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Dave P.

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David Pinsoff.

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Podcast about Evolutionary Psychology.

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Tagline is Actual Evolutionary Psychology.

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By actual evolutionary psychologists.

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And that this is what it is.

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Yep, let's continue listening.

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Okay.

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David, um Paul Smaldino is a I don't know how would you characterize

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Paul Smaldino?

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Um hmm polymathic, um good word, rigorous,

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insightful, um musical genius.

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He has a a band called Small Dinosaurs.

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Oh nice.

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Is this is it ongoing?

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I I don't know.

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I I we talked about it a bit.

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I think did we I think we talked about it a bit at the end of the episode.

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Yeah.

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Um, I don't know.

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I didn't listen yet.

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I haven't listened to the music.

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No, I haven't listened to the episode.

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Can I just you were there during the episode?

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But I honestly I do I sometimes re- I often re-listen to our episodes because I'm so I've

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heard other podcast hosts say this that they're so paying attention to the

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conversation that they don't hear everything.

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The first the the the the real time, but it's a different way of processing.

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Yeah, it's a different mindset because like when you're in the middle of a conversation,

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you're thinking about what you're gonna say next and what question you're gonna ask, and

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like you know, how to interact with the person.

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But when you're listening as a bystander or as a third party, you get to just take in the

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whole gestalt in a very different way.

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So what you're saying is we don't listen to the guest when we're interviewing.

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No, kidding.

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No, no, no, it's the opposite, actually.

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It's it's I I for me, it's like it's so much that I am so carefully listening to what

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they're saying and then trying to figure out where to go.

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Yeah.

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Um, yeah, this is probably very boring for listeners.

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But anyway, the point is I don't remember that from the Paul Smaldino episode.

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But what I do remember is the Lego um um solar system.

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Yes, we did talk about a Lego solar system.

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Yeah.

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So by now, you can't wait to hear this episode.

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So I mean we all what whilst we talked about mathematical models, yeah.

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The main thing is like why they're why math models models are stupid, we need more of them,

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which is you know a thing.

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We talked about the natural selection of bad science.

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Uh oh yeah, big yeah, we talked about that a lot.

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I mean, I think the the bottom line thing with Paul is that he is embodying and is a voice

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about it doesn't matter what people intend, whether it's social policy or good science or

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bad science, what matters is the selection criteria of the institutions.

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And he's doing modeling and empirical work, all about that.

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And I think the more that everybody sort of gets on board with whatever people intend, what

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we select for is what we get.

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I think that's a really uh important thing.

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And I think that's that's sort of what the conversation was about.

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Yeah, it was a it was a fun one.

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We also get into punk rock and covert signaling, sending signals that some people might get

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and some people might not get.

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So hopefully um this podcast will be full of signals that you get and uh no one else

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gets.

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Or maybe we want everyone to get it.

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I don't know.

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Uh you be the judge.

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You be the judge.

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All right.

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Enjoy enjoying the enjoyment.

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Um so yeah, let's get started.

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Um Paul is we're we we always do just an intro beforehand, so we can just get right

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into it.

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David, do you wanna do you wanna start um with this?

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Sure.

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Yeah.

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Well, I I am David Pinsoff.

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That is an identity.

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Uh I know I I I have a prediction, but I'm not I'll shut up.

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I'll let you ask the question, yeah.

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Well, it's not necessarily the type of identity that Paul Smaldino is interested in,

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because there's only one David Pinsov so far as I'm aware.

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Um but Paul, you're interested in identities that many people share and what functions they

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serve.

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Uh this is a huge topic in uh life as we know it in the United States and in many

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other places.

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Um there's lots of talk about identity, uh who I am, who we are.

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Uh you have a very interesting way of thinking about it.

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So, what is your way of thinking about it?

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Yeah, uh I mean I think identity is just sort of wrapped up.

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It's hard to be a person in the world and not have identity be something that

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you think about a lot.

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Uh and I think the way that a lot of people think

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about it is very sort of phenomenologically, meaning like the the experience of

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who I am in the world and what that means to me.

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Um, and I I think about things uh that that's true.

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Like that is a true thing about identity and a real thing and an important thing about

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identity.

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But I'm much more interested in its aspect as you know, the functional role of

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identity.

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What's identity for?

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And how do we use it purposefully?

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And I um this is where I think, you know, sort of evolutionary thinking, be it uh

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genetic evolution or uh sort of cultural evolution, which I tend to be a bit more

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uh interested in, but You know,

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the way we identify ourselves and the way we categorize others based on

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our identity categories.

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And identity is just a sort of a kind of category that we impose on other people.

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We use that for all kinds of adaptive decision making.

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Like how do we find partners for cooperation or finding a romantic

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partner or friends?

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How do we use what we perceive about something in order to gauge what kind of react

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uh interaction we can have with them, whether it's at the store or a stranger or at a party

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or in a classroom?

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Um how do we use identity to think about how we evaluate a

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source of information?

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Do we believe it?

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Do we accept it?

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Do we say this is the kind of thing that I'm definitely going to accept?

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Are we much more skeptical or rejecting of it?

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Do we use this to attack somebody or dismiss somebody?

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Um what kind of person am I also affects

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adaptive decision making.

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Like, do I am I bound by these rules because I am this kind of person and

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therefore tied to this kind of community and will judge be judged by this set of norms

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within this group?

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I think identity is really a kind of category that is at the core

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of so much of our behavior and decision making.

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And just to say, like all categories in general, like categorization is

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kind of uh one of the fundamental things that we do, right?

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That's foundational to how we behave and how we think, how we perceive the world.

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And I just think that identity is a really important social category that is

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more fleshed out and developed and nuanced in humans than in other species.

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Do you view identity as identical to categories in that it serves the same function as a

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category?

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Like if I were to categorize, you know, an animal, say birds, um, that's going to help me

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make predictions about um uh a new bird that I haven't seen before.

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I'm gonna assume that it flies, that it builds nests or whatever.

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And that's a useful category for me because it helps me make predictions uh about new cases

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I haven't seen.

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Similar similarly, if I if I see a new fruit, I'm gonna assume that it's sweet.

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Um and so these categories help structure our understanding of the world.

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Do you view identities as just the social version of that?

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It's just about uh helping us make better predictions about uh people and and and in what

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ways they're likely to behave or is it something more than that?

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yeah I be basically yes uh I think of identity as a a kind of

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category and you know uh being a a sort of special kind it has uh

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the features that it has.

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Um like the category identity is a kind of category that uh

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has certain features.

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But yeah, I do think that the main to me, the most helpful way to think about identity is

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as a kind of category.

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Okay.

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And David, can I well let me just poke on on that too a little bit, which is that, but

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there's also the part, and Paul, this is also like what you study.

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So I'm just saying what you've said is that it would be also as if like the fruit was also

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in a strategic uh stance towards the world.

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So unlike the fruit case where we're talking about the third-party perspective of well,

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what can I make an inference about for this fruit, there's also this sort of

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entrepreneurial or creative part of identity or strategic part of identity, negotiating

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identities or creating them or embodying them or broadcasting them.

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Right.

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And I I think that that's exactly right.

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And beyond that, it's not just uh that the fruit is, you know, behaving, right?

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Other people are behaving.

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Um, and so we have to think about strategic uh views on on what because due to their

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someone else's identity, how will they behave?

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But we also have to think about that they are another person with theory of mind who has,

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you know, categories about identity of their own.

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And we're all making decisions with limited information.

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And so understanding that another person may have their own categories of identity,

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they may have categorized me in a way that I wouldn't categorize myself.

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Or maybe I hope that they categorize me that is not necessarily entirely accurate, but it's

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useful strategically for them to categorize me in that way.

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And so I think the fact that you have lots of people categorizing each other in an

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interacting population makes one of the things that makes identity so interesting.

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And that would also explain then for what I'm hearing you say is why, and this is like a

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very David Pinsafian and Paul Smalldinian Smalldin.

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Small small small Dinonian.

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Whatever you go with, just don't it.

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I like it.

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Um is that uh there's a lot of negotiation.

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Like one of the one of the jobs in in social status competition

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is to say, here are the identities, or let's try out if this this

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we attribute this identity to you and act on behalf of this identity.

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Um That's right.

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And I we we often sort of put on, we try on roles, we try on identities in different

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contexts, and we sort of see how they fit.

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And we we modify how we view other people and we modify how we present ourselves,

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um, you know, at various times in our lives and also in different contexts.

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And so there are also like, you know, yeah.

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So this is one of the things that I think is so interesting about identity because it's

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actually extreme, it's not one thing, it's it's actually very multifaceted.

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And we have aspects of our identity that are pretty permanent.

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We have aspects that are maybe, you know, more malleable but over longer

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time scales.

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I mean, part of our identity is like what age category we are in, right?

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Um, and then there is the ones that are are very fluid and that change by context,

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um, like who we're with or what purpose we're in, um, what role we take.

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Uh, you maybe you're you're in a leadership role in like your you know

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garage band that you go practice with but you're actually in a subordinate role at work uh

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and I think yeah so we're really good at

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knowing what our role is in different contexts.

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And so I think this is one of the reasons it makes sense to think of identity as a kind of

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category.

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Yeah.

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One of the uh ways you study, or can you talk a little bit about how you study social

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identity?

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Because this is also a transition to sort of how you do your work, because you know, many

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listeners they might not know how you approach topics.

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And so you could imagine somebody just sort of approaching the the idea of identity and

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what you've just described with sort of paper and pencil surveys, like a social

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psychologist, no offense to that.

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But you know, the way you approach top any topic you study is with a bunch of cool

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tools that are, you know, you're pretty you're you're one of the the people doing these

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things and writing books about them.

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So can you talk a little bit about like how do you approach these and why do you use that

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tool?

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Sure, yeah.

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So I mean what you're talking about is is modeling, right?

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Is is mathematical and computational modeling.

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And and I do do those other things too a bit.

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Like the the you know behavioral experiments, because uh and it's important to

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actually you know see what people really do in like a lab setting in a behavioral setting

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it's also important to see what people do not in a lab setting right which is why sort of

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field work and naturalistic observation are important um but i i

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so i mostly do modeling i i the the bulk of my work i would say 80 to 90

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percent of the work i do is building mathematical or

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computational models.

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Um and I can I can talk more about how that works and what that is and uh etc.

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But you know, part of it is like I think the world is really complicated.

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I I mean I I know that the world is really complicated.

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Controversial, controversial take.

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Yeah, right.

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It's hot take.

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Uh it's just a few simple gizmos, just you know, you got your your atoms

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and you know they're all just uh yeah atoms and time atoms and time put that on a

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t-shirt uh yeah so I think it's really

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it's if it's really hard to think about all the things and we all have to make

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simplifications and we like every single person makes simplifications in our uh

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just going about the world, right?

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Going about our day.

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You don't think about every single molecule of dirt or sand or pavement on the street.

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You don't worry about the wind or the well, you know, unless unless you do, right?

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Unless the thing is important for your decision making.

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So most of the things that you know, when we're walking around doing our thing, we ignore

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tons of stuff if they're not likely to be important about the decisions we need to make.

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And when we build experiments, we come up with some paradigm

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where people behave in some way and we try to get them to do something.

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And we're usually not like, oh, what I'm really mostly interested in is like how people

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sitting at a laptop will click, you know, which one of these things at this time, like

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that's the main thing I'm interested in.

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You know, behavioral experiments these days are increasingly online, um, or like fill out a

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survey or whatever.

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We're interested in in what we try to do when we build experiments

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is capture some essence of the real question that we're interested in, whether it's about

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you know, identity or cognition or behavior or whatever it is.

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And that's limited.

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There's only so much you can do there.

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And it also leaves a lot of unexamined assumptions.

260
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And that's just the nature of the thing, right?

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When you deal with a very complex system, you have to ignore a lot of it in order to

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get any tractability and do science.

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and actually like isolate a signal that is strong enough to give you a sort of this is

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going this way and not that way.

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Um and as a supplement

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to that, which is sort of where I come in mostly, is taking trying to

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build sort of artificial systems, fake systems that capture the sort of essence

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of uh whatever dynamic that we're talking about, whatever the sort of components of the

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dynamic we're talking about, and think about the kinds of interactions, the properties that

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they have, and the way they interact and evolve over time, change over time.

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And sort of run that through mathematics or through computer simulation

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and sort of examine, okay, uh, based on these assumptions I made, have the

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outcomes that I predicted really happened?

274
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Have they is there something that I didn't expect?

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Uh, can I explain that?

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Um, that's a really powerful thing about modeling is when something I love it when

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something happens that you don't expect.

278
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And then because when something happens you don't expect in the real world, you're like, it

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could be a million things.

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I don't know.

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Maybe I don't have enough data to figure out what it is.

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But with the model, you actually've built the thing.

283
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You know everything that went into it.

284
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So if something happens that you don't expect, the main tool you have now is your brain to

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think, okay, well, you know, based on what I've put

286
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in, why did this thing come out?

287
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And sometimes it's because you made a mistake, and sometimes it's because you assumed

288
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something that you thought didn't matter, it turns out it mattered a lot.

289
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And sometimes it's because there were just unexpected consequences of what you

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did.

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The other really cool thing for me about doing these kinds of models are just the

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tremendous power to run variations and run lots of things.

293
00:18:10,090 --> 00:18:15,450
Like when we do experiments, it's you're so constrained by like how much money you have to

294
00:18:15,450 --> 00:18:21,370
pay participants or to go collect data or how much time a person is

295
00:18:21,370 --> 00:18:24,410
willing to sit and answer questions for, right?

296
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Or um, you know how many uh research assistance you can get to like run all

297
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the variants of your study.

298
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Or, you know, oh, it would actually be unethical to like kill a bunch of people or like

299
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poison them or like, you know, release a disease into the world.

300
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Like we definitely can't do that experimentally and we wouldn't want to, but we would kind

301
00:18:45,080 --> 00:18:49,480
of sometimes want to know what would happen on let's say a really long time scale that's

302
00:18:49,480 --> 00:18:53,290
really hard to study, especially when we're doing evolutionary thinking, right?

303
00:18:53,370 --> 00:18:59,050
It's like if you all you have is experiments, especially with humans, you mostly have

304
00:18:59,210 --> 00:19:05,130
at your tools, you have archaeology to look at the past and maybe some paleontology and

305
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history.

306
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And then you have you know this snapshot of what's happening now.

307
00:19:10,570 --> 00:19:15,370
And you try to make these really complicated inferences about like, okay, well, how did

308
00:19:15,370 --> 00:19:20,340
what we see now come out of what we think we know about the past.

309
00:19:20,980 --> 00:19:26,340
And using simulations, we can sort of say, well, okay, well, if the past was like that, and

310
00:19:26,340 --> 00:19:32,340
if these sort of general principles of change and behavior apply the way we think they do,

311
00:19:32,820 --> 00:19:37,460
then we can kind of simulate forward in time and look at different variations.

312
00:19:37,540 --> 00:19:43,230
We can run these simulations thousands of times With basically the same things.

313
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We can randomize a bunch of um starting conditions to try to get them to start over and

314
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over again.

315
00:19:49,070 --> 00:19:52,350
Um, so I I do this a lot, I really like it.

316
00:19:52,590 --> 00:19:58,350
Um I I one of the things uh and I I'm happy to talk a lot about this.

317
00:19:58,430 --> 00:20:04,270
Um I I wrote a textbook on doing this kind of modeling, sort of a general social

318
00:20:04,270 --> 00:20:10,000
science um approach, although um it definitely has sort of an evolutionary

319
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flavor.

320
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And uh I don't know I'm working actually on a second textbook uh that is more evolutionary

321
00:20:16,320 --> 00:20:22,160
in its uh approach uh explicitly um but um

322
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one of the things that I really like about this approach in addition to I think math is

323
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cool, I think computers are cool, I think these help me learn um is I'm also just very kind

324
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of ADD in my interests.

325
00:20:35,430 --> 00:20:41,110
and it takes like a really long time to set up a lab or a field site and I know this you

326
00:20:41,110 --> 00:20:45,430
know I have friends and colleagues who who do this kind of work right you you yourself Dave

327
00:20:45,590 --> 00:20:51,190
right like you know it takes so long to to get good experiments done

328
00:20:51,510 --> 00:20:57,030
yeah it's like making a movie as opposed to switching channels or channels but like

329
00:20:57,030 --> 00:21:01,990
watching it watching movies versus making movies it's like making making the setting up the

330
00:21:01,990 --> 00:21:03,910
lab is like making a movie it's really slow.

331
00:21:04,520 --> 00:21:05,560
That's a good analogy.

332
00:21:05,720 --> 00:21:11,240
And I think, like, you know, uh, instead of you know,

333
00:21:11,720 --> 00:21:17,560
if I just wrote the movies, yeah, and that's more like what I do, which is

334
00:21:17,640 --> 00:21:23,320
like I I know how the you know it works in theory, and I can just kind of like imagine it,

335
00:21:23,480 --> 00:21:28,890
except I can do more than imagine it and I can simulate it, but the once you sort of learn

336
00:21:28,890 --> 00:21:34,330
how to code and and do these kinds of analyses right like the startup time to getting

337
00:21:34,330 --> 00:21:40,170
simulations um the the thing that takes the longest honestly is the design part is

338
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just making sure that the model makes sense and is a good model and I think that's the

339
00:21:44,090 --> 00:21:49,930
hardest thing to do and uh but and once you can do that right running

340
00:21:51,320 --> 00:21:54,040
it running simulations is pretty low cost.

341
00:21:54,440 --> 00:21:59,160
Um, especially like if you're like me and are at a major university and you have access to

342
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a high performance computing cluster, you can run tens of thousands of

343
00:22:04,600 --> 00:22:06,440
simulations in a day.

344
00:22:06,680 --> 00:22:12,450
Uh I it's it's so funny to me when I talk to people who do simulations and they'll

345
00:22:12,450 --> 00:22:15,330
just say, Well, I don't want to do that.

346
00:22:15,410 --> 00:22:17,650
Like that's too many simulations to run.

347
00:22:17,730 --> 00:22:20,290
It's going to take like a week to get all that data.

348
00:22:20,530 --> 00:22:22,050
And I'm like, Are you kidding me?

349
00:22:22,210 --> 00:22:25,410
Like talk to an infant researcher or an anthropologist.

350
00:22:25,570 --> 00:22:26,930
Yeah, or field anthropologist.

351
00:22:27,010 --> 00:22:27,250
Yeah.

352
00:22:27,650 --> 00:22:30,690
Um, my wife has a PhD in developmental psychology.

353
00:22:30,770 --> 00:22:35,650
And so like she did this like you know longitudinal work that uh for her dissertation that

354
00:22:35,650 --> 00:22:39,330
took uh you know a year and a half just to collect all the data.

355
00:22:39,840 --> 00:22:44,240
And yeah, a field anthropologist who's spending, you know, months out of the air every

356
00:22:44,240 --> 00:22:49,680
every year going back, you know, over years to collect data for a single study.

357
00:22:49,840 --> 00:22:54,400
Um, so I find it sort of laughable when people complain about it taking like a week to do

358
00:22:54,400 --> 00:22:54,560
data.

359
00:22:54,800 --> 00:22:57,600
And it's not even like you have to be there for that week.

360
00:22:57,760 --> 00:23:01,040
You just like hit run and then walk away.

361
00:23:01,360 --> 00:23:07,070
Um so the the challenge really is uh, but you know, the bar

362
00:23:07,230 --> 00:23:12,590
is is is then the standard becomes like do you do something meaningful with it?

363
00:23:12,750 --> 00:23:16,270
Do you do something that makes sense that is develops insight, right?

364
00:23:16,430 --> 00:23:18,510
Because otherwise it's like what the fuck am I doing?

365
00:23:18,750 --> 00:23:19,070
Yeah.

366
00:23:19,230 --> 00:23:19,470
Yeah.

367
00:23:21,630 --> 00:23:22,270
Yeah.

368
00:23:22,670 --> 00:23:28,280
So it might be useful to like um take a concrete example of how this

369
00:23:28,280 --> 00:23:29,800
approach is used.

370
00:23:30,040 --> 00:23:36,040
Um so have you run simulations or developed models relating to identity

371
00:23:36,040 --> 00:23:40,200
that that you could walk us through like how you break apart the com the components?

372
00:23:40,440 --> 00:23:44,520
Or is there another model that you think is a good illustrative example of how this

373
00:23:44,520 --> 00:23:45,800
approach works?

374
00:23:46,200 --> 00:23:46,840
Yeah, sure.

375
00:23:46,920 --> 00:23:49,800
I'm I I think I mean you know the answer is yes to that.

376
00:23:50,040 --> 00:23:55,850
Uh uh uh let's talk about uh we can talk about the covert signaling stuff, because a lot a

377
00:23:55,850 --> 00:23:56,330
lot on that.

378
00:23:56,490 --> 00:23:57,450
Um okay.

379
00:23:57,770 --> 00:24:03,770
So uh this came out of

380
00:24:03,770 --> 00:24:09,770
an idea uh basically first of all that context would

381
00:24:10,330 --> 00:24:15,450
influence the the nature of identity as is if you think of identity not only as a category

382
00:24:15,530 --> 00:24:21,360
but as a communicated piece of information that I know, you know, I

383
00:24:21,360 --> 00:24:23,200
have I know things about myself.

384
00:24:23,520 --> 00:24:25,520
I, you know, am a certain way.

385
00:24:25,680 --> 00:24:30,080
I am affiliated with certain groups, I have certain commitments, and I meet you, a

386
00:24:30,080 --> 00:24:30,720
stranger.

387
00:24:31,040 --> 00:24:33,840
And I'm gonna communicate certain things to you.

388
00:24:34,080 --> 00:24:39,520
And there's an old literature uh from anthropology mostly and sociology, social psychology

389
00:24:39,680 --> 00:24:44,370
have sort of worked on this too, on uh like ethnic markers for identity tags.

