to—particularly among the richer countries—the trends in globalization, international free trade, and automation. All those things are making work more precarious, less remunerated. So I think that a more general process is going on, what we sometimes call neoliberalism—a lot more power for corporations relative to workers. It's true that even in countries like Germany and Sweden, the power of unions has declined over time.

Each country is a little bit different in terms of what their actual levels of inequality are, what is contributing to those levels of inequality, whether they're changing. But I think what's consistent is this sense that for working-class and middle-class people in high-income countries, their status is declining, and that a lot of that is the result of these global processes that have to do with global trade, automation, the power of finance, and just in general, that people at the top and highly educated people seem to be doing very well in the new economy. But because most people are not highly educated, they're not doing well. These things are pretty common across countries like the U.S., and it's also common that they haven't been dealt with. 

That's what gives rise to populism. It's a culmination of economic concerns and anxiety and insecurity, and forms of racism and anti-immigrant sentiment too. But I feel like you can't really disentangle those two things.

I asked before about who people believe should be responsible for reducing inequality in the U.S. Do you see similar trends across countries on that question as well? Or is this American belief in private companies an outlier?

What's really interesting is that there are a few countries like the U.S. Switzerland has very similar trends. Strangely, France too has a very high share of people saying that private companies [are responsible]. New Zealand too. The four big ones are the U.S., Switzerland, France, and New Zealand.

We think it probably has something to do with [the fact that] there's a lot of attention to excessively high-earning executives and wealthy people in all of these countries. Switzerland is pretty unusual in that they have a lot of nationwide ballot measures. A number of them have been about limiting executive pay. So it's been a very politicized issue in Switzerland. There have also been politicians who have really politicized the issue of high executive pay in France.

People aren’t so much thinking, “These great CEOs at these companies are going to reduce inequality.” No, they're more saying, “We don't want these corporations to be so unequal. We think it's unfair that the structure of inequality is so great within these companies.” That's our understanding of it when we look at the people who respond that way, and we look at their responses to other questions. It seems to me like it's really about the unfairness of corporations and, in particular, the low pay of most workers relative to that of the senior management.

Do you see a path forward today toward reducing income inequality in the U.S.?

There are both Republicans and Democrats who say they want to change the current structure of the economy, which has been termed “neoliberalism.” There's been a lot of discussion about needing to return power to the working class family, the middle-class family. Not just power, but economic security.

For the Republicans, for people like J.D. Vance and Josh Hawley, there are a number who want to shore up the traditional family. We kind of go back to the '50s—that's the “Make America Great Again” idea. Let's go back to a time in which there were breadwinners. The men were the breadwinners, but they made enough money to support their families, and then their wives could have children, and then they could take care of their children. 

Two-thirds of people want a major institution—the government or private companies—to reduce inequality.

Even in the modern perspective on this, in which [the breadwinner] could be a man or a woman, or it could be a same-gender household, an extended household, however you want to define today's households, they are having a hard time making ends meet. Childcare is really expensive. Housing is really expensive. There's a lot of writing now on the affordability crisis. All of this was exacerbated by the high inflation that was precipitated by the pandemic. 

We are definitely at a point where both parties are acknowledging that things need to change. The economic system, whether it's capitalism, whether it's neoliberalism, whatever you want to call it—globalization, technology, and automation. Something needs to change because all of these things are conspiring to limit the economic livelihoods and security of working and middle-class people. It's just that they haven't converged on the same solution.

Conversations about inequality can tend to get very partisan very quickly. What have you learned about how people on different sides of the political spectrum tend to think about inequality?

One of the ways we've tried to analyze this is to see whether there are partisan differences in responses to the questions about the government being the most responsible. We have one question that asks people about their support for the government reducing inequality, and then we have a completely separate question that asks people about their support for private companies reducing inequality. [Editor’s note: This is different than the multiple choice question about the group most responsible for reducing inequality discussed earlier in the conversation.]

When we look at responses to those two questions, we see a lot more similarity across all groups in the response to the private companies question. You have much more of a partisan difference in people's responses to the government responsibility question, but you have fewer partisan differences in response to the private companies being responsible for reducing inequality. 

Can you say more about the response to the private companies question?

Overall, there are more people, over a majority, who think that private companies should be responsible for reducing the pay [gap] between executives in major companies and unskilled workers in major companies.

It's usually like 57, 58, 59 percent who support that. The share who support the government's responsibility in reducing inequality is just under 50 percent. So it's like a 10 percentage point difference between the two.

When you just look at relative differences between the share of Republicans that support it versus the share of Democrats, that difference is much smaller when you're looking at the private companies. In other words, there's more agreement among partisan groups about the importance of private companies being responsible for reducing inequality. 

The last thing I’ll say is that if you combine the share of people who say the government is responsible or that private companies are responsible, it's about two-thirds of Americans. Two-thirds of people want a major institution—the government or private companies—to reduce inequality. That's a really different conclusion than if you only look at the government responsibility question, which is what everyone does. Then, what you get is that less than half of Americans want the government to reduce inequality. So that's where you get this conclusion that Americans are not very concerned about inequality.

The post Most Americans Dislike Income Inequality. But They Disagree About Who Should Fix It. appeared first on Behavioral Scientist.

Every day, another feature of society is poised for an AI revolution. How will AI change (big inhale) our jobs, relationships, parenting, schools, art, music, sex, war, public safety, public policy, public transport, scientific research, health care, food systems, factories, banks, insurance, retirement …? 

We can’t answer these questions about AI today. We don’t know where we’ll end up. But we can get a sense for where we’re headed. To do that, I spoke with Kristen Berman, who has worked at the intersection of behavioral science and technology in Silicon Valley for the past decade and a half.

I’ve known Kristen for about 10 years and I look to her to help me understand what’s going on in the tech world’s capitol. Since we both speak the same language when it comes to behavioral science, she has been an especially helpful translator.

Kristen is the founder and CEO of Irrational Labs, which helps companies build behavioral insights into their products and services. She is also the author of the Product Teardowns newsletter, where she reveals the behavioral principles underpinning today’s most popular apps and products. And she hosts The Irrational Mind podcast, where she interviews experts from a  range of fields—including winemakers, authors, designers, and product managers—to learn how they think about human behavior and behavior change. Before all of this, she worked on financial inclusion at Common Cents Lab and helped Google start its behavioral science team. (Full disclosure Irrational Labs has donated to Behavioral Scientist.)

So what’s her on-the-ground view of where AI is headed? 

In our conversation below, we touch on what she thinks AI developers could learn from behavioral scientists, how OnlyFans might improve your relationship with your doctor, the AI applications she is excited about, the ones that give her pause, and advice on what to do if AI is coming for your job.

Our conversation has been edited for length and clarity.  

Evan Nesterak: What is a blind spot that you think people developing AI have about human behavior?

Kristen Berman: One AI promise is a "do it for me” model. "Write my email for me. Tell me the best restaurants in this place for me." That is actually very aligned with behavioral science, with our methodology of making things easier for people. If we make something easier, more people will do it. It turns out a lot of things in the world are hard, and AI has the promise of making things easier.

I think the blind spot right now for these engineers is if you say, "Magically write my email for me," people don't really want it to send your email for you. People don't really trust the tone or the voice that it's in. We call it the Betty Crocker effect—the “add an egg” idea—which is that we value things that we contribute to more. We would probably trust AI more if we add our own two cents. My hunch is that this “add an egg” opportunity, where humans contribute and see their own effort, will be very important. 

At some point the tools will get so good and we will get familiar with them that we don't need to add an egg. We're just not there yet. 

Are there any other blind spots we should be worried about?

I think the biggest blind spot that anyone outside of the Valley could be worried about is the algorithmic bias that we are building and coding into our algorithms—biases that humans have. I'm very much in the camp of "AI will be easier to change than humans." Any kind of teaching that we do to get people to change their stereotypes or bias is very, very difficult.

