Selection with Paul Smaldino
2026-01-27 13:00:00.000 +00:00

Dave P. David Pinsoff. Podcast about Evolutionary Psychology. Tagline is Actual Evolutionary Psychology. By actual evolutionary psychologists. And that this is what it is. Yep, let's continue listening. Okay. David, um Paul Smaldino is a I don't know how would you characterize Paul Smaldino? Um hmm polymathic, um good word, rigorous, insightful, um musical genius. He has a a band called Small Dinosaurs. Oh nice. Is this is it ongoing? I I don't know. I I we talked about it a bit. I think did we I think we talked about it a bit at the end of the episode. Yeah. Um, I don't know. I didn't listen yet. I haven't listened to the music. No, I haven't listened to the episode. Can I just you were there during the episode?

But I honestly I do I sometimes re- I often re-listen to our episodes because I'm so I've heard other podcast hosts say this that they're so paying attention to the conversation that they don't hear everything. The first the the the the real time, but it's a different way of processing. Yeah, it's a different mindset because like when you're in the middle of a conversation, you're thinking about what you're gonna say next and what question you're gonna ask, and like you know, how to interact with the person. But when you're listening as a bystander or as a third party, you get to just take in the whole gestalt in a very different way. So what you're saying is we don't listen to the guest when we're interviewing.

No, kidding. No, no, no, it's the opposite, actually. It's it's I I for me, it's like it's so much that I am so carefully listening to what they're saying and then trying to figure out where to go. Yeah. Um, yeah, this is probably very boring for listeners. But anyway, the point is I don't remember that from the Paul Smaldino episode. But what I do remember is the Lego um um solar system. Yes, we did talk about a Lego solar system. Yeah. So by now, you can't wait to hear this episode. So I mean we all what whilst we talked about mathematical models, yeah. The main thing is like why they're why math models models are stupid, we need more of them, which is you know a thing. We talked about the natural selection of bad science.

Uh oh yeah, big yeah, we talked about that a lot. I mean, I think the the bottom line thing with Paul is that he is embodying and is a voice about it doesn't matter what people intend, whether it's social policy or good science or bad science, what matters is the selection criteria of the institutions. And he's doing modeling and empirical work, all about that. And I think the more that everybody sort of gets on board with whatever people intend, what we select for is what we get. I think that's a really uh important thing. And I think that's that's sort of what the conversation was about. Yeah, it was a it was a fun one. We also get into punk rock and covert signaling, sending signals that some people might get

and some people might not get. So hopefully um this podcast will be full of signals that you get and uh no one else gets. Or maybe we want everyone to get it. I don't know. Uh you be the judge. You be the judge. All right. Enjoy enjoying the enjoyment. Um so yeah, let's get started. Um Paul is we're we we always do just an intro beforehand, so we can just get right into it. David, do you wanna do you wanna start um with this? Sure. Yeah. Well, I I am David Pinsoff. That is an identity. Uh I know I I I have a prediction, but I'm not I'll shut up. I'll let you ask the question, yeah. Well, it's not necessarily the type of identity that Paul Smaldino is interested in, because there's only one David Pinsov so far as I'm aware.

Um but Paul, you're interested in identities that many people share and what functions they serve. Uh this is a huge topic in uh life as we know it in the United States and in many other places. Um there's lots of talk about identity, uh who I am, who we are. Uh you have a very interesting way of thinking about it. So, what is your way of thinking about it? Yeah, uh I mean I think identity is just sort of wrapped up. It's hard to be a person in the world and not have identity be something that you think about a lot. Uh and I think the way that a lot of people think about it is very sort of phenomenologically, meaning like the the experience of who I am in the world and what that means to me. Um, and I I think about things uh that that's true.

Like that is a true thing about identity and a real thing and an important thing about identity. But I'm much more interested in its aspect as you know, the functional role of identity. What's identity for? And how do we use it purposefully? And I um this is where I think, you know, sort of evolutionary thinking, be it uh genetic evolution or uh sort of cultural evolution, which I tend to be a bit more uh interested in, but You know, the way we identify ourselves and the way we categorize others based on our identity categories. And identity is just a sort of a kind of category that we impose on other people. We use that for all kinds of adaptive decision making. Like how do we find partners for cooperation or finding a romantic

partner or friends? How do we use what we perceive about something in order to gauge what kind of react uh interaction we can have with them, whether it's at the store or a stranger or at a party or in a classroom? Um how do we use identity to think about how we evaluate a source of information? Do we believe it? Do we accept it? Do we say this is the kind of thing that I'm definitely going to accept? Are we much more skeptical or rejecting of it? Do we use this to attack somebody or dismiss somebody? Um what kind of person am I also affects adaptive decision making. Like, do I am I bound by these rules because I am this kind of person and therefore tied to this kind of community and will judge be judged by this set of norms

within this group? I think identity is really a kind of category that is at the core of so much of our behavior and decision making. And just to say, like all categories in general, like categorization is kind of uh one of the fundamental things that we do, right? That's foundational to how we behave and how we think, how we perceive the world. And I just think that identity is a really important social category that is more fleshed out and developed and nuanced in humans than in other species. Do you view identity as identical to categories in that it serves the same function as a category? Like if I were to categorize, you know, an animal, say birds, um, that's going to help me make predictions about um uh a new bird that I haven't seen before.

I'm gonna assume that it flies, that it builds nests or whatever. And that's a useful category for me because it helps me make predictions uh about new cases I haven't seen. Similar similarly, if I if I see a new fruit, I'm gonna assume that it's sweet. Um and so these categories help structure our understanding of the world. Do you view identities as just the social version of that? It's just about uh helping us make better predictions about uh people and and and in what ways they're likely to behave or is it something more than that? yeah I be basically yes uh I think of identity as a a kind of category and you know uh being a a sort of special kind it has uh the features that it has. Um like the category identity is a kind of category that uh

has certain features. But yeah, I do think that the main to me, the most helpful way to think about identity is as a kind of category. Okay. And David, can I well let me just poke on on that too a little bit, which is that, but there's also the part, and Paul, this is also like what you study. So I'm just saying what you've said is that it would be also as if like the fruit was also in a strategic uh stance towards the world. So unlike the fruit case where we're talking about the third-party perspective of well, what can I make an inference about for this fruit, there's also this sort of entrepreneurial or creative part of identity or strategic part of identity, negotiating identities or creating them or embodying them or broadcasting them.

Right. And I I think that that's exactly right. And beyond that, it's not just uh that the fruit is, you know, behaving, right? Other people are behaving. Um, and so we have to think about strategic uh views on on what because due to their someone else's identity, how will they behave? But we also have to think about that they are another person with theory of mind who has, you know, categories about identity of their own. And we're all making decisions with limited information. And so understanding that another person may have their own categories of identity, they may have categorized me in a way that I wouldn't categorize myself. Or maybe I hope that they categorize me that is not necessarily entirely accurate, but it's

useful strategically for them to categorize me in that way. And so I think the fact that you have lots of people categorizing each other in an interacting population makes one of the things that makes identity so interesting. And that would also explain then for what I'm hearing you say is why, and this is like a very David Pinsafian and Paul Smalldinian Smalldin. Small small small Dinonian. Whatever you go with, just don't it. I like it. Um is that uh there's a lot of negotiation. Like one of the one of the jobs in in social status competition is to say, here are the identities, or let's try out if this this we attribute this identity to you and act on behalf of this identity. Um That's right.

And I we we often sort of put on, we try on roles, we try on identities in different contexts, and we sort of see how they fit. And we we modify how we view other people and we modify how we present ourselves, um, you know, at various times in our lives and also in different contexts. And so there are also like, you know, yeah. So this is one of the things that I think is so interesting about identity because it's actually extreme, it's not one thing, it's it's actually very multifaceted. And we have aspects of our identity that are pretty permanent. We have aspects that are maybe, you know, more malleable but over longer time scales. I mean, part of our identity is like what age category we are in, right?

Um, and then there is the ones that are are very fluid and that change by context, um, like who we're with or what purpose we're in, um, what role we take. Uh, you maybe you're you're in a leadership role in like your you know garage band that you go practice with but you're actually in a subordinate role at work uh and I think yeah so we're really good at knowing what our role is in different contexts. And so I think this is one of the reasons it makes sense to think of identity as a kind of category. Yeah. One of the uh ways you study, or can you talk a little bit about how you study social identity? Because this is also a transition to sort of how you do your work, because you know, many listeners they might not know how you approach topics.

And so you could imagine somebody just sort of approaching the the idea of identity and what you've just described with sort of paper and pencil surveys, like a social psychologist, no offense to that. But you know, the way you approach top any topic you study is with a bunch of cool tools that are, you know, you're pretty you're you're one of the the people doing these things and writing books about them. So can you talk a little bit about like how do you approach these and why do you use that tool? Sure, yeah. So I mean what you're talking about is is modeling, right? Is is mathematical and computational modeling. And and I do do those other things too a bit. Like the the you know behavioral experiments, because uh and it's important to

actually you know see what people really do in like a lab setting in a behavioral setting it's also important to see what people do not in a lab setting right which is why sort of field work and naturalistic observation are important um but i i so i mostly do modeling i i the the bulk of my work i would say 80 to 90 percent of the work i do is building mathematical or computational models. Um and I can I can talk more about how that works and what that is and uh etc. But you know, part of it is like I think the world is really complicated. I I mean I I know that the world is really complicated. Controversial, controversial take. Yeah, right. It's hot take. Uh it's just a few simple gizmos, just you know, you got your your atoms

and you know they're all just uh yeah atoms and time atoms and time put that on a t-shirt uh yeah so I think it's really it's if it's really hard to think about all the things and we all have to make simplifications and we like every single person makes simplifications in our uh just going about the world, right? Going about our day. You don't think about every single molecule of dirt or sand or pavement on the street. You don't worry about the wind or the well, you know, unless unless you do, right? Unless the thing is important for your decision making. So most of the things that you know, when we're walking around doing our thing, we ignore tons of stuff if they're not likely to be important about the decisions we need to make.

And when we build experiments, we come up with some paradigm where people behave in some way and we try to get them to do something. And we're usually not like, oh, what I'm really mostly interested in is like how people sitting at a laptop will click, you know, which one of these things at this time, like that's the main thing I'm interested in. You know, behavioral experiments these days are increasingly online, um, or like fill out a survey or whatever. We're interested in in what we try to do when we build experiments is capture some essence of the real question that we're interested in, whether it's about you know, identity or cognition or behavior or whatever it is. And that's limited. There's only so much you can do there.