390
00:24:44,530 --> 00:24:50,290
It's like, well, if we live in a world where people are different from each other and they

391
00:24:50,290 --> 00:24:56,050
meet strangers, it would be good to have a way sort of from a distance, at a safe distance,

392
00:24:56,210 --> 00:25:02,050
to be able to know very quickly some things about, you know, what this person is likely to

393
00:25:02,050 --> 00:25:04,420
be, uh, what this person is likely to be like.

394
00:25:04,660 --> 00:25:10,420
And so the way they dress, the way they, you know, decorate themselves, that's

395
00:25:10,420 --> 00:25:11,140
obvious, right?

396
00:25:11,300 --> 00:25:13,300
Their fashion, their, their, their clothing.

397
00:25:13,460 --> 00:25:15,380
You can see that really far away.

398
00:25:15,540 --> 00:25:19,860
Um then there are certain things like the way they move, certainly the way they talk, like

399
00:25:19,940 --> 00:25:24,500
obviously the way they they use language, the language they speak, but also the the accents

400
00:25:24,500 --> 00:25:26,980
they have, the the mannerisms of their speech.

401
00:25:27,140 --> 00:25:30,660
These are things we can really easily use to categorize people.

402
00:25:30,820 --> 00:25:32,330
And this really helps.

403
00:25:32,490 --> 00:25:38,170
Um, and it helps uh those kinds of ideas are really were really influential

404
00:25:38,730 --> 00:25:44,250
to me, but they're really kind of based on this idea of fairly

405
00:25:44,250 --> 00:25:50,010
small insular groups that where everyone in the group is an in group member and works with

406
00:25:50,010 --> 00:25:53,770
each other, and everyone from another group is an out group member and works with each

407
00:25:53,770 --> 00:25:53,930
other.

408
00:25:54,090 --> 00:25:58,410
And so we just want to know like if I meet somebody in the forest, are they a member of my

409
00:25:58,410 --> 00:26:03,720
sort of larger collective, you know, let's say tribal group or identity group, or are they

410
00:26:03,720 --> 00:26:09,560
from you know a different place or from a different and if so, what kind of person are

411
00:26:09,560 --> 00:26:09,720
they?

412
00:26:09,880 --> 00:26:13,000
Are they a a group that like we have relationships with?

413
00:26:13,160 --> 00:26:14,600
Are they someone that we're enemies with?

414
00:26:14,760 --> 00:26:15,880
I need to know.

415
00:26:16,680 --> 00:26:22,570
But now we live in really diverse societies where we're interacting constantly with people

416
00:26:22,890 --> 00:26:27,930
that we don't know, we've never met before, that nobody we know knows

417
00:26:28,090 --> 00:26:29,050
necessarily.

418
00:26:29,370 --> 00:26:35,210
Um, and we need to be able to make sort of rapid decisions about them.

419
00:26:35,290 --> 00:26:39,130
Because sometimes, like often, one of the things that really characterizes our, you know,

420
00:26:39,210 --> 00:26:44,330
modern societies is we sort of behave cooperatively with strangers every day.

421
00:26:44,820 --> 00:26:50,580
Just like you go to the store, you you know, buy things from the person to the store, you

422
00:26:50,580 --> 00:26:52,820
don't try to steal from the other customers, right?

423
00:26:52,900 --> 00:26:56,020
You like ride the bus or the train or get on a plane, right?

424
00:26:56,100 --> 00:26:57,620
Where we're all sort of calmly, right?

425
00:26:57,780 --> 00:27:03,300
Sarah Hurdy has this famous example of like, if you took like 200 chimpanzees and put them

426
00:27:03,300 --> 00:27:06,790
on an airplane in like a locked, you know, cabin in the sky.

427
00:27:06,870 --> 00:27:11,270
Like you would by the time that plane landed, you'd have like a bunch of dead chimpanzees

428
00:27:11,350 --> 00:27:12,950
because they'd all have killed each other.

429
00:27:13,190 --> 00:27:15,110
Um movie option idea.

430
00:27:15,350 --> 00:27:15,510
Yeah.

431
00:27:15,750 --> 00:27:15,830
Yeah.

432
00:27:15,990 --> 00:27:16,870
Chimps on a plane.

433
00:27:17,030 --> 00:27:18,150
Yeah, chimps on a plane.

434
00:27:18,950 --> 00:27:19,270
Yeah.

435
00:27:19,430 --> 00:27:20,310
I don't know about you guys.

436
00:27:20,390 --> 00:27:24,070
Every time I go to the grocery store, I form a coalition of males and I uh raid someone

437
00:27:24,070 --> 00:27:25,270
else's uh shopping cart.

438
00:27:25,670 --> 00:27:28,790
I was gonna say d I think David might be the exception to the what was that?

439
00:27:29,030 --> 00:27:30,390
So I I'm an exception to those rules.

440
00:27:30,630 --> 00:27:31,350
Yeah, exactly.

441
00:27:31,880 --> 00:27:32,680
that's fair.

442
00:27:32,840 --> 00:27:36,120
Uh I mean that's why I don't go to Ralph's.

443
00:27:36,280 --> 00:27:36,840
But yeah.

444
00:27:37,240 --> 00:27:43,160
Um the part of the the the sort of um

445
00:27:43,560 --> 00:27:49,400
so I I had this initial idea that like the way that society was structured,

446
00:27:49,640 --> 00:27:53,320
about the way the society was structured, and then the the sort of largeness and the

447
00:27:53,320 --> 00:27:59,090
diversity of the society would influence the sort of breadth versus depth trade-off in the

448
00:27:59,090 --> 00:28:04,290
way people signal their identity to each other and the sort of nature of the context

449
00:28:04,450 --> 00:28:05,730
specificity of that.

450
00:28:05,970 --> 00:28:09,410
And that's an idea that I've held on to for quite a long time.

451
00:28:09,570 --> 00:28:15,250
I think I I first started really thinking hard about that about maybe 10, 11 years ago, 12

452
00:28:15,250 --> 00:28:16,050
years ago.

453
00:28:17,660 --> 00:28:23,580
I really sort of I published like a sort of conceptual idea paper about it, you know,

454
00:28:23,740 --> 00:28:25,100
shortly thereafter.

455
00:28:25,500 --> 00:28:31,420
And uh just have like the first kind of major modeling paper on this

456
00:28:31,420 --> 00:28:33,900
idea coming out, hopefully coming out.

457
00:28:33,980 --> 00:28:37,100
Um we're just about to submit the final revisions uh soon.

458
00:28:37,260 --> 00:28:43,250
But um and i i'll talk more about that but but um one idea

459
00:28:43,250 --> 00:28:49,010
that kind of came along with that i i uh developed was this

460
00:28:49,010 --> 00:28:54,610
idea that we actually aren't just trying to honestly signal about

461
00:28:54,610 --> 00:29:00,610
ourselves um it's actually a lot of the time we we hide information we're not only

462
00:29:00,610 --> 00:29:06,480
dishonest but just sort of um uh sort of sneaky about, you know, or

463
00:29:06,560 --> 00:29:12,480
or um selective, let's say is the word um about how we talk about eat ourselves

464
00:29:12,480 --> 00:29:13,120
to other people.

465
00:29:13,200 --> 00:29:14,880
And that obviously we do that, right?

466
00:29:14,960 --> 00:29:19,280
We we just like you don't walk into a store and just start like yelling your political

467
00:29:19,280 --> 00:29:20,080
beliefs, right?

468
00:29:20,240 --> 00:29:24,800
You use, you know, you use uh uh judgment, right?

469
00:29:24,960 --> 00:29:25,680
Prudence, right?

470
00:29:25,760 --> 00:29:27,360
You're you're you're being savvy.

471
00:29:27,760 --> 00:29:32,570
Um But one of the things that's really interesting is what happens when you have a diverse

472
00:29:32,570 --> 00:29:38,570
society, and like often you find ourselves in positions, both like in person and

473
00:29:38,570 --> 00:29:43,290
certainly now online, uh, where there's a large audience, multiple listeners.

474
00:29:43,770 --> 00:29:49,530
And the thing that occurs then is every person, if you have people from

475
00:29:49,530 --> 00:29:55,530
diverse backgrounds with diverse experiences and assumptions and categories, if

476
00:29:55,530 --> 00:30:01,380
I say something that potentially gives information away about who

477
00:30:01,380 --> 00:30:05,540
I am, that's going to be interpreted differently by different people.

478
00:30:05,780 --> 00:30:11,540
I mean, the most simple version of that is like one person is like, great, they're like me.

479
00:30:11,620 --> 00:30:12,740
I might want to be friends with them.

480
00:30:12,820 --> 00:30:16,900
And another person is like, oh, they're part of that group that I hate.

481
00:30:17,140 --> 00:30:22,980
Like, I want to fight them or rob them or just like not help them or

482
00:30:22,980 --> 00:30:23,860
whatever.

483
00:30:24,500 --> 00:30:26,730
And what happens?

484
00:30:26,890 --> 00:30:28,250
How do I talk about myself?

485
00:30:28,410 --> 00:30:33,850
Or, you know, uh well, first a further version of that is like, what if, you know,

486
00:30:34,410 --> 00:30:40,250
if I'm a member, let's say, of an oppressed minority, I might want to talk about myself in

487
00:30:40,250 --> 00:30:44,170
a way that I need to find friends, I need to find people who will help me, I might need to

488
00:30:44,170 --> 00:30:46,490
find, you know, romantic partners.

489
00:30:47,540 --> 00:30:53,140
But I don't I don't want to risk outing myself in public and be

490
00:30:53,140 --> 00:30:57,940
potentially ostracized or hurt or imprisoned or killed.

491
00:30:58,180 --> 00:31:03,460
And so I need to be more selective about the way I speak.

492
00:31:03,620 --> 00:31:08,500
And one of the things that's really cool about human language, and not just human language

493
00:31:08,580 --> 00:31:13,380
in terms of spoken words, but also in terms of like the way that we communicate more

494
00:31:13,380 --> 00:31:18,690
broadly, is how nuanced, ambiguous, and

495
00:31:18,690 --> 00:31:20,130
multifaceted it is.

496
00:31:20,610 --> 00:31:24,850
There's the linguists have this term called implicature, which I really like, right?

497
00:31:24,930 --> 00:31:29,970
Which is me like the thing that the meaning that is implied by this the statement, right?

498
00:31:30,050 --> 00:31:31,490
This comes from pragmatics.

499
00:31:31,730 --> 00:31:37,410
So uh if I glance at an open window and say, it's a little chilly in here,

500
00:31:38,340 --> 00:31:43,940
I don't have to say like the implicature is maybe you should close the window, even though

501
00:31:43,940 --> 00:31:45,700
I didn't say it explicitly.

502
00:31:46,500 --> 00:31:52,260
And uh what's neat is that because we because

503
00:31:52,420 --> 00:31:58,420
language is complicated, because we have enough life experience to

504
00:31:58,420 --> 00:32:02,900
draw on previous categories, we are smart enough to think about lots of things in different

505
00:32:02,900 --> 00:32:04,420
ways, contexts, etc.

506
00:32:04,890 --> 00:32:09,290
I can say one thing that will have different implicatures to different people.

507
00:32:10,330 --> 00:32:16,250
And when this is used strategically for uh identity

508
00:32:16,250 --> 00:32:20,330
signaling, this is what we uh call covert signaling.

509
00:32:21,130 --> 00:32:27,130
So um, this is work that I originally developed with Tom Flampson and uh Richard McElreath,

510
00:32:27,450 --> 00:32:30,490
and have have continued to develop it with other colleagues.

511
00:32:30,730 --> 00:32:36,640
Um the this idea that um you

512
00:32:36,640 --> 00:32:42,320
know I could signal overtly and just that's like hey this is who I am let me tell you very

513
00:32:42,320 --> 00:32:47,040
clearly, unambiguously, this is something that you want to know about me, right?

514
00:32:47,440 --> 00:32:52,880
Like I'm a gay socialist who loves football and you know,

515
00:32:53,040 --> 00:32:55,600
whatever, hates square dancing.

516
00:32:56,320 --> 00:33:02,290
Um and that's gonna be

517
00:33:02,290 --> 00:33:06,290
really effective at making sure nobody is mistaken about who I am.

518
00:33:06,370 --> 00:33:11,010
And therefore, like it's gonna really you know maximize the fact that the the the chance

519
00:33:11,010 --> 00:33:15,330
that people who are similar to me are gonna know that they're similar to me and therefore,

520
00:33:15,410 --> 00:33:18,370
you know, might wanna extend relationships with me.

521
00:33:18,610 --> 00:33:24,210
On the other hand, though, if anyone is gonna be turned off by that or enraged by that,

522
00:33:24,460 --> 00:33:26,940
that's gonna be very bad for me.

523
00:33:27,580 --> 00:33:33,420
On the other hand, right, I could say something like, I really love this movie, or

524
00:33:33,500 --> 00:33:39,100
you know, I come in with a t-shirt that has an obscure reference, or just

525
00:33:39,420 --> 00:33:45,100
use jokes or fashion or other kinds of sometimes we call them dog whistles,

526
00:33:46,250 --> 00:33:51,850
Aspects of speech and communication that if you know what I'm talking about,

527
00:33:52,090 --> 00:33:57,850
if you are also aware of, you know, that these clothes or that movie

528
00:33:57,850 --> 00:34:02,810
reference or whatever, if you know those things, that the people who are likely to know

529
00:34:02,810 --> 00:34:07,930
those things are also likely to be similar to me and less similar to be members of other

530
00:34:07,930 --> 00:34:08,410
groups.

531
00:34:08,490 --> 00:34:13,130
And therefore, people who are similar to me will catch those references, whereas the people

532
00:34:13,130 --> 00:34:15,050
who are different from me will miss them.

533
00:34:15,420 --> 00:34:17,660
Because language can have multiple implicatures.

534
00:34:17,740 --> 00:34:19,020
I could say something.

535
00:34:19,180 --> 00:34:24,940
Um, this is one of the reasons why, like, you know, for most people who like who like

536
00:34:24,940 --> 00:34:26,540
humor, which is most people, right?

537
00:34:26,620 --> 00:34:30,460
Like, there's this old trope that like the funniest people you know are actually like your

538
00:34:30,460 --> 00:34:33,020
friends are the funniest people you know, always.

539
00:34:33,260 --> 00:34:36,380
Like the people who can make you laugh the most consistently are your friends.

540
00:34:36,460 --> 00:34:38,700
And there's like a very good reason for that, right?

541
00:34:38,860 --> 00:34:44,070
Like humor is an incredible tool For

542
00:34:44,230 --> 00:34:50,230
establishing that like we have shared values, shared references, and so I can make

543
00:34:50,230 --> 00:34:55,990
a joke and it will land really well with my friends who like were there

544
00:34:56,310 --> 00:35:00,950
at the thing that I'm referencing, or you know, understand that that thing is funny in the

545
00:35:00,950 --> 00:35:01,990
way that I meant it.

546
00:35:02,150 --> 00:35:05,670
Whereas other people might just say, you know, they might hear what I say.

547
00:35:05,830 --> 00:35:10,940
And it's not like I'm talking another language, they can hear the literal words I mean, but

548
00:35:10,940 --> 00:35:13,420
there is there's the this there's the text, right?

549
00:35:13,500 --> 00:35:14,780
And there's subtext.

550
00:35:15,500 --> 00:35:18,940
And subtext can act as this kind of covert signal.

551
00:35:19,260 --> 00:35:21,180
So that's the basic idea.

552
00:35:21,340 --> 00:35:25,100
And when we build these models, it's really helpful to start with a basic idea.

553
00:35:25,260 --> 00:35:28,460
This is what we call a verbal theory, what I've just talked about.

554
00:35:28,540 --> 00:35:34,380
It's it's an idea, it's talked about in words, um, it it seems to make

555
00:35:34,380 --> 00:35:40,010
sense, and then we build models in order to do a few things.

556
00:35:40,250 --> 00:35:46,010
One is like develop a proof of concept, like, okay, well, here's a way it

557
00:35:46,010 --> 00:35:50,330
could really work or might really work under certain assumptions.

558
00:35:50,570 --> 00:35:52,650
And here are also the boundary conditions.

559
00:35:52,810 --> 00:35:55,610
Here are sort of when it would or wouldn't work.

560
00:35:55,690 --> 00:35:58,970
And we can show how it would or wouldn't work.

561
00:35:59,050 --> 00:36:02,490
And we can even use this to make explicit predictions.

562
00:36:04,020 --> 00:36:09,460
At some case, if you have very good data and very good theory, you can even make um

563
00:36:09,700 --> 00:36:11,940
explicit quantitative predictions.

564
00:36:12,180 --> 00:36:18,180
But a lot of these models uh are used at least initially to develop more qualitative

565
00:36:18,180 --> 00:36:19,060
predictions.

566
00:36:19,620 --> 00:36:25,620
Um so uh some of the some of the the first modeling we did of this was to just sort

567
00:36:25,620 --> 00:36:30,980
of establish uh uh basically you create an artificial world

568
00:36:31,460 --> 00:36:36,430
where you you say, okay, well, the the world is going to be populated by people.

569
00:36:37,150 --> 00:36:39,550
What are the properties of those people?

570
00:36:39,630 --> 00:36:40,750
What do they do?

571
00:36:41,230 --> 00:36:46,750
Well, everyone has uh in these

572
00:36:46,750 --> 00:36:49,310
models some things about them.

573
00:36:49,870 --> 00:36:55,710
And to anyone else, to make things easier, we just said, well, let's just say any two pairs

574
00:36:55,710 --> 00:36:59,020
of people are either similar or not similar.

575
00:37:00,060 --> 00:37:05,260
And in a later model, we say, well, we can make explicit things, we give people like a

576
00:37:05,260 --> 00:37:09,820
vector of things about them, and we establish a cutoff where if you share enough things in

577
00:37:09,820 --> 00:37:12,220
common, you're similar, otherwise, you're dissimilar.

578
00:37:12,460 --> 00:37:16,060
And you could do this if you think that's a bad assumption, you could do it and you want

579
00:37:16,220 --> 00:37:17,340
you should do it another way.

580
00:37:17,500 --> 00:37:23,470
That's you've just identified one of the great things about modeling, which is that if if I

581
00:37:23,470 --> 00:37:28,750
do modeling and I say, look, I've made I've made this discovery with my model, you can say,

582
00:37:28,910 --> 00:37:33,950
well, you've made an assumption that doesn't hold in the systems that I've I'm interested

583
00:37:33,950 --> 00:37:34,190
in.

584
00:37:34,350 --> 00:37:35,310
And that's great.

585
00:37:35,550 --> 00:37:39,790
That means that we can you can take my model as a jumping off point and build on it and

586
00:37:39,790 --> 00:37:43,230
extend it and say, look, if you make these other assumptions, it doesn't hold.

587
00:37:43,390 --> 00:37:49,160
And that I think all goes towards building more robust, more falsifiable

588
00:37:49,480 --> 00:37:50,760
scientific theory.

589
00:37:51,320 --> 00:37:56,040
So we build these models and we assume that

590
00:37:57,000 --> 00:38:01,880
basically people are in a population where there's other people in the population that are

591
00:38:01,880 --> 00:38:03,800
similar or dissimilar to them.

592
00:38:04,440 --> 00:38:09,830
And in these first models, we say, well, let's not even worry about what signals they're

593
00:38:09,830 --> 00:38:11,270
using or how they're signaling.

594
00:38:11,430 --> 00:38:15,910
Let's just assume that what they're doing is signaling either overtly or covertly.

595
00:38:16,070 --> 00:38:21,990
And overt signaling has a probability of you know being identified by

596
00:38:22,630 --> 00:38:24,710
anyone who's around.

597
00:38:25,350 --> 00:38:27,830
And regardless if they're similar or dissimilar.

598
00:38:27,920 --> 00:38:33,360
And covert signaling is noisier, so even similar people are less likely to pick up on it.

599
00:38:33,440 --> 00:38:35,760
Sometimes you miss a joke even when your friend makes it.

600
00:38:36,000 --> 00:38:40,080
But dissimilar people are really unlikely to get it.

601
00:38:41,120 --> 00:38:46,560
And then it's this trade-off, right, between the increased

602
00:38:46,560 --> 00:38:52,480
fidelity of the covert signaling versus the uh avoiding sort of burning bridges with

603
00:38:52,480 --> 00:38:53,840
dissimilar individuals.

604
00:38:54,000 --> 00:38:59,890
And then we assume, well, okay, now people pair up to actually work on something

605
00:38:59,890 --> 00:39:00,610
together.

606
00:39:00,850 --> 00:39:06,370
And there are factors that influence the payoff that they get, how good that is, how good

607
00:39:06,370 --> 00:39:07,970
that that working together is.

608
00:39:08,290 --> 00:39:13,650
Say, well, it's better to work with someone who's similar to you, but it's if you have to

609
00:39:13,650 --> 00:39:17,570
work with someone who's dissimilar to you, it's better not to be aware of all your

610
00:39:17,570 --> 00:39:18,210
differences.

611
00:39:19,170 --> 00:39:19,490
Right?

612
00:39:19,680 --> 00:39:21,040
So it's better, you know.

613
00:39:21,200 --> 00:39:23,760
We we all like, you know, you can work with someone.

614
00:39:24,000 --> 00:39:28,320
Uh it's why, like, you know, we have these these heuristics of like don't talk about

615
00:39:28,320 --> 00:39:30,880
politics and religion at Thanksgiving or whatever, right?

616
00:39:31,040 --> 00:39:35,680
It's sometimes better not to not to highlight the differences in order to have a good time.

617
00:39:35,840 --> 00:39:38,480
And especially if you're trying to work with somebody.

618
00:39:38,720 --> 00:39:43,290
Um and we can vary things like, well, how bad is it?