I'm very much in the camp of "AI will be easier to change than humans."

I'll give you one example. For hiring, you can imagine that the AI is trained to accept more men because more men applied in the past and have been accepted. If you train the data on these types of hiring algorithms, you could then build in a bias against women. Well, it turns out people know this, and now you can train the AI to overcome the bias toward men. By understanding those types of probabilities and bias, you correct the training model to push it to the equitable solution. 

Human decision-making is tough. It would take a human to figure that out and convince their organization to make a change. We have the answer to how to overcome bias within AI. I am pretty stumped when it comes to changing humans and their own bias.

I was speaking with a friend who works in UX at a large financial firm. She said that there is a team working in parallel to hers that is essentially developing an AI that could take her role. She is also tasked with helping that team develop the AI. So she's stuck with the thought, "I'm helping develop an AI that could put me out of a job." 

This seems like tension a lot of employees are facing. We might hear company leaders say, "No, no, no, it's not going to take your job. It's going to make your job better, easier, more creative." Yet for some employees, there is a palpable sense that they’ll be replaced. What are you seeing play out in companies? 

Two things. One, companies are pretty nervous about telling their employees that their jobs are going to be taken. We've worked with a large financial advisor company, with thousands and thousands of advisors, and they publicly came out to their advisors and said, "AI will always be an assistant to you. It will not take your job." They call this idea human in the loop. A human will always be in the loop. I think this is probably false advertising and causing humans to have a little bit more complacency than is appropriate for this moment in time.

At Irrational Labs, we did a study where we asked a Prolific population of close to 800 people, "How likely is it that AI will take your job?" There are two ways to answer this question. In one world, you could be very fearful and terrified that AI will take your job. This probably will cause you deep unhappiness everyday, because you're worried. In another world, you could not be worried at all and say, "You know what? I'm smart, knowledgeable, empathetic, all of these things that make me human, and it just wouldn't take my job." 

You probably want a healthy fear, and that's not what we saw in the results. Only eight percent of the respondents said they believe that AI would take their job.

Even though we can be optimistic about society, it will be difficult to be optimistic about yourself if your job is at risk.

Your friend should be pretty nervous about AI taking her job, but not so nervous that she can’t sleep at night. She should be nervous enough to take reasonable steps to differentiate her knowledge, whether that be understanding how to control an AI to become the prompt engineer that would drive it, or moving fields. 

As a techno-optimist, I think our society, our GDP will be fine. We will not collapse with everyone unemployed because AI took their jobs. But that kind of hides the fact that some people in this mass job swapping will actually lose their jobs. So even though we can be optimistic about society, it will be difficult to be optimistic about yourself if your job is at risk.

The follow-up question in the Irrational Labs study was, "How likely do you think it'll be that other people's jobs are taken?" This, people are more worried about. When thinking about their own field, they responded that AI was two times more likely to take somebody else's job than their own. If it was somebody outside their field, the said AI was three times more likely to take somebody's jobs than their own. This represents the idea that we believe we are special, that we have something unique to offer, and this uniqueness, I'm going to label it essentialism, can help us keep our jobs.

I want to pick up on the essentialism idea, but before we do my friend wanted me to ask what advice you have for somebody who finds themselves in this kind of position?

My main recommendation is to use AI. Having an abstract understanding or fear of it may keep you in the status quo. So be the leader within your cohort or your peer group. Be the one to play around with the tools. It's actually very difficult now to do things well with AI. It still takes a lot of work. You can be one of those people who can figure out how to use it. When something does get displaced, you'll be the one that's going to stay at the company because you are one of the thought leaders there.

Coming back to this idea about human essentialism. What do AI developers think is fundamentally human that they wouldn't develop for? Maybe it's a moral or philosophical stance, "I am not going to develop for that." Or maybe it's something to do with a unique human ability, "Oh, humans are way better at that than what I think I could engineer." Or is it none of that, and anything is on the table?

I think anything is on the table. I'll give you an example. OnlyFans is one of the fastest growing sites that's ever existed. They just released some of their revenue and economics, and a large percentage of the population is on OnlyFans. And you'd think that these are really intimate relationships that people are creating, and yet the new thing is to have a bot be the creator. And so, you're actually talking to a bot. I don't know how to say …

Is this like a bot that is a porn bot, basically?

Yeah.

And then you're developing a human relationship with it?

Well, it's a real picture of a person that you may be buying, or a video of a person that you may be buying, but the conversation that you're having that you think is with a person could be, and will likely be in the future, a bot.

So if I'm the most advanced OnlyFans performer or worker, what I need in order to scale is to chat with more people, but because I'm only one person, there’s a limit to what I can do. So I created a bot that allows me to have chats with many more people around the world at one time for a certain fee. Now my business as one person has scaled incredibly. Is that kind of how it's working?

And they're negotiating. The bot is negotiating for you and doing better than you would do.

What do you mean, negotiating?

How OnlyFans works is you have to buy something from the creator, and that means there's a back and forth about how much it costs. Negotiating, you may say, is an essentially human characteristic. I can read your body language, I know your history, I understand the context. In reality, the bot is doing this quite well.

So basically, in this case, the person is still real, but these interactions and negotiations for what the real person will do are run by bots?

Yes.

Got it.

And this is actually very exciting for health care. I'll make a switch here.

Whoa. Okay.

Because you can imagine that if you met your doctor once, and you've had a great connection, now it's possible that when they message you—and maybe it's a bot—to take your medication or to come in for a checkup or ask you about symptoms after you've been prescribed a new medication, that you will engage with them.

While the OnlyFans seems like a crazy side market, these things will likely be pulled into highly regulated industries in some near future. It’s in these highly regulated industries like health and finance where the most good could be done. 

For instance, if you're going to get a loan for a mortgage, right now you should be calling multiple bankers to get quotes. The likelihood that people do that is very low because it takes a long time. You have to find a banker, you have to submit the loan application, usually you have to have a conversation with them, and all of this under the extreme time pressure of your mortgage or housing bid. If more people did that, rates would be more competitive. If these types of interactions were automated, it may actually have a pretty big upside for society.

Can you tell me more about the kinds of applications that you think will have a really positive impact on behavior? 

I do think more people will become creators. I'm somebody who doesn't draw. I don't sing. I don't make music. I'm bad at video editing. I don't think I have an aesthetic taste as much as other people do. That's kind of kept me out of the creative field. That's probably a shame because there's a level of personal exploration you can do when you're creative, and then there's a level of output that different people with different views can give, even if they don't feel like they are technically good in the artistic sense.

I can ask DALL-E to create an image, and then I can do 15 versions of it. My first one doesn't have to take me 30 hours to draw. I can say, "Now give me feedback on this image, and pretend you're somebody else giving me feedback." I think we will have people become doers, and maybe our world will be a little bit more fun because we'll all be creating. I'm excited for that.

Some of these tools are wild, but that doesn't mean people will use them, like them, or do anything differently because of them getting me to do something different from yesterday is very difficult.

The second thing, and this is the controversial one, is that we have these words like "loneliness epidemic." I actually think it's a friendship epidemic where people need more friends, more high quality friends, and that's actually a very difficult problem to solve because it's an offline problem. It takes a lot of vulnerability from people. There is an upside of having an AI as your friend or somebody just to get feedback on something, "Do you like this skirt I'm wearing?" or, "Hey, what do you think of this new video game?" I think it's actually very nice. 

You could say that, well, that will replace all of our real friends. It's possible. It's also possible to just be additive and we will actually have more. More is better when it comes to friendships and relationships. Again, I think I have a techno-optimist view on how we solve this loneliness epidemic with AI.