And it also leaves a lot of unexamined assumptions. And that's just the nature of the thing, right? When you deal with a very complex system, you have to ignore a lot of it in order to get any tractability and do science. and actually like isolate a signal that is strong enough to give you a sort of this is going this way and not that way. Um and as a supplement to that, which is sort of where I come in mostly, is taking trying to build sort of artificial systems, fake systems that capture the sort of essence of uh whatever dynamic that we're talking about, whatever the sort of components of the dynamic we're talking about, and think about the kinds of interactions, the properties that they have, and the way they interact and evolve over time, change over time.

And sort of run that through mathematics or through computer simulation and sort of examine, okay, uh, based on these assumptions I made, have the outcomes that I predicted really happened? Have they is there something that I didn't expect? Uh, can I explain that? Um, that's a really powerful thing about modeling is when something I love it when something happens that you don't expect. And then because when something happens you don't expect in the real world, you're like, it could be a million things. I don't know. Maybe I don't have enough data to figure out what it is. But with the model, you actually've built the thing. You know everything that went into it. So if something happens that you don't expect, the main tool you have now is your brain to

think, okay, well, you know, based on what I've put in, why did this thing come out? And sometimes it's because you made a mistake, and sometimes it's because you assumed something that you thought didn't matter, it turns out it mattered a lot. And sometimes it's because there were just unexpected consequences of what you did. The other really cool thing for me about doing these kinds of models are just the tremendous power to run variations and run lots of things. Like when we do experiments, it's you're so constrained by like how much money you have to pay participants or to go collect data or how much time a person is willing to sit and answer questions for, right? Or um, you know how many uh research assistance you can get to like run all

the variants of your study. Or, you know, oh, it would actually be unethical to like kill a bunch of people or like poison them or like, you know, release a disease into the world. Like we definitely can't do that experimentally and we wouldn't want to, but we would kind of sometimes want to know what would happen on let's say a really long time scale that's really hard to study, especially when we're doing evolutionary thinking, right? It's like if you all you have is experiments, especially with humans, you mostly have at your tools, you have archaeology to look at the past and maybe some paleontology and history. And then you have you know this snapshot of what's happening now. And you try to make these really complicated inferences about like, okay, well, how did

what we see now come out of what we think we know about the past. And using simulations, we can sort of say, well, okay, well, if the past was like that, and if these sort of general principles of change and behavior apply the way we think they do, then we can kind of simulate forward in time and look at different variations. We can run these simulations thousands of times With basically the same things. We can randomize a bunch of um starting conditions to try to get them to start over and over again. Um, so I I do this a lot, I really like it. Um I I one of the things uh and I I'm happy to talk a lot about this. Um I I wrote a textbook on doing this kind of modeling, sort of a general social

science um approach, although um it definitely has sort of an evolutionary flavor. And uh I don't know I'm working actually on a second textbook uh that is more evolutionary in its uh approach uh explicitly um but um one of the things that I really like about this approach in addition to I think math is cool, I think computers are cool, I think these help me learn um is I'm also just very kind of ADD in my interests. and it takes like a really long time to set up a lab or a field site and I know this you know I have friends and colleagues who who do this kind of work right you you yourself Dave right like you know it takes so long to to get good experiments done yeah it's like making a movie as opposed to switching channels or channels but like

watching it watching movies versus making movies it's like making making the setting up the lab is like making a movie it's really slow. That's a good analogy. And I think, like, you know, uh, instead of you know, if I just wrote the movies, yeah, and that's more like what I do, which is like I I know how the you know it works in theory, and I can just kind of like imagine it, except I can do more than imagine it and I can simulate it, but the once you sort of learn how to code and and do these kinds of analyses right like the startup time to getting simulations um the the thing that takes the longest honestly is the design part is just making sure that the model makes sense and is a good model and I think that's the

hardest thing to do and uh but and once you can do that right running it running simulations is pretty low cost. Um, especially like if you're like me and are at a major university and you have access to a high performance computing cluster, you can run tens of thousands of simulations in a day. Uh I it's it's so funny to me when I talk to people who do simulations and they'll just say, Well, I don't want to do that. Like that's too many simulations to run. It's going to take like a week to get all that data. And I'm like, Are you kidding me? Like talk to an infant researcher or an anthropologist. Yeah, or field anthropologist. Yeah. Um, my wife has a PhD in developmental psychology. And so like she did this like you know longitudinal work that uh for her dissertation that

took uh you know a year and a half just to collect all the data. And yeah, a field anthropologist who's spending, you know, months out of the air every every year going back, you know, over years to collect data for a single study. Um, so I find it sort of laughable when people complain about it taking like a week to do data. And it's not even like you have to be there for that week. You just like hit run and then walk away. Um so the the challenge really is uh, but you know, the bar is is is then the standard becomes like do you do something meaningful with it? Do you do something that makes sense that is develops insight, right? Because otherwise it's like what the fuck am I doing? Yeah. Yeah.

Yeah. So it might be useful to like um take a concrete example of how this approach is used. Um so have you run simulations or developed models relating to identity that that you could walk us through like how you break apart the com the components? Or is there another model that you think is a good illustrative example of how this approach works? Yeah, sure. I'm I I think I mean you know the answer is yes to that. Uh uh uh let's talk about uh we can talk about the covert signaling stuff, because a lot a lot on that. Um okay. So uh this came out of an idea uh basically first of all that context would influence the the nature of identity as is if you think of identity not only as a category but as a communicated piece of information that I know, you know, I

have I know things about myself. I, you know, am a certain way. I am affiliated with certain groups, I have certain commitments, and I meet you, a stranger. And I'm gonna communicate certain things to you. And there's an old literature uh from anthropology mostly and sociology, social psychology have sort of worked on this too, on uh like ethnic markers for identity tags. It's like, well, if we live in a world where people are different from each other and they meet strangers, it would be good to have a way sort of from a distance, at a safe distance, to be able to know very quickly some things about, you know, what this person is likely to be, uh, what this person is likely to be like. And so the way they dress, the way they, you know, decorate themselves, that's

obvious, right? Their fashion, their, their, their clothing. You can see that really far away. Um then there are certain things like the way they move, certainly the way they talk, like obviously the way they they use language, the language they speak, but also the the accents they have, the the mannerisms of their speech. These are things we can really easily use to categorize people. And this really helps. Um, and it helps uh those kinds of ideas are really were really influential to me, but they're really kind of based on this idea of fairly small insular groups that where everyone in the group is an in group member and works with each other, and everyone from another group is an out group member and works with each

other. And so we just want to know like if I meet somebody in the forest, are they a member of my sort of larger collective, you know, let's say tribal group or identity group, or are they from you know a different place or from a different and if so, what kind of person are they? Are they a a group that like we have relationships with? Are they someone that we're enemies with? I need to know. But now we live in really diverse societies where we're interacting constantly with people that we don't know, we've never met before, that nobody we know knows necessarily. Um, and we need to be able to make sort of rapid decisions about them. Because sometimes, like often, one of the things that really characterizes our, you know,

modern societies is we sort of behave cooperatively with strangers every day. Just like you go to the store, you you know, buy things from the person to the store, you don't try to steal from the other customers, right? You like ride the bus or the train or get on a plane, right? Where we're all sort of calmly, right? Sarah Hurdy has this famous example of like, if you took like 200 chimpanzees and put them on an airplane in like a locked, you know, cabin in the sky. Like you would by the time that plane landed, you'd have like a bunch of dead chimpanzees because they'd all have killed each other. Um movie option idea. Yeah. Yeah. Chimps on a plane. Yeah, chimps on a plane. Yeah. I don't know about you guys.

Every time I go to the grocery store, I form a coalition of males and I uh raid someone else's uh shopping cart. I was gonna say d I think David might be the exception to the what was that? So I I'm an exception to those rules. Yeah, exactly. that's fair. Uh I mean that's why I don't go to Ralph's. But yeah. Um the part of the the the sort of um so I I had this initial idea that like the way that society was structured, about the way the society was structured, and then the the sort of largeness and the diversity of the society would influence the sort of breadth versus depth trade-off in the way people signal their identity to each other and the sort of nature of the context specificity of that.

And that's an idea that I've held on to for quite a long time. I think I I first started really thinking hard about that about maybe 10, 11 years ago, 12 years ago. I really sort of I published like a sort of conceptual idea paper about it, you know, shortly thereafter. And uh just have like the first kind of major modeling paper on this idea coming out, hopefully coming out. Um we're just about to submit the final revisions uh soon. But um and i i'll talk more about that but but um one idea that kind of came along with that i i uh developed was this idea that we actually aren't just trying to honestly signal about ourselves um it's actually a lot of the time we we hide information we're not only

dishonest but just sort of um uh sort of sneaky about, you know, or or um selective, let's say is the word um about how we talk about eat ourselves to other people. And that obviously we do that, right? We we just like you don't walk into a store and just start like yelling your political beliefs, right? You use, you know, you use uh uh judgment, right? Prudence, right? You're you're you're being savvy. Um But one of the things that's really interesting is what happens when you have a diverse society, and like often you find ourselves in positions, both like in person and certainly now online, uh, where there's a large audience, multiple listeners. And the thing that occurs then is every person, if you have people from

diverse backgrounds with diverse experiences and assumptions and categories, if I say something that potentially gives information away about who I am, that's going to be interpreted differently by different people. I mean, the most simple version of that is like one person is like, great, they're like me. I might want to be friends with them. And another person is like, oh, they're part of that group that I hate. Like, I want to fight them or rob them or just like not help them or whatever. And what happens? How do I talk about myself? Or, you know, uh well, first a further version of that is like, what if, you know, if I'm a member, let's say, of an oppressed minority, I might want to talk about myself in

a way that I need to find friends, I need to find people who will help me, I might need to find, you know, romantic partners. But I don't I don't want to risk outing myself in public and be potentially ostracized or hurt or imprisoned or killed. And so I need to be more selective about the way I speak. And one of the things that's really cool about human language, and not just human language in terms of spoken words, but also in terms of like the way that we communicate more broadly, is how nuanced, ambiguous, and multifaceted it is. There's the linguists have this term called implicature, which I really like, right? Which is me like the thing that the meaning that is implied by this the statement, right?