619
00:39:43,450 --> 00:39:48,170
Like, is it a little bad to know you're dissimilar, or is it like really bad, like they

620
00:39:48,170 --> 00:39:49,050
kill you?

621
00:39:49,290 --> 00:39:55,290
Um and how often do you have to work with people who are different

622
00:39:55,290 --> 00:39:55,850
from you?

623
00:39:56,170 --> 00:40:01,930
Um is it really easy to like if you're if you can always use information that you have,

624
00:40:02,090 --> 00:40:07,770
your preferences about other people, and only choose people who you like and never have to

625
00:40:07,770 --> 00:40:12,560
work with people you don't like, yeah, maybe you should always you should signal overtly

626
00:40:12,560 --> 00:40:13,520
all the time.

627
00:40:13,920 --> 00:40:18,880
Uh but if sometimes you have to work with people, a fair amount of time you have to work

628
00:40:18,880 --> 00:40:24,880
with people uh that you don't like, um then yeah, the

629
00:40:24,880 --> 00:40:28,560
the model sort of predicts that we should expect more covert signaling.

630
00:40:28,800 --> 00:40:33,730
Now I'll I'll take a pause here, but I'll just say like we've that was sort of the

631
00:40:33,730 --> 00:40:38,450
groundwork, this kind of modeling with these kinds of qualitative predictions from the

632
00:40:38,450 --> 00:40:39,010
math.

633
00:40:39,170 --> 00:40:44,850
And we did simulations later, both where we did an agent-based sort of

634
00:40:44,850 --> 00:40:49,890
computational model where we could have minority and majority groups to make more explicit

635
00:40:49,890 --> 00:40:50,930
predictions there.

636
00:40:51,570 --> 00:40:57,410
We did uh an experiment using um sort of pre-Elon Musk Twitter

637
00:40:57,650 --> 00:41:03,100
to um to look at how people were actually signaling about their political identities online

638
00:41:03,260 --> 00:41:08,140
in different contexts, and how people we did in a behavioral experiment where to look at

639
00:41:08,140 --> 00:41:13,900
like what signals people chose to see if like the predictions of the model actually aligned

640
00:41:13,900 --> 00:41:17,420
with how people act in the world and and they do.

641
00:41:18,460 --> 00:41:24,290
And then, you know, later this, you know, the the work was sort of like

642
00:41:24,290 --> 00:41:30,210
I said, we in these models we said, well, we don't know, let's be agnostic about what

643
00:41:30,210 --> 00:41:34,850
people what signaling, what signals people are using, how they're using that, and even like

644
00:41:34,930 --> 00:41:39,250
you know, how they choose their signals, all it is is a strategy of covert signaling or

645
00:41:39,250 --> 00:41:40,530
overt signaling.

646
00:41:40,850 --> 00:41:46,720
Um but that led to sort of new ideas about the

647
00:41:46,720 --> 00:41:52,160
sort of lifespan of signals um and the idea that like

648
00:41:53,920 --> 00:41:59,680
well, if I'm using, let's say, a uh a r a reference, uh fashion

649
00:41:59,680 --> 00:42:05,270
choice or uh, you know, uh uh some words that I'm using, a joke that I'm using, and I'm

650
00:42:05,270 --> 00:42:09,350
doing this in public, like the whole idea is like there are third party listeners, and I'm

651
00:42:09,350 --> 00:42:12,870
using this because the th you know the people who are not like me don't know this

652
00:42:12,870 --> 00:42:14,870
reference, and the people who are like me do.

653
00:42:15,030 --> 00:42:17,110
Well, like people aren't stupid.

654
00:42:17,430 --> 00:42:23,110
Eventually, if everybody in my in-group is using these covert signals, people are

655
00:42:23,110 --> 00:42:26,710
gonna people from the out groups are gonna wise up, they're gonna notice.

656
00:42:26,950 --> 00:42:28,390
Like we notice all the time, right?

657
00:42:28,550 --> 00:42:31,720
We this is something that's like we call out the dog whistles.

658
00:42:31,880 --> 00:42:36,440
We call out the dog whistles, usually often years after they were effective.

659
00:42:36,680 --> 00:42:37,080
Yeah.

660
00:42:37,240 --> 00:42:37,800
Yep.

661
00:42:38,360 --> 00:42:40,760
And that's sort of the nature of it.

662
00:42:40,840 --> 00:42:43,720
So something that used to be covert is now overt.

663
00:42:44,360 --> 00:42:49,640
And uh we became interested in what

664
00:42:50,280 --> 00:42:56,110
are the dynamics of like does that imply that covert signals will have like a a

665
00:42:56,110 --> 00:42:57,790
natural lifespan.

666
00:42:58,030 --> 00:42:59,390
How long is that lifespan?

667
00:42:59,550 --> 00:43:01,710
What would determine that lifespan?

668
00:43:01,870 --> 00:43:07,710
Um and how exactly how long the the lifespan is, um we

669
00:43:07,710 --> 00:43:08,430
don't know.

670
00:43:08,750 --> 00:43:13,550
But the things that determine the lifespan now we do know from from you know, we we built a

671
00:43:13,550 --> 00:43:17,950
completely different kind of computational model and also a math model um to do this.

672
00:43:18,110 --> 00:43:22,110
Covert signaling, I think also this is one of the I'll I'll pause for a second.

673
00:43:22,270 --> 00:43:28,040
I I keep saying I'll pause and then not, but um one of the this is one of the

674
00:43:28,040 --> 00:43:29,480
additional benefits of modeling.

675
00:43:29,640 --> 00:43:34,360
It's not just that these things are hard to study because we're talking about timelines

676
00:43:34,520 --> 00:43:39,800
that are hard to study or and naturalistic interactions that are hard to study, but also

677
00:43:39,800 --> 00:43:45,080
because by definition, I'm talking about a a signal, a piece of

678
00:43:45,080 --> 00:43:51,000
communication the that you have to already be in the in-group to be able

679
00:43:51,000 --> 00:43:52,280
to identify.

680
00:43:53,640 --> 00:43:58,190
And that's really hard to study empirically.

681
00:43:58,430 --> 00:44:04,030
Um we we came up with some some workarounds uh for a couple of different um

682
00:44:04,270 --> 00:44:09,150
empirical studies, but uh they're limited, right?

683
00:44:09,470 --> 00:44:15,310
Um and and the the modeling really drove what we were looking for and helped us sort of see

684
00:44:15,310 --> 00:44:17,230
what kind of thing we were looking for.

685
00:44:18,240 --> 00:44:23,920
Yeah, if it's designed to not be detected uh by the experimenter, then that makes the

686
00:44:23,920 --> 00:44:26,880
experimenter's job uh extremely difficult.

687
00:44:27,680 --> 00:44:28,640
It does, yeah.

688
00:44:28,800 --> 00:44:34,720
So when we did this um uh with uh with Twitter data, and this is

689
00:44:34,720 --> 00:44:40,480
uh data collected in 2020, um we we specifically

690
00:44:40,480 --> 00:44:46,310
used you know tweets collected in the like weeks right before the 2020

691
00:44:46,310 --> 00:44:51,910
presidential election in the US we only used US participants and US based accounts

692
00:44:52,230 --> 00:44:58,230
and we figured well you know political identity was really salient I don't know if you guys

693
00:44:58,230 --> 00:45:01,350
remember 2020 is a really fun year.

694
00:45:01,590 --> 00:45:07,270
Um the uh it was like you know um

695
00:45:10,100 --> 00:45:13,300
It was uh wait, was this 2020 or 2016?

696
00:45:13,380 --> 00:45:14,660
Now I can't remember.

697
00:45:14,900 --> 00:45:20,820
Um either way, it was like, you know, this we're talking about like right

698
00:45:20,820 --> 00:45:24,740
before, you know, the the election, which was very heated.

699
00:45:24,900 --> 00:45:30,740
It was right um around when Amy Coney Barrett had been uh approved to the United States

700
00:45:30,740 --> 00:45:31,540
Supreme Court.

701
00:45:31,700 --> 00:45:34,420
It was just months after George Floyd was murdered.

702
00:45:34,580 --> 00:45:37,700
So like political identity was super salient.

703
00:45:38,180 --> 00:45:43,810
And what we did was collect a bunch of tweets based on

704
00:45:44,770 --> 00:45:50,130
accounts that were following a fair number of either far right or far-left news sites.

705
00:45:50,370 --> 00:45:54,290
So we're like, these people are probably going to be tweeting about politics.

706
00:45:55,010 --> 00:46:00,130
And then what we did was we got

707
00:46:00,370 --> 00:46:06,060
participants recruited through MTurk at the time, And we gave them a

708
00:46:06,060 --> 00:46:10,780
survey to determine their sort of where they fell on the political spectrum.

709
00:46:10,860 --> 00:46:14,060
Not just left and right, but how far left and how far right.

710
00:46:14,140 --> 00:46:19,820
So we could we sort of group people into four categories: radical right,

711
00:46:20,060 --> 00:46:23,340
moderate right, radical left, moderate left.

712
00:46:23,820 --> 00:46:29,470
And We then sort of gave people just lots and lots of tweets.

713
00:46:29,630 --> 00:46:33,710
And we said, all right, like, what do you think about this tweet?

714
00:46:33,790 --> 00:46:37,710
And, you know, more importantly, like, what can you tell about the likely political

715
00:46:37,710 --> 00:46:41,310
identity of this of the person who tweeted this just based on the text?

716
00:46:41,390 --> 00:46:45,070
And we we, you know, blanked out the account name and the picture.

717
00:46:45,150 --> 00:46:47,470
So all they saw was the text of the tweet.

718
00:46:48,110 --> 00:46:52,300
And uh, you know, sometimes it's really obvious, right?

719
00:46:52,460 --> 00:46:57,260
Like if someone was just like, you know, fuck Trump, like that's probably not someone on

720
00:46:57,260 --> 00:46:57,900
the right.

721
00:46:58,220 --> 00:47:03,340
Uh whereas, you know, there are things, other things that were a lot more subtle, like

722
00:47:03,340 --> 00:47:09,340
references to laws or, you know, sort of dog whistles that people use internally

723
00:47:09,340 --> 00:47:13,020
with an in-group, but don't really use with the out-group.

724
00:47:13,830 --> 00:47:19,590
And we found that there were a number of tweets that reliably

725
00:47:19,910 --> 00:47:25,910
people on the same side of the spectrum as the the tweeter on one side of the spectrum

726
00:47:26,070 --> 00:47:27,990
were like, Yeah, yeah, yeah, I know what this is.

727
00:47:28,070 --> 00:47:28,470
I like this.

728
00:47:28,550 --> 00:47:29,990
I'm I'm in favor of this.

729
00:47:30,230 --> 00:47:34,950
Whereas people on the other side, especially the moderates who are the majority, didn't

730
00:47:34,950 --> 00:47:35,190
get.

731
00:47:35,780 --> 00:47:40,260
So we had to sort of uh adapt the theory a little bit that we hadn't really thought about

732
00:47:40,260 --> 00:47:42,580
radicals and moderates in the modeling.

733
00:47:42,980 --> 00:47:45,220
But empirically, like it makes a lot of sense.

734
00:47:45,300 --> 00:47:50,900
Like the people who are really, you know, on like very left or very right,

735
00:47:51,220 --> 00:47:56,740
they they tend to also be sort of higher in their aggression and enmity for the other side.

736
00:47:56,820 --> 00:48:00,500
And they tend to be more savvy about the signals used by the other side.

737
00:48:02,350 --> 00:48:03,310
And yeah.

738
00:48:03,630 --> 00:48:07,470
They have more of an incentive to detect the dog whistles than say a moderate would.

739
00:48:07,790 --> 00:48:08,430
Exactly.

740
00:48:08,670 --> 00:48:12,510
Um and I and I wanna point out like they are in the minority though.

741
00:48:12,590 --> 00:48:17,070
Like it way more people are moderates than are radical.

742
00:48:19,310 --> 00:48:24,920
Is it that um the m the the radical were were better at detecting their

743
00:48:25,720 --> 00:48:31,000
same side dog whistles and the other side dog whistles, or what was the result?

744
00:48:31,720 --> 00:48:37,480
So basically, if somebody is signaling, if somebody is on the left and

745
00:48:37,640 --> 00:48:43,640
sending a signal that like communicates a left sort of you know biased message,

746
00:48:43,960 --> 00:48:47,720
everybody on the left understands that signal.

747
00:48:48,440 --> 00:48:54,310
If they're doing it covertly, The radicals on the right will also

748
00:48:54,630 --> 00:48:56,870
be fairly likely to notice it.

749
00:48:57,030 --> 00:48:58,310
But the moderates on the right won't.

750
00:48:58,630 --> 00:48:59,190
Yeah, got it.

751
00:48:59,270 --> 00:49:03,110
They're like higher information and even higher information about the other side in other

752
00:49:03,190 --> 00:49:03,350
words.

753
00:49:03,750 --> 00:49:03,990
That's right.

754
00:49:04,070 --> 00:49:04,230
Cool.

755
00:49:04,310 --> 00:49:04,790
Okay, yeah.

756
00:49:05,190 --> 00:49:08,470
So we basically use this difference between the moderates.

757
00:49:08,630 --> 00:49:13,350
So anything where like moderates on both sides could immediately identify as a political

758
00:49:13,350 --> 00:49:16,340
signal, we were like, that's an overt message.

759
00:49:16,500 --> 00:49:21,940
And things that everyone on one side, but only the radicals on the other side recognized

760
00:49:22,260 --> 00:49:24,180
were like, that's probably covert.

761
00:49:25,460 --> 00:49:31,380
And um in the, you know, so we did two things there, right?

762
00:49:31,460 --> 00:49:35,780
In the and this is all for sort of predictions that the whole design of this came out of

763
00:49:35,780 --> 00:49:37,300
the modeling that we had done.

764
00:49:38,100 --> 00:49:43,970
Um but we first just looked at like what does it look like on

765
00:49:43,970 --> 00:49:44,610
Twitter?

766
00:49:44,850 --> 00:49:48,690
And again, this is before Musk took it over, so it was Twitter, not X.

767
00:49:49,170 --> 00:49:54,610
Um, we just looked at the follower networks and sort of like who tweeted this?

768
00:49:55,570 --> 00:50:00,690
Who's what are let's we looked at all their followers and the kinds of news sites that they

769
00:50:00,850 --> 00:50:01,490
followed?

770
00:50:01,700 --> 00:50:07,540
And we looked at, you know, basically how partisan they were based on how mixed the the

771
00:50:07,540 --> 00:50:09,060
partisan sources were.

772
00:50:09,220 --> 00:50:11,780
So if you only followed right wing sources, you were right wing.

773
00:50:11,860 --> 00:50:14,900
If you only follow left wing sources, you were right left wing.

774
00:50:15,460 --> 00:50:20,740
If you followed both, you were more in the middle, and or at least more um

775
00:50:22,740 --> 00:50:24,180
harder to tell.

776
00:50:24,500 --> 00:50:30,350
And um and what we found is

777
00:50:30,350 --> 00:50:36,270
like the you know, the people who tweeted more overtly, most of their followers

778
00:50:36,270 --> 00:50:39,630
tended to be partisans on the same side of the spectrum.

779
00:50:39,710 --> 00:50:45,230
The people whose tweets were more covert tended to have more followers from both

780
00:50:45,230 --> 00:50:45,790
sides.

781
00:50:46,270 --> 00:50:52,240
And this is so this is um so r say radicals uh on either side.

782
00:50:52,400 --> 00:50:58,080
Um radicals with politically homogenous audiences um

783
00:50:58,720 --> 00:51:04,160
were more likely to signal overtly and radicals with uh more

784
00:51:04,240 --> 00:51:09,280
distributed or politically distributed audiences were more likely to signal covertly is

785
00:51:09,280 --> 00:51:15,120
that not exactly so we didn't we didn't re- we so the radical versus moderate

786
00:51:15,250 --> 00:51:19,250
those were our raiders, the people who were raiding tweets after we'd collected them,

787
00:51:19,330 --> 00:51:22,690
because we could actually ask them all these questions about their political identity.

788
00:51:22,850 --> 00:51:23,090
Got it.

789
00:51:23,570 --> 00:51:27,410
People who are actually producing the tweets, the only things we know about them are what

790
00:51:27,410 --> 00:51:30,050
news news accounts they follow.

791
00:51:30,370 --> 00:51:30,690
Got it.

792
00:51:31,010 --> 00:51:35,090
We're not really able to say that much about how radical they are, but we do know that

793
00:51:35,490 --> 00:51:40,370
they're probably at least fairly politically engaged because they're following lots of news

794
00:51:40,370 --> 00:51:43,540
sites and are tweeting about political things.

795
00:51:43,860 --> 00:51:44,100
Got it.

796
00:51:44,260 --> 00:51:47,940
And were the participants rating the radicalness or just the left-rightness?

797
00:51:48,740 --> 00:51:51,700
Um, so let me let me back up.

798
00:51:51,860 --> 00:51:52,020
Right.

799
00:51:52,180 --> 00:51:57,780
So we actually the first thing I did, right, or that we did was uh

800
00:52:00,980 --> 00:52:05,780
do some just ratings of tweets and ratings of so we we got people

801
00:52:06,790 --> 00:52:10,550
to ask, you know, answer questions just about their own political views, and that's how we

802
00:52:10,550 --> 00:52:11,830
could tell who they were.

803
00:52:11,990 --> 00:52:15,830
And then we asked them just to sort of rate these tweets that we had we had gotten.

804
00:52:15,990 --> 00:52:21,910
And we use that to sort of as a basically a metric, as an instrument to

805
00:52:21,910 --> 00:52:26,630
determine if the messages were being sent covertly or overtly.

806
00:52:29,180 --> 00:52:35,180
And when we just looked at who's sending what kinds of messages online and who's

807
00:52:35,180 --> 00:52:41,100
following them, we looked at not so the people who are sending, let's say,

808
00:52:41,340 --> 00:52:45,980
right wing tweets that are right wing, they're following lots of right-wing news sources.

809
00:52:46,140 --> 00:52:47,340
Who's following them?

810
00:52:47,580 --> 00:52:51,180
And what news sites are they of those people following?

811
00:52:53,070 --> 00:52:58,990
If that person who's following lots of right wing sites is tweeting out very overtly right

812
00:52:59,070 --> 00:53:04,430
wing messages, that correlates with most of their followers also

813
00:53:04,750 --> 00:53:08,270
being people who mostly follow right wing sites or sources.

814
00:53:09,070 --> 00:53:14,030
If they're tweeting more covertly, that means that a larger proportion of their audience,

815
00:53:14,190 --> 00:53:19,880
their followers, are following uh left wing sites, whether they're

816
00:53:19,880 --> 00:53:22,280
following both right or left or just left.

817
00:53:22,520 --> 00:53:23,160
Got it.

818
00:53:23,560 --> 00:53:29,400
Um, that signal was that effect was you know statistically significant and it was as

819
00:53:29,400 --> 00:53:34,440
they say, but it wasn't huge because Twitter is already fairly homophilus, like there's

820
00:53:34,440 --> 00:53:36,440
already a fair amount of assortment on Twitter.

821
00:53:36,520 --> 00:53:36,760
Yeah.

822
00:53:37,000 --> 00:53:42,440
So to boost the signal for this effect to test this, we did a behavioral experiment

823
00:53:43,080 --> 00:53:48,890
where we collected sets of tweets that are categorized as either left

824
00:53:48,890 --> 00:53:50,810
or right, overt or covert.

825
00:53:51,130 --> 00:53:53,210
So we have four groups of tweets.

826
00:53:53,930 --> 00:53:59,770
And then we we bring a participant in, now it's a new set of people, we say, hey,

827
00:54:00,730 --> 00:54:06,650
um you're gonna rate a bunch of these tweets, just like you know we

828
00:54:06,650 --> 00:54:07,370
did before.

829
00:54:07,450 --> 00:54:13,060
Uh uh and mostly we're just looking like, You know, we asked a few questions about them,

830
00:54:13,140 --> 00:54:17,540
like are they political, whatever, but also like once we determined that they were

831
00:54:17,540 --> 00:54:19,780
political, do you like it or dislike it?

832
00:54:19,940 --> 00:54:24,980
Do you have positive or negative feelings toward the person who sent this?

833
00:54:25,860 --> 00:54:28,500
And so everyone did that.

834
00:54:28,580 --> 00:54:34,070
And then we said, now, now you're gonna do an experiment, you're gonna have a

835
00:54:34,070 --> 00:54:40,070
task where we're gonna show you 40 tweets that are all, and

836
00:54:40,070 --> 00:54:45,830
we didn't tell them any of anything about the tweets, but actually they were

837
00:54:45,830 --> 00:54:51,670
all from people from the same side of the political spectrum as them, and half of them were

838
00:54:51,670 --> 00:54:53,190
covert and half of them were overt.

839
00:54:53,430 --> 00:54:54,870
They didn't know which was which.

840
00:54:55,110 --> 00:54:59,830
We just said, you're just gonna pick this, and we're just gonna and um

841
00:55:00,560 --> 00:55:06,560
there's gonna be a random set of 10 other people who rated who

842
00:55:06,560 --> 00:55:09,280
are gonna rate these tweets just like you rated them.

843
00:55:10,080 --> 00:55:15,920
And for every tweet that you select that most of your audience liked

844
00:55:16,240 --> 00:55:21,360
more than they disliked, you're gonna get more money than we say we were originally gonna

845
00:55:21,360 --> 00:55:21,840
give you.

846
00:55:22,720 --> 00:55:28,710
But for every tweet you pick that in this audience that you if most of them

847
00:55:28,790 --> 00:55:34,070
or or a majority of them dislike it, you're gonna lose money.

848
00:55:34,950 --> 00:55:36,790
And so you be careful.

849
00:55:36,870 --> 00:55:39,190
But you can select as many or as few as you want.