The third thing I'm excited about is self-driving cars. This is the biggest AI app that we ever have created. It will likely change the framework of society in the next decade. It already exists in San Francisco. When we think about AI, many times we think about it as a digital representation of our lives, and actually, self-driving cars are this physical representation of AI in our daily lives. And we know that commuting causes deep unhappiness. So if we can solve the commuting thing, we can solve some level of this time allocation problem.

This is a nice segue into my next question, because I think my answer to it would have been the loneliness chatbot. What are some of the applications you're most fearful about? What worries you, even as a techno-optimist?

This is the world in which every app can become a TikTok. I think that's scary. TikTok uses a lot of clever behavioral signals like how long have you stayed watching a video and AI to quickly create a map of your preferences and then serve you up very, very compelling videos. I challenge anyone to go on TikTok for 10 minutes and not stay on for an hour. Humans, regardless of their intellect or willpower, will likely fall to the algorithm. So we should be nervous about the attention economy. 

Again, the techno-optimist might say, "Well, we will have more algorithms to filter things from us, so I don't want to see all the news, and I want to see summaries of some things and not of others." Economist Tyler Cowen is a proponent of AI simplifying the attention economy because you get to create your own model of what you want. It's not somebody else's model; it's your model. So there could be problem-solving here. 

What do you wish AI developers would ask behavioral scientists? 

My sense is that a lot of AI developers, and I think this goes for any kind of technical product in general, are leading with the technical solution versus the insight on if people would use something, how they would change behavior, what preferences do they have? Some of these tools are wild, and that doesn't mean people will use them, like them, or do anything differently because of them. I think that is the humility that AI developers and technologists should be going into this with; getting me to do something different from yesterday is very difficult.

What do you wish behavioral scientists would ask AI developers?

"What's possible?" As a techno-optimist, I do think we're in for real change to our day-to-day, and this may not happen this year or next year, but over the next decade, we are likely to change a lot of our day-to-day workflows in our life. I think we should be curious about how AI can help in the problems that we're personally working on, whether it’s related to our internal productivity and operations or how we conduct our work or research.

Is there anything we didn't get to cover that you want to add?

I think folks reading probably think, "I could probably use AI more." There's probably a couple of apps you wanted to try and you haven't done it yet. Silicon Valley doesn't have this problem. Everyone is using the apps. You're meeting with people and they're working on some new AI app, so you're learning about it without having to read a blog post. That's actually not great for leaving folks behind. So there is this question: How do we help people bridge the intention-action gap so we're not leaving people behind?

The post Vibe Check: AI and Behavioral Science in Silicon Valley appeared first on Behavioral Scientist.

What do long-haul truck drivers talk about on the CB radio? Do ice cream truck drivers get sick of the ice cream truck song? What’s in a mystery novelist’s search engine history?

Dan Heath created his podcast, What It’s Like to Be…, to indulge what he calls a “missing curiosity” about what other people do at work. Assuming a 40-hour workweek, our jobs dictate what we’re doing, thinking, and feeling for about a third of our waking life. But even with the people we know best, Dan contends that we often don’t really know the answer to: What did you do at work today?

One year into the show, Dan has amassed an eclectic collage of answers. Leslie the TV meteorologist starts her workday at 3:30 a.m., picking through a mountain of atmospheric data until she goes live in Oklahoma City at 5:30. Eric the archaeologist also works early mornings, but he does his picking with an actual pickaxe, unearthing clues about what life was like thousands of years earlier at places like the Mound of Megiddo (biblical Armageddon). Beyond what people do, we also glimpse why they do it. Erin the ice cream truck driver talks about a girl who grew up coming to her truck. Now 25 and visiting her hometown, that girl made sure to stop by to see Erin for her childhood favorite, a cotton candy ice cream bar. 

These conversations are a refreshing complement to the scientific perspectives on work that we typically feature on Behavioral Scientist. One way we learn about work is by reading Barry Schwartz on how we’ve too often built organizations that strip our work of meaning, or Amy Edmondson on why people who feel psychologically safe report more mistakes but perform better. We learn from Cal Newport about the ways busyness makes us less productive, not more, and Nobel-winning economist Simon Johnson explains what AI might mean for work in the future.

Science is valuable precisely because of its capacity to uncover these kinds of patterns in what we do. But a focus on trends and tendencies can mask the individuals underneath. That’s why Dan’s approach is so valuable. Each conversation offers an intimate, n = 1 investigation about how someone spends their day. 

Recently, editor-in-chief Evan Nesterak spoke with Dan to ask about what he’s learned one year in, how this project has changed how he interacts with strangers, and the moments that have stuck with him—like the surprising sentimentality of interviewing a London cabbie in an increasingly Uber-dominated world. The conversation is below, edited for length and clarity.

Evan Nesterak: In your article introducing the podcast, you write, “Curiosity can be a means to an end. But couldn’t it also be … the end? A reward in itself?” Then you reflect on a few of the first conversations: “There is something that has been profoundly healthy for me about unclenching my brain and just listening. Relishing my own ignorance. And enjoying what it feels like to lose some of it.” 

So that was the spirit that you began the podcast with. Now that you've published over two dozen episodes, how is the experiment in slow curiosity going?

Dan Heath: I still stand by that mission, and this podcast continues to be a pleasure to work on. It's like that quote—there's a sense of relief in not having to scramble for the answers and to relax into observation and curiosity. 

There are continually surprises on the show, which is the fun part. Sometimes there are surprises driven by situational extremity—the long-haul truck drivers hauling 32,000 pounds of frozen dead rats. That is something that never would have intersected with my reality in the absence of the podcast. Right before we spoke today, I was interviewing an interior designer. She was talking about how she has a lot of rich New York clients. In one case, a piano had to go from one apartment to another, but you can't get a piano in the elevator. So they had to rig up a crane to hoist the piano through the skylight so that it could be transported to its new home. I love details like that. 

I don't think either of those speak to anything profound about the human condition, but I think that there is something exhilarating about getting to peek inside someone else's existence.

Before you recorded any of the pilot episodes, you had some expectations about what these conversations might be like. What was one expectation that you felt became reality?

To my great delight, there are people who share my unnatural curiosity about this piece of work. I always knew that if people listened to these episodes, they would get something from them. But I worried that it would be the way that we get something from asparagus. If we eat it, we probably feel good about ourselves, but we don't find ourselves walking around the street like, “Damn, I wish I had some asparagus right now.” My wish was that there would be people for whom this show was craveable, but I didn't know whether those people existed. I now know that they do. 

We did an elaborate listener survey a few months back, and one of the comments was from someone who said, “I find that this show has changed the way that I interact with other people in my life. I was eating out for dinner the other night and I found myself thinking, What would it be like to be this waiter? What's going on in their mind right now?” 

And I was like, man, I should print that on a bumper sticker for the show. That was the dream.

I like listening to the podcast during my commute. You might be a little hungry, you might be a little tired, and your tolerance for people might be going down. But listening to it, my mindset completely changes. I’m looking at the tram driver, or somebody gets on with a set of tools or art supplies or whatever, and I'm wondering, What's their day been like?

I appreciate that. For me personally, there's a similar therapeutic aspect where it's almost like an antidote to customer mindset. This is an embarrassing thing to admit, but sometimes when I'm having a really bad day, or a bad meal, or a bad flight, I'm composing the scathing review of the experience in my head. I hate that about myself and my brain, that there's this little critic that's going to get revenge. It's just silly and reptilian-brain-level stuff. 

I find that if I've just recorded an episode or listened to one during editing, it helps take the steam out. It makes me realize the obvious absurdity of venting at a flight attendant because the flight is delayed when you know full well they have nothing to do with it. So it's a reminder: It's not about you. Just being exposed to the complexity in different people's lives is a way of inoculating yourself against that sort of thing.

What are some of the conversations that have stayed with you most?

I don't claim this as the most important, but there were several times this past year when the theme of declining professions came up. The most recent instantiation of this was when I interviewed a London cabbie. There's almost no question in my mind that the London cabbie, as it has existed, will not be around 20 years from now. 