This comes from pragmatics. So uh if I glance at an open window and say, it's a little chilly in here, I don't have to say like the implicature is maybe you should close the window, even though I didn't say it explicitly. And uh what's neat is that because we because language is complicated, because we have enough life experience to draw on previous categories, we are smart enough to think about lots of things in different ways, contexts, etc. I can say one thing that will have different implicatures to different people. And when this is used strategically for uh identity signaling, this is what we uh call covert signaling. So um, this is work that I originally developed with Tom Flampson and uh Richard McElreath,

and have have continued to develop it with other colleagues. Um the this idea that um you know I could signal overtly and just that's like hey this is who I am let me tell you very clearly, unambiguously, this is something that you want to know about me, right? Like I'm a gay socialist who loves football and you know, whatever, hates square dancing. Um and that's gonna be really effective at making sure nobody is mistaken about who I am. And therefore, like it's gonna really you know maximize the fact that the the the chance that people who are similar to me are gonna know that they're similar to me and therefore, you know, might wanna extend relationships with me. On the other hand, though, if anyone is gonna be turned off by that or enraged by that,

that's gonna be very bad for me. On the other hand, right, I could say something like, I really love this movie, or you know, I come in with a t-shirt that has an obscure reference, or just use jokes or fashion or other kinds of sometimes we call them dog whistles, Aspects of speech and communication that if you know what I'm talking about, if you are also aware of, you know, that these clothes or that movie reference or whatever, if you know those things, that the people who are likely to know those things are also likely to be similar to me and less similar to be members of other groups. And therefore, people who are similar to me will catch those references, whereas the people who are different from me will miss them.

Because language can have multiple implicatures. I could say something. Um, this is one of the reasons why, like, you know, for most people who like who like humor, which is most people, right? Like, there's this old trope that like the funniest people you know are actually like your friends are the funniest people you know, always. Like the people who can make you laugh the most consistently are your friends. And there's like a very good reason for that, right? Like humor is an incredible tool For establishing that like we have shared values, shared references, and so I can make a joke and it will land really well with my friends who like were there at the thing that I'm referencing, or you know, understand that that thing is funny in the

way that I meant it. Whereas other people might just say, you know, they might hear what I say. And it's not like I'm talking another language, they can hear the literal words I mean, but there is there's the this there's the text, right? And there's subtext. And subtext can act as this kind of covert signal. So that's the basic idea. And when we build these models, it's really helpful to start with a basic idea. This is what we call a verbal theory, what I've just talked about. It's it's an idea, it's talked about in words, um, it it seems to make sense, and then we build models in order to do a few things. One is like develop a proof of concept, like, okay, well, here's a way it could really work or might really work under certain assumptions.

And here are also the boundary conditions. Here are sort of when it would or wouldn't work. And we can show how it would or wouldn't work. And we can even use this to make explicit predictions. At some case, if you have very good data and very good theory, you can even make um explicit quantitative predictions. But a lot of these models uh are used at least initially to develop more qualitative predictions. Um so uh some of the some of the the first modeling we did of this was to just sort of establish uh uh basically you create an artificial world where you you say, okay, well, the the world is going to be populated by people. What are the properties of those people? What do they do? Well, everyone has uh in these

models some things about them. And to anyone else, to make things easier, we just said, well, let's just say any two pairs of people are either similar or not similar. And in a later model, we say, well, we can make explicit things, we give people like a vector of things about them, and we establish a cutoff where if you share enough things in common, you're similar, otherwise, you're dissimilar. And you could do this if you think that's a bad assumption, you could do it and you want you should do it another way. That's you've just identified one of the great things about modeling, which is that if if I do modeling and I say, look, I've made I've made this discovery with my model, you can say,

well, you've made an assumption that doesn't hold in the systems that I've I'm interested in. And that's great. That means that we can you can take my model as a jumping off point and build on it and extend it and say, look, if you make these other assumptions, it doesn't hold. And that I think all goes towards building more robust, more falsifiable scientific theory. So we build these models and we assume that basically people are in a population where there's other people in the population that are similar or dissimilar to them. And in these first models, we say, well, let's not even worry about what signals they're using or how they're signaling. Let's just assume that what they're doing is signaling either overtly or covertly.

And overt signaling has a probability of you know being identified by anyone who's around. And regardless if they're similar or dissimilar. And covert signaling is noisier, so even similar people are less likely to pick up on it. Sometimes you miss a joke even when your friend makes it. But dissimilar people are really unlikely to get it. And then it's this trade-off, right, between the increased fidelity of the covert signaling versus the uh avoiding sort of burning bridges with dissimilar individuals. And then we assume, well, okay, now people pair up to actually work on something together. And there are factors that influence the payoff that they get, how good that is, how good that that working together is.

Say, well, it's better to work with someone who's similar to you, but it's if you have to work with someone who's dissimilar to you, it's better not to be aware of all your differences. Right? So it's better, you know. We we all like, you know, you can work with someone. Uh it's why, like, you know, we have these these heuristics of like don't talk about politics and religion at Thanksgiving or whatever, right? It's sometimes better not to not to highlight the differences in order to have a good time. And especially if you're trying to work with somebody. Um and we can vary things like, well, how bad is it? Like, is it a little bad to know you're dissimilar, or is it like really bad, like they

kill you? Um and how often do you have to work with people who are different from you? Um is it really easy to like if you're if you can always use information that you have, your preferences about other people, and only choose people who you like and never have to work with people you don't like, yeah, maybe you should always you should signal overtly all the time. Uh but if sometimes you have to work with people, a fair amount of time you have to work with people uh that you don't like, um then yeah, the the model sort of predicts that we should expect more covert signaling. Now I'll I'll take a pause here, but I'll just say like we've that was sort of the groundwork, this kind of modeling with these kinds of qualitative predictions from the

math. And we did simulations later, both where we did an agent-based sort of computational model where we could have minority and majority groups to make more explicit predictions there. We did uh an experiment using um sort of pre-Elon Musk Twitter to um to look at how people were actually signaling about their political identities online in different contexts, and how people we did in a behavioral experiment where to look at like what signals people chose to see if like the predictions of the model actually aligned with how people act in the world and and they do. And then, you know, later this, you know, the the work was sort of like I said, we in these models we said, well, we don't know, let's be agnostic about what

people what signaling, what signals people are using, how they're using that, and even like you know, how they choose their signals, all it is is a strategy of covert signaling or overt signaling. Um but that led to sort of new ideas about the sort of lifespan of signals um and the idea that like well, if I'm using, let's say, a uh a r a reference, uh fashion choice or uh, you know, uh uh some words that I'm using, a joke that I'm using, and I'm doing this in public, like the whole idea is like there are third party listeners, and I'm using this because the th you know the people who are not like me don't know this reference, and the people who are like me do. Well, like people aren't stupid.

Eventually, if everybody in my in-group is using these covert signals, people are gonna people from the out groups are gonna wise up, they're gonna notice. Like we notice all the time, right? We this is something that's like we call out the dog whistles. We call out the dog whistles, usually often years after they were effective. Yeah. Yep. And that's sort of the nature of it. So something that used to be covert is now overt. And uh we became interested in what are the dynamics of like does that imply that covert signals will have like a a natural lifespan. How long is that lifespan? What would determine that lifespan? Um and how exactly how long the the lifespan is, um we don't know. But the things that determine the lifespan now we do know from from you know, we we built a

completely different kind of computational model and also a math model um to do this. Covert signaling, I think also this is one of the I'll I'll pause for a second. I I keep saying I'll pause and then not, but um one of the this is one of the additional benefits of modeling. It's not just that these things are hard to study because we're talking about timelines that are hard to study or and naturalistic interactions that are hard to study, but also because by definition, I'm talking about a a signal, a piece of communication the that you have to already be in the in-group to be able to identify. And that's really hard to study empirically. Um we we came up with some some workarounds uh for a couple of different um

empirical studies, but uh they're limited, right? Um and and the the modeling really drove what we were looking for and helped us sort of see what kind of thing we were looking for. Yeah, if it's designed to not be detected uh by the experimenter, then that makes the experimenter's job uh extremely difficult. It does, yeah. So when we did this um uh with uh with Twitter data, and this is uh data collected in 2020, um we we specifically used you know tweets collected in the like weeks right before the 2020 presidential election in the US we only used US participants and US based accounts and we figured well you know political identity was really salient I don't know if you guys remember 2020 is a really fun year.

Um the uh it was like you know um It was uh wait, was this 2020 or 2016? Now I can't remember. Um either way, it was like, you know, this we're talking about like right before, you know, the the election, which was very heated. It was right um around when Amy Coney Barrett had been uh approved to the United States Supreme Court. It was just months after George Floyd was murdered. So like political identity was super salient. And what we did was collect a bunch of tweets based on accounts that were following a fair number of either far right or far-left news sites. So we're like, these people are probably going to be tweeting about politics. And then what we did was we got participants recruited through MTurk at the time, And we gave them a

survey to determine their sort of where they fell on the political spectrum. Not just left and right, but how far left and how far right. So we could we sort of group people into four categories: radical right, moderate right, radical left, moderate left. And We then sort of gave people just lots and lots of tweets. And we said, all right, like, what do you think about this tweet? And, you know, more importantly, like, what can you tell about the likely political identity of this of the person who tweeted this just based on the text? And we we, you know, blanked out the account name and the picture. So all they saw was the text of the tweet. And uh, you know, sometimes it's really obvious, right?

Like if someone was just like, you know, fuck Trump, like that's probably not someone on the right. Uh whereas, you know, there are things, other things that were a lot more subtle, like references to laws or, you know, sort of dog whistles that people use internally with an in-group, but don't really use with the out-group. And we found that there were a number of tweets that reliably people on the same side of the spectrum as the the tweeter on one side of the spectrum were like, Yeah, yeah, yeah, I know what this is. I like this. I'm I'm in favor of this. Whereas people on the other side, especially the moderates who are the majority, didn't get. So we had to sort of uh adapt the theory a little bit that we hadn't really thought about

radicals and moderates in the modeling. But empirically, like it makes a lot of sense. Like the people who are really, you know, on like very left or very right, they they tend to also be sort of higher in their aggression and enmity for the other side. And they tend to be more savvy about the signals used by the other side. And yeah. They have more of an incentive to detect the dog whistles than say a moderate would. Exactly. Um and I and I wanna point out like they are in the minority though. Like it way more people are moderates than are radical. Is it that um the m the the radical were were better at detecting their same side dog whistles and the other side dog whistles, or what was the result?