850
00:55:39,510 --> 00:55:43,590
And we're like, there's a minimum amount you can get, but let's say, you know, I don't

851
00:55:43,590 --> 00:55:44,150
remember what it was.

852
00:55:44,230 --> 00:55:45,670
It was like five dollars.

853
00:55:45,940 --> 00:55:50,100
If all of them hate it, if you pick all the tweets and all of your audience hates it, you

854
00:55:50,100 --> 00:55:51,380
might only get two dollars.

855
00:55:51,540 --> 00:55:54,500
But maybe if you go up, you can go like as many as eight dollars.

856
00:55:54,580 --> 00:55:56,500
I don't remember the exact numbers.

857
00:55:57,300 --> 00:56:03,060
And uh and all we told them about their audience was how many of the ten

858
00:56:03,380 --> 00:56:06,420
were on the same side of the political spectrum as them.

859
00:56:07,940 --> 00:56:10,660
And you know, unsurprisingly, right?

860
00:56:10,740 --> 00:56:15,830
People are real this is like in in hindsight fairly obvious, but like, you know, the it was

861
00:56:15,830 --> 00:56:17,030
sort of a proof of concept.

862
00:56:17,110 --> 00:56:18,710
People are really good at this.

863
00:56:19,030 --> 00:56:24,870
Um when people when when people knew that the majority of their

864
00:56:24,870 --> 00:56:29,350
audience was on the same side as them, they picked almost everything.

865
00:56:29,510 --> 00:56:33,030
They were like, all these, I agree with all these these messages.

866
00:56:33,190 --> 00:56:35,510
I want to send them all to this audience.

867
00:56:35,750 --> 00:56:41,740
When people knew that the audience was made up of a fair number, like a mix, they

868
00:56:41,740 --> 00:56:42,940
were more careful.

869
00:56:43,180 --> 00:56:47,580
And when they knew that a lot of the audience, a majority of their audience was different

870
00:56:47,580 --> 00:56:50,380
from them, they were even more careful in two ways.

871
00:56:50,540 --> 00:56:54,140
They just selected way fewer tweets overall.

872
00:56:54,300 --> 00:56:57,900
And of the ones they selected, a higher percentage of them were covert.

873
00:57:00,620 --> 00:57:04,810
And so like, it's a fairly

874
00:57:05,610 --> 00:57:11,450
artificial construct construction, but the idea was to sort of show that we could

875
00:57:11,610 --> 00:57:15,530
that people actually do this in the real world, make these strategic considerations in the

876
00:57:15,690 --> 00:57:18,490
real world that align with the kinds of modeling that we did.

877
00:57:18,570 --> 00:57:23,930
And I think the conclusions that we make from the model are broader than the ones we can

878
00:57:23,930 --> 00:57:24,890
make from the experiment.

879
00:57:25,050 --> 00:57:30,900
But the experiment is just an illustration that, yeah, people can behave in

880
00:57:30,900 --> 00:57:33,540
this way that we predicted that they be would behave.

881
00:57:34,660 --> 00:57:37,780
I see an interesting uh extension of this.

882
00:57:38,020 --> 00:57:42,980
Um so it it would be interesting to look at people who are on the same side politically,

883
00:57:43,060 --> 00:57:48,500
say say left-wing people, um, and have them evaluate um

884
00:57:48,980 --> 00:57:54,790
the political uh allegiances of of the tweeter uh who is either tweeting a

885
00:57:54,790 --> 00:57:59,510
covert signal or an overt signal, and whether they think the people who are tweeting uh

886
00:57:59,670 --> 00:58:05,270
overt signals are more radical or more extreme uh than the people tweeting covert

887
00:58:05,270 --> 00:58:10,310
signals, even though they know e even in cases where they know the the the the meaning of

888
00:58:10,310 --> 00:58:11,830
the covert signal.

889
00:58:12,140 --> 00:58:17,580
And so the idea there would be that if I'm sending an overt signal, I'm sending a costly

890
00:58:17,580 --> 00:58:23,340
signal of my loyalty to uh to leftists because I'm I am burning bridges

891
00:58:23,660 --> 00:58:26,860
with uh moderates and with right wing people.

892
00:58:27,100 --> 00:58:31,900
Whereas if I have a more mixed social network, I can't afford to burn those bridges.

893
00:58:32,060 --> 00:58:37,340
Uh and so I'm going to be uh uh unlikely to be as committed

894
00:58:37,340 --> 00:58:38,140
politically.

895
00:58:38,790 --> 00:58:40,150
I think that's exactly right.

896
00:58:40,230 --> 00:58:45,270
And I this is actually, yeah, what you're talking about were uh is related to a lot of the

897
00:58:45,270 --> 00:58:49,110
conversations we were having as we first you know started doing this kind of work.

898
00:58:49,270 --> 00:58:54,870
And w one of the the things that really kind of inspired uh, especially the this experiment

899
00:58:54,870 --> 00:59:00,790
that I just talked about, was um uh I, you know, I had done the modeling and and and I

900
00:59:00,790 --> 00:59:06,660
just kept thinking about, you know, uh I think the first paper was published in 2018,

901
00:59:06,820 --> 00:59:08,660
but it was it was written.

902
00:59:08,900 --> 00:59:14,180
The first draft of that paper was was finished, you know, I think uh sometime in

903
00:59:14,180 --> 00:59:15,140
2015.

904
00:59:15,860 --> 00:59:21,540
And, you know, something happened around 2015, 2016 in the

905
00:59:21,540 --> 00:59:22,820
US, especially, right?

906
00:59:22,900 --> 00:59:27,380
All of a sudden it just felt like, and you know, we've been seeing it happen more and more.

907
00:59:27,460 --> 00:59:32,850
Like all these kind of Extreme white

908
00:59:33,010 --> 00:59:38,930
national racist, you know, fascist sympathizers just

909
00:59:38,930 --> 00:59:40,530
started coming out of the woodwork.

910
00:59:40,690 --> 00:59:44,610
Like all of a sudden, they just were there were more and more of them.

911
00:59:44,690 --> 00:59:49,970
Like people were on Stormfront, uh, which is like a right-wing kind of fascist uh

912
00:59:50,130 --> 00:59:51,970
Nazi website.

913
00:59:52,130 --> 00:59:57,250
And they were just we had that march in um Charlottesville.

914
00:59:57,810 --> 01:00:03,780
Um and it became very clear, like, you

915
01:00:03,780 --> 01:00:05,300
know, why is this happening?

916
01:00:05,460 --> 01:00:07,620
Or the you know, question arose, like, why is this happening?

917
01:00:07,780 --> 01:00:11,620
And and two possibilities sort of immediately present themselves.

918
01:00:11,780 --> 01:00:17,300
Like, one, people have been recruited to this cause that weren't previously of this

919
01:00:17,300 --> 01:00:17,780
mindset.

920
01:00:17,940 --> 01:00:22,500
But all of a sudden, this has become much more attractive uh for people, and people uh it's

921
01:00:22,500 --> 01:00:26,500
much more compelling, and a lot of people who previously didn't think this way now think

922
01:00:26,500 --> 01:00:27,140
this way.

923
01:00:28,070 --> 01:00:33,910
Another hypothesis, though, is that people there were there were there

924
01:00:33,910 --> 01:00:38,870
had been for quite some time a lot of people who felt this way, but were much more quiet

925
01:00:38,870 --> 01:00:39,510
about it.

926
01:00:39,910 --> 01:00:44,550
That maybe they talked this way in their insular social networks, but they would never talk

927
01:00:44,550 --> 01:00:49,670
this way among you know in public because of the negative consequences.

928
01:00:50,500 --> 01:00:56,420
And between the kind of assortment that the internet and social media facilitate,

929
01:00:56,740 --> 01:01:02,340
so it becomes much more easy to find networks of like-minded people online,

930
01:01:02,740 --> 01:01:06,100
and that that has good things and bad things about it.

931
01:01:06,900 --> 01:01:12,810
And you know, the rise of a political leader, specifically

932
01:01:12,810 --> 01:01:18,730
Donald Trump and others, who seemed ideologically aligned

933
01:01:18,730 --> 01:01:23,210
with these, sort of emboldened people and said, I think their perceptions of both their

934
01:01:23,210 --> 01:01:28,330
social networks and the potential payoffs, costs, and benefits of signaling

935
01:01:28,650 --> 01:01:29,450
changed.

936
01:01:29,690 --> 01:01:35,530
And so they went from signaling either only in private or covertly in public,

937
01:01:36,740 --> 01:01:40,100
to being much more emboldened to signal c uh overtly.

938
01:01:40,180 --> 01:01:42,820
And that was really where where the idea came from.

939
01:01:42,980 --> 01:01:48,580
And and the initial uh you know uh plan to do this study was much more ambitious, but it

940
01:01:48,580 --> 01:01:49,540
turned out to be really hard.

941
01:01:49,620 --> 01:01:52,180
So we we did the smaller version of it.

942
01:01:52,660 --> 01:01:53,860
Um right.

943
01:01:54,180 --> 01:01:58,370
I mean it's occurring to me that another interesting extension of this would be to look at

944
01:01:58,370 --> 01:02:00,930
perceptions of group formidability.

945
01:02:01,090 --> 01:02:05,250
And you might expect that groups that are signaling overtly will be perceived as more

946
01:02:05,250 --> 01:02:10,450
formidable or more of a political threat, uh, and perhaps even you know larger in number

947
01:02:10,610 --> 01:02:12,690
than groups that are signaling covertly.

948
01:02:12,850 --> 01:02:17,730
Because if I have a huge social network of like-minded peers, I can afford to burn bridges

949
01:02:17,730 --> 01:02:18,690
with everyone else.

950
01:02:18,850 --> 01:02:24,280
Uh, I can afford to signal overtly, but if I'm a tiny minority, um, let's say you know I'm

951
01:02:24,280 --> 01:02:28,760
a I'm a minority of dissidents in a totalitarian regime, I have to signal very, very, very

952
01:02:28,760 --> 01:02:33,560
covertly because if I get detected, you know, off to the gulags I go, right?

953
01:02:33,800 --> 01:02:39,640
But if I'm in the majority, uh, I can afford to signal as overtly as I want uh without care

954
01:02:39,640 --> 01:02:43,880
for you know burning bridges because there are so few bridges to burn or they're not very

955
01:02:43,880 --> 01:02:45,560
valuable bridges in the first place.

956
01:02:45,800 --> 01:02:49,720
And so it might be that if I'm looking, even if I know with certainty the political

957
01:02:49,720 --> 01:02:55,010
allegiances of the of the person, um, You know, if they're s if they're signaling overtly,

958
01:02:55,090 --> 01:02:59,810
I'm going to be more threatened and estimate that they uh have a more homogenous social

959
01:02:59,810 --> 01:03:03,970
network, they have uh a larger social network of like-minded peers than if they're

960
01:03:03,970 --> 01:03:05,570
signaling covertly.

961
01:03:06,130 --> 01:03:06,770
That's right.

962
01:03:06,930 --> 01:03:08,530
Uh yeah, that's exactly right.

963
01:03:08,850 --> 01:03:12,210
And would it also, I mean, the other question would be the uh same side.

964
01:03:12,370 --> 01:03:18,370
So if I'm uh a persecuted minority, for example, and I see somebody else explicitly,

965
01:03:18,610 --> 01:03:24,400
overtly signaling, that should be minimally interesting to me and

966
01:03:24,400 --> 01:03:29,200
maybe a little bit positive, if not really positive, because it's doing two things.

967
01:03:29,360 --> 01:03:32,080
One is it's sort of they're the canary in the coal mine.

968
01:03:32,320 --> 01:03:34,000
So are they going to go to the gulag or not?

969
01:03:34,160 --> 01:03:38,800
Because it tells me, you know, how many of their how many other people like me are there.

970
01:03:38,960 --> 01:03:44,770
Um, but then it also is maybe my group is more formidable than I thought.

971
01:03:44,850 --> 01:03:47,810
And maybe I don't have to uh signal covertly.

972
01:03:48,050 --> 01:03:53,890
So you can imagine that there's also an appetite for overt signaling of your own side

973
01:03:54,130 --> 01:03:58,770
when you're experiencing at some level in your brain that you're having to do it covertly

974
01:03:58,770 --> 01:03:59,650
for the time being.

975
01:04:00,130 --> 01:04:01,010
Yeah, that's right.

976
01:04:01,090 --> 01:04:05,330
And this is actually something I'm super interested in because I think it's really hard to

977
01:04:05,330 --> 01:04:10,690
explain um from a kind of strategic perspective.

978
01:04:10,930 --> 01:04:14,300
But I think uh which is the sort of like

979
01:04:16,940 --> 01:04:22,300
you know, especially in political situations when dissidents kind of go above and beyond,

980
01:04:22,460 --> 01:04:23,900
like they're oppressed.

981
01:04:23,980 --> 01:04:28,620
And if they signal in a particular way, they're gonna be oppressed more, but they choose to

982
01:04:28,620 --> 01:04:30,860
do it anyway in a kind of act of science.

983
01:04:30,940 --> 01:04:35,580
I find this really interesting um and inspiring in a lot of ways.

984
01:04:35,900 --> 01:04:41,890
And, you know, I think I can we can make kind of gesso stories about

985
01:04:41,970 --> 01:04:47,810
or you know uh uh speculate about that a lot of these people, and I think the evidence at

986
01:04:47,810 --> 01:04:53,570
least partially supports this, that like a lot of these people are like the ones who

987
01:04:53,570 --> 01:04:57,970
feel like there is no hope in the mainstream system.

988
01:04:58,130 --> 01:05:04,100
There's just um uh Benwat de Corson and I think is

989
01:05:04,260 --> 01:05:10,180
Dan Nettle the other author, they have these papers uh on um you

990
01:05:10,180 --> 01:05:15,700
know the the on on risk and uh

991
01:05:16,580 --> 01:05:20,660
and there are other people who've who've written about this too, but basically like risk

992
01:05:20,660 --> 01:05:21,460
and thresholds.

993
01:05:21,700 --> 01:05:22,180
Yeah, yeah.

994
01:05:22,500 --> 01:05:27,860
And uh, you know, how like people who are sort of just above the threshold where like you

995
01:05:27,860 --> 01:05:31,070
have everything things are going okay for you as long as you don't rock the boat.

996
01:05:31,150 --> 01:05:33,550
Like you'll be fine as long as you don't rock the boat.

997
01:05:33,790 --> 01:05:38,110
Those people are incentivized to not take any risks, to be very careful.

998
01:05:38,350 --> 01:05:42,990
But the people who've fallen below that threshold who are basically fucked either way.

999
01:05:43,230 --> 01:05:47,870
Like if they keep going on the way they've always been going, they've got nothing anyway.

1000
01:05:47,950 --> 01:05:50,910
So they might as well try and take a chance.

1001
01:05:51,470 --> 01:05:56,760
And I think one of the things that's also underappreciated is like those people can form

1002
01:05:56,760 --> 01:06:02,680
coalitions and raise themselves up, rise them uh raise themselves up out of that

1003
01:06:02,760 --> 01:06:08,520
that gulf explicitly by taking these risks and by by sort

1004
01:06:08,520 --> 01:06:10,280
of going against the mainstream.

1005
01:06:10,360 --> 01:06:14,760
Um one of my favorite books that I read in the last uh number of years was this book called

1006
01:06:14,760 --> 01:06:16,680
um burning down the house.

1007
01:06:16,920 --> 01:06:18,050
Do you know this Dave?

1008
01:06:18,370 --> 01:06:24,290
I know the song, but uh yeah, not not the um not the talking head song, which is great, but

1009
01:06:24,290 --> 01:06:27,410
it's uh house spelled in the German way, H-A-U-S.

1010
01:06:27,730 --> 01:06:28,290
Oh nice.

1011
01:06:28,450 --> 01:06:32,690
Uh and the the subtitle is Punk Rock Revolution and the Fall of the Berlin Wall.

1012
01:06:32,930 --> 01:06:33,410
Oh, nice.

1013
01:06:33,490 --> 01:06:34,290
I shouldn't read that.

1014
01:06:34,370 --> 01:06:34,450
Yeah.

1015
01:06:34,770 --> 01:06:40,160
It's written by this journalist, Tim Moore, and it's about basically punk rockers in East

1016
01:06:40,160 --> 01:06:46,000
Germany in the nineteen eighties and kind of highlighting these people like it just

1017
01:06:46,000 --> 01:06:50,960
makes like the sort of American punk scene around the same time feel just completely uh

1018
01:06:51,120 --> 01:06:53,600
like a bunch of posers because suburban.

1019
01:06:53,680 --> 01:06:53,760
Yeah.

1020
01:06:54,000 --> 01:06:55,520
Yeah, it's totally suburban, right?

1021
01:06:55,600 --> 01:06:57,920
It was uh like no risk.

1022
01:06:58,000 --> 01:06:58,320
No risk.

1023
01:06:58,640 --> 01:07:03,520
German punks were being like arrested uh by the Stasi and thrown in jail.

1024
01:07:03,680 --> 01:07:08,730
And then and yeah, getting getting beaten up, getting literally thrown in prison, not being

1025
01:07:08,730 --> 01:07:09,690
able to get jobs.

1026
01:07:09,850 --> 01:07:15,690
They the bands weren't actually allowed to play in public because you needed a permit given

1027
01:07:15,690 --> 01:07:17,610
by the government to play punk rock.

1028
01:07:17,690 --> 01:07:20,970
So you had all the shows were underground or in churches.

1029
01:07:21,370 --> 01:07:27,290
Um but uh m Tim Moore argues that basically, you know, there was an effect of

1030
01:07:27,450 --> 01:07:32,740
of these people sort of actively resisting and you can't help but notice them on the

1031
01:07:32,740 --> 01:07:33,140
street.

1032
01:07:33,220 --> 01:07:38,180
And when you see them continuously getting harassed and oppressed, uh despite not having

1033
01:07:38,180 --> 01:07:44,180
done anything, uh that this he argues that uh this helped sort of turn the tide

1034
01:07:44,180 --> 01:07:49,780
against the GDP uh or the DDR, sorry, um in uh

1035
01:07:50,020 --> 01:07:51,070
in East Germany.

1036
01:07:51,310 --> 01:07:52,030
Yeah, that's awesome.

1037
01:07:52,190 --> 01:07:56,190
It also reminds me of Michael Monkrief we had on, and he was talking about um uh drag

1038
01:07:56,190 --> 01:08:01,790
queens, and it was a similar thing where it was, you know, 1950s, and it was these guys who

1039
01:08:01,790 --> 01:08:03,630
were down and out, low status.

1040
01:08:03,790 --> 01:08:09,230
So it was exactly this, they're kind of screwed either way, and they um they they they're

1041
01:08:09,230 --> 01:08:14,910
using this high variance strategy um to help their own status, but then they're

1042
01:08:14,910 --> 01:08:20,900
doing this work to push on and provoke, you know, for for you know,

1043
01:08:21,140 --> 01:08:22,020
reaction basically.

1044
01:08:22,100 --> 01:08:22,660
And it was David.

1045
01:08:22,900 --> 01:08:23,460
No, that's right.

1046
01:08:23,780 --> 01:08:28,020
If there's um do you ever read there's uh Hunter Thompson's first book, Hell's Angels.

1047
01:08:28,580 --> 01:08:29,380
Yeah, that's a great book.

1048
01:08:29,460 --> 01:08:31,460
I love great author.

1049
01:08:31,620 --> 01:08:36,420
Good if you want to be a better uh author or read writer, um, read read good right writers,

1050
01:08:36,500 --> 01:08:37,060
and Hunter S.

1051
01:08:37,220 --> 01:08:37,860
Thompson's great.

1052
01:08:37,940 --> 01:08:38,100
Yeah.

1053
01:08:38,340 --> 01:08:39,300
Yeah, agreed.

1054
01:08:39,540 --> 01:08:42,740
Um, yeah, Hell's Angels uh is is really great.

1055
01:08:42,820 --> 01:08:47,950
I mean, he talks about them too in that book, like you know, not ro in this kind of

1056
01:08:47,950 --> 01:08:50,270
romantic way, but he's like, these are the the dregs.

1057
01:08:50,510 --> 01:08:56,110
This is the love the people who have nothing in society who are uh and they've banded

1058
01:08:56,110 --> 01:08:58,670
together to to sort of form this outlaw group.

1059
01:08:58,910 --> 01:09:04,030
And um this actually this kind of thinking has has gotten I've become more and more

1060
01:09:04,030 --> 01:09:10,030
interested in this kind of dynamic between mainstream society and

1061
01:09:11,150 --> 01:09:13,630
groups, you know, kind of at the edges.

1062
01:09:14,110 --> 01:09:17,900
Um Yeah, it's super cool.

1063
01:09:18,140 --> 01:09:22,620
I think one of the one of the things I want to ask, speaking of groups at the edges, is

1064
01:09:22,620 --> 01:09:23,180
science.

1065
01:09:23,340 --> 01:09:28,460
Um and um you have this paper with Richard uh

1066
01:09:28,540 --> 01:09:31,980
McAlbright about the natural selection of bad science.

1067
01:09:32,140 --> 01:09:35,820
Um, and I and there's two things I just want to preface for the audience to say.

1068
01:09:35,980 --> 01:09:40,970
One is that everything you've been talking about, I think it's really important, and I want

1069
01:09:40,970 --> 01:09:46,570
to be explicit about is that I think the work you're doing is part of a

1070
01:09:46,570 --> 01:09:51,530
movement to recognize that there are unintended consequences.

1071
01:09:51,770 --> 01:09:56,410
So I think like a simple model of the world is sort of like what people intend is what

1072
01:09:56,410 --> 01:09:57,050
happens.

1073
01:09:57,290 --> 01:10:02,730
But I think the reason to do modeling or social study social dynamics and

1074
01:10:03,370 --> 01:10:08,440
is to is to say, look, selection, what you select for

1075
01:10:09,240 --> 01:10:10,520
is what you get.

