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This is Evolutionary Psychology at the podcast.

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Actual Evolutionary Psychology by Actual Evolutionary Psychologists.

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Today we have a repeat customer, Dan Conroy Beam.

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Our first repeat customer.

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I believe the first guest that we've had on for a second time.

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Yeah.

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Is that right?

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Or no, I guess well, I guess we had uh Christina on as a co-host.

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Yeah, we had Christina on as a co-host.

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Yeah.

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And we will have the Zach Garfield.

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Uh Zach Garfield episode hasn't been the Ray Hames one isn't out yet.

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Or or it might have just come out.

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But this is our first repeat guest in the guest.

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Substantive, yes, exactly.

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Yeah.

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And no one better than Dan Conroy Beam because uh Dan is deriving

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from scratch, he is uh god among um uh

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silicone based agents, recreating the evolution of sex, sex roles,

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and gender.

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Gender.

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Yeah.

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So this is all about gender.

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Not all about, but half about.

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Or the complex interrelations between sex, sex roles, and

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gender.

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And I found it just really um, I don't know.

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Like I I knew a lot of the stuff, you know, reading Dawkins and uh the evolutionary bios

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stuff, but it was great to just hear Dan synthesize it and review it and

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show the elegance of these ideas and how they can be cashed out mathematically,

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uh, and review just the linkages between you know the evolutionary biology of sex and

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sex roles and how that manifests in gender in a complex uh

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cultural species such as Homo sapiens.

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I I found it to be really uh enlightening and enriching.

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Um, yeah.

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Yeah.

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And I think like, you know, pinning biology versus culture, we always know that that's

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wrong.

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But even going through this in as an exercise in working out the details as carefully

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and frankly in an unbiased way as one can, as Dan did,

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it was even for me sort of a check on my own.

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Uh um, you know, I was still sort of pitting biology against culture.

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And I don't think uh, you know, there's obviously lots of heat and temperature

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cultural issue wise around gender, especially now.

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I don't think any I think this trans um uh this is uh

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orthogonal to all of that, or at least it it it um it rises above all of that because it

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really talks about how there is learning and there are socials and it is dynamic, but it's

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also very much grounded in um uh sex.

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but what sex is is more abstract, even if it's biological and it's it's it's more weird.

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It's making the familiar strange.

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And even both sides in the culture war, well I think what Dan is describing

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is a far stranger, weirder world than either s either sides um would

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would imagine.

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I think John Tuby once said that scientists sort of think that, like, you know, reality is

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like one side or the other for any at any moment in time in a scientific debate.

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But reality is far weirder and far far far way further out than even what either

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side is arguing typically, um, if you look across the history of science.

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For sure.

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And because this is such a hot button topic, I feel like Dan has done such a good job of

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just providing a level head and a North Star through which to navigate these complicated

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and messy ideas.

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And I've I just thought it was a really fun and interesting conversation.

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I'm sure you'll enjoy it.

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Yeah, I I loved it.

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And there's so much to I found it inspiring to just be motivated to think about new things

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to think about.

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And it made my life as a kid growing up and just who I thought was a role model just

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make more sense too.

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Yeah, totally.

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Enjoy.

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All right.

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Enjoy.

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Uh Dan, we've already had you on.

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We're not gonna do any intros.

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You're well, okay.

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You're doing you do Asian-based modeling and empirical work based on that

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modeling.

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You're doing a new area, uh, you're you're investigating a new thing.

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I don't know, maybe you disagree that it's new.

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Uh what are you doing?

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Um can you either talk about what you're doing now and why you got into it and

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or what agent-based modeling is for people who might not know?

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Um choosing adventure.

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Uh well and thanks for having me back.

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Hey guys, good to see you.

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Welcome.

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Um really been enjoying a pod.

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I think it's only two episodes that I are still on my to watch list.

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Wow, that's good.

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Nice.

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We got a super fam.

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That's cool.

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Oh yeah, that's great.

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We gotta give you we gotta give you a sticker.

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Uh David, you actually this is we're we'll cut this out, but we need to order stickers for

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HPus.

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Oh yeah.

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Yes you do.

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Yes you do.

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I'm actually I'm excited to see yeah just from hungry people at conferences and like visits

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and stuff like you have you have a listener base and I'm excited to see like at HPES to

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talk to people see what see what they're thinking about it.

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Yeah but this is a horrible can't believe I can't believe Dan said that thing he said.

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But anyway yes enough enough of our um uh sticker procurement um side

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sidebar.

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Apologies, Dan.

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So yeah, either agent based modeling and or what are you doing now?

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Um I mean, I guess we uh we could start with agent based modeling, I guess.

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I mean, like the bigger picture thing.

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So I mean, you say it like I'm doing a new thing.

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I don't really feel like I'm doing a new thing.

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Uh I'm doing a thing that maybe people haven't done enough, but I'm definitely like

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standing on the shoulders of giants.

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Um I mean, agent based modeling is just one technique for

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formally modeling psychological hypotheses.

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It's a computer simulation technique where what you do is you simulate

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populations of autonomous entities, which we call agents.

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Autonomous just meaning they execute behaviors in some fashion.

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They do so according to decision rules that are inside their own head, uh and they execute

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those based on information that they capture from their own environment.

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So you just basically simulate a little kind of proto person with a little simple

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brain, put them in some kind of environment and set them free to behave however they are

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going to behave.

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And this is really good for modeling all kinds of systems.

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It's especially good for modeling social systems.

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So situations where you think you have either some understanding of some bit

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of psychology and you want to know, you know, if you put a hundred people together that

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have this kind of mind, what's the sort of social phenomenon that's going to come out of

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that?

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Or a situation where you think you have a good understanding of the social phenomenon, but

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you're trying to reverse engineer what individual level psychologies are sufficient to

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produce that.

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So you can, you know, create a little silicone labor laboratory, right?

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Um And test out a bunch of different individual-level psychologies and see which of them

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are sufficient to produce that social level phenomenon you're interested in.

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I'm not the first person to do this.

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I mean, I like I have certainly been inspired.

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Like, I do this in the context of largely mating decisions, although, as you know, Dave,

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have been branching out into other kinds of social, social partner decisions, things like

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group formation, friend choice.

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Uh, but you know, there have been lots of people in the evolution and behavior space that

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who have used similar kinds of models.

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I mean, you know, I was super inspired as an undergrad, as we all probably were, by like,

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you know, Axle Rod's prisoners dilemma tournaments.

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I was really inspired by um, you know the blind watchmaker.

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It's blind watchmaker, right?

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That was one of my favorite Dawkins books.

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And that's where he has the you know simulations where he's like evolving the different

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shapes to look like different animals.

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You know, the work of people like Jeffrey Miller and Peter Todd back in the day when they

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were, they were doing agent-based models of great choice.

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And when I came across that I was like, oh, this is this is how we should have been

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studying mating all along.

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Uh normally has done this similar work to, you know, of course like Paul Smaldino, who I

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know you've had on the pod.

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Um so there's lots of people over time who have done this.

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It it just like hasn't maybe caught on to quite the degree that I think it should.

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And AJ based modeling it's really good for math

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uh is hard to represent lots of interactions and lots

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of factors.

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My understanding is uh in that with an agent-based model,

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to some degree you can sort of look at the complex emergent things that

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you wouldn't be able to tell by looking at, you know, you you you stipulate the rules in

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each individual agent, but the properties that what ends up happening is not trivial

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or even predictable ahead of time, which is part of why you do it, is would be

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my understanding in a way.

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Yeah.

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I mean, I I would say a couple of things.

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One, agent-based modeling is a really good technique if you are interested in building

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formal models, but like me are not particularly skilled at math.

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So, you know, you get to instead of having to like, you know,

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set up and solve like a series of partial differential equations or something like that,

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you get to just sort of like, you just have to specialize specify the decision rules inside

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the agent's head.