So basically, if somebody is signaling, if somebody is on the left and sending a signal that like communicates a left sort of you know biased message, everybody on the left understands that signal. If they're doing it covertly, The radicals on the right will also be fairly likely to notice it. But the moderates on the right won't. Yeah, got it. They're like higher information and even higher information about the other side in other words. That's right. Cool. Okay, yeah. So we basically use this difference between the moderates. So anything where like moderates on both sides could immediately identify as a political signal, we were like, that's an overt message. And things that everyone on one side, but only the radicals on the other side recognized

were like, that's probably covert. And um in the, you know, so we did two things there, right? In the and this is all for sort of predictions that the whole design of this came out of the modeling that we had done. Um but we first just looked at like what does it look like on Twitter? And again, this is before Musk took it over, so it was Twitter, not X. Um, we just looked at the follower networks and sort of like who tweeted this? Who's what are let's we looked at all their followers and the kinds of news sites that they followed? And we looked at, you know, basically how partisan they were based on how mixed the the partisan sources were. So if you only followed right wing sources, you were right wing.

If you only follow left wing sources, you were right left wing. If you followed both, you were more in the middle, and or at least more um harder to tell. And um and what we found is like the you know, the people who tweeted more overtly, most of their followers tended to be partisans on the same side of the spectrum. The people whose tweets were more covert tended to have more followers from both sides. And this is so this is um so r say radicals uh on either side. Um radicals with politically homogenous audiences um were more likely to signal overtly and radicals with uh more distributed or politically distributed audiences were more likely to signal covertly is that not exactly so we didn't we didn't re- we so the radical versus moderate

those were our raiders, the people who were raiding tweets after we'd collected them, because we could actually ask them all these questions about their political identity. Got it. People who are actually producing the tweets, the only things we know about them are what news news accounts they follow. Got it. We're not really able to say that much about how radical they are, but we do know that they're probably at least fairly politically engaged because they're following lots of news sites and are tweeting about political things. Got it. And were the participants rating the radicalness or just the left-rightness? Um, so let me let me back up. Right. So we actually the first thing I did, right, or that we did was uh

do some just ratings of tweets and ratings of so we we got people to ask, you know, answer questions just about their own political views, and that's how we could tell who they were. And then we asked them just to sort of rate these tweets that we had we had gotten. And we use that to sort of as a basically a metric, as an instrument to determine if the messages were being sent covertly or overtly. And when we just looked at who's sending what kinds of messages online and who's following them, we looked at not so the people who are sending, let's say, right wing tweets that are right wing, they're following lots of right-wing news sources. Who's following them? And what news sites are they of those people following?

If that person who's following lots of right wing sites is tweeting out very overtly right wing messages, that correlates with most of their followers also being people who mostly follow right wing sites or sources. If they're tweeting more covertly, that means that a larger proportion of their audience, their followers, are following uh left wing sites, whether they're following both right or left or just left. Got it. Um, that signal was that effect was you know statistically significant and it was as they say, but it wasn't huge because Twitter is already fairly homophilus, like there's already a fair amount of assortment on Twitter. Yeah. So to boost the signal for this effect to test this, we did a behavioral experiment

where we collected sets of tweets that are categorized as either left or right, overt or covert. So we have four groups of tweets. And then we we bring a participant in, now it's a new set of people, we say, hey, um you're gonna rate a bunch of these tweets, just like you know we did before. Uh uh and mostly we're just looking like, You know, we asked a few questions about them, like are they political, whatever, but also like once we determined that they were political, do you like it or dislike it? Do you have positive or negative feelings toward the person who sent this? And so everyone did that. And then we said, now, now you're gonna do an experiment, you're gonna have a task where we're gonna show you 40 tweets that are all, and

we didn't tell them any of anything about the tweets, but actually they were all from people from the same side of the political spectrum as them, and half of them were covert and half of them were overt. They didn't know which was which. We just said, you're just gonna pick this, and we're just gonna and um there's gonna be a random set of 10 other people who rated who are gonna rate these tweets just like you rated them. And for every tweet that you select that most of your audience liked more than they disliked, you're gonna get more money than we say we were originally gonna give you. But for every tweet you pick that in this audience that you if most of them or or a majority of them dislike it, you're gonna lose money.

And so you be careful. But you can select as many or as few as you want. And we're like, there's a minimum amount you can get, but let's say, you know, I don't remember what it was. It was like five dollars. If all of them hate it, if you pick all the tweets and all of your audience hates it, you might only get two dollars. But maybe if you go up, you can go like as many as eight dollars. I don't remember the exact numbers. And uh and all we told them about their audience was how many of the ten were on the same side of the political spectrum as them. And you know, unsurprisingly, right? People are real this is like in in hindsight fairly obvious, but like, you know, the it was sort of a proof of concept.

People are really good at this. Um when people when when people knew that the majority of their audience was on the same side as them, they picked almost everything. They were like, all these, I agree with all these these messages. I want to send them all to this audience. When people knew that the audience was made up of a fair number, like a mix, they were more careful. And when they knew that a lot of the audience, a majority of their audience was different from them, they were even more careful in two ways. They just selected way fewer tweets overall. And of the ones they selected, a higher percentage of them were covert. And so like, it's a fairly artificial construct construction, but the idea was to sort of show that we could

that people actually do this in the real world, make these strategic considerations in the real world that align with the kinds of modeling that we did. And I think the conclusions that we make from the model are broader than the ones we can make from the experiment. But the experiment is just an illustration that, yeah, people can behave in this way that we predicted that they be would behave. I see an interesting uh extension of this. Um so it it would be interesting to look at people who are on the same side politically, say say left-wing people, um, and have them evaluate um the political uh allegiances of of the tweeter uh who is either tweeting a covert signal or an overt signal, and whether they think the people who are tweeting uh

overt signals are more radical or more extreme uh than the people tweeting covert signals, even though they know e even in cases where they know the the the the meaning of the covert signal. And so the idea there would be that if I'm sending an overt signal, I'm sending a costly signal of my loyalty to uh to leftists because I'm I am burning bridges with uh moderates and with right wing people. Whereas if I have a more mixed social network, I can't afford to burn those bridges. Uh and so I'm going to be uh uh unlikely to be as committed politically. I think that's exactly right. And I this is actually, yeah, what you're talking about were uh is related to a lot of the conversations we were having as we first you know started doing this kind of work.

And w one of the the things that really kind of inspired uh, especially the this experiment that I just talked about, was um uh I, you know, I had done the modeling and and and I just kept thinking about, you know, uh I think the first paper was published in 2018, but it was it was written. The first draft of that paper was was finished, you know, I think uh sometime in 2015. And, you know, something happened around 2015, 2016 in the US, especially, right? All of a sudden it just felt like, and you know, we've been seeing it happen more and more. Like all these kind of Extreme white national racist, you know, fascist sympathizers just started coming out of the woodwork. Like all of a sudden, they just were there were more and more of them.

Like people were on Stormfront, uh, which is like a right-wing kind of fascist uh Nazi website. And they were just we had that march in um Charlottesville. Um and it became very clear, like, you know, why is this happening? Or the you know, question arose, like, why is this happening? And and two possibilities sort of immediately present themselves. Like, one, people have been recruited to this cause that weren't previously of this mindset. But all of a sudden, this has become much more attractive uh for people, and people uh it's much more compelling, and a lot of people who previously didn't think this way now think this way. Another hypothesis, though, is that people there were there were there

had been for quite some time a lot of people who felt this way, but were much more quiet about it. That maybe they talked this way in their insular social networks, but they would never talk this way among you know in public because of the negative consequences. And between the kind of assortment that the internet and social media facilitate, so it becomes much more easy to find networks of like-minded people online, and that that has good things and bad things about it. And you know, the rise of a political leader, specifically Donald Trump and others, who seemed ideologically aligned with these, sort of emboldened people and said, I think their perceptions of both their social networks and the potential payoffs, costs, and benefits of signaling

changed. And so they went from signaling either only in private or covertly in public, to being much more emboldened to signal c uh overtly. And that was really where where the idea came from. And and the initial uh you know uh plan to do this study was much more ambitious, but it turned out to be really hard. So we we did the smaller version of it. Um right. I mean it's occurring to me that another interesting extension of this would be to look at perceptions of group formidability. And you might expect that groups that are signaling overtly will be perceived as more formidable or more of a political threat, uh, and perhaps even you know larger in number than groups that are signaling covertly.

Because if I have a huge social network of like-minded peers, I can afford to burn bridges with everyone else. Uh, I can afford to signal overtly, but if I'm a tiny minority, um, let's say you know I'm a I'm a minority of dissidents in a totalitarian regime, I have to signal very, very, very covertly because if I get detected, you know, off to the gulags I go, right? But if I'm in the majority, uh, I can afford to signal as overtly as I want uh without care for you know burning bridges because there are so few bridges to burn or they're not very valuable bridges in the first place. And so it might be that if I'm looking, even if I know with certainty the political allegiances of the of the person, um, You know, if they're s if they're signaling overtly,

I'm going to be more threatened and estimate that they uh have a more homogenous social network, they have uh a larger social network of like-minded peers than if they're signaling covertly. That's right. Uh yeah, that's exactly right. And would it also, I mean, the other question would be the uh same side. So if I'm uh a persecuted minority, for example, and I see somebody else explicitly, overtly signaling, that should be minimally interesting to me and maybe a little bit positive, if not really positive, because it's doing two things. One is it's sort of they're the canary in the coal mine. So are they going to go to the gulag or not? Because it tells me, you know, how many of their how many other people like me are there.