1076
01:10:11,480 --> 01:10:13,240
Intentions be damned.

1077
01:10:13,480 --> 01:10:16,520
And so I think that's really important.

1078
01:10:16,680 --> 01:10:21,320
And I think that in the same way this applies to social identities, you've done work, kind

1079
01:10:21,320 --> 01:10:27,240
of meta science work, about how do intentions versus selection,

1080
01:10:27,400 --> 01:10:30,760
so like what are we selecting for, for example, when we do science?

1081
01:10:31,000 --> 01:10:36,690
And might it be that there's unintended consequences that even things that seem good

1082
01:10:37,010 --> 01:10:42,850
or nobody's with malicious intent or everybody's just sort of trying to get

1083
01:10:42,850 --> 01:10:47,730
by in the in the aggregate produce these bad

1084
01:10:48,050 --> 01:10:49,170
dynamics?

1085
01:10:49,410 --> 01:10:51,330
I think that idea is really powerful.

1086
01:10:51,410 --> 01:10:57,400
And I don't think outside of maybe the modeling work or the modeling world, I

1087
01:10:57,400 --> 01:10:59,960
think they're not everybody fully appreciates that.

1088
01:11:00,040 --> 01:11:02,440
And so I want to just say, like, I think what you're doing is really important.

1089
01:11:02,680 --> 01:11:07,880
Can you talk a little bit about um the natural selection of

1090
01:11:08,200 --> 01:11:09,640
bad science?

1091
01:11:10,360 --> 01:11:10,840
Yeah.

1092
01:11:11,080 --> 01:11:13,160
Uh uh thanks thanks for that.

1093
01:11:13,320 --> 01:11:19,320
Um so I mean, for uh first of all, I'll just say like I I think what you said is is

1094
01:11:19,320 --> 01:11:21,000
spot on and and really important.

1095
01:11:21,240 --> 01:11:26,010
And I think that you know It's a naive view, right?

1096
01:11:26,170 --> 01:11:32,090
If people think that they can understand social behavior, the way that people

1097
01:11:32,090 --> 01:11:37,610
behave, and almost all behavior is social behavior to some extent, um when you're talking

1098
01:11:37,610 --> 01:11:43,050
about humans, uh, by only studying how individuals behave in kind of isolation, right?

1099
01:11:43,210 --> 01:11:48,700
Like the lots of people interacting in a, you know,

1100
01:11:48,860 --> 01:11:54,620
cultural and social context over time produces behaviors that are direct

1101
01:11:54,620 --> 01:11:58,780
consequences of the organization, the way those things, which is you know, more like sort

1102
01:11:58,780 --> 01:12:03,260
of sociological thinking, but um and I I think that's really important.

1103
01:12:03,580 --> 01:12:06,140
Um so okay.

1104
01:12:06,620 --> 01:12:12,230
Um So the natural selection of bad science was this paper,

1105
01:12:12,470 --> 01:12:17,030
this project that kind of came out a little bit about, you know, uh Richard and I, this was

1106
01:12:17,030 --> 01:12:17,750
a long time ago.

1107
01:12:17,830 --> 01:12:22,070
We started working on this, um 2014, 2015.

1108
01:12:22,230 --> 01:12:24,230
The paper came out in 2016.

1109
01:12:24,390 --> 01:12:29,910
Um, and I think the context is important then because um

1110
01:12:30,390 --> 01:12:36,360
you guys may recall, like this was kind of right in the aftermath of

1111
01:12:36,360 --> 01:12:41,240
uh what's sometimes called the replication crisis, uh, where which was really kind of

1112
01:12:41,240 --> 01:12:47,000
started in psychology as being recognized, but gradually people noticed it in other fields,

1113
01:12:47,160 --> 01:12:52,440
biomedicine and you know, lots of, lots of fields, um, which was basically that

1114
01:12:53,400 --> 01:12:59,210
uh a lot of the standard methods that had uh become standard in uh a

1115
01:12:59,210 --> 01:13:02,570
bunch of these fields were producing results that didn't replicate.

1116
01:13:02,810 --> 01:13:07,210
And it all like the the one of the main catalysts, it wasn't the only one, but one of the

1117
01:13:07,210 --> 01:13:12,410
main catalysts that really kind of launched this investigation was this publication in this

1118
01:13:12,410 --> 01:13:16,730
top journal in in social psychology by uh um by Daryl Bem.

1119
01:13:17,130 --> 01:13:18,090
Daryl Bem, yeah, yeah.

1120
01:13:18,330 --> 01:13:24,240
Yeah, he claimed to have uh evidence for um uh seeing the future,

1121
01:13:24,400 --> 01:13:30,320
predicting the future, that uh uh undergraduates could choose which uh

1122
01:13:31,120 --> 01:13:35,520
uh which curtain had a uh pornographic image behind it.

1123
01:13:35,680 --> 01:13:39,600
Uh and it wasn't even a real curtain, it was uh like a computer screen.

1124
01:13:39,760 --> 01:13:44,960
And the point was like the computer chose which curtain to put the image behind

1125
01:13:45,250 --> 01:13:48,050
after the students had made their guess.

1126
01:13:48,290 --> 01:13:51,330
So there was literally no way for the students to guess.

1127
01:13:51,410 --> 01:13:57,170
And yet he's like, well, it was actually they they guessed correctly like 53% of the

1128
01:13:57,170 --> 01:13:57,490
time.

1129
01:13:57,890 --> 01:14:03,010
And you know, we did a t-test and found that that was statistically significant.

1130
01:14:03,490 --> 01:14:09,380
And it like it's it's just ludicrously terrible methodology,

1131
01:14:09,540 --> 01:14:14,740
just the thinking and I got really upset uh for another reason too about it, or kind of was

1132
01:14:14,740 --> 01:14:17,780
up in arms about it, because I was like, this is a physics result.

1133
01:14:18,340 --> 01:14:24,260
If you're claiming to like have evidence that everything we know

1134
01:14:24,260 --> 01:14:27,890
about causality Is wrong.

1135
01:14:28,050 --> 01:14:31,730
Like that we there's another substance in the other thing.

1136
01:14:32,050 --> 01:14:34,290
This is like the foundation of everything we do.

1137
01:14:34,450 --> 01:14:36,370
This is a massive claim.

1138
01:14:36,610 --> 01:14:42,530
And your evidence is just like you know, uh some like a like

1139
01:14:42,530 --> 01:14:48,210
20 undergraduates clicked a button uh in in slightly above chance.

1140
01:14:49,000 --> 01:14:53,080
And you know, when people started investigating, they found that like actually he had

1141
01:14:53,080 --> 01:14:57,480
showed them a whole bunch of different kinds of images, and pornographic images were the

1142
01:14:57,480 --> 01:14:59,000
only ones that had a high enough.

1143
01:14:59,240 --> 01:15:05,000
So it's like it's like the whole like uh there's this XKCD comic uh about

1144
01:15:05,160 --> 01:15:10,920
where like he's joking about uh scientists giving people different color jelly beans

1145
01:15:10,920 --> 01:15:15,950
and see if any of them will cure cancer, and then you just try all the different colors and

1146
01:15:15,950 --> 01:15:21,310
finally one of them is like, oh yeah, like fewer people who had green jelly beans have

1147
01:15:21,310 --> 01:15:21,870
cancer.

1148
01:15:22,030 --> 01:15:26,990
And then like the headline is like, uh, green jelly beans cause can uh cure cancer.

1149
01:15:27,310 --> 01:15:32,350
But it's if you try a million things, like statistically, one of them will have a

1150
01:15:32,350 --> 01:15:32,990
correlation.

1151
01:15:33,070 --> 01:15:38,350
But it's like there's that website, Spurious Correlations, where it's just amazing and

1152
01:15:38,350 --> 01:15:39,390
everyone should look at it.

1153
01:15:39,470 --> 01:15:45,420
It's like uh, you know the the number of uh like kevin bacon movies in a

1154
01:15:45,420 --> 01:15:48,540
given decade correlates with like deaths by drowning.

1155
01:15:48,940 --> 01:15:51,020
Um I mean tremors.

1156
01:15:51,500 --> 01:15:53,100
That one is actually causal I think.

1157
01:15:53,740 --> 01:15:54,140
Yeah.

1158
01:15:55,100 --> 01:15:59,740
I can I I can understand how seeing Kevin Bacon would would make you less careful around

1159
01:15:59,740 --> 01:16:00,540
bodies of water.

1160
01:16:01,980 --> 01:16:02,540
I agree with that.

1161
01:16:02,860 --> 01:16:06,550
Can I can I interject too this is like uh inside scoop information.

1162
01:16:06,710 --> 01:16:12,630
I, as a graduate student, submitted a paper to that journal and the same action

1163
01:16:12,630 --> 01:16:18,390
editor that handled the BEM article, and my paper was uh rejected.

1164
01:16:18,550 --> 01:16:21,510
And it was like careful, it was sort of an obvious thing.

1165
01:16:21,590 --> 01:16:26,550
It was like accents are categorized, they had tons of control conditions, and it was not

1166
01:16:26,550 --> 01:16:28,950
accepted at the same time that the BEM paper was.

1167
01:16:29,110 --> 01:16:34,950
So it's it's also for me like very egregious in that it's also it w it wasn't just sort of

1168
01:16:34,950 --> 01:16:40,880
like people were doing collecting careful science, and then

1169
01:16:40,880 --> 01:16:42,320
this just kind of got in.

1170
01:16:42,400 --> 01:16:47,200
It was just from my experience, it was obviously careful science was just being like, no,

1171
01:16:47,280 --> 01:16:50,720
this is nonsense, and then ESP, yeah, come on in.

1172
01:16:50,800 --> 01:16:51,040
Yeah.

1173
01:16:51,360 --> 01:16:51,520
Yeah.

1174
01:16:51,760 --> 01:16:55,920
It seems like it's it's the opposite of what you'd want to be doing as I feel as a

1175
01:16:55,920 --> 01:17:01,830
knowledge accumulating enterprise, which is taking into account past knowledge and trying

1176
01:17:01,830 --> 01:17:06,630
to integrate new knowledge with the past edifice of knowledge and coming into your

1177
01:17:06,630 --> 01:17:12,150
hypothesis with a set of well-informed priors based on prior research and prior knowledge,

1178
01:17:12,310 --> 01:17:17,670
as opposed to just starting your investigation in a vacuum completely in ignorance of

1179
01:17:17,670 --> 01:17:19,910
centuries of scientific research.

1180
01:17:20,790 --> 01:17:24,870
And that's, I think, what we are all about as evolutionary social scientists is we want to

1181
01:17:24,870 --> 01:17:29,480
integrate our knowledge of human beings with past knowledge that we've already accumulated

1182
01:17:29,480 --> 01:17:31,320
about how evolution works, right?

1183
01:17:31,800 --> 01:17:36,280
Uh and and and the fact that social science does not avail itself of that tremendously

1184
01:17:36,280 --> 01:17:42,040
useful tool of using everything we know to inform our research is just mind-boggling.

1185
01:17:42,120 --> 01:17:44,680
But anyway, Paul has a reason why people do that.

1186
01:17:45,080 --> 01:17:46,440
Yeah, why do people do that?

1187
01:17:46,520 --> 01:17:46,680
Yes.

1188
01:17:47,000 --> 01:17:47,240
Yeah.

1189
01:17:47,480 --> 01:17:50,510
So um I I agree.

1190
01:17:50,590 --> 01:17:55,950
And yeah, so part of this project was was to sort of maybe highlight a potential reason for

1191
01:17:55,950 --> 01:18:00,830
why uh people are not as good scientists as they should be or could be.

1192
01:18:01,230 --> 01:18:06,830
Um which was basically, you know, a griping about like uh various things in

1193
01:18:06,830 --> 01:18:08,670
academia kind of came out of it.

1194
01:18:08,830 --> 01:18:14,300
Um and seeing, you know, these headlines, exactly the kind of thing you're talking about,

1195
01:18:14,380 --> 01:18:20,140
Dave, like, yeah, the these ridiculous studies getting uh you know

1196
01:18:21,100 --> 01:18:26,380
highlighted, put into, you know, top journals and by people at Ivy League universities

1197
01:18:26,540 --> 01:18:32,540
while this like very careful, methodical, creative research uh was was getting passed over

1198
01:18:32,620 --> 01:18:37,420
or or shunted or uh into lower you know uh ranked journals.

1199
01:18:37,820 --> 01:18:43,570
And um, you know, one of the things that's

1200
01:18:43,570 --> 01:18:49,330
true about academics and scientists is that they're human beings and they live in a

1201
01:18:49,330 --> 01:18:54,850
world w you know that is characterized we live in an unequal hierarchical

1202
01:18:54,850 --> 01:19:00,530
society where having wealth and acclaim and prestige

1203
01:19:01,200 --> 01:19:06,560
gives you hired gives you as you know people certain kinds of satisfaction.

1204
01:19:06,640 --> 01:19:08,000
It's things that people want.

1205
01:19:08,480 --> 01:19:12,800
And it's also the case that not everyone can get those things.

1206
01:19:13,200 --> 01:19:18,960
Which, you know, immediately anyone who thinks about evolution to think, well, okay,

1207
01:19:19,120 --> 01:19:22,160
scientists vary, they're different from each other.

1208
01:19:22,400 --> 01:19:26,880
Um there's a something that you know

1209
01:19:28,550 --> 01:19:34,550
Allows for the transmission of scientific knowledge, where you know scientists train

1210
01:19:34,550 --> 01:19:39,030
other scientists via universities and postdoctoral fellowships and trainings.

1211
01:19:39,110 --> 01:19:41,270
So there's inheritance in that way.

1212
01:19:41,430 --> 01:19:43,590
And there's selection, right?

1213
01:19:44,070 --> 01:19:50,070
So not only uh so these are all the the these are all the ingredients you need

1214
01:19:50,070 --> 01:19:52,070
for an evolutionary dynamic.

1215
01:19:52,390 --> 01:19:58,080
And the question then is, well, you know, what

1216
01:19:58,080 --> 01:20:01,600
ways do they vary, and especially how are they selected?

1217
01:20:02,080 --> 01:20:07,760
And so we built a model because the truth is they vary in lots of ways, and they're

1218
01:20:07,760 --> 01:20:09,520
selected in lots of ways.

1219
01:20:09,840 --> 01:20:15,760
But we wanted to focus on the quality or rigor

1220
01:20:17,110 --> 01:20:18,630
of the methodology.

1221
01:20:18,870 --> 01:20:24,630
So we imagined, you know, we created a model where we have um we actually did a

1222
01:20:24,630 --> 01:20:28,230
previous model where we had a population model of hypotheses.

1223
01:20:28,470 --> 01:20:33,030
I actually I like this paper a lot, and it's uh uh often overlooked.

1224
01:20:33,190 --> 01:20:39,060
So that this Richard and I did this previous paper where we looked at uh just a population

1225
01:20:39,060 --> 01:20:44,980
of hypotheses that you know where they were tested with certain amounts of error

1226
01:20:45,140 --> 01:20:49,700
for both false negatives and false positives and there were certain kinds of communication

1227
01:20:49,700 --> 01:20:55,620
bias like publication bias and certain kinds of replication strategies and we looked at

1228
01:20:55,700 --> 01:21:00,510
all right well like under different assumptions what would we be able to say about the

1229
01:21:00,510 --> 01:21:05,630
probability that a hypothesis is true given a certain record of supporting and

1230
01:21:05,630 --> 01:21:08,270
non-supporting results in the published record?

1231
01:21:08,910 --> 01:21:12,990
And so we so we had that model done.

1232
01:21:13,870 --> 01:21:19,470
And we started thinking, well, you know, that model just said, like, well, there's an

1233
01:21:19,470 --> 01:21:20,430
error rate.

1234
01:21:20,670 --> 01:21:25,870
There's a power, the statistical power, you know, the ability to find true results and

1235
01:21:25,870 --> 01:21:27,710
avoid false negatives.

1236
01:21:28,800 --> 01:21:34,400
What if we created a population of artificial science agents that varied in their

1237
01:21:34,400 --> 01:21:35,040
methods?

1238
01:21:35,360 --> 01:21:41,360
They said, well, okay, well, you know, everyone has a uh we we just said,

1239
01:21:41,440 --> 01:21:47,360
all right, all the agents have some sort of intrinsic methods that can vary in how rigorous

1240
01:21:47,360 --> 01:21:50,590
they are and how powerful they are.

1241
01:21:50,910 --> 01:21:52,510
And those things trade off.

1242
01:21:52,670 --> 01:21:58,270
So we can avoid false positives by being extremely

1243
01:21:58,270 --> 01:22:01,550
careful, but then we might miss a lot of true positives.

1244
01:22:01,790 --> 01:22:06,990
We can find all the true positives by having very high power, by you know, saying

1245
01:22:06,990 --> 01:22:11,150
everything is true, that would be good, but that's not good because then we get a lot of

1246
01:22:11,150 --> 01:22:12,990
false false positives.

1247
01:22:13,310 --> 01:22:17,820
So we want to be able to say the things that are true are true and the things that are

1248
01:22:17,820 --> 01:22:19,100
false are false.

1249
01:22:19,420 --> 01:22:22,780
And to do that takes time and effort.

1250
01:22:22,940 --> 01:22:24,780
It's being careful.

1251
01:22:25,580 --> 01:22:31,580
But that means that you know you're not you're less likely to submit, you know, to get

1252
01:22:31,580 --> 01:22:35,420
false positives, which gives you fewer things to submit to journals.

1253
01:22:35,580 --> 01:22:38,620
And it's going to take you longer between studies.

1254
01:22:39,340 --> 01:22:43,890
And so that's actually the kind of science that we think we want, which is careful science

1255
01:22:43,970 --> 01:22:46,450
where the things we say are true are actually true.

1256
01:22:46,690 --> 01:22:49,090
But then there's the selection pressure.

1257
01:22:49,250 --> 01:22:51,650
And in this case, we did a simplified version.

1258
01:22:51,810 --> 01:22:57,650
So in reality, right, the reason why scientists get hired and promoted and get

1259
01:22:57,650 --> 01:23:02,850
grant funding, which allows them to, you know, train more graduate students,

1260
01:23:03,010 --> 01:23:06,930
undergraduates, allows them to get more prestigious jobs where they have more influence.

1261
01:23:07,250 --> 01:23:13,100
It comes down to a lot of things, but some of the biggest ones will be how many papers they

1262
01:23:13,100 --> 01:23:18,220
publish, how prestigious and influential those papers are, how much grant money they get,

1263
01:23:18,380 --> 01:23:18,940
etc.

1264
01:23:19,420 --> 01:23:25,020
And uh we in the model, we just distilled this down to one thing, which is just how many

1265
01:23:25,020 --> 01:23:26,140
papers you publish.

1266
01:23:26,380 --> 01:23:31,980
And you we you we could build a model with other things, but in this simple version, uh we

1267
01:23:31,980 --> 01:23:32,940
did it this way.

1268
01:23:33,180 --> 01:23:35,130
And we also allowed for replication.

1269
01:23:35,210 --> 01:23:39,130
So we said, all right, if somebody else replicates your study and it doesn't replicate,

1270
01:23:39,210 --> 01:23:42,090
that's bad for you and that hurts your prestige.

1271
01:23:42,250 --> 01:23:46,010
But otherwise, the more the more papers you publish, the more prestige you have.

1272
01:23:46,250 --> 01:23:51,210
And the more prestige you have, the more likely you are to transmit your methods to someone

1273
01:23:51,210 --> 01:23:56,810
else in the future because they you get a good job at a prestigious university, you get

1274
01:23:56,810 --> 01:24:01,130
lots of grant funding, your papers are influential and people apply to work with you, et

1275
01:24:01,130 --> 01:24:01,770
cetera.

1276
01:24:02,330 --> 01:24:08,180
And this, we just kind of simulated this dynamic over sort of generations of

1277
01:24:08,180 --> 01:24:14,020
scientists and showed that if the selection, the major selection

1278
01:24:14,020 --> 01:24:19,860
pressure is how many papers you publish, all things equal, that's going to

1279
01:24:19,860 --> 01:24:25,780
select for a continual decrease in the rigor that we

1280
01:24:25,780 --> 01:24:26,100
see.

1281
01:24:26,260 --> 01:24:28,710
Now, you know, it's a model.

1282
01:24:29,110 --> 01:24:31,190
And like I said, there are other things.

1283
01:24:31,990 --> 01:24:37,670
But if it's wrong, right, then we should see the opposite happening, right?

1284
01:24:37,750 --> 01:24:40,230
Because what we actually want is things to be true.

1285
01:24:40,470 --> 01:24:45,430
So we should see a gradual tend uh trend for better and better methods over time.

1286
01:24:45,590 --> 01:24:51,590
If selection is actually favoring methods that are geared at improving truth, uh,

1287
01:24:51,670 --> 01:24:57,100
that have, you know, uh evidence that they will actually help us get

1288
01:24:57,660 --> 01:24:59,980
epistemic value out of our studies.

1289
01:25:01,260 --> 01:25:05,500
But you know, there's a lot of evidence from a number of fields that that hasn't happened.

1290
01:25:05,900 --> 01:25:11,580
I mean, two of the ones I like to highlight is like, you know, there's this famous uh

1291
01:25:11,580 --> 01:25:15,500
paper by Paul Meal, who's a philosopher of science in the 1960s.

1292
01:25:15,740 --> 01:25:21,550
He wrote this paper in the 1960s basically saying, hey, psychologists are really not

1293
01:25:21,550 --> 01:25:26,030
using p-values correctly, this sort of null hypothesis significance testing.

1294
01:25:26,270 --> 01:25:27,950
You're doing it really badly.

1295
01:25:28,110 --> 01:25:29,950
You don't understand what p-values are.

1296
01:25:30,030 --> 01:25:35,950
A p-value is not the probability that your hypothesis is

1297
01:25:35,950 --> 01:25:36,270
true.