I felt a real sense of depression after that episode. To be a London cabbie, you have to pass what some people have called the hardest exam in the world in any domain. It's called “The Knowledge,” which is an appropriately majestic name for the test, where you have to memorize not only every street and road in Greater London, but also landmarks, statues. You have to know restaurants to the point where the examiner might ask you to take them from place to place and only mention the name of the chef, rather than the address or the name of the restaurant. So you have to know not just London as a map, but London as an ecosystem.

Then Uber comes along. Now if you want a ride in London, you can either ride with the people who have basically rebuilt their brains, or the person that signed up on Uber that day because they wanted to take people around in their Camry. Uber is probably cheaper, and probably a little more convenient. I just keep turning this around in my mind: How sad should we be about London cabbies going away? I personally feel very sad, but is it irrational? Would I have mourned the phase-out of carriage drivers?

There's this sense that we're supposed to just let go of things like that in the spirit of technological progress. But what really burns about this particular example is it doesn't feel like, Oh, technology has opened this new door and we've gone from the horse to the car. This feels like regulatory arbitrage. The reason Uber is going to replace the London cabbie is not because they're better, but because they've done away with any standard that existed for this other set of people. 

So that's one thing that I didn't see coming that I don't know quite what to do with. It feels overly romantic to think, “Oh, woe is me. Every career that goes away is a lost language or an extinct species.” But it just feels wrong that something as proud and as distinguished as this career could be wiped away. 

What job would you most like to do of the people you've interviewed so far and why?

Two that I interviewed almost back-to-back were a mystery novelist and a forensic accountant. I found myself fantasizing about a mystery novel series with a forensic accountant as the protagonist. So I was going to combine both of them into one master profession. Maybe I still will someday—I'm not ruling it out. 

At the end of the podcast, you help us speak the language of your guest’s profession. You ask them for a word or phrase that only those in the profession would know. What's a word or phrase you learned that sticks out in your memory?

One of my favorites was when the Secret Service agent talked about the phrase “left of boom.” Boom is the bad thing, and left of boom is what you're going to do to prevent the bad thing from happening. So you want to be left of boom. The crisis PR communicator used the expression "break into jail," which means when companies that have done something stupid actually communicate too much about it, which then makes other people aware and interested and appalled, though they otherwise wouldn't have heard of it. And the archeologist explained what a “sherd” is—a sherd is a broken piece of pottery, as distinct from a shard, which is a broken piece of glass. I like that one too.

I’m curious if you think there are lessons that people who are working in behavioral science might take from these types of conversations.

For me, the best role the podcast could play for a researcher is in the discovery phase. All of us in any profession, researchers included, live in a bubble with people around us that have similar jobs, similar socioeconomic status, and so on. I think listening to the way people talk about their work and the forces that play on them—what delights them, what stresses them out, recurring tensions in their role—I feel like there are a lot of seeds for academic inquiry in there. My brother Chip and I, in one of the books we cowrote, said that data is basically an aggregation of individual stories, and one of the most powerful ways we can learn more is by diving down to the level of those stories. That's what the podcast does.

The post So, What It’s Like to Be…? A Year of Conversations about Work appeared first on Behavioral Scientist.

"A thousand years of history and contemporary evidence make one thing abundantly clear,” write newly minted Nobel laureates Daron Acemoglu and Simon Johnson in their book Power and Progress. “There is nothing automatic about new technologies bringing widespread prosperity. Whether they do or not is an economic, social, and political choice." 

This summer, I interviewed Johnson about the relationship between technological progress and prosperity, including how societies have made these choices in the past and what our decisions about the current wave of AI could mean for our future.

The occasion for our conversation was Behavioral Scientist’s Summer Book Club, Techno-Visions. We read Kurt Vonnegut’s Player Piano about a future society run by a special class of engineers, their supercomputer, and the people at the mercy of both. Player Piano is Vonnegut’s warning of what could come to pass if we prioritize the needs of machines over people. It’s a vision of a possible future that hasn’t proven as false as we might have hoped. 

“At the heart of Vonnegut’s Player Piano and our own uncertainty about the future lies the same question,” I wrote in the introduction to the book club. “Where exactly is the technology we’re developing taking us?” 

In our conversation, Johnson helped make sense of this question through the lens of economic history. He also shared his perspective on the kinds of work AI will likely transform, how to better evaluate the utopian and dystopian visions of AI, and the tension between building what we can versus what we need.  

Our conversation was originally intended exclusively for the Summer Book Club. But after Johnson was awarded the Nobel, we decided to bring the conversation to our entire audience. It has been edited for length and clarity. 

Evan Nesterak: The level of investment and interest in AI makes it seem as if our society is on the verge of a tremendous change. Depending on who you talk to, that change might be catastrophic or it might be wonderful. Do you think we're on the precipice of some unprecedented societal transformation? Or do you think change will be less pronounced than it’s being made out to be?

Simon Johnson: My current assessment—and of course in this age of technological development we have to reserve the right to change our opinions tomorrow if we see more new things coming—is we are not on the precipice or about to experience a sudden acceleration. But if you take the long historical view, the United States has for most of the past 150 years generated technology and changed itself in the light of that technology faster than any human society has ever done. The United States has reinvented itself sometimes for better and sometimes for worse, repeatedly. My business as usual involves a lot of societal transformation, but not the sudden acceleration and not the sudden bliss point, or singularity, or other things that people imagine.

What sectors do you think are most likely to transform, and who might find themselves displaced? 

AI is a form of automation. Instead of replacing people doing manual tasks with their hands, we're replacing people doing tasks with their minds. The key element is, How routine is that task? This is something that my colleague at MIT, David Autor, has built his career around measuring. He, Daron Acemoglu, and I run a center at MIT that attempts to follow these issues.

It seems to us and to others who look at this is people who have jobs with a relatively routine component [are at risk of being displaced]. Customer call centers handling routine matters, for example, or people doing paperwork that is essentially filing the same thing over and over again, or people who translate or modify existing materials where the modifications are all quite small. One CEO called these positions—and this is not my term and it's not a nice term, but he's a CEO and he employs people, so I think it tells you something—he called them “cut-and-paste jobs.”

When you talk to people at LinkedIn who have a lot of data on postings, positions, and transitions, they talk about it being not the bottom 20 percent in job compensation and not people who are doing a lot of interaction person to person. But between the 20th and 30th percentile, there are a lot of people who have relatively routine white-collar jobs and those people I think are most immediately in the line of fire.

A fundamental point you make in Power and Progress is that prosperity as a result of technological progress, far from being guaranteed, is a choice. Could you tell us more about the several of those choices societies have made, and when it's gone right, and when it's gone south?

The modern story begins about 250 years ago with the advent of what we now call industrial technology—machines being applied to various tasks, either previously done by humans or not done by humans at all. What's really interesting about the modern age is we have continued to innovate and create new machines to do things. Productivity has increased, and productivity changes have been pretty steady. 

But shared prosperity, the key word is shared, that has been very different. For the first 80 to 100 years in the Industrial Revolution, people built enormous factories never previously imagined. They put together the silk factories, then the wool factories, and then of course the cotton factories. Most of the people who worked in those factories did not live better than they or their forebears had. In fact, a lot of people who were in the cotton industry as skilled weavers, for example, they did well when spinning was mechanized at the end of the 18th century. They did badly when weaving was mechanized. And that generation and their kids struggled to gain any kind of prosperity or decent life in that modern economy.

Change also came as machinery spread and as industrialization became a broader process—a lot more opportunities and new tasks were created. I would highlight the creation of the railways. Railways were providing long-distance transportation, obviously, which had previously been offered by horse and coach, which is pretty uncomfortable and you weren't able to move a lot of stuff, or by canals, which was extremely slow.