Um, but then it also is maybe my group is more formidable than I thought. And maybe I don't have to uh signal covertly. So you can imagine that there's also an appetite for overt signaling of your own side when you're experiencing at some level in your brain that you're having to do it covertly for the time being. Yeah, that's right. And this is actually something I'm super interested in because I think it's really hard to explain um from a kind of strategic perspective. But I think uh which is the sort of like you know, especially in political situations when dissidents kind of go above and beyond, like they're oppressed. And if they signal in a particular way, they're gonna be oppressed more, but they choose to

do it anyway in a kind of act of science. I find this really interesting um and inspiring in a lot of ways. And, you know, I think I can we can make kind of gesso stories about or you know uh uh speculate about that a lot of these people, and I think the evidence at least partially supports this, that like a lot of these people are like the ones who feel like there is no hope in the mainstream system. There's just um uh Benwat de Corson and I think is Dan Nettle the other author, they have these papers uh on um you know the the on on risk and uh and there are other people who've who've written about this too, but basically like risk and thresholds. Yeah, yeah. And uh, you know, how like people who are sort of just above the threshold where like you

have everything things are going okay for you as long as you don't rock the boat. Like you'll be fine as long as you don't rock the boat. Those people are incentivized to not take any risks, to be very careful. But the people who've fallen below that threshold who are basically fucked either way. Like if they keep going on the way they've always been going, they've got nothing anyway. So they might as well try and take a chance. And I think one of the things that's also underappreciated is like those people can form coalitions and raise themselves up, rise them uh raise themselves up out of that that gulf explicitly by taking these risks and by by sort of going against the mainstream. Um one of my favorite books that I read in the last uh number of years was this book called

um burning down the house. Do you know this Dave? I know the song, but uh yeah, not not the um not the talking head song, which is great, but it's uh house spelled in the German way, H-A-U-S. Oh nice. Uh and the the subtitle is Punk Rock Revolution and the Fall of the Berlin Wall. Oh, nice. I shouldn't read that. Yeah. It's written by this journalist, Tim Moore, and it's about basically punk rockers in East Germany in the nineteen eighties and kind of highlighting these people like it just makes like the sort of American punk scene around the same time feel just completely uh like a bunch of posers because suburban. Yeah. Yeah, it's totally suburban, right? It was uh like no risk. No risk. German punks were being like arrested uh by the Stasi and thrown in jail.

And then and yeah, getting getting beaten up, getting literally thrown in prison, not being able to get jobs. They the bands weren't actually allowed to play in public because you needed a permit given by the government to play punk rock. So you had all the shows were underground or in churches. Um but uh m Tim Moore argues that basically, you know, there was an effect of of these people sort of actively resisting and you can't help but notice them on the street. And when you see them continuously getting harassed and oppressed, uh despite not having done anything, uh that this he argues that uh this helped sort of turn the tide against the GDP uh or the DDR, sorry, um in uh in East Germany. Yeah, that's awesome.

It also reminds me of Michael Monkrief we had on, and he was talking about um uh drag queens, and it was a similar thing where it was, you know, 1950s, and it was these guys who were down and out, low status. So it was exactly this, they're kind of screwed either way, and they um they they they're using this high variance strategy um to help their own status, but then they're doing this work to push on and provoke, you know, for for you know, reaction basically. And it was David. No, that's right. If there's um do you ever read there's uh Hunter Thompson's first book, Hell's Angels. Yeah, that's a great book. I love great author. Good if you want to be a better uh author or read writer, um, read read good right writers,

and Hunter S. Thompson's great. Yeah. Yeah, agreed. Um, yeah, Hell's Angels uh is is really great. I mean, he talks about them too in that book, like you know, not ro in this kind of romantic way, but he's like, these are the the dregs. This is the love the people who have nothing in society who are uh and they've banded together to to sort of form this outlaw group. And um this actually this kind of thinking has has gotten I've become more and more interested in this kind of dynamic between mainstream society and groups, you know, kind of at the edges. Um Yeah, it's super cool. I think one of the one of the things I want to ask, speaking of groups at the edges, is science. Um and um you have this paper with Richard uh

McAlbright about the natural selection of bad science. Um, and I and there's two things I just want to preface for the audience to say. One is that everything you've been talking about, I think it's really important, and I want to be explicit about is that I think the work you're doing is part of a movement to recognize that there are unintended consequences. So I think like a simple model of the world is sort of like what people intend is what happens. But I think the reason to do modeling or social study social dynamics and is to is to say, look, selection, what you select for is what you get. Intentions be damned. And so I think that's really important. And I think that in the same way this applies to social identities, you've done work, kind

of meta science work, about how do intentions versus selection, so like what are we selecting for, for example, when we do science? And might it be that there's unintended consequences that even things that seem good or nobody's with malicious intent or everybody's just sort of trying to get by in the in the aggregate produce these bad dynamics? I think that idea is really powerful. And I don't think outside of maybe the modeling work or the modeling world, I think they're not everybody fully appreciates that. And so I want to just say, like, I think what you're doing is really important. Can you talk a little bit about um the natural selection of bad science? Yeah. Uh uh thanks thanks for that.

Um so I mean, for uh first of all, I'll just say like I I think what you said is is spot on and and really important. And I think that you know It's a naive view, right? If people think that they can understand social behavior, the way that people behave, and almost all behavior is social behavior to some extent, um when you're talking about humans, uh, by only studying how individuals behave in kind of isolation, right? Like the lots of people interacting in a, you know, cultural and social context over time produces behaviors that are direct consequences of the organization, the way those things, which is you know, more like sort of sociological thinking, but um and I I think that's really important.

Um so okay. Um So the natural selection of bad science was this paper, this project that kind of came out a little bit about, you know, uh Richard and I, this was a long time ago. We started working on this, um 2014, 2015. The paper came out in 2016. Um, and I think the context is important then because um you guys may recall, like this was kind of right in the aftermath of uh what's sometimes called the replication crisis, uh, where which was really kind of started in psychology as being recognized, but gradually people noticed it in other fields, biomedicine and you know, lots of, lots of fields, um, which was basically that uh a lot of the standard methods that had uh become standard in uh a

bunch of these fields were producing results that didn't replicate. And it all like the the one of the main catalysts, it wasn't the only one, but one of the main catalysts that really kind of launched this investigation was this publication in this top journal in in social psychology by uh um by Daryl Bem. Daryl Bem, yeah, yeah. Yeah, he claimed to have uh evidence for um uh seeing the future, predicting the future, that uh uh undergraduates could choose which uh uh which curtain had a uh pornographic image behind it. Uh and it wasn't even a real curtain, it was uh like a computer screen. And the point was like the computer chose which curtain to put the image behind after the students had made their guess.

So there was literally no way for the students to guess. And yet he's like, well, it was actually they they guessed correctly like 53% of the time. And you know, we did a t-test and found that that was statistically significant. And it like it's it's just ludicrously terrible methodology, just the thinking and I got really upset uh for another reason too about it, or kind of was up in arms about it, because I was like, this is a physics result. If you're claiming to like have evidence that everything we know about causality Is wrong. Like that we there's another substance in the other thing. This is like the foundation of everything we do. This is a massive claim. And your evidence is just like you know, uh some like a like

20 undergraduates clicked a button uh in in slightly above chance. And you know, when people started investigating, they found that like actually he had showed them a whole bunch of different kinds of images, and pornographic images were the only ones that had a high enough. So it's like it's like the whole like uh there's this XKCD comic uh about where like he's joking about uh scientists giving people different color jelly beans and see if any of them will cure cancer, and then you just try all the different colors and finally one of them is like, oh yeah, like fewer people who had green jelly beans have cancer. And then like the headline is like, uh, green jelly beans cause can uh cure cancer.

But it's if you try a million things, like statistically, one of them will have a correlation. But it's like there's that website, Spurious Correlations, where it's just amazing and everyone should look at it. It's like uh, you know the the number of uh like kevin bacon movies in a given decade correlates with like deaths by drowning. Um I mean tremors. That one is actually causal I think. Yeah. I can I I can understand how seeing Kevin Bacon would would make you less careful around bodies of water. I agree with that. Can I can I interject too this is like uh inside scoop information. I, as a graduate student, submitted a paper to that journal and the same action editor that handled the BEM article, and my paper was uh rejected.

And it was like careful, it was sort of an obvious thing. It was like accents are categorized, they had tons of control conditions, and it was not accepted at the same time that the BEM paper was. So it's it's also for me like very egregious in that it's also it w it wasn't just sort of like people were doing collecting careful science, and then this just kind of got in. It was just from my experience, it was obviously careful science was just being like, no, this is nonsense, and then ESP, yeah, come on in. Yeah. Yeah. It seems like it's it's the opposite of what you'd want to be doing as I feel as a knowledge accumulating enterprise, which is taking into account past knowledge and trying to integrate new knowledge with the past edifice of knowledge and coming into your

hypothesis with a set of well-informed priors based on prior research and prior knowledge, as opposed to just starting your investigation in a vacuum completely in ignorance of centuries of scientific research. And that's, I think, what we are all about as evolutionary social scientists is we want to integrate our knowledge of human beings with past knowledge that we've already accumulated about how evolution works, right? Uh and and and the fact that social science does not avail itself of that tremendously useful tool of using everything we know to inform our research is just mind-boggling. But anyway, Paul has a reason why people do that. Yeah, why do people do that? Yes. Yeah. So um I I agree.

And yeah, so part of this project was was to sort of maybe highlight a potential reason for why uh people are not as good scientists as they should be or could be. Um which was basically, you know, a griping about like uh various things in academia kind of came out of it. Um and seeing, you know, these headlines, exactly the kind of thing you're talking about, Dave, like, yeah, the these ridiculous studies getting uh you know highlighted, put into, you know, top journals and by people at Ivy League universities while this like very careful, methodical, creative research uh was was getting passed over or or shunted or uh into lower you know uh ranked journals. And um, you know, one of the things that's

true about academics and scientists is that they're human beings and they live in a world w you know that is characterized we live in an unequal hierarchical society where having wealth and acclaim and prestige gives you hired gives you as you know people certain kinds of satisfaction. It's things that people want. And it's also the case that not everyone can get those things. Which, you know, immediately anyone who thinks about evolution to think, well, okay, scientists vary, they're different from each other. Um there's a something that you know Allows for the transmission of scientific knowledge, where you know scientists train other scientists via universities and postdoctoral fellowships and trainings.

So there's inheritance in that way. And there's selection, right? So not only uh so these are all the the these are all the ingredients you need for an evolutionary dynamic. And the question then is, well, you know, what ways do they vary, and especially how are they selected? And so we built a model because the truth is they vary in lots of ways, and they're selected in lots of ways. But we wanted to focus on the quality or rigor of the methodology. So we imagined, you know, we created a model where we have um we actually did a previous model where we had a population model of hypotheses. I actually I like this paper a lot, and it's uh uh often overlooked. So that this Richard and I did this previous paper where we looked at uh just a population

of hypotheses that you know where they were tested with certain amounts of error for both false negatives and false positives and there were certain kinds of communication bias like publication bias and certain kinds of replication strategies and we looked at all right well like under different assumptions what would we be able to say about the probability that a hypothesis is true given a certain record of supporting and non-supporting results in the published record? And so we so we had that model done. And we started thinking, well, you know, that model just said, like, well, there's an error rate. There's a power, the statistical power, you know, the ability to find true results and avoid false negatives.