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And then the computer handles the rest, right?

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Because it's it's all actual interactions between agents.

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And so a lot of the stuff that like a lot of the techniques that you would need for like uh

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analytic models um you just sort of like offload to the computer solve it by simulation

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right um so that's part of why i got into it originally because i was like i want to do

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these kinds of things but like i never took you know beyond high school calculus

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so um At the time when I was a grad student, this was like the easiest way in.

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But yeah, I do think there's virtue in that.

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Yeah, when you when you're trying to model uh systems that you think are the result of

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partially random, uh large numbers of partially random interactions between

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lots of parts, You can solve those things with math, but you have to make a lot of

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simplifying assumptions that uh sort of push you further

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and further away from what you're actually trying to model.

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And I think in some senses, you know, depending on your question, an agent-based model can

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be a more realistic representation of the system you're trying to study.

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That's not always a virtue, but but sometimes I think it is.

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Yeah.

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And it forces you to be explicit about things.

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Like I imagine as you're having a student, for example, come on, they um I imagine

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it's a sort of thing of like, oh, I realize I have to specify this, I have to specify this.

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Things that I didn't even really think about.

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I have to have an idea about how it works.

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Yeah, I think that's a major virtue of formal modeling, period.

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And I think agent-based modeling is like, you know no different from other modeling

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techniques in that it really draws these things out of you.

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And you know, David, I know you've done uh also a lot of like formal we were just talking

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about your hand paper, like a lot of formal modeling as well.

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I think this is a a huge virtue of trying to formalize your hypotheses,

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your theories, period.

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I mean I always tell students, yeah, like I've never sat down to write an

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agent-based model and not realized an assumption I didn't realize I was making.

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Um uh this is like just a universal process.

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And I think, you know, it depends, it depends on how far you want to get into this, but

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like so much of our theorizing in psychology is purely verbal.

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Um, and that's useful.

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Um It's, you know, honestly, like we're humans, is how we communicate at some level.

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Our our hypothesizing needs to be verbal.

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And you can get pretty quickly, you can get much farther into a complicated

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idea when you're working with it purely verbally than if you try to formalize it at every

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step.

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But part of the trade-off with that is like, yeah, you're sort of relying on a lot of your

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intuitive machinery in ways that you don't necessarily realize you're doing.

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Uh, and so it's easy to bake in assumptions that you're not realizing you're

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making, uh, or uh be overly vague and broad

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uh in ways that give you lots of degrees of freedom for your hypothesis to

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appear as though it's explaining a lot of things.

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Um and when you sit down and you have to say exactly what is the information that

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is available, exactly how is that processed into exactly what outputs, uh, there

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aren't as many shortcuts.

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Um and so you sort of realize, I mean, uh I might have mentioned this last time.

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Did I talk about this last time I was on the podcast?

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I don't remember.

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Um I don't know, I don't listen to the podcast.

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No, I don't remember what time you were the first guest in, I think.

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Is that right?

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It was Jamie, Jamie or you?

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I forget.

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Sorry, Jamie, or you better not have Jamie.

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I think I think it was Dan.

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I think it was Dan.

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Yeah, I think we're and we're getting up on a year.

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So this is maybe maybe maybe you should be our one year um episode.

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We'll see how it works out.

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I don't know.

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I'll take that.

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I mean part part of my like whole entryway into doing all this stuff was you know, I was I

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was at the University of Texas, I was interested in dating psychology.

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Um, there was some work coming out around the time, including from uh Paul Eastwick,

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who was also at the University of Texas at the time.

244
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He was like the floor below us.

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Uh, so we were having these kind of like occasional interactions in the building.

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Um, but you know, there were these papers coming out that were uh we talked about the speed

247
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dating stuff last time, um, these speed dating papers that were like, can we predict what

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mate choices people will make in a live in-person interaction?

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And the answer was kind of no.

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And so there's all this like hullabaloo, like, maybe our understanding of mating psychology

251
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is like totally wrong.

252
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We need to upend everything we know.

253
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Um and I was sort of like skeptical that that was true.

254
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And I was like, there's maybe simpler explanations for why we struggle to predict people's

255
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choices in live interactions.

256
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And I was like, maybe, you know, one explanation is just that

257
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I don't even remember where I started.

258
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But you know, I was like, maybe one explanation is just that like people want lots of

259
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different things, right?

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Have multiple different preferences, they've got to balance against them.

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And so maybe the person you met at the speed dating event who best met your

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kindness preferences was really far off your intelligence preferences, right?

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Um, and so if we only look at like how much did the partner you choose you chose

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match this one preference, we're missing those like trade-offs that people are making

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across dimensions.

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Um maybe that's what's going on.

267
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Um that feels intuitively true to me, but like, is that really sufficient to produce the

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kind of data that we're seeing in these speed dating studies?

269
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I just finished taking this like Monte Carlo analysis class that I had to take because it

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was like total serendipity.

271
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I like forgot to register for classes one semester.

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So the required stats class I needed to take filled up, but they wouldn't make an extra

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spot for me.

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And so I ended up taking this other stats class and that like totally changed my life.

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Um but I had just taken this like Monte Carlo analysis class and I was like, all right, I

276
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bet I like I could do one of the models that we do for that class.

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Like I could just like you know make up some data that looks kind of like make choice data.

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I could like simulate a process that looks kind of like what I'm thinking, like you know,

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people making choices across multiple dimensions.

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I could see what kind of data that makes across a large number of samples, and that would

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tell me, you know, is this sort of process actually capable of producing this kind of data?

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I didn't even know, I had never heard the word agent-based modeling at that time.

283
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Like I didn't know that this was a thing.

284
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I thought I was just kind of like hacking this together.

285
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Um So I started to try it out and I was like, okay, so like I'll simulate some data that

286
00:17:02,490 --> 00:17:03,850
looks kind of like made choice data.

287
00:17:03,930 --> 00:17:06,090
I sort of know what that looks like from the lab.

288
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Um and then you know, I need to have these agents.

289
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I didn't I didn't know they were called agents at the time.

290
00:17:11,450 --> 00:17:15,850
I was like, I need to have these like synthetic people, you know, sort of meet with each

291
00:17:15,850 --> 00:17:21,050
other and they need to like evaluate how attracted they are to one another, I guess, in

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order to like make a decision that they want to see each other again.

293
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So, oh shoot, like, okay, I can give them preferences, I can give them traits, but like,

294
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how do I make them decide from that how attractive they are to one another?

295
00:17:34,240 --> 00:17:37,440
Actually, like crap, there's a lot of different ways you could do that.

296
00:17:37,760 --> 00:17:42,800
Like maybe I could say the preferences are like a threshold, and if you're above a

297
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threshold, like then you're attractive.

298
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That's how Peter Todd said it works.

299
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But maybe I can make it like a linear model where the preferences are like weights and they

300
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sort of like like a regression.

301
00:17:52,000 --> 00:17:55,670
They like, you know, multiply the trait by the preference and then add it all up.

302
00:17:55,750 --> 00:17:59,030
That's sort of like how David is, David Buss has talked about it before.

303
00:17:59,270 --> 00:18:03,110
Oh man, like there's lots of different ways this could happen.

304
00:18:03,430 --> 00:18:09,030
I'd never thought about like this is an important part of the process that we

305
00:18:09,030 --> 00:18:12,710
haven't actually described how it works.

306
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Uh, and it wasn't until like I had to sit down and like make it happen in a computer that I

307
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realized, like, oh, this is a linchpin assumption in how mate choice works, that

308
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the entire field is just like mouth studying.

309
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So I think that's the that's a key virtue of like forcing yourself to do these things.

310
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Uh as you know, Dave, I now have in my lab, sort of like above the main meeting

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table.