All of a sudden, you could pick up milk at the farmer's gate at four o’clock in the morning and get it into London in time for people to buy fresh milk in the market. You could go to the seaside for a day to Blackpool, or Brighton, or one of these other places that belong in British folklore, and see things differently, and meet different people. That had never been possible.

This is obviously a bit metaphorical but also instructive. The number of horses involved in transportation in Britain went up as the railway spread, not down, because horses were used for local connections. If you read a Sherlock Holmes novel, he takes the train out to Sussex, he comes back by train, and he takes a horse-drawn cab back to Baker Street.

Now that obviously didn't last forever. The horse was replaced by the motorcar. But there was an overall expansion of opportunities, there was an increased demand for labor. A lot of these jobs in the 19th century, good jobs, important jobs, did not require a formal education.

Changing the nature of technology did play out in a way that helped a lot of people, primarily because it increased the demand for people who didn't already have a lot of formal education. It created new opportunities, people got more education, and the trade union movement developed, a very important part of sharing prosperity. 

So there's your negative and your positive, all in the first 150 years.

Over the past year or two, we've been inundated with different visions for the future based on AI. What I appreciated about Power and Progress was that you were looking at what happened in history when similar visions were being proposed to the public. As we read about AI or we hear tech executives say things like, “This is going to completely change your life for the better,” what questions should we be asking to be thoughtful consumers of these visions? 

I think the problem with tech visions, and these exaggerated views on either side, are that they focus on the technology itself. What we should really focus on is, What's the problem you're trying to solve? What is it that you're trying to address? Is it homelessness? Is it inequality? Is it something else about opportunity? There's a long list.

Just saying, "We have invented this thing. It will have fantastic effects." Well, many people have said that. Historically, it turns out to be much more complicated.

We should focus on, What is it we want to change about our society or the way we live individually or collectively? And how do we mobilize technology to improve childcare, to improve healthcare, to prevent or anticipate the development of certain kinds of cancers?

Because if you don't articulate those goals, and share them, and get people to buy into them and mobilize resources, chances are you won't achieve it.

If the U.S. positioned itself more as the problem-solving hub of the world looking for win-win solutions, that would be quite transformational. I think the potential is there, and that would generate a lot of good jobs in the U.S., and it would help a lot of people around the world. But it's not the zeitgeist.

You write in Power and Progress that a lot of the early pioneers developing computer technology were rebels or hackers pushing back against large corporate structures. Could you talk about the origins of modern computing technology and what the hopes of those early technologists were versus where we ended up today, with the concentration of power and influence in a handful of executives at a handful of companies?

This is sort of the paradox of the modern computer industry. Remember, initially, the computer business was highly concentrated, in part because of the research and development money needed, and in part because of the winner-take-all characteristics of a government contract. IBM in the early 1970s was the most valuable company on the American stock market. 

The hackers who rose up around semiconductors started to think about ways you could hack your way around or build alternatives to this very Vonnegut type of corporate structure—top down, rigid, hierarchical. Would it be possible to build something that was more anarchic, more decentralized, more empowering? 

And of course that is what they built. We are using technology descended from that, you and I today. People with a lot of education have done well over the past three to four decades. It's not just a few tech people who've done well. But there is a big winner-take-all dimension to this. 

Neal Stephenson's Snow Crash is one of the most prescient science fiction novels, because he anticipated the way technology would erode a lot of existing social norms and compacts, and also put a tremendous amount of power in the hands of a few, in his case, telecoms executives. 

It's a paradox that they believed they were creating something that would spread power much more broadly. They put a lot of opportunity in the hands of highly educated people, and people with some specialized skills, who could build global brands, and they contribute to the disempowering of many Americans. As we've discussed already, it's a winner-take-all digital world.

One of the things that I've heard predicted is that it will be possible for a single person to build a billion-dollar company alone by leveraging the capabilities of AI. Is this possible? And even if it is possible, wouldn’t it rebalance over time if multiple people have access to the same powerful technology?

I think it is entirely plausible that the so-called winner-take-all characteristics of the digital transformation we've seen over the past three decades plays out in a way that continues and exacerbates inequality across multiple dimensions. Sectors will change, technologies will change. It's not that there's a lock-in forever. There's competitive pressure in an economy like the United States, but there is this irresistible tendency for concentration. That does mean at the individual level, like you said, one person can leverage their skills and their insights through algorithms and have an enormous impact, an enormous reach, and a big market cap.

I could imagine the next science fiction novel, perhaps building on Player Piano, where there is a single person running a company that has immense control and leads to a two-tiered society. Somebody from this era will have to write that one. 

Or the AI will run itself. Autonomous AI where it receives some initial human instruction. This is of course a fascination in the science fiction literature among the techno-optimists, but the techno-pessimists too, that AI will create further AI, and be better at creating further AI than we will. And that further AI will receive instructions, some of which could be intentionally malicious, some of which could be not intentionally malicious but will just turn out to be bad for humans. I think it's a serious possibility that we should worry about.

I want to ask for your take on a conversation I had with a friend who runs a technology company. He was arguing that AI is going to make productivity higher across the board. My sense is that we only have so many hours in the day to consume something, whether it's a book, or Netflix, or time with our family, whatever it might be. So if we're able to create all these new things much, much faster, there's still a limit on what one individual can consume. The question is, Where is all that productivity going to go? There's a fundamental limit on how much more I could add to my life. Even if we could produce at 10x, I don’t feel I could add 10x to my life. 

One important dimension of productivity and one difficulty of measuring productivity in the modern world is that people change what they consume over time. We try to measure that by the value, by how much they pay for it, but that's pretty imperfect. So yes, watching 10 times Netflix probably is not appealing, and probably not good for your health either. But other things may change, your capabilities or something about how you spend either your working time or your leisure time will change almost certainly.

One thing that economists have expected for a long time—but economists are really bad at predicting the future—is that we'd have a lot more leisure. One way you might see high productivity is everybody only works three days a week, which is also not so far from the Vonnegut vision in Player Piano. Except there, some people are working full-time and feel very fulfilled, and some people are doing makework and feel pretty frustrated.

But what if the gains from higher productivity then and now were more evenly spread? And what if we all agreed to dial back on the intensity of the working life? Wouldn't that be better? More leisure, more time to yourself, more creative time, more time for your hobbies.

Productivity can and will continue to increase. I don't think it's going to accelerate massively, as I said already. And you're right, there are limits on what we consume and of course limits on what the planet can produce. If everybody lived at the income level of the United States, you'd need several more planets to produce that amount of stuff. But one way we might take high productivity gains is by using resources more effectively, all kinds of resources, and also by respecting the planet and also by limiting climate change and so on. 

There seems to be a difference between articulating a goal and then marshaling the tech to that goal, versus creating the technology simply because we can create it. There’s a line that Jeff Goldblum's character says in Jurassic Park, "Your scientists were so preoccupied with whether or not they could that they didn't stop to think if they should.” I think that's a theme Vonnegut is exploring in Player Piano, an idea about technology that he was partially inspired to explore by Norbert Wiener, whom you quote in the opening of Power and Progress. 

I'm very glad you picked up on the Norbert Wiener point because my interpretation is exactly that Vonnegut was reading Wiener's work then using it in the right way. The epigraph for Power and Progress is a quote from Wiener, 1949: 

"If we combine our machine potentials of a factory with the valuation of human beings on which our present factory system is based, we are in for an Industrial Revolution of unmitigated cruelty. We must be willing to deal in facts rather than fashionable ideologies if we wish to get through this period unharmed." 

I think Wiener and Vonnegut should be read together. Wiener, of course, was a brilliant mathematician and engineer of cybernetics, a forerunner of AI, if you like.

So I think that, and the point of writing the book was to say, all of this is within our control. This is not the 1750s. We are not struggling for political representation. We are not oppressed by the remnants of an aristocratic class or whatever merchant industrialists are rising. This is a deeply democratic, articulate, educated society in the United States and also other parts of the world. We can and should have these debates, and we should make the choices, and we should decide what we want to pursue.