What if we created a population of artificial science agents that varied in their methods? They said, well, okay, well, you know, everyone has a uh we we just said, all right, all the agents have some sort of intrinsic methods that can vary in how rigorous they are and how powerful they are. And those things trade off. So we can avoid false positives by being extremely careful, but then we might miss a lot of true positives. We can find all the true positives by having very high power, by you know, saying everything is true, that would be good, but that's not good because then we get a lot of false false positives. So we want to be able to say the things that are true are true and the things that are

false are false. And to do that takes time and effort. It's being careful. But that means that you know you're not you're less likely to submit, you know, to get false positives, which gives you fewer things to submit to journals. And it's going to take you longer between studies. And so that's actually the kind of science that we think we want, which is careful science where the things we say are true are actually true. But then there's the selection pressure. And in this case, we did a simplified version. So in reality, right, the reason why scientists get hired and promoted and get grant funding, which allows them to, you know, train more graduate students, undergraduates, allows them to get more prestigious jobs where they have more influence.

It comes down to a lot of things, but some of the biggest ones will be how many papers they publish, how prestigious and influential those papers are, how much grant money they get, etc. And uh we in the model, we just distilled this down to one thing, which is just how many papers you publish. And you we you we could build a model with other things, but in this simple version, uh we did it this way. And we also allowed for replication. So we said, all right, if somebody else replicates your study and it doesn't replicate, that's bad for you and that hurts your prestige. But otherwise, the more the more papers you publish, the more prestige you have. And the more prestige you have, the more likely you are to transmit your methods to someone

else in the future because they you get a good job at a prestigious university, you get lots of grant funding, your papers are influential and people apply to work with you, et cetera. And this, we just kind of simulated this dynamic over sort of generations of scientists and showed that if the selection, the major selection pressure is how many papers you publish, all things equal, that's going to select for a continual decrease in the rigor that we see. Now, you know, it's a model. And like I said, there are other things. But if it's wrong, right, then we should see the opposite happening, right? Because what we actually want is things to be true. So we should see a gradual tend uh trend for better and better methods over time.

If selection is actually favoring methods that are geared at improving truth, uh, that have, you know, uh evidence that they will actually help us get epistemic value out of our studies. But you know, there's a lot of evidence from a number of fields that that hasn't happened. I mean, two of the ones I like to highlight is like, you know, there's this famous uh paper by Paul Meal, who's a philosopher of science in the 1960s. He wrote this paper in the 1960s basically saying, hey, psychologists are really not using p-values correctly, this sort of null hypothesis significance testing. You're doing it really badly. You don't understand what p-values are. A p-value is not the probability that your hypothesis is

true. And it's not the probability that the null hypothesis is false. It's actually just the probability that you saw the data you saw conditional on the null hypothesis being true. But that doesn't actually support any other specific hypothesis. And you would think that, you know, in the 1960s, that was 60 years ago, that would have an effect. But just less than a decade ago, the American Statistical Association published this like statement on p-values, just reiterating all the same things that Paul Meal says. He's like, nobody, we did a survey, people don't understand p values, they're not using statistics correctly. Well, why do you think that is? Well, understanding statistics is hard. It takes a lot of time when you could be running studies.

And if you really understand statistics, you're going to be a lot more qualified in how you promote the results you got. You're going to be a lot more cautious. Whereas if you don't, you might just say, look, we found this amazing thing. Green jelly beans cure cancer. And that's going to get attention. And in a world where there's not good vetting of these kinds of results, where scientists are policing themselves, you can actually get selection for misunderstanding. Is this um would you view this analogously to uh life history theory where there are trade-offs between quantity versus quality of offspring? You could either invest very intensively in a small number of offspring, um, if they're you

know, if you expect them to live a long time, uh versus if you are in a high predation environment where most of your offspring are expected to die, uh, you might want to hedge your bets and have a large number of offspring lay a bunch of different eggs in hopes that and invest very little in any of them in hopes that at least a few of them uh will will survive. Um would you view this uh trade-off between quantity and quality as analogous to the trade-off between quantity and quality in scientific research? I think loosely analogous, yeah. I mean, uh to on the you know, to some extent, first of all, like I mean, all evolution, right, is about trade-offs. Right. So anything evolutionary is going to be about trade-offs, but I think even more so, right,

this is an interesting analogy because um in a lot of scientific fields, people get, you know, the jobs and fellowships that junior scientists get right out of graduate school or even during graduate school matter a lot. Um there was a really neat study about a decade ago by this Dutch group, and they looked at um the people who got this um this very prestigious um funding uh award. Um so it gives you a lot of money to do your research. It's also very prestigious, helps you get jobs. And they looked at basically the people who are who are ranked just below the threshold and just above the threshold. The people who got the award, but barely, versus the people who barely missed out on the award.

And then they tracked them for a number of years after, and their paths diverge wildly. There's a huge effect of these early successes. So the the value of edging past being perceived as edging past your peers early on is it ends up in our system being really high in a lot of fields. And so if you take on as a graduate student a very complicated project that's you're you're spending, you know, being very rigorous, very careful about and taking a lot of time and doesn't really lead to a lot of publications. Maybe you're maybe a couple of like smaller publications that are um you're publishing kind of on the way to something big. It's not big yet, but maybe it will be amazing later on, versus someone who's just cranking

out those studies and trying and aiming for things that are the sort of the most catchy and most newsworthy and are the least careful. Well, what's gonna happen, right, is if you have a system that values that second person over the first one. And it's not every system, not every field is like this. But when it is like that, then that first person doesn't get a job. They either get a low ranked job where they're not going to have as many resources to continue doing their work, or they're going to leave academia and do something else. And so the science that gets done overall Um gets worse. And this is actually why I think it's so important to think about the the sort of cultural and institutional incentives and not just individual behavior.

Like we all know about perverse incentives, but there's there are some people who think, well, look, you just have to be better than that. You just have to be better than that. You just have to do the work that you know is good. And yeah, well, that sounds really noble. Um but and let's imagine you know you do and you feel really satisfied. Yeah. Maybe you get lucky, but if you take, you know, a hundred people like that and a hundred people who are just, you know, playing the system in the using the techniques that the system selects for, well, the system is going to select for those people. Yes. And so it can't just be about individual behavior. It has to be also about, you know, the sort of cultural and institutional incentives, the

mechanisms that select for certain people. I mean, I didn't we didn't think about this at the time. I completely wasn't turned on to it back then. But I mean, this is very similar to what, you know, Noam Chomsky and Edward Herman were talking about in Manufacturing Consent on their book on the media in the 1980s. This is also a kind of sort of institutional selection where they're saying, you know, the the people who are in control of, you know, media organizations are hiring people and promoting people who espouse certain views and think about things in certain ways and not hiring others. And even if every single person who goes into journalism is completely noble and thinks they're doing the best they can, the people who think along the party lines are going to be

the ones that end up in positions of authority and influence, and the ones who think differently are not. Yeah, it's or that it's uh the the whatever will be an audience, whatever increases the A-B testing of like what gets more eyeballs on this, so we can sell more ads. Um, so yeah, if it's um uh yeah, the idea that selection is what matters and not intention. I think politics too, right? Like you can you could say, you know, um how do we minimize crime? Well we should do things that like punish criminals, right? As opposed to like you know minimal standard wage or something, like Dan Nunnell's suggesting. Um the idea that you you can't intuit intentionally what will produce the outcome you want, and you have to just rely on the idea that there's selection,

I think is a really humbling thing. And I think your work, when I've read it, every time I do something, I think to myself, like honestly, like the I often think of the the natural selection of bad science work where I'm like, what am I selecting by clicking on this? Or what are we selecting if we make a decision about who to hire based on this criteria? Um, how are we contributing? How are we like a cog in the wheel of the institution? Yeah. Yeah. Yeah. I mean, it's even I mean, I I feel like we this might even be a case of mismatch where we overestimate the effectiveness of appeals to morality and conscience. That if we just scold our way to better science or wag our finger to better science, that

that's going to solve the problem. We just need to encourage people to have integrity and to do better. Well, the problem with that view is that the people who are most susceptible to those appeals to conscience are going to be selected against and under the current incentive incentive structure. And the people who are least susceptible to those appeals are going to rise to the top, right? Yeah. So you end up if you want what you want is a world where integrity and, you know, morality, let's say, are favored, you actually have to change the system so that the people who act that way are actually the ones that rise to the top and are the ones that get rewarded and the other ones get don't. And I think, I mean, with politics, I mean, obviously we're seeing the effects of uh

selection every day uh right now. But I mean, you know, even just at the most basic level, uh, you know, we we have an electoral system with in the US, this like long drawn out, kind of very media based um electoral system where where people run for office over months and what what what does that do right it selects for the people who are really good at running for office but the people who are really good at running for office in that system aren't necessarily the people who are really good at governing right yeah or or even like you know gerrymandering or you know it's even worse like the selection I mean yeah yeah like it's yeah yeah you would uh uh yeah exactly the the selection or even yeah

yeah tons of examples of uh if you can if you can put your thumb on the scale of the selection process, it's really leads to crazy outcomes. Yes. It's a counterintuitive, like we're we're not inclined to view things in terms of incentive structure problems as opposed to character or or ethical problems. Yeah. I I again it may be a kind of mismatch. I don't know. But it seems like our first inclination is we gotta boo the bad people. We gotta figure out who the bad people are and discourage them and we gotta praise the good people, and that's the way to solve the problem as opposed to viewing it as an incentive-based problem that we Yeah, although I do want to maybe push back on that slightly because if it becomes normative to boo the bad people

and punish the bad people, and the mechanisms for bad people succeeding are not there, right? Or are shut down actively, that does change it into a selective system. Sure. So booing the bad people can be a you know a precursor or let's say an early, you know, sign of more normative, more institutionalized change. But you have to get all the way there. It just can't be yelling from the bleachers. Yeah. Totally. Yeah. Yeah. I I feel I just just went around. Like I just I can't emphasize enough. I wish like we don't have ads on this, but like the PSA of like selection and not intention is really what matters. I feel like that's such an important sort of public uh education thing. Like I wish there was something in school, even where I don't know, like just game theory

models or something. I mean, I don't know, I don't know what you do with that, but just the idea. I think I just I even for myself, I still catch myself going in the wrong direction as David's talking about where I just sort of really overemphasize people's intentions and motivations or wagging a finger as opposed to people are doing what makes sense given the incentive structure in the environment and that's the thing you gotta really target. I mean it's hard because it's you know it's a bigger than one person. It's hard and I it's not it's it's not all one or all the other either. Yeah no no that's fair. Yeah. Right. Especially like the problem is there are also incentive structures for changing the

incentive structure. And those can be fucked too. And that's the problem. Right. Yeah. Yeah. You know, like the people who r who rise to positions uh of power who are capable of changing the incentive structure. Well, that is itself a whole selection process that you're not getting the people who are best able to to to do that. Yeah. No one wants to change the incentive structure to be something that wouldn't have selected for them. Correct. Exactly. Yeah. Exactly. Yeah. Um Paul. Yeah. No, go go ahead. Yeah. No, I was just gonna say, I mean, we saw that in science too, with a lot of pushback uh against reform uh a decade ago, where a lot of people who had succeeded very well, you know, with the old system were very against these sort of uh increased scrutiny.