312
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Um, I have a there's a famous quote, uh Richard Feynman from his chalkboard at the time of

313
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his death, What I cannot create, I do not understand.

314
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And that's sort of like a core lab ethos.

315
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Like if you understand how some system works, you should be able to build a copy of it in a

316
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computer.

317
00:19:00,550 --> 00:19:02,070
David, you were gonna say something.

318
00:19:02,550 --> 00:19:05,830
No, I mean it's just um very, very cool stuff.

319
00:19:05,990 --> 00:19:11,960
So I'm curious Now that we have this background of uh agent-based modeling and

320
00:19:11,960 --> 00:19:17,800
we know your methodology, uh what kind of new stuff do you have cooking

321
00:19:17,960 --> 00:19:21,320
in the kitchen with this methodology?

322
00:19:22,680 --> 00:19:28,520
And it doesn't matter if it's if it's uncooked, because uh we can come in and we can

323
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hope.

324
00:19:29,320 --> 00:19:30,120
Well, yeah.

325
00:19:30,280 --> 00:19:33,080
Um I feel like I'm being pushed in a certain direction.

326
00:19:33,480 --> 00:19:33,880
Exactly.

327
00:19:35,480 --> 00:19:38,070
Um well, I mean, so you know, Dave knows.

328
00:19:38,310 --> 00:19:44,070
Um uh I've got a big project that I've been working on for the last uh

329
00:19:44,230 --> 00:19:50,150
year or so that's that's sort of putting together a collection of agent-based models

330
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on the evolution of sex and gender.

331
00:19:53,190 --> 00:19:59,030
Um, this is something that's been on the back of my mind for like several years now.

332
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Um just being very unsatisfied with

333
00:20:05,400 --> 00:20:11,400
not really knowing how to think about these concepts and not really feeling like there was

334
00:20:11,720 --> 00:20:15,080
clear guidance, at least that I'm aware of, in the literature.

335
00:20:15,160 --> 00:20:20,520
And so it's mostly just an exercise of like me ironing out my own thoughts on these

336
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concepts.

337
00:20:21,000 --> 00:20:24,120
And then hopefully other people also find it useful and interesting.

338
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We'll see.

339
00:20:25,590 --> 00:20:30,870
Um but yeah, I mean I'm on I'm on sabbatical this year.

340
00:20:30,950 --> 00:20:35,430
So this was one of my like big sabbatical goals was to finally push out like a big paper

341
00:20:35,430 --> 00:20:41,030
that's sort of like me just sketching out my thoughts on like what are sex and gender.

342
00:20:41,590 --> 00:20:44,070
I mean I use it as podcasts, but go ahead.

343
00:20:44,390 --> 00:20:49,110
Do do you view them as um so are do you are you analyzing them together or as separate

344
00:20:49,110 --> 00:20:50,310
things or as interacting things?

345
00:20:50,390 --> 00:20:52,550
So how what are your what are your thoughts there?

346
00:20:53,420 --> 00:20:55,020
I was gonna ask you guys what your thoughts were.

347
00:20:55,340 --> 00:20:55,980
Yeah, I love this.

348
00:20:56,060 --> 00:20:59,260
This is I remember there was like a point, I don't know how it was months ago, where you

349
00:20:59,260 --> 00:21:03,660
were Dan was going around and just like very wonderfully serving he's like, what do you

350
00:21:03,660 --> 00:21:04,380
think gender is?

351
00:21:04,540 --> 00:21:08,460
Like in a like a like you know like a clipboard and a pencil in hand, which I thought was

352
00:21:08,620 --> 00:21:12,860
lovely scientific method of just like how would we define gender or is it different than

353
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sex?

354
00:21:13,180 --> 00:21:13,900
Yeah, what is it?

355
00:21:13,980 --> 00:21:14,300
Yeah.

356
00:21:15,110 --> 00:21:18,710
Well now that now that we're like local colleagues, Dave knows this, but this is my gimmick

357
00:21:18,710 --> 00:21:19,590
for basically every project.

358
00:21:19,750 --> 00:21:23,670
Like you can always predict what I'm working on like six months in advance because at the

359
00:21:23,670 --> 00:21:26,950
bar I'll just start asking people, What do you think about X?

360
00:21:28,630 --> 00:21:30,310
That's a fantastic way to do it.

361
00:21:30,390 --> 00:21:30,790
I love that.

362
00:21:30,870 --> 00:21:32,630
Did you when did you start doing that?

363
00:21:32,950 --> 00:21:33,510
I don't know.

364
00:21:33,670 --> 00:21:35,270
I think it's just uh I don't know.

365
00:21:35,430 --> 00:21:39,030
I mean I maybe it was the fundamental attribution error, which is also something I need to

366
00:21:39,030 --> 00:21:39,270
write.

367
00:21:39,590 --> 00:21:40,470
Yes, exactly.

368
00:21:41,030 --> 00:21:41,670
Your favorite.

369
00:21:43,440 --> 00:21:46,960
I remember Dan Dan in the hallway being like, what does the fundamental attribution error

370
00:21:46,960 --> 00:21:47,280
predict?

371
00:21:47,360 --> 00:21:53,280
Does it predict that um internal versus external uh uh will be higher um then

372
00:21:53,360 --> 00:21:54,240
like ordinally?

373
00:21:54,320 --> 00:21:55,840
Like what's the relationship between those two?

374
00:21:56,000 --> 00:22:01,840
Or is it just that um that your internal is going to be lower for another person than it is

375
00:22:01,840 --> 00:22:02,400
for yourself?

376
00:22:02,480 --> 00:22:06,160
But it doesn't matter what the absolute magnitude is between external and internal.

377
00:22:06,560 --> 00:22:08,480
Great questions, but anyway.

378
00:22:09,440 --> 00:22:11,930
I mean, uh, let me throw out a wrong idea.

379
00:22:12,090 --> 00:22:13,690
You can tell me w why it's wrong.

380
00:22:13,850 --> 00:22:18,490
I mean, I feel like part of the answer might be that like so sex, we already have a bunch

381
00:22:18,490 --> 00:22:21,290
of evolutionary models on the evolution of sex.

382
00:22:21,450 --> 00:22:26,250
Uh, you know, it's about, you know, uh differences in um parental investment, operational

383
00:22:26,250 --> 00:22:30,570
sex ratio, potential reproductive rate, and these things lead to you know the different sex

384
00:22:30,570 --> 00:22:32,570
roles we see throughout the animal kingdom.

385
00:22:33,280 --> 00:22:36,960
Gender is an interesting thing because um it's a cultural thing.

386
00:22:37,280 --> 00:22:42,560
Um I would as a first pass on like part of what's going on, it might be that you have sex,

387
00:22:42,960 --> 00:22:47,120
which represents these, you know, a parental investment reproductive strategies.

388
00:22:47,680 --> 00:22:53,280
And then maybe gender is like how you signal this, because it's this thing isn't on the

389
00:22:53,280 --> 00:22:57,120
surface for other people to know, like uh necessarily.

390
00:22:58,190 --> 00:23:03,710
um it could be how you signal your sex um it so it could be kind of signaling

391
00:23:03,710 --> 00:23:09,630
equilibrium um it could be a kind of yeah maybe let me let me

392
00:23:09,630 --> 00:23:13,870
start with start with that idea and see I like that idea I like that and you would go

393
00:23:13,870 --> 00:23:17,870
straight to signaling that would be I would

394
00:23:19,870 --> 00:23:23,840
uh I like that idea and that I think that's actually pretty dang clear.

395
00:23:23,920 --> 00:23:26,320
I think you just got like two thirds of the paper.

396
00:23:26,560 --> 00:23:30,320
Um I mean l let me ask you, you guys are the host, but let me ask you that.