1298
01:25:36,350 --> 01:25:39,390
And it's not the probability that the null hypothesis is false.

1299
01:25:39,470 --> 01:25:45,340
It's actually just the probability that you saw the data you saw conditional on the null

1300
01:25:45,340 --> 01:25:47,180
hypothesis being true.

1301
01:25:47,980 --> 01:25:51,820
But that doesn't actually support any other specific hypothesis.

1302
01:25:53,180 --> 01:25:58,860
And you would think that, you know, in the 1960s, that was 60 years ago,

1303
01:25:59,020 --> 01:26:00,140
that would have an effect.

1304
01:26:00,380 --> 01:26:05,500
But just less than a decade ago, the American Statistical Association published this like

1305
01:26:05,500 --> 01:26:09,580
statement on p-values, just reiterating all the same things that Paul Meal says.

1306
01:26:09,740 --> 01:26:13,610
He's like, nobody, we did a survey, people don't understand p values, they're not using

1307
01:26:13,610 --> 01:26:14,650
statistics correctly.

1308
01:26:14,810 --> 01:26:16,170
Well, why do you think that is?

1309
01:26:16,410 --> 01:26:18,250
Well, understanding statistics is hard.

1310
01:26:18,330 --> 01:26:20,650
It takes a lot of time when you could be running studies.

1311
01:26:20,810 --> 01:26:26,730
And if you really understand statistics, you're going to be a lot more qualified in how

1312
01:26:26,730 --> 01:26:28,010
you promote the results you got.

1313
01:26:28,090 --> 01:26:29,370
You're going to be a lot more cautious.

1314
01:26:29,530 --> 01:26:33,210
Whereas if you don't, you might just say, look, we found this amazing thing.

1315
01:26:33,530 --> 01:26:35,690
Green jelly beans cure cancer.

1316
01:26:36,480 --> 01:26:38,880
And that's going to get attention.

1317
01:26:39,120 --> 01:26:42,560
And in a world where there's not good vetting of these kinds of results, where scientists

1318
01:26:42,640 --> 01:26:47,520
are policing themselves, you can actually get selection for misunderstanding.

1319
01:26:49,040 --> 01:26:54,080
Is this um would you view this analogously to uh life history theory where there are

1320
01:26:54,080 --> 01:26:57,440
trade-offs between quantity versus quality of offspring?

1321
01:26:57,600 --> 01:27:02,560
You could either invest very intensively in a small number of offspring, um, if they're you

1322
01:27:02,560 --> 01:27:08,070
know, if you expect them to live a long time, uh versus if you are in a high predation

1323
01:27:08,070 --> 01:27:12,390
environment where most of your offspring are expected to die, uh, you might want to hedge

1324
01:27:12,390 --> 01:27:16,710
your bets and have a large number of offspring lay a bunch of different eggs in hopes that

1325
01:27:16,950 --> 01:27:21,990
and invest very little in any of them in hopes that at least a few of them uh will will

1326
01:27:21,990 --> 01:27:22,710
survive.

1327
01:27:22,870 --> 01:27:27,510
Um would you view this uh trade-off between quantity and quality as analogous to the

1328
01:27:27,510 --> 01:27:30,630
trade-off between quantity and quality in scientific research?

1329
01:27:31,510 --> 01:27:33,460
I think loosely analogous, yeah.

1330
01:27:33,620 --> 01:27:38,180
I mean, uh to on the you know, to some extent, first of all, like I mean, all evolution,

1331
01:27:38,260 --> 01:27:39,540
right, is about trade-offs.

1332
01:27:39,700 --> 01:27:39,940
Right.

1333
01:27:40,180 --> 01:27:44,740
So anything evolutionary is going to be about trade-offs, but I think even more so, right,

1334
01:27:44,900 --> 01:27:50,900
this is an interesting analogy because um in a lot of scientific

1335
01:27:50,900 --> 01:27:56,820
fields, people get, you know, the jobs and

1336
01:27:56,980 --> 01:28:02,910
fellowships that junior scientists get right out of graduate school or even during

1337
01:28:02,910 --> 01:28:04,990
graduate school matter a lot.

1338
01:28:05,630 --> 01:28:11,390
Um there was a really neat study about a decade ago by this Dutch group, and they looked at

1339
01:28:11,470 --> 01:28:16,990
um the people who got this um this very prestigious um funding uh award.

1340
01:28:17,470 --> 01:28:20,110
Um so it gives you a lot of money to do your research.

1341
01:28:20,190 --> 01:28:22,030
It's also very prestigious, helps you get jobs.

1342
01:28:22,110 --> 01:28:27,360
And they looked at basically the people who are who are ranked just below the threshold and

1343
01:28:27,360 --> 01:28:28,240
just above the threshold.

1344
01:28:28,560 --> 01:28:33,520
The people who got the award, but barely, versus the people who barely missed out on the

1345
01:28:33,520 --> 01:28:33,840
award.

1346
01:28:33,920 --> 01:28:39,040
And then they tracked them for a number of years after, and their paths diverge wildly.

1347
01:28:39,440 --> 01:28:43,200
There's a huge effect of these early successes.

1348
01:28:43,680 --> 01:28:49,670
So the the value of edging past being perceived as

1349
01:28:49,670 --> 01:28:55,270
edging past your peers early on is it ends up in our system being really high in a lot of

1350
01:28:55,270 --> 01:28:55,830
fields.

1351
01:28:56,070 --> 01:29:01,910
And so if you take on as a graduate student a very complicated project

1352
01:29:01,990 --> 01:29:06,390
that's you're you're spending, you know, being very rigorous, very careful about and taking

1353
01:29:06,390 --> 01:29:09,270
a lot of time and doesn't really lead to a lot of publications.

1354
01:29:09,430 --> 01:29:14,760
Maybe you're maybe a couple of like smaller publications that are um you're publishing kind

1355
01:29:14,760 --> 01:29:16,600
of on the way to something big.

1356
01:29:16,680 --> 01:29:22,280
It's not big yet, but maybe it will be amazing later on, versus someone who's just cranking

1357
01:29:22,280 --> 01:29:27,880
out those studies and trying and aiming for things that are the sort of the most catchy and

1358
01:29:27,880 --> 01:29:30,760
most newsworthy and are the least careful.

1359
01:29:31,000 --> 01:29:36,230
Well, what's gonna happen, right, is if you have a system that

1360
01:29:36,230 --> 01:29:39,270
values that second person over the first one.

1361
01:29:40,310 --> 01:29:43,350
And it's not every system, not every field is like this.

1362
01:29:43,750 --> 01:29:49,030
But when it is like that, then that first person doesn't get a job.

1363
01:29:49,190 --> 01:29:52,710
They either get a low ranked job where they're not going to have as many resources to

1364
01:29:52,710 --> 01:29:56,550
continue doing their work, or they're going to leave academia and do something else.

1365
01:29:56,790 --> 01:30:02,680
And so the science that gets done overall Um gets

1366
01:30:02,680 --> 01:30:03,000
worse.

1367
01:30:03,080 --> 01:30:07,640
And this is actually why I think it's so important to think about the the sort of cultural

1368
01:30:07,640 --> 01:30:10,920
and institutional incentives and not just individual behavior.

1369
01:30:11,560 --> 01:30:15,960
Like we all know about perverse incentives, but

1370
01:30:18,520 --> 01:30:24,170
there's there are some people who think, well, look, you just have to be better than that.

1371
01:30:24,330 --> 01:30:25,770
You just have to be better than that.

1372
01:30:25,850 --> 01:30:28,730
You just have to do the work that you know is good.

1373
01:30:29,690 --> 01:30:33,290
And yeah, well, that sounds really noble.

1374
01:30:33,690 --> 01:30:38,730
Um but and let's imagine you know you do and you feel really satisfied.

1375
01:30:38,970 --> 01:30:39,290
Yeah.

1376
01:30:39,530 --> 01:30:45,220
Maybe you get lucky, but if you take, you know, a hundred people like that and a hundred

1377
01:30:45,380 --> 01:30:50,740
people who are just, you know, playing the system in the using the techniques that the

1378
01:30:50,740 --> 01:30:54,260
system selects for, well, the system is going to select for those people.

1379
01:30:54,500 --> 01:30:54,900
Yes.

1380
01:30:55,140 --> 01:30:57,860
And so it can't just be about individual behavior.

1381
01:30:57,940 --> 01:31:03,700
It has to be also about, you know, the sort of cultural and institutional incentives, the

1382
01:31:03,700 --> 01:31:07,150
mechanisms that select for certain people.

1383
01:31:07,630 --> 01:31:10,430
I mean, I didn't we didn't think about this at the time.

1384
01:31:10,590 --> 01:31:12,750
I completely wasn't turned on to it back then.

1385
01:31:12,830 --> 01:31:18,670
But I mean, this is very similar to what, you know, Noam Chomsky and Edward Herman were

1386
01:31:18,670 --> 01:31:23,950
talking about in Manufacturing Consent on their book on the media in the 1980s.

1387
01:31:24,110 --> 01:31:29,300
This is also a kind of sort of institutional selection where they're saying, you know, the

1388
01:31:29,540 --> 01:31:35,540
the people who are in control of, you know, media organizations are hiring

1389
01:31:35,540 --> 01:31:41,300
people and promoting people who espouse certain views and think about things in

1390
01:31:41,300 --> 01:31:43,300
certain ways and not hiring others.

1391
01:31:43,460 --> 01:31:48,420
And even if every single person who goes into journalism is completely noble and thinks

1392
01:31:48,420 --> 01:31:53,620
they're doing the best they can, the people who think along the party lines are going to be

1393
01:31:53,620 --> 01:31:58,090
the ones that end up in positions of authority and influence, and the ones who think

1394
01:31:58,090 --> 01:31:59,370
differently are not.

1395
01:31:59,930 --> 01:32:05,690
Yeah, it's or that it's uh the the whatever will be an audience, whatever increases the A-B

1396
01:32:05,850 --> 01:32:09,770
testing of like what gets more eyeballs on this, so we can sell more ads.

1397
01:32:10,090 --> 01:32:15,530
Um, so yeah, if it's um uh yeah, the idea that

1398
01:32:15,530 --> 01:32:18,920
selection is what matters and not intention.

1399
01:32:19,160 --> 01:32:20,840
I think politics too, right?

1400
01:32:20,920 --> 01:32:26,760
Like you can you could say, you know, um how do we minimize crime?

1401
01:32:27,000 --> 01:32:30,040
Well we should do things that like punish criminals, right?

1402
01:32:30,120 --> 01:32:34,200
As opposed to like you know minimal standard wage or something, like Dan Nunnell's

1403
01:32:34,360 --> 01:32:35,080
suggesting.

1404
01:32:35,320 --> 01:32:40,930
Um the idea that you you can't intuit intentionally what will

1405
01:32:40,930 --> 01:32:45,650
produce the outcome you want, and you have to just rely on the idea that there's selection,

1406
01:32:45,810 --> 01:32:47,330
I think is a really humbling thing.

1407
01:32:47,410 --> 01:32:51,810
And I think your work, when I've read it, every time I do something, I think to myself,

1408
01:32:52,050 --> 01:32:56,130
like honestly, like the I often think of the the natural selection of bad science work

1409
01:32:56,210 --> 01:32:59,490
where I'm like, what am I selecting by clicking on this?

1410
01:32:59,730 --> 01:33:05,650
Or what are we selecting if we make a decision about who to hire based on this criteria?

1411
01:33:06,000 --> 01:33:07,680
Um, how are we contributing?

1412
01:33:07,840 --> 01:33:11,200
How are we like a cog in the wheel of the institution?

1413
01:33:11,600 --> 01:33:12,080
Yeah.

1414
01:33:12,320 --> 01:33:12,640
Yeah.

1415
01:33:13,040 --> 01:33:13,280
Yeah.

1416
01:33:13,440 --> 01:33:18,240
I mean, it's even I mean, I I feel like we this might even be a case of mismatch where we

1417
01:33:18,240 --> 01:33:22,560
overestimate the effectiveness of appeals to morality and conscience.

1418
01:33:22,720 --> 01:33:28,320
That if we just scold our way to better science or wag our finger to better science, that

1419
01:33:28,320 --> 01:33:29,280
that's going to solve the problem.

1420
01:33:29,360 --> 01:33:32,320
We just need to encourage people to have integrity and to do better.

1421
01:33:32,480 --> 01:33:37,350
Well, the problem with that view is that the people who are most susceptible to those

1422
01:33:37,350 --> 01:33:41,670
appeals to conscience are going to be selected against and under the current incentive

1423
01:33:41,830 --> 01:33:42,390
incentive structure.

1424
01:33:42,710 --> 01:33:46,790
And the people who are least susceptible to those appeals are going to rise to the top,

1425
01:33:46,950 --> 01:33:47,110
right?

1426
01:33:47,430 --> 01:33:47,590
Yeah.

1427
01:33:47,750 --> 01:33:52,310
So you end up if you want what you want is a world where integrity and, you know, morality,

1428
01:33:52,390 --> 01:33:58,390
let's say, are favored, you actually have to change the system so that the people who act

1429
01:33:58,390 --> 01:34:03,700
that way are actually the ones that rise to the top and are the ones that get rewarded and

1430
01:34:03,700 --> 01:34:04,900
the other ones get don't.

1431
01:34:05,140 --> 01:34:09,780
And I think, I mean, with politics, I mean, obviously we're seeing the effects of uh

1432
01:34:09,780 --> 01:34:13,060
selection every day uh right now.

1433
01:34:13,700 --> 01:34:18,820
But I mean, you know, even just at the most basic level, uh, you know, we we have an

1434
01:34:18,820 --> 01:34:24,180
electoral system with in the US, this like long drawn out, kind of very media

1435
01:34:24,340 --> 01:34:30,070
based um electoral system where where people run for office over months

1436
01:34:30,310 --> 01:34:34,070
and what what what does that do right it selects for the people who are really good at

1437
01:34:34,070 --> 01:34:37,910
running for office but the people who are really good at running for office in that system

1438
01:34:37,910 --> 01:34:42,150
aren't necessarily the people who are really good at governing right yeah or or even like

1439
01:34:42,230 --> 01:34:46,230
you know gerrymandering or you know it's even worse like the selection I mean yeah yeah

1440
01:34:46,310 --> 01:34:51,990
like it's yeah yeah you would uh uh yeah exactly the the selection or even yeah

1441
01:34:52,070 --> 01:34:57,600
yeah tons of examples of uh if you can if you can put your thumb on the scale of the

1442
01:34:57,600 --> 01:35:01,520
selection process, it's really leads to crazy outcomes.

1443
01:35:02,000 --> 01:35:02,240
Yes.

1444
01:35:02,400 --> 01:35:07,040
It's a counterintuitive, like we're we're not inclined to view things in terms of incentive

1445
01:35:07,040 --> 01:35:11,040
structure problems as opposed to character or or ethical problems.

1446
01:35:11,200 --> 01:35:11,360
Yeah.

1447
01:35:11,520 --> 01:35:14,080
I I again it may be a kind of mismatch.

1448
01:35:14,160 --> 01:35:14,640
I don't know.

1449
01:35:14,720 --> 01:35:17,830
But it seems like our first inclination is we gotta boo the bad people.

1450
01:35:17,990 --> 01:35:21,590
We gotta figure out who the bad people are and discourage them and we gotta praise the good

1451
01:35:21,590 --> 01:35:25,110
people, and that's the way to solve the problem as opposed to viewing it as an

1452
01:35:25,110 --> 01:35:30,070
incentive-based problem that we Yeah, although I do want to maybe push back on that

1453
01:35:30,070 --> 01:35:35,830
slightly because if it becomes normative to boo the bad people

1454
01:35:36,150 --> 01:35:41,990
and punish the bad people, and the mechanisms for bad people succeeding

1455
01:35:42,310 --> 01:35:44,310
are not there, right?

1456
01:35:44,540 --> 01:35:49,980
Or are shut down actively, that does change it into a selective system.

1457
01:35:50,220 --> 01:35:50,380
Sure.

1458
01:35:50,780 --> 01:35:55,660
So booing the bad people can be a you know a

1459
01:35:55,660 --> 01:36:00,220
precursor or let's say an early, you know, sign of

1460
01:36:01,500 --> 01:36:03,900
more normative, more institutionalized change.

1461
01:36:04,140 --> 01:36:05,660
But you have to get all the way there.

1462
01:36:06,060 --> 01:36:08,540
It just can't be yelling from the bleachers.

1463
01:36:08,860 --> 01:36:09,180
Yeah.

1464
01:36:09,420 --> 01:36:09,980
Totally.

1465
01:36:10,130 --> 01:36:10,370
Yeah.

1466
01:36:10,450 --> 01:36:10,690
Yeah.

1467
01:36:10,930 --> 01:36:12,770
I I feel I just just went around.

1468
01:36:12,850 --> 01:36:14,290
Like I just I can't emphasize enough.

1469
01:36:14,370 --> 01:36:19,650
I wish like we don't have ads on this, but like the PSA of like selection and not intention

1470
01:36:19,970 --> 01:36:21,410
is really what matters.

1471
01:36:21,650 --> 01:36:27,090
I feel like that's such an important sort of public uh education thing.

1472
01:36:27,250 --> 01:36:31,010
Like I wish there was something in school, even where I don't know, like just game theory

1473
01:36:31,010 --> 01:36:31,890
models or something.

1474
01:36:32,050 --> 01:36:35,010
I mean, I don't know, I don't know what you do with that, but just the idea.

1475
01:36:35,170 --> 01:36:41,020
I think I just I even for myself, I still catch myself going in the wrong direction

1476
01:36:41,100 --> 01:36:46,460
as David's talking about where I just sort of really overemphasize people's intentions and

1477
01:36:46,460 --> 01:36:52,140
motivations or wagging a finger as opposed to people are doing what makes sense given the

1478
01:36:52,140 --> 01:36:55,340
incentive structure in the environment and that's the thing you gotta really target.

1479
01:36:55,420 --> 01:36:58,140
I mean it's hard because it's you know it's a bigger than one person.

1480
01:36:58,460 --> 01:37:02,380
It's hard and I it's not it's it's not all one or all the other either.

1481
01:37:02,780 --> 01:37:03,820
Yeah no no that's fair.

1482
01:37:03,900 --> 01:37:04,060
Yeah.

1483
01:37:04,300 --> 01:37:04,380
Right.

1484
01:37:04,620 --> 01:37:09,450
Especially like the problem is there are also incentive structures for changing the

1485
01:37:09,450 --> 01:37:10,250
incentive structure.

1486
01:37:10,410 --> 01:37:11,770
And those can be fucked too.

1487
01:37:12,330 --> 01:37:13,130
And that's the problem.

1488
01:37:13,770 --> 01:37:13,850
Right.

1489
01:37:14,890 --> 01:37:15,210
Yeah.

1490
01:37:15,450 --> 01:37:15,610
Yeah.

1491
01:37:15,850 --> 01:37:19,530
You know, like the people who r who rise to positions uh of power who are capable of

1492
01:37:19,530 --> 01:37:20,730
changing the incentive structure.

1493
01:37:20,890 --> 01:37:24,890
Well, that is itself a whole selection process that you're not getting the people who are

1494
01:37:24,890 --> 01:37:26,650
best able to to to do that.

1495
01:37:26,970 --> 01:37:27,210
Yeah.

1496
01:37:27,450 --> 01:37:31,130
No one wants to change the incentive structure to be something that wouldn't have selected

1497
01:37:31,130 --> 01:37:31,450
for them.

1498
01:37:31,690 --> 01:37:32,010
Correct.

1499
01:37:32,330 --> 01:37:32,970
Exactly.

1500
01:37:33,290 --> 01:37:33,610
Yeah.

1501
01:37:33,820 --> 01:37:34,380
Exactly.

1502
01:37:34,620 --> 01:37:34,940
Yeah.

1503
01:37:35,100 --> 01:37:35,900
Um Paul.

1504
01:37:37,020 --> 01:37:37,180
Yeah.

1505
01:37:37,420 --> 01:37:38,140
No, go go ahead.

1506
01:37:38,220 --> 01:37:38,300
Yeah.

1507
01:37:38,620 --> 01:37:42,780
No, I was just gonna say, I mean, we saw that in science too, with a lot of pushback uh

1508
01:37:43,100 --> 01:37:48,300
against reform uh a decade ago, where a lot of people who had succeeded very well, you

1509
01:37:48,300 --> 01:37:53,180
know, with the old system were very against these sort of uh increased scrutiny.

1510
01:37:53,500 --> 01:37:53,740
Yes.

1511
01:37:54,220 --> 01:37:54,380
Yes.

1512
01:37:54,620 --> 01:37:55,500
Yes, I do remember.

1513
01:37:55,580 --> 01:37:59,500
I mean, I I was in grad school during the whole when the replication crisis was like

1514
01:37:59,500 --> 01:38:00,860
reaching its its peak.

1515
01:38:01,020 --> 01:38:05,910
And I remember there was a lot of like political squabbles between the old guard were

1516
01:38:05,990 --> 01:38:09,350
saying everything's fine, this is just a bunch of bunch of bullshit.

1517
01:38:09,430 --> 01:38:12,470
These are like replication bullies, replication police, or whatever.

1518
01:38:12,630 --> 01:38:14,790
And like the new guard was like, no, this is a serious problem.

1519
01:38:14,870 --> 01:38:16,790
We need to really change things.

1520
01:38:16,950 --> 01:38:22,710
Uh and it was not uh uh a coincidence that the old guard, the people who were fighting for

1521
01:38:22,710 --> 01:38:26,310
the status quo, were the people who were the most privileged, the most prestigious, uh,

1522
01:38:26,480 --> 01:38:28,240
with the highest positions in the current system.