Yes. Yes. Yes, I do remember. I mean, I I was in grad school during the whole when the replication crisis was like reaching its its peak. And I remember there was a lot of like political squabbles between the old guard were saying everything's fine, this is just a bunch of bunch of bullshit. These are like replication bullies, replication police, or whatever. And like the new guard was like, no, this is a serious problem. We need to really change things. Uh and it was not uh uh a coincidence that the old guard, the people who were fighting for the status quo, were the people who were the most privileged, the most prestigious, uh, with the highest positions in the current system. They were obviously the stakeholders of the status quo, and the the new guard were the

people who were uh most uh likely to benefit from a a new system. Um David Pinsafian terms, yeah. Exactly. It was I was in the middle of a status game that was on the brink of collapse, and there were stakeholders in the current status game that were trying to preserve it, and uh stakeholders in a new status game that were trying to undermine it. Uh and um I think, you know, uh it uh things have improved. I mean I maybe you can correct me if I'm wrong, Paul, but um I I think um we have at least seen some progress. I I would like to see more, but I think we're certainly better than we were in the peak of the replication crisis. Would you would you disagree with that? Well yeah, yes and no. I I think that we have yes, I think there are a lot of things that are better.

Um I think that a lot more the people are a lot more savvy about statistics than they were and methodological design. I think people are generally, you know, graduate students coming out of the social sciences and psychology are better at coding and better at statistics. They're increasingly thinking about causality. I still think that the sort of like Like myopic view of you know, I will do one study that changes the world, and without really thinking about how it is linked to the the larger, you know uh constellation or you know, uh blanket of human behavior, the breadth of human behavior, I think that's still missing a lot of the time, and and it leads to a lot of work that I feel like could be good if there was some more thought that went into it.

But I've been I've been disappointed in a lot of the work that's come out. And you know, maybe this is just there's the old um adage that 90% of everything is crap, and maybe that's just always true. So but I think like that the good 10% uh is is is pretty good. Now there's a lot of good stuff. The the main point of progress I've seen is that you can now get more status for a failed replication attempt than you could have in the old era. Now, if I fail to replicate someone else's work, I could get that published. Uh and I could get some attention for that. Uh it used to be that if you failed to replicate someone's work, no one would pay attention and no one would care. So that I think is at least some progress.

Yeah, it's an improvement. Um the the thing that I haven't seen as much progress as I would like is in terms of theory. Yeah, I was gonna say, yeah, yeah. And and and the and the uh people think of theory as like an airy, superfluous thing, but it actually feeds directly into into your your model, uh, which is that if you if you have an incentive structure where you are ignoring past knowledge and you are preferentially seeking out low prior hypotheses, And the lower prior your hypothesis if it pans out, the higher your status, well, that is going to further exacerbate the trend. So if I'm hunting for counterintuitive findings that clash with everything we have known in the past, and I get more prestige if my finding is more shocking and more titillating,

well, um that is a recipe for even worse science and even less replicable work. Because if I'm pumping out lots of different studies and looking for the low prior uh hypothesis, uh, well, chances are one of them is going to turn out to be right. And if I publish that, I'm gonna get tons of status for discovering this new flashy counterintuitive thing, right? But if we're all uh in a different incentive structure where instead of uh hunting for the lowest prior hypotheses, we're hunting for the highest prior hypotheses, well then all of a sudden the dynamics are different. Uh if if we have a theoretical edifice that uh is causing us to preferentially look for uh data that is more likely to be uh confirmed and hypotheses that are more likely to bear

fruit, that is going to uh help the replication crisis uh much more because any given finding is just going to be more likely to to replicate because it has a prior edifice of knowledge behind it. I don't know. What do you what do you think about that that way of thinking? I I I understand that way of thinking, but I I also think that there's a a problem that potentially comes out of it. Um, which is that we we want to the things that are counterintuitive or surprising, let's say, are actually the more valuable if they're right. So you know if I tell you that like, hey, I dropped a thousand pennies and all of them fell down, Like that's not a interesting like I yeah, I can replicate that, but like no one

cares, right? We know it's gonna happen. If I tell you that like I discovered a material that actually goes up when you drop it and we never knew about it before, that's amazing. Um I mean it's just a helium balloon, right? But like it's still seems amaz it's amazing still. Helium balloons are incredible. They're the best. Don't don't poo poo helium balloons. They're amazing. It's magic. Things, you know, things that are counterintuitive, they're they're surprising. We pay attention to surprising things for like an a you know a very good evolutionary reason, right? That it has information. Uh and the the issue, right, is like if every single person individually is looking for the most surprising thing because individuals get rewarded when they find

something really surprising. Well, everyone's gonna look, try to like find something regardless, especially if some of these other incentives are also in place and people are not looking for things with good reason, they're just kind of, you know, if you find something surprising and it's true and it's real, that's valuable. But if you find something that you say is surprising, but actually if you think about it for a minute, it's like it's not, it's only surprising because like you've been like really myopically and your field has been myopically looking in one place, um, you know, uh another area of research I've been getting more and more interested in is the sort of like collective intelligence, collective uh problem solving stuff.

And you know, if if we stop thinking about scientists as a bunch of individuals that all are individually incentivized to be the individually best scientist, but think about you know teams and collectives as what would be the best strategy if if we have aligned interests that we're all trying to find uh good stuff? Well, to some extent it would it would be to get it wouldn't be to have every single person working on exactly the same problem. You would want an enough idiosyncrasity, enough you know, uh iconoclasts That people are looking in different places, that people are trying things that uh might not work. Yeah. And they're not punished for failing as long as they're doing it well, as long as they're

being rigorous and careful in the way that they're doing it. And if you have people who are if what's being, you know, sort of scrutinized is is this a good idea and is the way you're doing it good, then let people do all kinds of things. And if we can if we can evaluate the methods, but you know, and the methods can include the method of selecting a hypothesis, it's tricky because evaluation is by its very nature a variance-reducing process. Yeah. And variance reducing is good if you want to maintain high quality that's um you know corresponds to a fixed set of criteria. But you know, as we know well, right, evolution works on two things, right? Selection and variance. And you know, it's the fundamental theorem of evolution, right?

Is that the speed of evolution is proportional to the variance in the population. So higher variance actually makes a s a population more adaptable and more resilient. So we also need mechanisms to inject variation into this system if we want it to be you know to be making progress. So yeah, no I I I I agree with that. Yeah, no, it's it's um it's analogous to like uh you know if I have a a stock portfolio and I want to invest in a bunch of different stocks and I want to ideally invest in stocks that are likely to give me a return, but maybe I I'll take a risk you know, in a few cases. Maybe I'll, you know, I'll I'll include a few risky stocks on my portfolio too. And that's probably a good thing, right?

And the same thing is true of science. You know, we wanna uh you know uh invest in uh uh theories and ideas that are likely to bear fruit, but maybe a small percentage of us can take some risks on some wilder ideas and we'll and we'll see what happens. But I think the current distribution is lopsided, where we have too many people investing in the risky strategy and not enough incentive for figuring out when those risky findings are actually correct or not. And not enough people investing in the more uh uh promising ideas. Um, and we get a lopsided uh distribution of research. Um ideally, we would have um uh you know projects that have some high likelihood of bearing fruit. Um but the problem is that a lot of people are investing in projects that have a low

likelihood of bearing fruit. And and the deeper problem is they don't even realize that because they haven't bothered to familiarize themselves with past research and with past knowledge uh and and with uh say evolutionary theory, economics, whatever, whatever is going to give them an edge in uh uh making sharper predictions that are more likely to bear fruit. I don't know. I don't know. It's hard. It's hard to say. I mean, like science is there's a lot of scientists studying a lot of different things, but I think in the in the sort of behavioral sciences, I worry that it's it's worse than that, right? That you have actually like um low quality, low risk soci uh work being done. It's likely to uh to find something that uh

doesn't kind of matter. Yeah. Yeah. I mean, and I like one of you might even be end up being a reviewer for this. I just submitted a paper that is just to be really quick. Um, I got really frustrated because I sent it was it was very much inspired by you, but inspired by you, Paul, because I sent I sent a paper to I'm gonna say perspectives on psych science. It's good, you know, it had a lot of theory, theory crisis. Um, no, it didn't go, but it was the editor desk rejected it for reasons that I was like, wow, this is selecting against theory, good theory. And so I so I actually wrote a rant paper, I submitted to perspective. So here's the premise: is that um if you ask for data concurrent with

theory, uh, so if you select theory based on whether it it comes along with data with the theory, it selects for bad theory because the it selects for basically the kinds of theories that it's easy to generate data about. Um, so you're actually or at least theory that you don't need necessarily. It could be you don't need so like so for precisely theories that are about difficult things that we are not even sure how to well, like challenges to like we we I have a theory that there is this mechanism and there's a deductive logic that like there should be this like parenting mechanism, right? It would be impossible for organisms to exist if it didn't exist, but we don't know how to look for it yet.