397
00:23:30,400 --> 00:23:31,840
I mean, I've asked David this before, so it's not fair.

398
00:23:31,920 --> 00:23:33,200
So maybe I'm asking just David.

399
00:23:33,360 --> 00:23:38,400
Like, what do you think is the average if you ask the average psychologist what sex is?

400
00:23:38,640 --> 00:23:41,920
Because you just said like, yeah, we have all these like evolutionary biological models,

401
00:23:42,080 --> 00:23:45,360
but like, do you think psychologists are aware of those?

402
00:23:47,990 --> 00:23:49,830
Probably most psychologists are.

403
00:23:49,990 --> 00:23:50,710
I mean, it depends.

404
00:23:50,790 --> 00:23:51,510
I don't know.

405
00:23:51,750 --> 00:23:55,350
It depends on how familiar they are with evolutionary psychology in general.

406
00:23:55,590 --> 00:23:57,190
And I'll at the end of the day.

407
00:24:02,230 --> 00:24:05,910
I think most psychologists would probably go like genitalia.

408
00:24:06,710 --> 00:24:12,550
Some would probably go genes, and a much smaller percentage would do gamete investment.

409
00:24:13,040 --> 00:24:13,760
Yeah, yeah.

410
00:24:13,920 --> 00:24:19,840
And I I think the the the you know first day Trump executive order, I think that's

411
00:24:19,840 --> 00:24:21,120
kind of shifted things a little bit.

412
00:24:21,200 --> 00:24:25,040
I was thinking about this project before that executive order and that executive order

413
00:24:25,200 --> 00:24:28,800
really blew a hole in in a lot of my thinking.

414
00:24:28,960 --> 00:24:34,960
Um and that was the executive order that defined gender by j it didn't define gender

415
00:24:34,960 --> 00:24:35,200
at all.

416
00:24:38,000 --> 00:24:38,080
Okay.

417
00:24:38,400 --> 00:24:42,170
It doesn't quite say this, but it tries to erase gender as a concept entirely.

418
00:24:42,330 --> 00:24:43,450
That's my read of it at least.

419
00:24:43,610 --> 00:24:49,210
Uh but it defines as being about it's complicated when you get more into this, but it

420
00:24:49,290 --> 00:24:51,370
defines sex as being something about gamete size.

421
00:24:51,450 --> 00:24:55,770
So I think a lot of gamete size be got onto a lot of people's minds much more than it had

422
00:24:55,770 --> 00:24:57,130
been before that executive work.

423
00:24:57,450 --> 00:24:57,770
Yeah.

424
00:24:58,490 --> 00:25:02,330
But yeah, I mean, my my perspective on this, like, you know, I'm a human mating researcher.

425
00:25:02,490 --> 00:25:06,410
Obviously, sex and gender are like really important concepts when you're thinking about you

426
00:25:06,410 --> 00:25:07,530
know human relationships.

427
00:25:07,610 --> 00:25:11,040
And so, and I've you know published on sex differences a lot like these are things that

428
00:25:11,040 --> 00:25:15,840
I've been forced to think about it at least in some degree and you know coming up as like a

429
00:25:15,840 --> 00:25:21,040
psychologist you're sort of aware there are these concepts sex and gender they're supposed

430
00:25:21,040 --> 00:25:26,080
to be different things you're supposed to talk about them differently um but when you try

431
00:25:26,080 --> 00:25:30,720
to like look at like what is the guidance that you get from psychologists about like what

432
00:25:30,720 --> 00:25:36,210
is sex itself and what is gender and why are those different things I just I

433
00:25:36,210 --> 00:25:40,850
personally have never deeply understood what I was supposed to be talking about.

434
00:25:41,010 --> 00:25:45,170
Um, and when I try to read the literature for like clear guidance on like what are the

435
00:25:45,170 --> 00:25:49,730
definitions or what are our models of these things, I find them really unsatisfying.

436
00:25:49,890 --> 00:25:54,770
Uh so if you look at like definitions of sex in the literature, if you look at like the

437
00:25:54,770 --> 00:25:59,410
APA's guidance on like what sex is, I don't have it in front of me, but it's something

438
00:25:59,410 --> 00:26:04,100
like, you know, sex is a person's biological status.

439
00:26:04,340 --> 00:26:10,260
Uh, that's literally that's a near direct quote, and I have no idea what what that

440
00:26:10,260 --> 00:26:10,900
means.

441
00:26:11,300 --> 00:26:16,900
Um, and it's you know indicated by a number of traits, and they list off a number of traits

442
00:26:16,980 --> 00:26:22,500
that's like genitalia, gonads, you know, sex chromosomes, hormones.

443
00:26:22,690 --> 00:26:27,090
they list off a number of indicators of sex, which I think is the correct.

444
00:26:27,170 --> 00:26:28,130
Those things are indicators.

445
00:26:28,210 --> 00:26:29,490
They're not sex in and of itself.

446
00:26:29,650 --> 00:26:34,850
A lot of other psychologists will define sex as being like what chromosomes you have, which

447
00:26:34,850 --> 00:26:36,770
I think is crazy.

448
00:26:37,970 --> 00:26:43,490
Or like, you know, they'll define sex as being like what sex you're assigned based on your

449
00:26:43,490 --> 00:26:47,420
genitalia, which I think is also like, I get what they're going for, but that's not quite

450
00:26:47,420 --> 00:26:48,060
right either.

451
00:26:48,300 --> 00:26:53,420
Um, but yeah, like the APA's guidance, it doesn't say like what biological status of what.

452
00:26:53,580 --> 00:26:58,060
Like I have lots of statuses as a biological organism.

453
00:26:58,220 --> 00:27:00,940
Like, what what are we talking about when we talk about sex?

454
00:27:01,260 --> 00:27:03,180
Why are those things the indicators of sex?

455
00:27:03,260 --> 00:27:05,020
And they don't mention gamete size at all.

456
00:27:05,180 --> 00:27:09,260
The APA's definition of sex does not reference gametes at all.

457
00:27:09,420 --> 00:27:12,770
Uh and I think that's I think that's a problem.

458
00:27:13,010 --> 00:27:18,690
Uh one, because a lot of the things that psychologists focus on as

459
00:27:18,690 --> 00:27:24,610
being indicators of or definitions of sex are things that only apply to humans, right?

460
00:27:24,690 --> 00:27:29,970
Like sex chromosomes, they focus a lot on do you have X or Y chromosomes?

461
00:27:30,050 --> 00:27:34,930
Do you have certain like you know chromosomal disorders that lead to uh you know different

462
00:27:34,930 --> 00:27:36,610
sex development disorders?

463
00:27:36,850 --> 00:27:41,980
Uh you know, it's fair enough for psychologists to be overly focused on humans, but like XY

464
00:27:41,980 --> 00:27:46,140
chromosomes, that's not definitive of sex across the diversity of life on this planet,

465
00:27:46,220 --> 00:27:46,380
right?

466
00:27:46,460 --> 00:27:51,100
Like that's how mammals do sex determination, but like birds don't have XY chromosomes,

467
00:27:51,180 --> 00:27:52,300
they have ZW chromosomes.

468
00:27:52,380 --> 00:27:55,260
Like lots of reptile reptiles don't have sex chromosomes at all.

469
00:27:55,340 --> 00:27:58,060
Like they do sex determination just by temperature, right?

470
00:27:58,140 --> 00:28:03,690
So like there's a whole wider world of sex out there uh that is not determined by X, Y

471
00:28:03,690 --> 00:28:04,090
chromosomes.

472
00:28:04,170 --> 00:28:05,850
You wanted to define it in terms of genitalia.

473
00:28:05,930 --> 00:28:07,930
There's a whole diversity of genitalia out there, right?

474
00:28:08,010 --> 00:28:11,210
The way that we do genitalia is not the only way to do genitalia.