But you're right that the default remains that we will be drawn repeatedly back to what people can invent, the path of least resistance. Sometimes that delivers good things. Sometimes you can tweak it to deliver good things. And sometimes it delivers really bad outcomes from most people. So let's be aware of that, and let's think about technology on that basis.

This article builds on an earlier version published for Behavioral Scientist’s Summer Book Club, Techno-Visions.

The post A Conversation with Simon Johnson about Technology and Prosperity appeared first on Behavioral Scientist.

When I picked up Michael Muthikrishna’s book, A Theory of Everyone, I wasn’t expecting energy to play a leading role. But Muthukrishna makes a compelling case that energy should be central in how we understand ourselves and how we design our world. 

By energy, Muthukrishna means everything that helps power life—photosynthesis, ATP, fossil fuels, renewables, and, possibly in the future, fusion. Energy is “the single most important quantity in the universe,” he writes.

Muthukrishna’s goal in A Theory of Everyone is to provide a unifying theory of who humans are and where we’re going. This means understanding the development of life across millions of years and human life across hundreds of thousands. The common constraint for all life, he writes, is the ability to find and use energy. 

“We cannot move without energy, cannot reproduce, cannot do anything at all,” he writes. “At the heart of this theory of everyone is a quest for the capture and control of energy.”

I previously spoke to Muthukrishna about how a theory of everyone could help social and behavioral science progress from a young science to a more mature one. In part two of our conversation, we discuss energy’s relationship to our behavior, particularly as it relates to cooperation. Muthukrishna argues that today’s falling energy ceiling—meaning energy is becoming harder to access—is shifting humanity from a place of overall abundance, triggered by the Industrial Revolution, back to one of scarcity. 

“Till the 1700s the energy ceiling remained low. The amount of work our ancestors could do was a function of their own manual labor and the labor of their work animals,” he writes. “With so little excess energy, no matter what we did, no matter how clever we were, no matter how hard we worked, there was a limit to what we could achieve.” 

Harnessing fossil fuels during the Industrial Revolution changed that, leading to an explosion in population, innovation, health, and wealth. Since then, he writes, “we have come to take energy for granted.”

Muthukrishna wants us to stop taking energy for granted. “Continued progress, peace, and the civilization they create require us to get to the next energy level,” he writes. Many of the societal crises simmering around the world—like unequal concentrations of wealth, antidemocratic sentiments, immigration pressures—will get worse as the energy ceiling continues to fall, he argues.

People who care about solving these problems need to understand not just their psychological or behavioral dimensions but also how energy factors in. “Once you see the links between energy, innovation, cooperation, and evolution,” he writes, “you can’t unsee them.”

Our conversation has been edited for length and clarity. 

Evan Nesterak: Why is understanding energy essential to understanding ourselves?

Michael Muthukrishna: My interest in energy comes from my interest in cooperation. One of my contributions to the literature is to say that we’ve got all these mechanisms for cooperation: inclusive fitness and kin selection, direct reciprocity (you scratch my back, I scratch yours), indirect reciprocity, reputation (I don't know you, but I know of you, so I'll work with you because I heard you’re a good guy). But there’s a problem. All those mechanisms exist at the same time, and they actually undermine one another.

Take corruption. Everybody thinks corruption is a puzzle to be explained. It’s not. Corruption is easily understandable. It’s the natural state for humans—the kinds of things we call corruption are really lower scales of cooperation undermining higher scales.

The kinds of things we call corruption are really lower scales of cooperation undermining higher scales.

If a president gives a contract to their son, we say, Oh, that’s nepotism. But it’s inclusive fitness, kin selection, undermining our institutions. If a manager gives a job to a friend, or a friend of a friend, that is direct or indirect reciprocity undermining our meritocracy.

If it is the case that lower scales of cooperation are more stable and undermine higher scales, how do we live in a world where you and I could have met in the same room and we wouldn’t be too afraid of one another? If this were a few centuries ago, and we’re from different parts of the world, it would be a weird encounter. There’d be some trust issues, right? 

If you look at graphs of pretty much any metric of human progress (like child survival rates, size of polities, energy capture, overall wealth, health care, global population) you notice something weird. Everything is flat from the beginning of history until about the Industrial Revolution, and then there’s basically a vertical takeoff to the point where, as Ian Morris puts it, it makes a mockery of everything that came before.

When we build these mathematical models of cooperation, we deliberately set them up as dilemmas. They are only interesting insofar as it is better for me to do something for me than it is to do something for the group. But we forget the model is just a model and the world is the world. 

The question isn’t, “Do I contribute to the group or am I a defector?” In reality the question is, “What level do I cooperate at? Do I just do things for myself? Do I do it for my family, for my friends, my region, my group, my country, or the world?” Different people cooperate at different scales.

We built some models of this using a stag-hunt game [discussed here]. We looked to see how you could transition between these levels. There’s pretty much nothing that would allow you to transition between these levels except literal energy capture.

Just energy?

Energy is different to everything else. When I was explaining this to some of my colleagues in economics, they asked things like, “But humans need water as well. Why isn’t water the most important thing?”

I'll tell you why. Because if you have enough energy, if you have literally unlimited energy, you can get water. But it doesn't matter how much water you have if you can’t get energy.

Energy has moved us into a positive-sum situation. If you have enough energy, it incentivizes, in terms of payoffs, working together to access and use that energy to outcompete others. The story of humanity is that we cooperate and we compete, and then we cooperate in groups to compete with other cooperative groups. The scale of cooperation that is adaptive is the scale at which I am not better off in a larger group.

You write that we need to think about energy differently today than we have over the past century. We can no longer assume abundance. 

If you look at energy return on investment, which is a measure of excess energy, all of the metrics look the same. The return on energy has been collapsing. In 1919, one barrel of oil found you another thousand barrels. That’s a huge amount of excess energy. In 1950, a barrel of oil got you another 100. By 2010, one barrel of oil got you another five.

You can think of the space of the possible (what we are cooperating to access, if you like) as being constrained by that energy ceiling and an efficiency floor. Because a lot of modern engineering, and certainly economics and psychology, was formed after the Industrial Revolution, it’s focused on efficiency. It’s not worried about the ceiling because the ceiling was so high for so long. That’s why economists think that we’ll always be able to find the technology to get us out of the situation we’re currently in. It’s not true. 

What level do I cooperate at? Do I just do things for myself? Do I do it for my family, for my friends, my region, my group, my country, or the world?

Efficiency has been decreasing and there’s a limit. At some level, there’s a certain number of joules needed to heat a building. And you can be as efficient as you want by keeping it all in, but there’s a minimum amount. There’s a limit to efficiency. The ceiling is collapsing on us. So the space of the possible is shrinking, and it’s shrinking our capacity for cooperation.

So the constraint on our behavior is our ability to tap into the energy of the planet?

On an island, for example, larger animals shrink as they evolve. There’s just not enough energy to become a large animal. Same thing with groups. It’s easy to be prosocial when there’s more to go around, and there’s more to go around the more energy-rich and cooperative you are. 

Imagine you’re driving your car in a car park and some asshole drives in and takes your spot. You're going to behave very differently if there are tons of spaces versus if you've been driving around for 30 minutes and that was the one you suddenly found.

The payoff matrix is becoming more of that dilemma, the less energy that we have. That is the situation that we find ourselves in today. All of this talk about degrowth, it’s nonsense. It’s going to make things worse, because it’s going to return us to a zero-sum situation. If the economy slows down, it means that your win is my loss, because we’re taking from a limited pie. The alternative approach to climate change, to the climate crisis, to conservation, and so on is energy abundance.

If our energy ceiling is shrinking, which is going to erode cooperation, what’s the thing we should be focusing our cooperation on?