We don't know how to study it. The idea that, you know, that gets selected, that paper gets selected out because it doesn't have any data with it, right? It it it falls out of the um it falls out of the narrative of what we're even discussing. So the idea is that I mean, uh the the short version is um asking for data concurrent with theory selects for bad theory and I sent it to perspectives and it was rejected because it it didn't have data. The ultimate. Yeah. So so I love this. And so this is a very much inspired by Paul Smaldino I think, but it comes back to this thing of um that, you know, it's just what are you selecting for? Right. No, and we have you from I mean this is also, you know, my sort of push to like be deep in

your field, but like look at other fields too, because otherwise you get totally insular and you you you you allow things to become normative uh a and that shouldn't be. Um like that. I mean there's so many examples from other fields of theories being published and then you know not finding you know experimental evidence for them for years, sometimes decades. Yeah. Yeah. Paul, I want to just because we're running short on time or we're ending here, um, you we haven't, you know, we've we've ebbed in flowed in terms of like talking to guests about like themselves or like their their life. Um but I am curious because you know you have this um in my in my opinion, you know, you have like a very fierce uh scientific identity

in the sense of, you know, you're you're really pushing not in like a um a pushy way, but you're um you're a spokesperson for doing models. I mean, you have papers that are like, you know, models are stupid and we need more of them. Like that's a very special sort of stance to take. Like I'm an expert in something and it's stupid, but it's a good thing. Like that's a very special thing. It feels very different to me than what we talked about before, which is sort of people have these status games and they're just like, I'm the best. Like to say something like, I'm an expert in something that's stupid and we need more of it is very subversive. So I'm just sort of curious, like we talked about um, you know, the punk rock uh East

German thing. I mean, are have you always sort of wanted to is that you? Like are you the punk rocker in East Germany? Uh if for Saga. Well punk rocker would never admit. Yeah, but well, you know. I yeah, I don't know. Uh I don't know. I mean I like I do think like a little bit a little bit of a punk attitude I think is healthy. And uh I I I definitely you know respond to that. And I also just feel like for me, like I've tried being more mainstream and it doesn't work. Like I don't feel comfortable and people reject it. Like uh I tried to give more like um when I was on the job market looking for academic permanent jobs, I tried to give much more typical jobs like the way that I had seen other

people give them, and it just never worked, and I didn't get anything. And then I finally was just like, you know, screw it. I'm just gonna give the the the talk that I wanna give. And I I gave a talk at UC Merced called, I think the talk was called that I got that got me my job was called Learning About Social Evolution with Extremely Unrealistic Models. And I was like, these models are totally unrealistic. But let's see what we can learn from them anyway. And I think that, you know, we we we tell this like fake narrative that like we're the science we're doing is like so precise and we know so much. And I think like acknowledging that like we're flailing and we like it's not like people don't know anything.

We know a lot of things, but there's also just so much we don't know and being a little m humble about that, I think it is actually a real strength. Gives, well, for me anyway, I feel like it gives me a power. Um, and I think also like I just my trajectory, like I studied physics in as an undergrad. I didn't take a single class on biology, cognitive science, or psychology until grad school. And when I did, I was like, really? This that we're are we this is what we're seriously doing? And I I love the topics. I love they're my favorite kinds of questions. I think like psychology, cognitive science, the social sciences have the best questions. They're the things I'm most interested in. And sometimes and there's it's not like everyone's doing crap work but me.

Like, obviously, it's not what I'm saying. There's so many people doing work that I think is amazing and inspiring. But there's also like plenty of stuff where I'm like, come on, come on, guys. Um, and I think that, you know, being a little bit willing to push back on that. Also, if I'm gonna say other people's be, you know, critical of other people's work, then like it's beholden to me to be just as critical about my own work. And so I I really try to be like I am very careful and I haven't always succeeded, but I try you know, to be pretty cautious in the uh in the claims that I'm making. And uh and but that doesn't mean that I don't I love speculating. Like I it hate I hate it when people are afraid to speculate because they're like, well, I

don't know if I don't have the evidence. And I'm like, right, that's why you speculate, and then you see maybe you're right and maybe you're wrong. Like, but if you are an expert, then like your speculation should be better than people who are not an expert, right? So like the stuff we can prove is such a small amount of the things, a small subset of the things we actually want to talk about. Yeah. So um I I think that like I'm all for like, you know, I don't know. Um I I I like I don't think I'm that special in the like sort of field of scientists, but I have carved out my little niche. And uh I I just keep you know, I'll just say the last thing I'll say is like I've I've done this thing, this stuff on like working

on models of science and talking about like science and improving science, and I and it's my most cited stuff. Like the natural selection of bad science, that paper is by far my most cited paper. Um, but the idea that working on science itself is like my main life's mission is is like I reject that. I don't want that to be the case. And I I'm happy to be known for that, but I um there's so many other things. Like I'm interested in culture and the world and human behavior more generally. And it's just you know, that's just one aspect of it. And I I um I don't know. I don't know where I'm going with that. But uh Yeah. I I I also think this all comes back to like the original identity thing where we're all

looking around and like psychologists are like, it's sort of the emperor has no clothes. We're sort of you're you're anticipating like what's gonna keep what's gonna give me status, how are other people gonna react to stuff? And there's this, I think there's a paper about this where it's sort of the surprisingly popular thing where um I remember we had um for our wedding meal we asked people like we have to have like these three entrees and and we had like I don't know something like quail and everyone was like oh I would love quail but I don't think anyone else is going to and that it turned out to be a really good idea that we had quail for the meal because everybody wanted but nobody thought anyone else did what they thought they did.

And what this paper is about surprisingly popular is something where you respond to something but you don't think anyone else will. And I think like the the value of like having a paper like models are stupid, we should have more of them, is it removes that, it's it's sort of that courage to sort of be like, um, it's it's standing out, like it's it's rocking the boat a little bit. But then but then people can be like, oh, there is somebody else who's like these things are useful, but they are kind of stupid. And we don't have to like pretend like everybody agrees that they're not stupid. You know, and maybe it it kind of um it g it gives some error to otherwise everybody's like pretending like, well, I think everybody must think they're

right. I might think they're stupid, but nobody else does. And you know, you being a little bit courageous to be like, Well, they're dumb, but let's use them. Like, I think that helps people realize that they're not alone. Yeah, I I think I think that's right. Um I just I can't remember what book it's in, but there was this I remember a long time ago reading this this something by Kurt Vonnegut where he talks about in one of his books and about how like one of the great things about writing is that like somebody you've never met can can you know read something that like expresses a perspective on the world and says hey me too. I think that way too. that like that feeling of, hey, I'm not alone in thinking this is hugely powerful.

And it's like, you know, one of the things that really like ties me, and I think a lot of us to like the works of art that we love and the works of writing fiction or non that we love. And so I think like, you know, to the extent that like I can be that for even like a handful of people in, you know, is is that that's huge to me. I I love Yeah. Well it it was for me, honestly. Like honestly the the the like I'm like this is ridiculous. Um there's good work and there's clearly selection for status. Um it was really uh it it it it kept me in this to to to know that other people were thinking the same way. To tie it back full circle it is perhaps the feeling of a covert signal well received. Exactly.

And to be to be part of a community that you didn't know existed. So Paul, is there anything uh that we should have asked you uh during this time? Or did we did we get it all? I mean, I'm sure that you got a very small amount, but because we all contain multitudes, but uh I feel very, very happy with the conversation. I thought I I had a lot of fun. Yeah. Can you uh um uh sh t demonstrate at least one thing behind like what there for the people who are just listening, but like you know, we also are on YouTube. Um, you have guitars and you've got a solar system or something behind you. What is the well I'm not gonna play guitar on the podcast? Okay, no, that's uh that's for subscribe. We don't have subscribers, but if we did, that would be bonus content.

You have to pay to listen to Paul. Uh you you can um the the some of the music by my old uh garage band, the small dinosaurs, is available on so it's called small dinosaurs. I will put a link in the show notes. So it's small dinosaurs. Yeah. Okay. Um and then this is a Lego technic. It's uh sun and moon and uh an earth, and the earth spins and the sun spins, and the earth goes around the sun, and the moon goes around the earth, and I'm twisting a little lever, and it can show you like where the position the relative positions of the sun, the moon, and the earth are at different times of year. Awesome. And uh it's it's a very old technology, but Lego made one and I got it for Christmas. And we were talking about it earlier.

I think it's really neat. Um so you know, props to Lego. So awesome. Yeah. Lego, please sponsor the podcast. Oh, look at that. At the very end, I'm shilling to corporate. Yeah, anyway, but yeah. I mean, if you're gonna shill to a corporation, Lego's a pretty good one. Yeah, I mean a lot of uh a lot of wonderful changes to human brains done t by Lego, you know, and that's that's saying a lot. That sounds like really dark. It's a very reductive way to put it. I don't know if you know about Lego's secret laboratory where they're uh changing people's brains. It's the you know, exactly. I'm just the you know, I don't know, like Coca-Cola's just dump people full of sugar. Lego, it's like it's you know, it's like it's been but yeah.

I feel like I kind of think in Legos and I feel like that's a good thing. I don't know, to to think in terms of how the the pieces fit together and how it works. I don't know. And in like perseverance and planning and mental rotation and creativity and autonomy and all those things. But yeah, no, no, fair enough. All right. Well, uh maybe on that note, I don't know. Paul, do you want to have the last word? Potato. Awesome. All right. On that note. All right, Paul. Yeah, I'll hit I'll hit um I'll stop recording, but uh this is great, man. Yep. Oh yeah. Thanks. This is fun. David, I really enjoyed this conversation with Paul. Did you enjoy it? I loved it. I had a blast. All right. Um, and if you did enjoy it, please help us by spreading the word.

If you just tell one other person, then um then they'll might listen to it as well. I can't tell you how many people who I've emailed as like even a um a guest to come on who said I don't know, I didn't know about the podcast. So um, and then they listen to it and at least um you know 90% of the time they say, Oh, it actually was pretty good. Yeah, that's true. I mean, unless they're all lying to us uh to so they don't hurt our feelings. Uh yeah, maybe the people who never got back to me are like that was bad. I mean, occasionally people spl leave bad reviews, but that's okay. But if you're one of the people who does enjoy the podcast and is willing to tell us the truth about that, um then maybe share that truth with others by uh

saying a kind word about us on social media, leaving us a kind review, visiting us on Patreon. Uh there are many ways to show your support, and uh your support is what keeps us going and keeps this thing going. So please show it. That and caffeine. Yeah. You You'd send us some coffee grounds, uh, preferably in a fanny pack. That would be fantastic. And we'll leave you with that image until we see you next week or hear from you next week. See you next week.