475
00:28:11,610 --> 00:28:13,210
You want to define it in terms of hormones?

476
00:28:13,370 --> 00:28:18,730
Like plants have sexes, but don't have, you know, at least hormones like we have.

477
00:28:18,810 --> 00:28:21,290
You could argue they have their own kind of hormones, but like they don't have

478
00:28:21,290 --> 00:28:23,370
testosterone, they don't have estradyl.

479
00:28:24,440 --> 00:28:29,960
so the definitions of sex that like psychologists use are really, really

480
00:28:29,960 --> 00:28:35,880
anthropocentric and don't capture like the concept of sex more broadly, which I

481
00:28:35,880 --> 00:28:36,440
think is a problem.

482
00:28:36,680 --> 00:28:40,520
Psychologists might think it's fine, but I I think that's really unsatisfying.

483
00:28:40,840 --> 00:28:46,360
And then you think about gender, um, like what are definitions of gender?

484
00:28:46,440 --> 00:28:48,450
They're also just really vague.

485
00:28:48,530 --> 00:28:53,650
Like, you know, they'll say things, people will say things like sex is biological and

486
00:28:53,650 --> 00:28:55,730
gender is like cultural.

487
00:28:55,890 --> 00:29:01,890
Um, like quite, I mean, that's not uh, I hope that's not a too unfair

488
00:29:01,890 --> 00:29:07,890
gloss of uh a lot of ideas, but like one, that irritates me because culture is biological.

489
00:29:07,970 --> 00:29:12,290
Uh, but two, it also irritates me because, and you know, people will recognize this, like

490
00:29:12,370 --> 00:29:14,930
it's not clear where that boundary actually is.

491
00:29:15,090 --> 00:29:20,940
Like there are many things that uh there are ways in which things you

492
00:29:20,940 --> 00:29:24,620
might want to call cultural intersect with things you might want to call biological.

493
00:29:24,860 --> 00:29:28,380
And so, where should you like delineate the boundary between what sex and gender becomes

494
00:29:28,380 --> 00:29:28,940
really unclear?

495
00:29:29,100 --> 00:29:31,420
And it's also just like not clear what gender is supposed to be.

496
00:29:31,500 --> 00:29:37,180
Like people will offer definitions of gender that are literally like, you know, aspects of

497
00:29:37,180 --> 00:29:37,980
femininity.

498
00:29:38,140 --> 00:29:40,140
And it's like, what does that mean?

499
00:29:40,380 --> 00:29:44,650
How do I, you know, we're just talking about like what I can't create, I don't understand.

500
00:29:44,730 --> 00:29:47,370
Like, how do I build a robot that has aspects of femininity?

501
00:29:47,530 --> 00:29:52,250
And also, like, if femininity is just how you refer to like womanness, then like it's

502
00:29:52,250 --> 00:29:52,970
totally circular.

503
00:29:53,050 --> 00:29:56,410
Like, there's no information in that definition as to what gender actually is.

504
00:29:56,650 --> 00:30:02,650
Uh, so I've been trying to sketch out sort of models, agent-based models, uh,

505
00:30:02,730 --> 00:30:07,820
but also you know, agent-based models as representations of like theoretical ideas for like

506
00:30:08,060 --> 00:30:11,980
how should we as evolutionary psychologists think about these concepts?

507
00:30:12,140 --> 00:30:12,940
Are these different things?

508
00:30:13,020 --> 00:30:14,460
And if so, what are they?

509
00:30:14,620 --> 00:30:19,020
Um and hopefully that's also useful to like other psychologists more broadly.

510
00:30:19,900 --> 00:30:22,220
So I'm I've been writing this like law on paper.

511
00:30:22,300 --> 00:30:23,420
It's way too long right now.

512
00:30:23,500 --> 00:30:26,940
I I just finished like the very rough first draft on Tuesday.

513
00:30:27,820 --> 00:30:30,220
These ideas are still like very actively cooking.

514
00:30:31,210 --> 00:30:31,530
cool.

515
00:30:31,690 --> 00:30:36,650
Like you know, part one is sort of like this this rant I just gave, like what what are sex

516
00:30:36,650 --> 00:30:37,690
and gender in the first place?

517
00:30:37,930 --> 00:30:41,770
Part two is kind of like sketching how I think we should think about sex, and part three is

518
00:30:41,770 --> 00:30:43,610
kind of sketching how I think we should think about gender.

519
00:30:43,930 --> 00:30:44,090
Yeah.

520
00:30:44,170 --> 00:30:49,690
And Dan, you gave a a talk uh at the Center for Evolutionary Psychology, and you re- Um,

521
00:30:50,330 --> 00:30:56,240
in addition to reinventing agent-based modeling when you were um back in Texas, Uh in

522
00:30:56,240 --> 00:31:02,240
your model, I if I followed you um sort of re-derived the evolution of

523
00:31:02,240 --> 00:31:03,840
sex in your model, which was kind of cool.

524
00:31:04,000 --> 00:31:06,800
You started very much from like first principles.

525
00:31:07,600 --> 00:31:07,840
Yeah.

526
00:31:07,920 --> 00:31:13,760
So I'm I'm being a little self-indulgent, uh, to be honest, in in this project, uh,

527
00:31:14,080 --> 00:31:16,640
which is partly contributing to its excessive length.

528
00:31:16,720 --> 00:31:20,480
And I know reviewers are probably gonna hammer me for, but I just really want to do it.

529
00:31:20,800 --> 00:31:26,190
Um so yeah, what I'm trying to do, which I never done before, is, you know, usually when

530
00:31:26,190 --> 00:31:31,230
you do agent-based models, you know, you have a system that you want to model, you kind of

531
00:31:31,230 --> 00:31:35,870
like write the code for that, you create your little agents, you know, hit run, you let it

532
00:31:35,870 --> 00:31:40,590
run for a while, you know, at some point it stabilizes, or you're just like sick of waiting

533
00:31:40,590 --> 00:31:41,070
for it to run.

534
00:31:41,150 --> 00:31:46,270
So you pause it, you have your little population, you analyze them, you report some figures

535
00:31:46,270 --> 00:31:50,820
and statistics in the paper, and you just kind of like freeze those agents in time forever,

536
00:31:50,980 --> 00:31:55,300
put them on a shelf and like never really worry about them ever again.

537
00:31:55,540 --> 00:32:01,460
Um in this project, I'm trying to uh sort of work with a set

538
00:32:01,460 --> 00:32:06,180
of populations of agents throughout uh a variety of different models.

539
00:32:06,260 --> 00:32:08,900
Uh and again, yeah, starting from first principles.

540
00:32:08,980 --> 00:32:12,100
So like the first question is like why do we even have sex?

541
00:32:13,300 --> 00:32:18,570
So a build an agent-based model that is a uh sort of

542
00:32:18,570 --> 00:32:22,250
recapitulation of John Tuby's model of the evolution of sex.

543
00:32:22,330 --> 00:32:26,890
Like this is, you know, this turns out to be a major question that we still don't have like

544
00:32:26,890 --> 00:32:29,050
a super satisfying answer to.

545
00:32:29,210 --> 00:32:31,290
Like, why do we have sex in the first place?

546
00:32:31,450 --> 00:32:34,090
Why are we not all asexually reproducing organisms?

547
00:32:34,410 --> 00:32:37,770
Asexual reproduction just seems like the better way to reproduce.

548
00:32:37,930 --> 00:32:40,970
Like you don't have to pay all the costs of searching for a mate.

549
00:32:41,140 --> 00:32:44,180
you certainly don't have to pay all the costs of like establishing and maintaining a

550
00:32:44,180 --> 00:32:45,460
relationship like we do.