1523
01:38:28,400 --> 01:38:32,880
They were obviously the stakeholders of the status quo, and the the new guard were the

1524
01:38:32,880 --> 01:38:37,040
people who were uh most uh likely to benefit from a a new system.

1525
01:38:37,280 --> 01:38:42,720
Um David Pinsafian terms, yeah.

1526
01:38:43,120 --> 01:38:43,440
Exactly.

1527
01:38:43,520 --> 01:38:46,880
It was I was in the middle of a status game that was on the brink of collapse, and there

1528
01:38:46,880 --> 01:38:50,560
were stakeholders in the current status game that were trying to preserve it, and uh

1529
01:38:50,640 --> 01:38:54,320
stakeholders in a new status game that were trying to undermine it.

1530
01:38:54,480 --> 01:38:58,710
Uh and um I think, you know, uh it uh things have improved.

1531
01:38:58,870 --> 01:39:04,150
I mean I maybe you can correct me if I'm wrong, Paul, but um I I think um we have at least

1532
01:39:04,150 --> 01:39:05,030
seen some progress.

1533
01:39:05,190 --> 01:39:09,430
I I would like to see more, but I think we're certainly better than we were in the peak of

1534
01:39:09,430 --> 01:39:10,390
the replication crisis.

1535
01:39:10,470 --> 01:39:11,750
Would you would you disagree with that?

1536
01:39:12,150 --> 01:39:14,470
Well yeah, yes and no.

1537
01:39:14,630 --> 01:39:18,470
I I think that we have yes, I think there are a lot of things that are better.

1538
01:39:18,710 --> 01:39:24,560
Um I think that a lot more the people are a lot more savvy about statistics than they were

1539
01:39:24,560 --> 01:39:26,480
and methodological design.

1540
01:39:27,600 --> 01:39:32,000
I think people are generally, you know, graduate students coming out of the social sciences

1541
01:39:32,000 --> 01:39:35,120
and psychology are better at coding and better at statistics.

1542
01:39:35,280 --> 01:39:37,840
They're increasingly thinking about causality.

1543
01:39:39,520 --> 01:39:45,190
I still think that the sort of like Like myopic view

1544
01:39:45,430 --> 01:39:50,790
of you know, I will do one study that changes the world, and without really thinking about

1545
01:39:50,790 --> 01:39:56,150
how it is linked to the the larger, you know uh

1546
01:39:58,070 --> 01:40:03,910
constellation or you know, uh blanket of human behavior, the breadth of

1547
01:40:03,910 --> 01:40:07,990
human behavior, I think that's still missing a lot of the time, and and it leads to a lot

1548
01:40:07,990 --> 01:40:12,920
of work that I feel like could be good if there was some more thought that went into it.

1549
01:40:13,000 --> 01:40:16,120
But I've been I've been disappointed in a lot of the work that's come out.

1550
01:40:16,280 --> 01:40:22,120
And you know, maybe this is just there's the old um adage that 90% of everything is crap,

1551
01:40:22,200 --> 01:40:24,680
and maybe that's just always true.

1552
01:40:25,720 --> 01:40:30,760
So but I think like that the good 10% uh is is is pretty good.

1553
01:40:30,920 --> 01:40:32,360
Now there's a lot of good stuff.

1554
01:40:32,850 --> 01:40:37,810
The the main point of progress I've seen is that you can now get more status for a failed

1555
01:40:37,810 --> 01:40:41,570
replication attempt than you could have in the old era.

1556
01:40:41,810 --> 01:40:45,490
Now, if I fail to replicate someone else's work, I could get that published.

1557
01:40:45,570 --> 01:40:47,170
Uh and I could get some attention for that.

1558
01:40:47,250 --> 01:40:50,690
Uh it used to be that if you failed to replicate someone's work, no one would pay attention

1559
01:40:50,770 --> 01:40:51,570
and no one would care.

1560
01:40:51,730 --> 01:40:53,250
So that I think is at least some progress.

1561
01:40:53,490 --> 01:40:54,290
Yeah, it's an improvement.

1562
01:40:54,530 --> 01:40:59,240
Um the the thing that I haven't seen as much progress as I would like is in terms of

1563
01:40:59,240 --> 01:40:59,480
theory.

1564
01:40:59,880 --> 01:41:01,480
Yeah, I was gonna say, yeah, yeah.

1565
01:41:01,800 --> 01:41:06,840
And and and the and the uh people think of theory as like an airy, superfluous thing, but

1566
01:41:06,840 --> 01:41:12,120
it actually feeds directly into into your your model, uh, which is that if you if you have

1567
01:41:12,120 --> 01:41:17,080
an incentive structure where you are ignoring past knowledge and you are preferentially

1568
01:41:17,080 --> 01:41:22,050
seeking out low prior hypotheses, And the lower prior your hypothesis

1569
01:41:23,410 --> 01:41:28,130
if it pans out, the higher your status, well, that is going to further exacerbate the

1570
01:41:28,130 --> 01:41:28,290
trend.

1571
01:41:28,450 --> 01:41:32,610
So if I'm hunting for counterintuitive findings that clash with everything we have known in

1572
01:41:32,610 --> 01:41:38,610
the past, and I get more prestige if my finding is more shocking and more titillating,

1573
01:41:38,930 --> 01:41:44,660
well, um that is a recipe for even worse science and even less replicable

1574
01:41:44,660 --> 01:41:44,980
work.

1575
01:41:45,220 --> 01:41:49,460
Because if I'm pumping out lots of different studies and looking for the low prior uh

1576
01:41:49,460 --> 01:41:52,980
hypothesis, uh, well, chances are one of them is going to turn out to be right.

1577
01:41:53,060 --> 01:41:56,820
And if I publish that, I'm gonna get tons of status for discovering this new flashy

1578
01:41:56,900 --> 01:41:58,500
counterintuitive thing, right?

1579
01:41:58,740 --> 01:42:03,540
But if we're all uh in a different incentive structure where instead of uh hunting for the

1580
01:42:03,540 --> 01:42:07,780
lowest prior hypotheses, we're hunting for the highest prior hypotheses, well then all of a

1581
01:42:07,780 --> 01:42:09,810
sudden the dynamics are different.

1582
01:42:09,970 --> 01:42:15,170
Uh if if we have a theoretical edifice that uh is causing us to preferentially look for uh

1583
01:42:15,170 --> 01:42:19,890
data that is more likely to be uh confirmed and hypotheses that are more likely to bear

1584
01:42:19,890 --> 01:42:25,410
fruit, that is going to uh help the replication crisis uh much more because any given

1585
01:42:25,410 --> 01:42:29,650
finding is just going to be more likely to to replicate because it has a prior edifice of

1586
01:42:29,650 --> 01:42:30,530
knowledge behind it.

1587
01:42:30,690 --> 01:42:31,090
I don't know.

1588
01:42:31,170 --> 01:42:33,010
What do you what do you think about that that way of thinking?

1589
01:42:33,400 --> 01:42:39,080
I I I understand that way of thinking, but I I also think that there's a a problem that

1590
01:42:39,080 --> 01:42:40,840
potentially comes out of it.

1591
01:42:41,000 --> 01:42:46,440
Um, which is that we we want to the things that are

1592
01:42:46,440 --> 01:42:52,200
counterintuitive or surprising, let's say, are actually the more valuable if

1593
01:42:52,200 --> 01:42:53,080
they're right.

1594
01:42:53,880 --> 01:42:59,400
So you know if I tell you that like, hey, I dropped a thousand pennies and all of them fell

1595
01:42:59,400 --> 01:43:04,610
down, Like that's not a interesting like I yeah, I can replicate that, but like no one

1596
01:43:04,610 --> 01:43:05,090
cares, right?

1597
01:43:05,170 --> 01:43:06,530
We know it's gonna happen.

1598
01:43:07,650 --> 01:43:12,690
If I tell you that like I discovered a material that actually goes up when you drop it and

1599
01:43:12,690 --> 01:43:15,570
we never knew about it before, that's amazing.

1600
01:43:15,730 --> 01:43:18,210
Um I mean it's just a helium balloon, right?

1601
01:43:18,290 --> 01:43:20,930
But like it's still seems amaz it's amazing still.

1602
01:43:21,410 --> 01:43:22,610
Helium balloons are incredible.

1603
01:43:23,010 --> 01:43:23,490
They're the best.

1604
01:43:23,650 --> 01:43:25,170
Don't don't poo poo helium balloons.

1605
01:43:25,250 --> 01:43:25,650
They're amazing.

1606
01:43:25,730 --> 01:43:26,290
It's magic.

1607
01:43:27,580 --> 01:43:30,540
Things, you know, things that are counterintuitive, they're they're surprising.

1608
01:43:30,700 --> 01:43:34,620
We pay attention to surprising things for like an a you know a very good evolutionary

1609
01:43:34,620 --> 01:43:35,180
reason, right?

1610
01:43:35,260 --> 01:43:36,700
That it has information.

1611
01:43:37,020 --> 01:43:42,540
Uh and the the issue, right, is like if every single person individually is

1612
01:43:42,540 --> 01:43:47,260
looking for the most surprising thing because individuals get rewarded when they find

1613
01:43:47,260 --> 01:43:48,780
something really surprising.

1614
01:43:49,020 --> 01:43:54,860
Well, everyone's gonna look, try to like find something regardless, especially

1615
01:43:54,860 --> 01:44:00,450
if some of these other incentives are also in place and people are not

1616
01:44:00,930 --> 01:44:05,010
looking for things with good reason, they're just kind of, you know, if you find something

1617
01:44:05,010 --> 01:44:08,450
surprising and it's true and it's real, that's valuable.

1618
01:44:09,010 --> 01:44:12,770
But if you find something that you say is surprising, but actually if you think about it

1619
01:44:12,770 --> 01:44:16,770
for a minute, it's like it's not, it's only surprising because like you've been like really

1620
01:44:16,770 --> 01:44:22,700
myopically and your field has been myopically looking in one place, um, you know,

1621
01:44:22,860 --> 01:44:26,700
uh another area of research I've been getting more and more interested in is the sort of

1622
01:44:26,700 --> 01:44:31,180
like collective intelligence, collective uh problem solving stuff.

1623
01:44:31,260 --> 01:44:36,860
And you know, if if we stop thinking about scientists as a bunch of individuals

1624
01:44:37,020 --> 01:44:42,860
that all are individually incentivized to be the individually best scientist, but

1625
01:44:42,860 --> 01:44:48,110
think about you know teams and collectives as what would be the best strategy if if we have

1626
01:44:48,110 --> 01:44:52,510
aligned interests that we're all trying to find uh good stuff?

1627
01:44:52,750 --> 01:44:57,710
Well, to some extent it would it would be to get it wouldn't be to have every single person

1628
01:44:57,710 --> 01:44:59,550
working on exactly the same problem.

1629
01:45:00,190 --> 01:45:06,030
You would want an enough idiosyncrasity, enough

1630
01:45:06,190 --> 01:45:11,860
you know, uh iconoclasts That people are looking in different places, that people are

1631
01:45:11,860 --> 01:45:15,060
trying things that uh might not work.

1632
01:45:15,380 --> 01:45:15,700
Yeah.

1633
01:45:15,940 --> 01:45:20,740
And they're not punished for failing as long as they're doing it well, as long as they're

1634
01:45:20,740 --> 01:45:24,740
being rigorous and careful in the way that they're doing it.

1635
01:45:25,060 --> 01:45:30,740
And if you have people who are if what's being, you know, sort of scrutinized is

1636
01:45:31,380 --> 01:45:37,290
is this a good idea and is the way you're doing it good, then

1637
01:45:37,530 --> 01:45:39,770
let people do all kinds of things.

1638
01:45:40,090 --> 01:45:45,850
And if we can if we can evaluate the methods, but you know, and the

1639
01:45:45,850 --> 01:45:51,770
methods can include the method of selecting a hypothesis, it's tricky because

1640
01:45:52,570 --> 01:45:57,530
evaluation is by its very nature a

1641
01:45:57,530 --> 01:45:59,850
variance-reducing process.

1642
01:46:00,410 --> 01:46:00,970
Yeah.

1643
01:46:01,320 --> 01:46:06,200
And variance reducing is good if you want to maintain high quality

1644
01:46:06,600 --> 01:46:11,480
that's um you know corresponds to a fixed set of criteria.

1645
01:46:11,720 --> 01:46:15,800
But you know, as we know well, right, evolution works on two things, right?

1646
01:46:15,960 --> 01:46:17,480
Selection and variance.

1647
01:46:18,040 --> 01:46:22,120
And you know, it's the fundamental theorem of evolution, right?

1648
01:46:22,200 --> 01:46:26,680
Is that the speed of evolution is proportional to the variance in the population.

1649
01:46:26,840 --> 01:46:32,750
So higher variance actually makes a s a population more adaptable and more resilient.

1650
01:46:32,990 --> 01:46:38,910
So we also need mechanisms to inject variation into this system if we

1651
01:46:38,910 --> 01:46:40,830
want it to be you know to be making progress.

1652
01:46:41,070 --> 01:46:43,790
So yeah, no I I I I agree with that.

1653
01:46:43,870 --> 01:46:49,310
Yeah, no, it's it's um it's analogous to like uh you know if I have a a stock portfolio and

1654
01:46:49,310 --> 01:46:53,870
I want to invest in a bunch of different stocks and I want to ideally invest in stocks that

1655
01:46:53,870 --> 01:46:57,960
are likely to give me a return, but maybe I I'll take a risk you know, in a few cases.

1656
01:46:58,120 --> 01:47:02,040
Maybe I'll, you know, I'll I'll include a few risky stocks on my portfolio too.

1657
01:47:02,120 --> 01:47:03,640
And that's probably a good thing, right?

1658
01:47:03,800 --> 01:47:05,160
And the same thing is true of science.

1659
01:47:05,240 --> 01:47:10,040
You know, we wanna uh you know uh invest in uh uh theories and ideas that are likely to

1660
01:47:10,040 --> 01:47:13,960
bear fruit, but maybe a small percentage of us can take some risks on some wilder ideas and

1661
01:47:13,960 --> 01:47:15,000
we'll and we'll see what happens.

1662
01:47:15,240 --> 01:47:19,160
But I think the current distribution is lopsided, where we have too many people investing

1663
01:47:19,160 --> 01:47:24,210
in the risky strategy and not enough incentive for figuring out when those risky findings

1664
01:47:24,210 --> 01:47:25,570
are actually correct or not.

1665
01:47:25,970 --> 01:47:30,530
And not enough people investing in the more uh uh promising ideas.

1666
01:47:30,690 --> 01:47:35,890
Um, and we get a lopsided uh distribution of research.

1667
01:47:35,970 --> 01:47:41,890
Um ideally, we would have um uh you know projects that have some high

1668
01:47:41,890 --> 01:47:43,570
likelihood of bearing fruit.

1669
01:47:43,730 --> 01:47:47,700
Um but the problem is that a lot of people are investing in projects that have a low

1670
01:47:47,700 --> 01:47:48,740
likelihood of bearing fruit.

1671
01:47:48,900 --> 01:47:53,220
And and the deeper problem is they don't even realize that because they haven't bothered to

1672
01:47:53,220 --> 01:47:58,980
familiarize themselves with past research and with past knowledge uh and and with uh say

1673
01:47:59,220 --> 01:48:04,020
evolutionary theory, economics, whatever, whatever is going to give them an edge in uh uh

1674
01:48:04,260 --> 01:48:08,020
making sharper predictions that are more likely to bear fruit.

1675
01:48:08,180 --> 01:48:08,820
I don't know.

1676
01:48:09,290 --> 01:48:09,770
I don't know.

1677
01:48:09,850 --> 01:48:10,170
It's hard.

1678
01:48:10,250 --> 01:48:10,810
It's hard to say.

1679
01:48:10,890 --> 01:48:14,410
I mean, like science is there's a lot of scientists studying a lot of different things, but

1680
01:48:14,410 --> 01:48:20,010
I think in the in the sort of behavioral sciences, I worry that it's it's worse than that,

1681
01:48:20,090 --> 01:48:20,330
right?

1682
01:48:20,410 --> 01:48:25,930
That you have actually like um low quality, low risk soci

1683
01:48:26,250 --> 01:48:27,530
uh work being done.

1684
01:48:28,570 --> 01:48:34,170
It's likely to uh to find something that uh

1685
01:48:34,650 --> 01:48:36,650
doesn't kind of matter.

1686
01:48:36,890 --> 01:48:37,130
Yeah.

1687
01:48:37,370 --> 01:48:37,530
Yeah.

1688
01:48:39,100 --> 01:48:42,300
I mean, and I like one of you might even be end up being a reviewer for this.

1689
01:48:42,380 --> 01:48:45,980
I just submitted a paper that is just to be really quick.

1690
01:48:46,140 --> 01:48:50,540
Um, I got really frustrated because I sent it was it was very much inspired by you, but

1691
01:48:50,540 --> 01:48:55,580
inspired by you, Paul, because I sent I sent a paper to I'm gonna say perspectives on psych

1692
01:48:55,660 --> 01:48:55,980
science.

1693
01:48:56,060 --> 01:48:59,020
It's good, you know, it had a lot of theory, theory crisis.

1694
01:48:59,180 --> 01:49:03,930
Um, no, it didn't go, but it was the editor desk rejected it for reasons that I was like,

1695
01:49:04,010 --> 01:49:07,210
wow, this is selecting against theory, good theory.

1696
01:49:07,450 --> 01:49:11,450
And so I so I actually wrote a rant paper, I submitted to perspective.

1697
01:49:11,530 --> 01:49:17,450
So here's the premise: is that um if you ask for data concurrent with

1698
01:49:17,450 --> 01:49:23,290
theory, uh, so if you select theory based on whether it it comes along with

1699
01:49:23,290 --> 01:49:29,280
data with the theory, it selects for bad theory because

1700
01:49:29,840 --> 01:49:34,560
the it selects for basically the kinds of theories that it's easy to generate data about.

1701
01:49:34,720 --> 01:49:38,240
Um, so you're actually or at least theory that you don't need necessarily.

1702
01:49:38,400 --> 01:49:43,760
It could be you don't need so like so for precisely theories that are about difficult

1703
01:49:43,760 --> 01:49:49,410
things that we are not even sure how to well, like challenges to like we we I have a theory

1704
01:49:49,410 --> 01:49:53,250
that there is this mechanism and there's a deductive logic that like there should be this

1705
01:49:53,330 --> 01:49:54,770
like parenting mechanism, right?

1706
01:49:55,010 --> 01:49:59,490
It would be impossible for organisms to exist if it didn't exist, but we don't know how to

1707
01:49:59,490 --> 01:50:00,210
look for it yet.

1708
01:50:00,370 --> 01:50:01,650
We don't know how to study it.

1709
01:50:01,890 --> 01:50:05,650
The idea that, you know, that gets selected, that paper gets selected out because it

1710
01:50:05,650 --> 01:50:07,090
doesn't have any data with it, right?

1711
01:50:07,330 --> 01:50:12,370
It it it falls out of the um it falls out of the narrative of what we're even discussing.

1712
01:50:12,530 --> 01:50:17,560
So the idea is that I mean, uh the the short version is um asking for data concurrent with

1713
01:50:17,560 --> 01:50:22,360
theory selects for bad theory and I sent it to perspectives and it was rejected because it

1714
01:50:22,440 --> 01:50:23,960
it didn't have data.

1715
01:50:25,640 --> 01:50:26,200
The ultimate.

1716
01:50:26,920 --> 01:50:27,080
Yeah.

1717
01:50:27,160 --> 01:50:28,280
So so I love this.

1718
01:50:28,360 --> 01:50:32,280
And so this is a very much inspired by Paul Smaldino I think, but it comes back to this

1719
01:50:32,280 --> 01:50:35,800
thing of um that, you know, it's just what are you selecting for?

1720
01:50:35,960 --> 01:50:36,120
Right.

1721
01:50:36,770 --> 01:50:42,130
No, and we have you from I mean this is also, you know, my sort of push to like be deep in

1722
01:50:42,130 --> 01:50:46,370
your field, but like look at other fields too, because otherwise you get totally insular

1723
01:50:46,450 --> 01:50:52,050
and you you you you allow things to become normative uh a and that

1724
01:50:52,290 --> 01:50:53,170
shouldn't be.

1725
01:50:53,490 --> 01:50:54,610
Um like that.

1726
01:50:54,770 --> 01:51:00,530
I mean there's so many examples from other fields of theories being published

1727
01:51:00,840 --> 01:51:06,280
and then you know not finding you know experimental evidence for them for years,

1728
01:51:06,440 --> 01:51:07,560
sometimes decades.

1729
01:51:07,880 --> 01:51:08,200
Yeah.

1730
01:51:08,360 --> 01:51:08,760
Yeah.

1731
01:51:09,160 --> 01:51:14,120
Paul, I want to just because we're running short on time or we're ending here, um, you we

1732
01:51:14,120 --> 01:51:18,040
haven't, you know, we've we've ebbed in flowed in terms of like talking to guests about

1733
01:51:18,040 --> 01:51:20,280
like themselves or like their their life.

1734
01:51:20,440 --> 01:51:26,350
Um but I am curious because you know you have this um in my

1735
01:51:26,430 --> 01:51:32,430
in my opinion, you know, you have like a very fierce uh scientific identity

1736
01:51:32,510 --> 01:51:38,270
in the sense of, you know, you're you're really pushing not in like a um a pushy way, but

1737
01:51:38,270 --> 01:51:41,550
you're um you're a spokesperson for doing models.

1738
01:51:41,630 --> 01:51:44,670
I mean, you have papers that are like, you know, models are stupid and we need more of

1739
01:51:44,670 --> 01:51:44,910
them.

1740
01:51:45,070 --> 01:51:47,470
Like that's a very special sort of stance to take.

1741
01:51:47,550 --> 01:51:52,020
Like I'm an expert in something and it's stupid, but it's a good thing.