One thing is to staunch the things that are undermining cooperation, the fractures in society. The second thing is to use our cooperative capacity to bring more people on board to tackle the problems we face. That means equalizing opportunity, which means tackling things like inequality.

This is brain power that could be used to unlock cheaper energy that then makes everyone better off?

That’s right. Unlock cheaper energy, unlock more innovative ways to use energy, or make that next scientific discovery that completely shifts the way we live (AI, the internet, transistors, all the way back to fire). 

There’s a time constraint on this. We have the maximum probability, within our lifetime, of cracking that very difficult problem by letting a thousand flowers bloom. Some people think we just need to wait for Einstein. You don’t know who the next Einstein is. You don’t know who the next Newton or Leibniz are. You want to create a world where every generation has the opportunity to try to run that race from a relatively equal starting point, so that the best take us ever forward, and that makes all of us better off.

If there’s truly energy abundance, we’d get that vertical take-off once more. . . Right now, everything is slowing down.

In terms of energy, what should we be aiming for right now? I think the low-hanging fruit, the easy answer here, is we should be investing in the technology we have right now, which is nuclear. We know we can do this. But we’re suffering from the fears an earlier generation faced with an awesome new technology that looked a little scary. But we’ve tamed it. We’ve figured out ways to deal with that more effectively. There are still issues, which I describe in my book, proliferation being one of them.

The next step that we should be doubling down on is fusion. Fusion is a game-changer. The next step from fusion is I guess antimatter reactions. Beyond that, it’s so far in the future we won’t look like humans.

If energy becomes virtually free, the promise of fusion, what becomes the constraint on human progress? Is it time to innovation? Is it something else?

If there’s truly that kind of energy abundance, we can go back to the rapid rise in the graph I described earlier. We’d get that vertical take-off once more. Right now, everything is slowing down.

So if we have cheap energy, are we in kumbaya mode at that point?

No, not immediately. Cheap energy allows for the possibility of higher scales of cooperation, but you can still be trapped for a very long time in suboptimal, lower equilibria. The Democratic Republic of the Congo has plenty of energy, but it’s a shit show. The people accessing that energy are not the people of the Democratic Republic of the Congo. It is a small set of elites collaborating with foreign oil companies and other companies that are accessing their resources for the benefit of the people in another country and not the DRC. Even with abundant energy, we have to get past some of the fractures that we’ve created in our society and there’s a lag on that.

Let’s say we do have energy abundance. Aren’t there psychological, sociological, or reasons that might hold us back? 

The fractures are always there. The question is when we might cross those boundaries and try something new. Under conditions of abundance, we might want to try something new because what we can do together is more beneficial per person than it would be for us to go at each other’s throats. If there isn’t enough to go around, then I do need to lock down into my group and differences matter a lot more. But when you’re in an abundant society, it allows for the possibility of trying something and it being beneficial.

Abundant energy is not a complete solution, but it is a better position than energy scarcity. 

I would say something a little stronger than that. I would say that energy abundance is a prerequisite for high scales of cooperation and a peaceful, prosperous society.

Read part 1 of our interview: Michael Muthukrishna wants to integrate the science of human beings, from genes to culture to our environments, into ‘a theory of everyone.’ Doing so, he says, is key to advancing social and behavioral science. “Right now . . . we’re in that pre-Darwinian world where you notice that there are butterflies and birds have different beaks. Or the alchemy world where Newton is turning lead into gold,” he told me. In part 1 of our interview, we discuss how that might happen. — Evan Nesterak, Editor-in-Chief

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The post Understanding Energy Is Essential to Understanding Ourselves: A Q&A with Michael Muthukrishna (Part 2) appeared first on Behavioral Scientist.

No one can accuse Michael Muthukrishna of thinking small. His first book, titled A Theory of Everyone, sets out to explain “who we are, how we got here, and where we’re going.”

Muthukrishna, a professor of economic psychology at the London School of Economics, weaves together thinking from psychology, economics, evolutionary biology, anthropology, and other fields to offer a theory of us. 

“Physicists refer to a unifying theory that connects diverse effective theoretical frameworks, such as general relativity—the physics of the very vast—and quantum mechanics—the physics of the extremely tiny—as a theory of everything,” he writes. He describes his attempt to integrate the science of the vast and tiny worlds of human beings, including genes, culture, and the environment, as “a theory of everyone.”

An important goal of articulating this theory, Muthukrishna says, is to help move social and behavioral science from a young science to a more mature one.

“A young science behaves like a child. It spends most of its time observing the world and coming up with explanations for what it sees,” he writes. “It gets into everything, plays with switches, knobs, and runs whatever experiments it comes up with. But it doesn’t yet know how to properly make sense of what it sees, how things connect with one another, or how to confidently act in the world.”

The next stage, Muthukrishna writes, is a sort of scientific puberty. “Like human puberty, this is an exciting, embarrassing, and often awkward affair, and requires some major changes. Chief among these is the discovery of an overarching theoretical framework that can sift sense from nonsense, make trustworthy and useful predictions, and offer pathways from discoveries to technologies.”

Where are the social and behavioral sciences currently? “Right now . . . we’re in that pre-Darwinian world where you notice that there are butterflies and birds have different beaks. Or the alchemy world where Newton is turning lead into gold,” he told me. 

This progression of social and behavioral science is the subject of the first of a two-part interview with Muthukrisha. In the second part, we’ll consider how energy, its sources and relative abundance, has shaped who we are and limits (or doesn’t) who we can become. 

The interview has been edited for length and clarity.

Evan Nesterak: We meet in an elevator. I learn you’ve written a book and I ask you what it’s about. I say, “Tell me the theory of everyone.” What do you say?

Michael Muthukrishna: This is a bit like asking me to give the elevator pitch for general relativity! The theory of everyone, at its core, is that there are four laws that have always governed the evolution of all of life. They manifest in different ways for different life, but they are the same laws, and they apply just as much to bacteria as to businesses, to cells and to societies. They apply just as much to our individual behavior as they do to our collective behavior. 

Those laws are the availability of energy, the law of energy; the law of innovation, by which we innovate new and efficient ways to use that energy to out compete others; the law of cooperation, by which we work together to outcompete others, but also to facilitate innovation; and the law of evolution, which has three ingredients: diversity, transmission, and selection. It works for genetic evolution, and it also works for cultural evolution.

The theory of everyone calls to mind a tension I’ve been interested in for some time, one that goes back to Ken Gergen’s paper “Social Psychology as History.” In other sciences, like physics, our theories of how planets move around the sun doesn’t change how they move. But our theories of human nature can change behavior. For instance, how we design our institutions will change based on who we think people are, which will then influence how people behave, influencing who we think people are, and so on. How do you think about that argument in terms of a theory of everyone? The challenge of setting down laws for a moving target, so to speak. 

In A Theory of Everyone, I’m not trying to give you physics, which is designed around fairly well-behaved systems. I think the successes of biology are a much better model for a behavioral scientist. In biology you don’t have that predictive power in quite the same way. What you do have is the rules by which a biological system evolves and changes. 

What I’m trying to say in A Theory of Everyone is these are the rules by which the system works. It's energy. It's innovation.. It’s cooperation. It’s the laws of evolution. It is the fact that humans are a dual-inherited animal with three sources of information guiding our behavior: genetics from our parents, culture from our societies, and a short lifetime of experience. Those are just the rules. 

Now, what is that software [the part determined by cultural evolution]? That’s a different question. I have a paper with a similar title, “Psychology as a Historical Science.” The software is a result of this path dependence. If your ancestors were struggling over resources, maybe you’re more tightly cooperative, you’re maybe a little less generous to out-groups. You can escape that, but that’s your starting point. 

If our world looked different, if we had gone down a different historical path, then my specific policy suggestions in the second half of the book would be different because the suggestions come out of the theory, but not in the sense of they’re the only conclusion. They’re the way that the theory interacts with the world as it is today. It’s the way the theory interacts with humans as they generally are, as well as humans as they specifically are in our cultural context, in this moment in history.