551
00:32:45,620 --> 00:32:51,060
Um, and you know, if you're a gene inside an asexually reproducing organism's body, like

552
00:32:51,220 --> 00:32:57,140
you know, suppose your organism produces two offspring per generation, you know you're

553
00:32:57,140 --> 00:32:59,460
getting two copies of yourself per generation, right?

554
00:32:59,540 --> 00:33:04,020
But if you're a gene inside a sexually reproducing a uh organism's body, right, every one

555
00:33:04,020 --> 00:33:06,740
of your offspring only have a 50% chance of inheriting you.

556
00:33:06,900 --> 00:33:12,810
So like if the reproduction rate is fixed, like your your replication rate as a gene is

557
00:33:12,810 --> 00:33:13,290
halved, right?

558
00:33:13,610 --> 00:33:14,970
Like, why would you do that?

559
00:33:15,290 --> 00:33:20,330
Um, so there are some hypotheses out there.

560
00:33:20,410 --> 00:33:22,890
I think this might have been John Tiwee's first paper.

561
00:33:23,050 --> 00:33:26,250
Uh I actually I keep saying that, but I need to verify that it's true.

562
00:33:26,410 --> 00:33:32,300
But like he offered uh a model that at

563
00:33:32,300 --> 00:33:33,260
the time was novel.

564
00:33:33,660 --> 00:33:34,700
I think he got kind of unfortunate.

565
00:33:34,940 --> 00:33:37,100
He was a little bit unlucky in that W.D.

566
00:33:37,260 --> 00:33:40,460
Hamilton proposed a very similar model at the same time.

567
00:33:40,620 --> 00:33:45,100
So I think Hamilton's version is the one that like most people know, but I'm gonna credit

568
00:33:45,100 --> 00:33:47,660
it to John because he independently came up with it.

569
00:33:47,900 --> 00:33:51,660
Um John's idea was that it has to do with pathogens, right?

570
00:33:51,740 --> 00:33:57,130
Like so we're all in this long standing coevolutionary arms race with pathogens.

571
00:33:57,290 --> 00:34:02,090
They want to invade our bodies, you know, consume our bodies to produce copies of

572
00:34:02,090 --> 00:34:02,650
themselves.

573
00:34:02,810 --> 00:34:04,250
That's good for their reproduction.

574
00:34:04,330 --> 00:34:05,770
It's bad for our reproduction.

575
00:34:05,930 --> 00:34:08,490
So we evolve defenses to try to keep them out.

576
00:34:08,650 --> 00:34:11,370
They evolve counter defenses to try to get in.

577
00:34:11,690 --> 00:34:13,210
You know, that goes on forever.

578
00:34:13,450 --> 00:34:18,900
The problem for us is, you know, as large-bodied, long-lived, you know, slow reproducing

579
00:34:18,900 --> 00:34:22,100
organisms, pathogens reproduce much more quickly than we do, right?

580
00:34:22,180 --> 00:34:27,220
Like the pathogens that are in us right now can have, you know, orders of magnitude more

581
00:34:27,220 --> 00:34:33,060
generations to adapt to our defenses than we have to evolve, you know,

582
00:34:33,220 --> 00:34:34,420
new defenses.

583
00:34:34,660 --> 00:34:39,620
Um and so John's idea was that the function of sex is, you know, if you're an asexual

584
00:34:39,620 --> 00:34:45,610
reproducing organism, you're invaded by some pathogen, it gets many generations to

585
00:34:45,610 --> 00:34:48,970
evolve unique counteroffenses to your defenses.

586
00:34:49,050 --> 00:34:51,530
So it's really good at exploiting your physiology.

587
00:34:51,690 --> 00:34:56,090
Then you produce offspring, which are genetic clones of yourself, but to the extent that

588
00:34:56,090 --> 00:35:01,050
your defenses are heritable, the pathogens that are well adapted to you are immediately

589
00:35:01,050 --> 00:35:03,210
well adapted to infecting your offspring.

590
00:35:03,290 --> 00:35:04,890
And so you're never going to shake them off.

591
00:35:05,520 --> 00:35:10,320
Uh, but if you reproduce sexually, what you're doing is you're taking half of your genes,

592
00:35:10,480 --> 00:35:13,600
randomly scrambling them with half of David's genes.

593
00:35:13,760 --> 00:35:18,480
Uh, you're producing an offspring that is genetically similar to both of you, uh, but is

594
00:35:18,480 --> 00:35:19,600
genetically unique, right?

595
00:35:19,680 --> 00:35:24,320
So the pathogens infecting, you know, that are well adapted to either of you are no longer

596
00:35:24,800 --> 00:35:27,760
perfectly adapted to invading your offspring.

597
00:35:27,920 --> 00:35:33,230
Um, and so that, John argued, could be a sort of constant selection pressure that would

598
00:35:33,230 --> 00:35:37,390
favor the evolution of sexual reproduction over and above its sort of baseline costs.

599
00:35:37,550 --> 00:35:41,630
And you can build an agent-based model, a simple agent-based model, where you know simulate

600
00:35:41,630 --> 00:35:46,670
agents that are infected by a pathogen agent, they can reproduce asexually or sexually, and

601
00:35:46,670 --> 00:35:51,070
you can show if you turn up the dial on like how bad it is to be infected by a pathogen.

602
00:35:51,230 --> 00:35:54,910
There is a point eventually where selection will favor sexual reproduction over asexual

603
00:35:54,910 --> 00:35:55,870
reproduction.

604
00:35:56,360 --> 00:36:02,200
That doesn't have much to do with the rest of the rest of the project, but I just

605
00:36:02,200 --> 00:36:06,280
wanted to build an agent-based model of that idea because I love that idea.

606
00:36:06,600 --> 00:36:11,400
Yeah, it's one of the great insights of evolutionary biology that like and one of the great

607
00:36:11,400 --> 00:36:13,960
m mysteries of biology is why we sexually reproduce.

608
00:36:14,040 --> 00:36:18,040
And it's a particularly, I think particularly satisfying solution to that problem.

609
00:36:18,200 --> 00:36:23,240
Uh and it's cool that you know you can cash it out mathematically uh in a model.

610
00:36:23,800 --> 00:36:29,550
Um it's also a good example of like asking a question you wouldn't ordinarily think.

611
00:36:29,790 --> 00:36:34,430
Like it's I think it's a great example of like even seeing the mystery there at all and not

612
00:36:34,430 --> 00:36:37,550
just assuming, well, of course there's sex and just taking it for granted.

613
00:36:37,790 --> 00:36:37,950
Yeah.

614
00:36:38,110 --> 00:36:38,590
Um yeah.

615
00:36:38,830 --> 00:36:44,270
And I also I like it's like a nice example of like the germ like pathogens um

616
00:36:44,510 --> 00:36:50,280
seem like such a window dressing thing from like a modern everyday perspective.

617
00:36:50,360 --> 00:36:51,800
Like that's what Lysol's for.

618
00:36:51,960 --> 00:36:57,240
Like the idea that like we're built around the fact that pathogens exist, I think to a very

619
00:36:57,240 --> 00:36:58,760
modern uh intuition.

620
00:36:58,840 --> 00:37:00,680
It feels like, oh, pathogens, whatever.

621
00:37:00,760 --> 00:37:02,040
That's like the stuff in the carpet.

622
00:37:02,120 --> 00:37:04,120
I do, you know, I build rockets.

623
00:37:04,280 --> 00:37:09,080
You know, it's like, no, you are designed around and to defeat pathogens, and you have to

624
00:37:09,080 --> 00:37:15,070
take um the uh the world is much more powerful, even the microscopic world is much

625
00:37:15,070 --> 00:37:16,670
more powerful than you might think.

626
00:37:18,270 --> 00:37:18,510
Yeah.

627
00:37:18,670 --> 00:37:19,230
Yeah.

628
00:37:19,630 --> 00:37:22,910
So anyway, were you going to continue with the with the journey?