1742
01:51:52,180 --> 01:51:53,940
Like that's a very special thing.

1743
01:51:54,100 --> 01:51:57,700
It feels very different to me than what we talked about before, which is sort of people

1744
01:51:57,700 --> 01:52:00,740
have these status games and they're just like, I'm the best.

1745
01:52:00,900 --> 01:52:04,260
Like to say something like, I'm an expert in something that's stupid and we need more of it

1746
01:52:04,340 --> 01:52:05,460
is very subversive.

1747
01:52:05,620 --> 01:52:11,620
So I'm just sort of curious, like we talked about um, you know, the punk rock uh East

1748
01:52:11,620 --> 01:52:13,220
German thing.

1749
01:52:14,070 --> 01:52:18,230
I mean, are have you always sort of wanted to is that you?

1750
01:52:18,390 --> 01:52:20,470
Like are you the punk rocker in East Germany?

1751
01:52:20,550 --> 01:52:21,350
Uh if for Saga.

1752
01:52:21,750 --> 01:52:23,350
Well punk rocker would never admit.

1753
01:52:24,390 --> 01:52:25,430
Yeah, but well, you know.

1754
01:52:26,550 --> 01:52:27,750
I yeah, I don't know.

1755
01:52:27,830 --> 01:52:28,630
Uh I don't know.

1756
01:52:28,710 --> 01:52:34,630
I mean I like I do think like a little bit a little bit of a punk attitude

1757
01:52:34,710 --> 01:52:35,830
I think is healthy.

1758
01:52:36,070 --> 01:52:41,020
And uh I I I definitely you know respond to that.

1759
01:52:41,100 --> 01:52:45,180
And I also just feel like for me, like I've tried being more mainstream and it doesn't

1760
01:52:45,180 --> 01:52:45,420
work.

1761
01:52:45,660 --> 01:52:48,380
Like I don't feel comfortable and people reject it.

1762
01:52:48,700 --> 01:52:53,740
Like uh I tried to give more like um when I was on the job market looking for academic

1763
01:52:53,820 --> 01:52:57,900
permanent jobs, I tried to give much more typical jobs like the way that I had seen other

1764
01:52:57,900 --> 01:53:00,780
people give them, and it just never worked, and I didn't get anything.

1765
01:53:01,020 --> 01:53:04,050
And then I finally was just like, you know, screw it.

1766
01:53:04,130 --> 01:53:07,970
I'm just gonna give the the the talk that I wanna give.

1767
01:53:08,130 --> 01:53:13,810
And I I gave a talk at UC Merced called, I think the talk was called that I got that got me

1768
01:53:13,810 --> 01:53:18,130
my job was called Learning About Social Evolution with Extremely Unrealistic Models.

1769
01:53:19,250 --> 01:53:21,970
And I was like, these models are totally unrealistic.

1770
01:53:22,290 --> 01:53:24,450
But let's see what we can learn from them anyway.

1771
01:53:25,010 --> 01:53:30,960
And I think that, you know, we we we tell this like fake narrative that

1772
01:53:31,040 --> 01:53:35,200
like we're the science we're doing is like so precise and we know so much.

1773
01:53:35,280 --> 01:53:40,480
And I think like acknowledging that like we're flailing and we like it's not like people

1774
01:53:40,480 --> 01:53:41,120
don't know anything.

1775
01:53:41,200 --> 01:53:46,320
We know a lot of things, but there's also just so much we don't know and being a little m

1776
01:53:46,480 --> 01:53:50,110
humble about that, I think it is actually a real strength.

1777
01:53:50,270 --> 01:53:53,790
Gives, well, for me anyway, I feel like it gives me a power.

1778
01:53:53,950 --> 01:53:59,550
Um, and I think also like I just my trajectory, like I studied

1779
01:53:59,550 --> 01:54:00,990
physics in as an undergrad.

1780
01:54:01,230 --> 01:54:06,510
I didn't take a single class on biology, cognitive science, or psychology until grad

1781
01:54:06,510 --> 01:54:07,070
school.

1782
01:54:07,310 --> 01:54:09,550
And when I did, I was like, really?

1783
01:54:10,190 --> 01:54:13,710
This that we're are we this is what we're seriously doing?

1784
01:54:13,870 --> 01:54:15,540
And I I love the topics.

1785
01:54:15,620 --> 01:54:17,540
I love they're my favorite kinds of questions.

1786
01:54:17,620 --> 01:54:21,700
I think like psychology, cognitive science, the social sciences have the best questions.

1787
01:54:21,780 --> 01:54:23,780
They're the things I'm most interested in.

1788
01:54:24,020 --> 01:54:28,260
And sometimes and there's it's not like everyone's doing crap work but me.

1789
01:54:28,340 --> 01:54:29,700
Like, obviously, it's not what I'm saying.

1790
01:54:29,780 --> 01:54:33,940
There's so many people doing work that I think is amazing and inspiring.

1791
01:54:34,180 --> 01:54:38,580
But there's also like plenty of stuff where I'm like, come on, come on, guys.

1792
01:54:38,900 --> 01:54:44,170
Um, and I think that, you know,

1793
01:54:44,730 --> 01:54:48,170
being a little bit willing to push back on that.

1794
01:54:49,530 --> 01:54:53,850
Also, if I'm gonna say other people's be, you know, critical of other people's work, then

1795
01:54:53,930 --> 01:54:58,490
like it's beholden to me to be just as critical about my own work.

1796
01:54:58,970 --> 01:55:04,730
And so I I really try to be like I am very careful and I haven't always

1797
01:55:04,730 --> 01:55:10,720
succeeded, but I try you know, to be pretty cautious in the uh in the claims

1798
01:55:10,720 --> 01:55:11,520
that I'm making.

1799
01:55:11,760 --> 01:55:16,640
And uh and but that doesn't mean that I don't I love speculating.

1800
01:55:16,800 --> 01:55:20,240
Like I it hate I hate it when people are afraid to speculate because they're like, well, I

1801
01:55:20,240 --> 01:55:21,360
don't know if I don't have the evidence.

1802
01:55:21,520 --> 01:55:25,840
And I'm like, right, that's why you speculate, and then you see maybe you're right and

1803
01:55:25,840 --> 01:55:26,560
maybe you're wrong.

1804
01:55:26,640 --> 01:55:31,590
Like, but if you are an expert, then like your speculation should be better than people who

1805
01:55:31,590 --> 01:55:32,950
are not an expert, right?

1806
01:55:33,110 --> 01:55:38,710
So like the stuff we can prove is such a small amount of the things, a small subset of the

1807
01:55:38,710 --> 01:55:40,150
things we actually want to talk about.

1808
01:55:40,230 --> 01:55:40,550
Yeah.

1809
01:55:40,870 --> 01:55:46,070
So um I I think that like I'm all for like,

1810
01:55:46,870 --> 01:55:48,310
you know, I don't know.

1811
01:55:48,550 --> 01:55:53,830
Um I I I like I don't think I'm that special in the like sort of

1812
01:55:54,860 --> 01:55:58,620
field of scientists, but I have carved out my little niche.

1813
01:55:58,860 --> 01:56:04,860
And uh I I just keep you know, I'll just

1814
01:56:04,860 --> 01:56:08,780
say the last thing I'll say is like I've I've done this thing, this stuff on like working

1815
01:56:08,780 --> 01:56:13,740
on models of science and talking about like science and improving science, and I and it's

1816
01:56:13,740 --> 01:56:15,100
my most cited stuff.

1817
01:56:15,260 --> 01:56:20,140
Like the natural selection of bad science, that paper is by far my most cited paper.

1818
01:56:20,380 --> 01:56:25,930
Um, but the idea that working on science itself is like

1819
01:56:26,250 --> 01:56:29,770
my main life's mission is is like I reject that.

1820
01:56:29,930 --> 01:56:31,530
I don't want that to be the case.

1821
01:56:31,690 --> 01:56:36,410
And I I'm happy to be known for that, but I um there's so many other things.

1822
01:56:36,570 --> 01:56:40,810
Like I'm interested in culture and the world and human behavior more generally.

1823
01:56:40,890 --> 01:56:44,090
And it's just you know, that's just one aspect of it.

1824
01:56:44,330 --> 01:56:48,860
And I I um I don't know.

1825
01:56:49,020 --> 01:56:50,540
I don't know where I'm going with that.

1826
01:56:50,700 --> 01:56:51,580
But uh Yeah.

1827
01:56:51,740 --> 01:56:57,340
I I I also think this all comes back to like the original identity thing where we're all

1828
01:56:57,340 --> 01:57:02,060
looking around and like psychologists are like, it's sort of the emperor has no clothes.

1829
01:57:02,140 --> 01:57:07,100
We're sort of you're you're anticipating like what's gonna keep what's gonna give me

1830
01:57:07,100 --> 01:57:09,740
status, how are other people gonna react to stuff?

1831
01:57:10,060 --> 01:57:14,940
And there's this, I think there's a paper about this where it's sort of the surprisingly

1832
01:57:14,940 --> 01:57:20,930
popular thing where um I remember we had um for our wedding meal we asked people like we

1833
01:57:20,930 --> 01:57:24,610
have to have like these three entrees and and we had like I don't know something like quail

1834
01:57:24,930 --> 01:57:29,730
and everyone was like oh I would love quail but I don't think anyone else is going to and

1835
01:57:29,890 --> 01:57:33,970
that it turned out to be a really good idea that we had quail for the meal because

1836
01:57:33,970 --> 01:57:36,770
everybody wanted but nobody thought anyone else did what they thought they did.

1837
01:57:36,850 --> 01:57:40,370
And what this paper is about surprisingly popular is something where you respond to

1838
01:57:40,370 --> 01:57:42,370
something but you don't think anyone else will.

1839
01:57:42,720 --> 01:57:47,760
And I think like the the value of like having a paper like models are stupid, we should

1840
01:57:47,760 --> 01:57:53,280
have more of them, is it removes that, it's it's sort of that courage to sort of be like,

1841
01:57:53,680 --> 01:57:57,600
um, it's it's standing out, like it's it's rocking the boat a little bit.

1842
01:57:57,760 --> 01:58:01,680
But then but then people can be like, oh, there is somebody else who's like these things

1843
01:58:01,680 --> 01:58:03,590
are useful, but they are kind of stupid.

1844
01:58:03,670 --> 01:58:08,310
And we don't have to like pretend like everybody agrees that they're not stupid.

1845
01:58:08,470 --> 01:58:14,230
You know, and maybe it it kind of um it g it gives some error to

1846
01:58:14,390 --> 01:58:19,030
otherwise everybody's like pretending like, well, I think everybody must think they're

1847
01:58:19,030 --> 01:58:19,270
right.

1848
01:58:19,430 --> 01:58:21,910
I might think they're stupid, but nobody else does.

1849
01:58:22,150 --> 01:58:26,390
And you know, you being a little bit courageous to be like, Well, they're dumb, but let's

1850
01:58:26,390 --> 01:58:26,870
use them.

1851
01:58:27,030 --> 01:58:30,550
Like, I think that helps people realize that they're not alone.

1852
01:58:30,900 --> 01:58:33,540
Yeah, I I think I think that's right.

1853
01:58:33,700 --> 01:58:38,740
Um I just I can't remember what book it's in, but there was this I remember a long time ago

1854
01:58:38,740 --> 01:58:42,500
reading this this something by Kurt Vonnegut where he talks about in one of his books and

1855
01:58:42,500 --> 01:58:48,180
about how like one of the great things about writing is that like somebody you've

1856
01:58:48,180 --> 01:58:53,540
never met can can you know read something that like expresses a perspective on the world

1857
01:58:53,540 --> 01:58:54,820
and says hey me too.

1858
01:58:54,980 --> 01:58:56,020
I think that way too.

1859
01:58:56,710 --> 01:59:01,910
that like that feeling of, hey, I'm not alone in thinking this is hugely powerful.

1860
01:59:01,990 --> 01:59:05,830
And it's like, you know, one of the things that really like ties me, and I think a lot of

1861
01:59:05,830 --> 01:59:10,470
us to like the works of art that we love and the works of writing fiction or non that we

1862
01:59:10,470 --> 01:59:10,710
love.

1863
01:59:10,790 --> 01:59:15,990
And so I think like, you know, to the extent that like I can be that for even like a

1864
01:59:15,990 --> 01:59:20,070
handful of people in, you know, is is that that's huge to me.

1865
01:59:20,150 --> 01:59:21,500
I I love Yeah.

1866
01:59:22,460 --> 01:59:23,820
Well it it was for me, honestly.

1867
01:59:23,900 --> 01:59:27,420
Like honestly the the the like I'm like this is ridiculous.

1868
01:59:27,580 --> 01:59:31,980
Um there's good work and there's clearly selection for status.

1869
01:59:32,220 --> 01:59:38,220
Um it was really uh it it it it kept me in this to to to know that other people

1870
01:59:38,220 --> 01:59:39,340
were thinking the same way.

1871
01:59:40,060 --> 01:59:46,010
To tie it back full circle it is perhaps the feeling of a covert signal well

1872
01:59:46,090 --> 01:59:46,730
received.

1873
01:59:47,850 --> 01:59:48,170
Exactly.

1874
01:59:48,570 --> 01:59:52,330
And to be to be part of a community that you didn't know existed.

1875
01:59:53,850 --> 01:59:59,690
So Paul, is there anything uh that we should have asked you uh during this

1876
01:59:59,690 --> 01:59:59,930
time?

1877
02:00:00,090 --> 02:00:01,930
Or did we did we get it all?

1878
02:00:02,810 --> 02:00:08,410
I mean, I'm sure that you got a very small amount, but because we all

1879
02:00:08,410 --> 02:00:12,560
contain multitudes, but uh I feel very, very happy with the conversation.

1880
02:00:12,640 --> 02:00:13,760
I thought I I had a lot of fun.

1881
02:00:14,000 --> 02:00:14,160
Yeah.

1882
02:00:15,200 --> 02:00:21,040
Can you uh um uh sh t demonstrate at least one thing behind like what there for the people

1883
02:00:21,040 --> 02:00:23,600
who are just listening, but like you know, we also are on YouTube.

1884
02:00:24,000 --> 02:00:27,840
Um, you have guitars and you've got a solar system or something behind you.

1885
02:00:28,000 --> 02:00:30,720
What is the well I'm not gonna play guitar on the podcast?

1886
02:00:31,200 --> 02:00:33,200
Okay, no, that's uh that's for subscribe.

1887
02:00:33,440 --> 02:00:36,080
We don't have subscribers, but if we did, that would be bonus content.

1888
02:00:36,160 --> 02:00:37,440
You have to pay to listen to Paul.

1889
02:00:38,000 --> 02:00:43,990
Uh you you can um the the some of the music by my old uh garage band, the

1890
02:00:43,990 --> 02:00:48,310
small dinosaurs, is available on so it's called small dinosaurs.

1891
02:00:48,470 --> 02:00:49,750
I will put a link in the show notes.

1892
02:00:49,830 --> 02:00:51,110
So it's small dinosaurs.

1893
02:00:51,430 --> 02:00:51,750
Yeah.

1894
02:00:51,910 --> 02:00:52,150
Okay.

1895
02:00:52,390 --> 02:00:54,870
Um and then this is a Lego technic.

1896
02:00:54,950 --> 02:01:00,230
It's uh sun and moon and uh an earth, and the earth spins and the sun spins, and the earth

1897
02:01:00,230 --> 02:01:04,880
goes around the sun, and the moon goes around the earth, and I'm twisting a little lever,

1898
02:01:05,040 --> 02:01:09,840
and it can show you like where the position the relative positions of the sun, the moon,

1899
02:01:10,080 --> 02:01:12,400
and the earth are at different times of year.

1900
02:01:12,640 --> 02:01:12,960
Awesome.

1901
02:01:13,200 --> 02:01:17,760
And uh it's it's a very old technology, but Lego made one and I got it for Christmas.

1902
02:01:17,920 --> 02:01:19,440
And we were talking about it earlier.

1903
02:01:19,600 --> 02:01:20,720
I think it's really neat.

1904
02:01:21,200 --> 02:01:22,880
Um so you know, props to Lego.

1905
02:01:23,200 --> 02:01:24,640
So awesome.

1906
02:01:24,800 --> 02:01:25,280
Yeah.

1907
02:01:25,650 --> 02:01:27,650
Lego, please sponsor the podcast.

1908
02:01:29,490 --> 02:01:30,290
Oh, look at that.

1909
02:01:30,450 --> 02:01:32,210
At the very end, I'm shilling to corporate.

1910
02:01:32,290 --> 02:01:33,250
Yeah, anyway, but yeah.

1911
02:01:33,570 --> 02:01:36,450
I mean, if you're gonna shill to a corporation, Lego's a pretty good one.

1912
02:01:36,770 --> 02:01:42,450
Yeah, I mean a lot of uh a lot of wonderful changes to human brains done t by Lego,

1913
02:01:42,850 --> 02:01:44,530
you know, and that's that's saying a lot.

1914
02:01:44,850 --> 02:01:46,610
That sounds like really dark.

1915
02:01:47,490 --> 02:01:49,330
It's a very reductive way to put it.

1916
02:01:49,970 --> 02:01:53,730
I don't know if you know about Lego's secret laboratory where they're uh changing people's

1917
02:01:53,730 --> 02:01:53,890
brains.

1918
02:01:54,610 --> 02:01:55,860
It's the you know, exactly.

1919
02:01:56,020 --> 02:01:59,700
I'm just the you know, I don't know, like Coca-Cola's just dump people full of sugar.

1920
02:01:59,780 --> 02:02:03,300
Lego, it's like it's you know, it's like it's been but yeah.

1921
02:02:03,380 --> 02:02:06,660
I feel like I kind of think in Legos and I feel like that's a good thing.

1922
02:02:06,820 --> 02:02:10,900
I don't know, to to think in terms of how the the pieces fit together and how it works.

1923
02:02:10,980 --> 02:02:11,380
I don't know.

1924
02:02:11,780 --> 02:02:17,620
And in like perseverance and planning and mental rotation and creativity and autonomy

1925
02:02:17,780 --> 02:02:19,060
and all those things.

1926
02:02:19,220 --> 02:02:20,740
But yeah, no, no, fair enough.

1927
02:02:20,820 --> 02:02:21,140
All right.

1928
02:02:21,300 --> 02:02:24,530
Well, uh maybe on that note, I don't know.

1929
02:02:24,610 --> 02:02:26,450
Paul, do you want to have the last word?

1930
02:02:27,810 --> 02:02:28,610
Potato.

1931
02:02:28,850 --> 02:02:29,170
Awesome.

1932
02:02:29,330 --> 02:02:29,490
All right.

1933
02:02:29,650 --> 02:02:29,970
On that note.

1934
02:02:31,810 --> 02:02:32,530
All right, Paul.

1935
02:02:32,610 --> 02:02:36,930
Yeah, I'll hit I'll hit um I'll stop recording, but uh this is great, man.

1936
02:02:37,090 --> 02:02:37,490
Yep.

1937
02:02:37,650 --> 02:02:38,210
Oh yeah.

1938
02:02:38,290 --> 02:02:38,530
Thanks.

1939
02:02:38,690 --> 02:02:39,490
This is fun.

1940
02:02:42,370 --> 02:02:45,410
David, I really enjoyed this conversation with Paul.

1941
02:02:45,650 --> 02:02:46,850
Did you enjoy it?

1942
02:02:47,100 --> 02:02:47,740
I loved it.

1943
02:02:47,900 --> 02:02:48,540
I had a blast.

1944
02:02:48,860 --> 02:02:49,260
All right.

1945
02:02:49,420 --> 02:02:53,820
Um, and if you did enjoy it, please help us by spreading the word.

1946
02:02:53,980 --> 02:02:59,340
If you just tell one other person, then um then they'll might listen to it as well.

1947
02:02:59,500 --> 02:03:05,500
I can't tell you how many people who I've emailed as like even a um a guest to come on who

1948
02:03:05,500 --> 02:03:08,140
said I don't know, I didn't know about the podcast.

1949
02:03:08,300 --> 02:03:14,170
So um, and then they listen to it and at least um you know 90% of the

1950
02:03:14,170 --> 02:03:16,810
time they say, Oh, it actually was pretty good.

1951
02:03:17,210 --> 02:03:17,930
Yeah, that's true.

1952
02:03:18,010 --> 02:03:21,450
I mean, unless they're all lying to us uh to so they don't hurt our feelings.

1953
02:03:21,530 --> 02:03:25,770
Uh yeah, maybe the people who never got back to me are like that was bad.

1954
02:03:25,930 --> 02:03:29,770
I mean, occasionally people spl leave bad reviews, but that's okay.

1955
02:03:30,250 --> 02:03:34,260
But if you're one of the people who does enjoy the podcast and is willing to tell us the

1956
02:03:34,260 --> 02:03:39,460
truth about that, um then maybe share that truth with others by uh

1957
02:03:39,860 --> 02:03:45,220
saying a kind word about us on social media, leaving us a kind review, visiting us on

1958
02:03:45,220 --> 02:03:46,100
Patreon.

1959
02:03:46,260 --> 02:03:52,100
Uh there are many ways to show your support, and uh your support is what keeps us going

1960
02:03:52,100 --> 02:03:53,300
and keeps this thing going.

1961
02:03:53,460 --> 02:03:54,500
So please show it.

1962
02:03:54,820 --> 02:03:56,260
That and caffeine.

1963
02:03:56,500 --> 02:03:56,900
Yeah.

1964
02:03:57,140 --> 02:04:00,790
You You'd send us some coffee grounds, uh, preferably in a fanny pack.

1965
02:04:00,870 --> 02:04:02,550
That would be fantastic.

1966
02:04:04,950 --> 02:04:10,150
And we'll leave you with that image until we see you next week or hear from you next week.

1967
02:04:10,790 --> 02:04:11,750
See you next week.