I’ve been thinking about your idea that the social and behavioral sciences need to move to the next stage of development as a science, from alchemy to chemistry as you put it. What feels like alchemy to you? Where are we trying to lead into gold?

If you look at the heuristics and biases we use, default bias or loss aversion for instance, because we’ve just discovered them completely empirically, we have no basis upon which to say, Will this generalize? So we resort to saying things like, There are individual differences, and, Context matters. But it doesn't have to be that way. 

You’re saying social and behavioral science needs to become more formalized. That we need to be asking what’s the theory connecting observations like default bias or loss aversion. 

When Mendeleev and Lavoisier were coming up with the periodic table, they had a bunch of evidence in front of them. Then they were able to see these patterns. If we think about loss aversion from a theoretical perspective, you can’t just look at the empirical data. It would be shocking if people of low income in more deprived environments didn’t have some kind of loss aversion because that kind of loss really is catastrophic for them. Whereas if you're a fairly affluent person, you may or may not show loss aversion in the way that it’s measured for these small stakes or even large stake games. 

Defaults are another example that we’re looking at cross-culturally. We want to understand, Is this really about trust? Is it about social norms? These things that we really understand well. Or is it these more amorphous, lead-into-gold type things like [psychological] inertia? What does that even mean? Why would we have inertia? Under what conditions? 

Laying things on a theoretical foundation is that move from alchemy to chemistry. But the very first step before you even get to laying it on deeper theoretical foundations is you have to have the right model.

Laying things on a theoretical foundation is that move from alchemy to chemistry. But the very first step . . . is you have to have the right model.

The analogy that I use in the book is that you can develop a pretty accurate model of the solar system with Earth in the center and the Sun rotating around the Earth. That was the Ptolemaic model, and it was incredibly predictive. It was predictive because the model wasn’t a real model. It had circular orbits and it had little epicycles that corrected for the fact that in reality the orbits are elliptical and so on. 

In Copernicus’s original model, and in part why I think Galileo had such a hard time, he still had circular orbits even though he put the sun in the center. He still needed these little epicycles.

I sometimes think a lot of economic theory looks like that. You have these hidden assumptions that you’re stuck with and then you have to compensate. You’re like, Here’s the utility model, people don’t behave like the Nash equilibrium suggests. Maybe they have inequity aversion, maybe they have this or that. You're trying to patch it with all these things. 

But the moment you realize, Oh, the Sun is in the center and the orbits are elliptical is when you go, Aha, now everything follows from that. We can build a cleaner, more parsimonious model of the universe.

The key insight for pushing forward the behavioral sciences is that humans are a new kind of animal. We’re governed not just by millions of years of genetic evolution that apply across the animal kingdom but also by thousands of years of cultural evolution. That is the deep software that plays out differently in different parts of the world. 

We have a new paper under review where we show people the Coffer illusion. Can I show this to you?

Sure.

Tell me, do you see rectangles or circles?

I see rectangles. Wait, now I see circles as well.

It turns out when we tried this in the U.K. and in the U.S, 80 percent of people see only the rectangles, and something like another 19 percent of people see rectangles and then circles.

That would be me. 

That’s me, and that’s every author of this paper. When we ran this in Namibia, 48 percent of people only saw circles and another 48 percent saw the circles first and then managed to see the rectangles.

Wow. What’s the explanation there?

We don’t know, but it’s consistent with the carpentered corner hypothesis. I’m looking at a computer screen that has neat, clean edges. Rectangles are not a natural thing. In the natural environment, you don’t have these clean edges, trees don’t grow like that. Everything is jagged, and circles and curved edges are much more common. Your visual system is attuned to seeing that more than you see these things. 

We did this in villages. When we go to the semiurban environments, people are still seeing a lot of circles compared to the West, but it’s starting to balance out. 

That’s an example of software. Humans everywhere have the same visual system; if that perception can change, you have to imagine how much the software varies around the world. 

But we’re not butterfly collectors. It’s not enough to just do the first step, which is to show the effect. You have to explain it. We have millions of years of genetic evolution, thousands of years of cultural evolution, and then a short lifetime of experience that is also shaped by our ecology and our experiences. Things like risk aversion and the marshmallow test and so on are going to be products of these things.

So you're basically saying we've been doing a lot of ...

Blind empiricism.

We had to start somewhere to get a foothold, and people have been running experiments to do that. But it sounds like you're saying, we're ready to move on, to ask, how does this all fit together? 

Exactly. Don’t be like a home chef following recipes. Become a chef that understands flavors. Science isn’t induction or deduction, it’s abduction, where you’re moving toward the best explanation, connecting the theory to the evidence. Otherwise, you can’t narrow down the large space of possibilities. I think we're at the point where we can move to Newtonian physics, if that makes sense. That would be a massive improvement. F = m * a. We’re at that point.

Where we can get a good enough understanding of the world that we can model it really, really well, and then eventually we'll come to …

Quantum mechanics.

But with the first move to Newtonian physics, we’re modeling our world really, really well.

That’s right. We're at the point where we can transition into modeling it. Right now, we’re just empiricists, we're just measuring stuff. We’re in that pre-Darwinian world where you just notice that there are butterflies and birds that have different beaks and so on. Or, you’re in that alchemy world where Newton is turning  lead into gold.

So how do we get to the next stage?

It’s dual inheritance theory—the modeling of genetic evolution and cultural evolution, along with the lifetime of experience.

How does the way we do behavioral science change with dual-inheritance theory in mind?

First, there’s a lot of basic science to be done trying to lay these heuristics and biases and ways of nudging people onto firmer theoretical foundations. There have been a few attempts in this direction, but we want to make those first models.

The best work I’ve seen in this sphere are people like Charles Efferson, who wants to move people away from female genital cutting. He's got a really nice model of how to understand whether it’s a coordination problem or if it's a norm problem, whom you should target depending on that, and what you should measure. 

That is what I would call applied cultural evolutionary behavioral science. You have a mathematical model, and it's making testable predictions. It is not an abstract model out of nowhere like we sometimes build in economics, but one that is actually grounded in a framework of models, around social learning and cooperation and norm psychology, as well as error management theory or genetic evolution, and so on.

You’re building these models, they’ve got precise, testable predictions. You test them in the lab across the world, you check if your expectations are there, then you take them and you start to apply them. 

And now you’re thinking with a periodic table, you’re thinking with a theoretical framework. You’re not just using your own life intuitions about what you think is going to work and not work, which are limited. 

Is there anything we didn’t get to that you’d like to add?

The book itself not only lays out a theory of everyone (in terms of cooperation and innovation and intelligence and how humans came to be where we are) but also specifically applies it to issues that we care about. Many of the problems we see in the world, we currently see as disconnected—the climate crisis, immigration troubles, rise of right wing authoritarianism, innovation stagnation, society falling apart, inflation, our education system not keeping up. They are not disconnected. They are part of the same process. By solving the root, we fix the whole damn thing.

In part two of our interview, Muthukrishna and I discuss how energy—everything from photosynthesis to ATP, fossil fuels to fission—shape and constrain our behavior. “We cannot move without energy, cannot reproduce, cannot do anything at all,” Muthukrishna says. “And yet since the Industrial Revolution, which unlocked unfathomable quantities of unexploited energy in the form of fossil fuels, we have stopped thinking about it."

"Ultimately energy is at the heart of all that we do and all we can do," he writes. "And when we see energy in this light, we are like the fish finally seeing the water around us. Suddenly our experiences and potential futures come into clearer vision.” Read part 2 here. — Evan Nesterak, Editor-in-Chief

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The post Moving Behavioral Science from Alchemy to Chemistry: A Conversation with Michael Muthukrishna (Part 1) appeared first on Behavioral Scientist.