629
00:37:23,070 --> 00:37:25,630
So we we got the evolution of sex comes from viruses.

630
00:37:25,870 --> 00:37:26,910
What's the next step here?

631
00:37:27,630 --> 00:37:28,110
Yeah, yeah, yeah.

632
00:37:28,350 --> 00:37:30,510
So I mean that gives us sexual reproduction.

633
00:37:30,590 --> 00:37:30,830
Right.

634
00:37:30,990 --> 00:37:34,830
So then what I do is I take those agents that have now evolved sexual reproduction.

635
00:37:35,070 --> 00:37:38,030
And we ask, well, where does where do sexes come from?

636
00:37:38,110 --> 00:37:38,270
Right?

637
00:37:38,350 --> 00:37:39,470
Why, why would there be sexes?

638
00:37:39,630 --> 00:37:41,230
You have another sexual reproduction.

639
00:37:41,470 --> 00:37:46,820
And so here I'm just like, again, the whole like first part of this paper is like

640
00:37:46,820 --> 00:37:52,660
recapitulating models by people smarter than me, but trying to do them in a sort of unified

641
00:37:52,660 --> 00:37:54,180
and simplified way.

642
00:37:54,500 --> 00:38:00,020
Um so here uh I'm building off of models by people like, you know, Coco and

643
00:38:00,020 --> 00:38:03,300
Parker and you know, again, Tube and Cosmics.

644
00:38:03,380 --> 00:38:08,420
Honestly, Lita uh was angry at me for not including her model in the in the draft I

645
00:38:08,420 --> 00:38:11,650
presented to the CEP, but it'll make it into the paper.

646
00:38:11,810 --> 00:38:14,770
Um in the supplement, probably, but it'll be there.

647
00:38:14,930 --> 00:38:18,930
Uh so you know the question is why do we have sexes?

648
00:38:19,090 --> 00:38:23,170
And earlier, David, I mean you gave a definition of sexes in terms of like differences in

649
00:38:23,170 --> 00:38:25,810
parental investment and operational sex ratio and stuff like that.

650
00:38:25,970 --> 00:38:27,970
I don't think that stuff is actually sex either.

651
00:38:28,130 --> 00:38:33,970
I think those things are things that importantly follow from sex, uh, but they're not like

652
00:38:33,970 --> 00:38:35,890
definitive of what sex is.

653
00:38:36,290 --> 00:38:42,180
Um and I think like biologists have roughly known this answer for a

654
00:38:42,180 --> 00:38:44,420
long time, but psychologists just don't know about it.

655
00:38:44,500 --> 00:38:49,380
So this I see this part of the paper as like just communicating the established biology to

656
00:38:49,380 --> 00:38:50,180
psychologists.

657
00:38:50,340 --> 00:38:52,100
Uh where does where does sex come from?

658
00:38:52,260 --> 00:38:56,820
Well, if you reproduce sexually, the way you do it is you produce gametes, sex cells,

659
00:38:56,900 --> 00:38:57,060
right?

660
00:38:57,220 --> 00:39:01,860
Those contain some kind of genetic payload that's like these are the genes that I want my

661
00:39:01,860 --> 00:39:07,690
offspring to be constructed from, as well as some just like general cellular machinery

662
00:39:07,770 --> 00:39:11,930
and like energy and nutrient stores that like allow the cell to function.

663
00:39:12,170 --> 00:39:17,770
Those gametes fuse together and you know if that fusion is successful, if there's enough

664
00:39:17,770 --> 00:39:22,010
energy and nutrient stores, if there's like the correct kind of machinery, then that fused

665
00:39:22,010 --> 00:39:24,010
zygote can develop into an offspring.

666
00:39:24,940 --> 00:39:30,940
So there's some probably like minimal investment that each uh

667
00:39:30,940 --> 00:39:33,420
sort of parent organism needs to make.

668
00:39:33,580 --> 00:39:38,140
Like if you each put in, there's some minimum investment a zygote needs to be successful.

669
00:39:38,300 --> 00:39:43,100
If each parent puts in 50% of that into the zygotes, when those zygotes fuse, you know,

670
00:39:43,180 --> 00:39:45,340
they have some probability of success, right?

671
00:39:45,570 --> 00:39:48,690
Um, the way the model works is sort of built off of that kind of assumption.

672
00:39:48,850 --> 00:39:53,730
We're assuming there's some kind of like minimum sort of energy threshold that a zygote

673
00:39:53,730 --> 00:39:58,450
needs uh to be able to develop successfully into an organism when two gametes fuse, right?

674
00:39:58,850 --> 00:40:04,770
Um that said, uh as long as both parents put in that sort of

675
00:40:04,770 --> 00:40:10,260
50% minimum, there's you know some probability of zygote success, but there's inevitably

676
00:40:10,260 --> 00:40:14,500
going to be variation in how much parent organisms invest in their gametes, right?

677
00:40:14,660 --> 00:40:17,540
And that variation is going to have consequences, right?

678
00:40:17,620 --> 00:40:22,980
There's going to be an inevitable sort of quality-quantity trade-off in gamete production.

679
00:40:23,060 --> 00:40:28,900
Uh, so I can put more uh energy and nutrients into each of my gametes

680
00:40:29,150 --> 00:40:34,750
that is probably going to increase the likely success of those gametes in developing into

681
00:40:34,750 --> 00:40:35,950
successful offspring.

682
00:40:36,270 --> 00:40:40,750
But if each of those gametes is more expensive, that means I can't afford to produce as

683
00:40:40,750 --> 00:40:43,310
many of them given a fixed energy budget, right?

684
00:40:43,630 --> 00:40:49,310
So I can make bigger, more nourishing gametes, but the consequence of that is I'm not going

685
00:40:49,310 --> 00:40:51,150
to have as many reproductive opportunities.

686
00:40:51,420 --> 00:40:57,340
On the other hand, I can sort of jeep out, I can make smaller, uh, less nourishing gametes.

687
00:40:57,420 --> 00:41:01,980
Uh, they're gonna be less likely to develop into successful offspring when they fuse with

688
00:41:01,980 --> 00:41:06,700
others, but you know, the upside is by investing less into each gamete, I can afford to

689
00:41:06,700 --> 00:41:08,460
make more of them with a fixed budget, right?

690
00:41:08,620 --> 00:41:12,460
Uh this is again like very inspired by Dawkins, right?

691
00:41:12,540 --> 00:41:16,220
The the description of the evolution of sex, I think, in the selfish gene kind of follows

692
00:41:16,220 --> 00:41:17,020
exactly this logic.

693
00:41:17,100 --> 00:41:20,220
He was also sort of basing this off of like Parker's ideas.

694
00:41:22,050 --> 00:41:27,730
So, you know, you could think you know, one potentially optimal strategy is everybody just

695
00:41:27,730 --> 00:41:28,930
invests 50%.

696
00:41:30,290 --> 00:41:36,050
But another potentially optimal strategy is you can make lots of really

697
00:41:36,050 --> 00:41:41,890
small gametes, which provide very little investment into the resulting offspring, but

698
00:41:41,890 --> 00:41:44,370
give you lots and lots of opportunities to produce offspring.

699
00:41:44,610 --> 00:41:48,500
And you can make those gametes such that they specifically target relatively large

700
00:41:48,500 --> 00:41:48,980
partners.

701
00:41:49,060 --> 00:41:53,380
And that sort of balances the cost and benefits of both extremes of that investment

702
00:41:53,380 --> 00:41:54,340
trade-off, right?

703
00:41:54,500 --> 00:41:58,980
If you make lots of small gametes that specifically target large gametes, then your

704
00:41:59,140 --> 00:42:04,020
offspring are relatively likely to be successful because they're targeting gametes that

705
00:42:04,020 --> 00:42:08,290
provide the energy and nutrients that you're not providing, but you get lots of

706
00:42:08,290 --> 00:42:09,650
reproductive opportunities.

707
00:42:10,130 --> 00:42:14,690
So you can set up, you know, we take those agents that have involved sexual reproduction.

708
00:42:14,850 --> 00:42:16,050
Now they make gametes.

709
00:42:16,130 --> 00:42:18,530
They have a gene that regulates do you make big or small ones?

710
00:42:18,690 --> 00:42:20,210
They have a fixed energy budget.

711
00:42:20,290 --> 00:42:25,410
And they have another gene that regulates like what size of gamete do your gametes target.

712
00:42:25,650 --> 00:42:30,130
And you can show, you can start off as population where there's like uniform, whatever

713
00:42:30,130 --> 00:42:33,090
initial distribution of gamete production you want, but you can start it off where there's

714
00:42:33,090 --> 00:42:37,320
like a uniform distribution of gamete size, kick that off.

715
00:42:37,480 --> 00:42:42,040
And very rapidly, what you see is the evolution of a bimodal distribution in gamete size,

716
00:42:42,120 --> 00:42:47,960
where there's some individuals that specialize in producing as small of gametes as they can

717
00:42:48,040 --> 00:42:50,680
that target partners that are as large as they can.

718
00:42:50,760 --> 00:42:54,920
And then there's other individuals that specialize in producing relatively large gametes,

719
00:42:55,000 --> 00:42:57,000
and there's not very much selection on their preferences.

720
00:42:57,270 --> 00:42:59,910
And so that that is the evolution of sexes, right?

721
00:43:00,310 --> 00:43:02,870
And I think that's how we need to think about sexes.

722
00:43:03,270 --> 00:43:09,190
Um, what sex is, is there is this inevitable quality-quantity trade-off

723
00:43:09,190 --> 00:43:10,230
in gamete production.

724
00:43:10,390 --> 00:43:13,510
This is, by the way, a part where I'm still trying to work out the language of the

725
00:43:13,510 --> 00:43:14,070
argument.

726
00:43:14,150 --> 00:43:18,790
Uh, but there is this inevitable quality-quantity trade-off in gamete production.

727
00:43:19,280 --> 00:43:25,040
There is, given the details of a particular species like life history and

728
00:43:25,200 --> 00:43:31,200
ecology, there are going to be some optima in terms of where you

729
00:43:31,200 --> 00:43:33,760
fall on that trade off distribution.

730
00:43:33,920 --> 00:43:36,160
So there are isogamous species out there.

731
00:43:36,240 --> 00:43:40,320
There are species where the optimum is everybody contributes the same amount to their

732
00:43:40,320 --> 00:43:40,960
gametes.

733
00:43:41,040 --> 00:43:42,480
All gametes are equally sized.

734
00:43:42,560 --> 00:43:46,530
There are there are no sexes, or maybe I would like to say there's just one sex.

735
00:43:46,850 --> 00:43:51,250
Um, but there are a large number of species where the optima are just the two extreme ends

736
00:43:51,250 --> 00:43:52,850
of that trade-off distribution, right?

737
00:43:53,010 --> 00:43:57,810
Like one optimum is produce gametes basically as small as you can, the other optimum is

738
00:43:57,810 --> 00:43:59,730
produce gametes basically as large as you can.

739
00:43:59,890 --> 00:44:04,690
And those optima is what I want to argue is what we should think of as the sexes for that

740
00:44:04,690 --> 00:44:05,330
species.

741
00:44:07,360 --> 00:44:13,360
Dan, can I ask from an intuitive perspective, like already

742
00:44:13,360 --> 00:44:19,360
existing as a like a huge human, it feels like the investment in gametes

743
00:44:19,360 --> 00:44:22,720
is trivial, even when they're um large.

744
00:44:23,040 --> 00:44:23,200
Yeah.

745
00:44:23,680 --> 00:44:28,800
Is the idea that phylogenetically, i.e., over evolutionary time,

746
00:44:29,790 --> 00:44:35,790
for even small critters, that was that really was a big investment or a

747
00:44:35,790 --> 00:44:37,390
big investment difference.

748
00:44:37,710 --> 00:44:43,550
And then there's a bunch of concomitant differences that follow from

749
00:44:43,870 --> 00:44:49,630
that change in investment, um, such that, you know, the generally speaking, there's

750
00:44:49,630 --> 00:44:53,550
obligate investment on the larger gamete producer.

751
00:44:53,710 --> 00:44:59,600
Is the is the idea that it's sort of a um a timing thing in the sense of the

752
00:44:59,600 --> 00:45:05,200
gamete difference came first and then the the big macroscopic

753
00:45:06,160 --> 00:45:12,080
sex differences or dimorphisms came later because from the again from like the the

754
00:45:12,160 --> 00:45:16,640
you know like the the it would be trivial for me as an adult male if I could

755
00:45:16,640 --> 00:45:22,080
physiologically to produce an egg like that's nothing right but we're talking about it as

756
00:45:22,080 --> 00:45:27,070
if it's this enormous difference but I think that might be a bias perspective.

757
00:45:27,230 --> 00:45:30,590
Or like I think you know what I mean, but I mean that's that that's sort of the the

758
00:45:30,590 --> 00:45:30,990
question.

759
00:45:31,150 --> 00:45:33,390
I think people might be wondering when they're hearing that.

760
00:45:33,470 --> 00:45:35,070
And I'm I'm wondering it too.

761
00:45:35,150 --> 00:45:35,790
Yeah.

762
00:45:36,270 --> 00:45:36,510
Yeah.

763
00:45:36,590 --> 00:45:39,470
And I think I think a lot of psychologists are going to wonder that.

764
00:45:39,550 --> 00:45:44,030
Like I I want to communicate this to like a broader psychological audience than just

765
00:45:44,030 --> 00:45:45,070
evolutionary psychologists.

766
00:45:45,150 --> 00:45:48,590
So I do think that's like um that will be an issue.

767
00:45:48,750 --> 00:45:54,620
Um and I mean we're also like immediately falling into assuming

768
00:45:54,700 --> 00:46:00,540
uh I always forget the term for this, but like uh gonakora species where it's like either

769
00:46:00,540 --> 00:46:02,380
as an individual you specialize in one.

770
00:46:02,780 --> 00:46:03,340
Yeah, and that's right.

771
00:46:03,420 --> 00:46:05,660
Yeah, no, totally totally that need not be the case.

772
00:46:05,740 --> 00:46:06,780
There's lots of things.

773
00:46:07,020 --> 00:46:07,340
Yeah, yeah.

774
00:46:09,020 --> 00:46:09,660
Yeah, exactly.

775
00:46:09,740 --> 00:46:10,460
Yeah, yeah, yeah.

776
00:46:10,780 --> 00:46:14,540
Lots of fish where yeah, you you can do both at the same time or either a different point

777
00:46:14,700 --> 00:46:14,860
of time.

778
00:46:16,890 --> 00:46:18,410
My bias, my bias is showing.

779
00:46:18,570 --> 00:46:19,050
My species.

780
00:46:20,970 --> 00:46:21,210
Okay.

781
00:46:21,850 --> 00:46:27,770
But yeah, I mean, the argument is that we're talking about sort of very uh early evolution

782
00:46:27,770 --> 00:46:28,410
of sex.

783
00:46:28,650 --> 00:46:33,530
Um at a time when gamete production would be like a more sizable chunk of your overall

784
00:46:33,530 --> 00:46:34,570
energy budget.

785
00:46:34,810 --> 00:46:39,780
Um and I think what happens is that the reason why this

786
00:46:39,780 --> 00:46:45,700
bimodality in uh gamete production matters is because that does set

787
00:46:45,700 --> 00:46:48,740
up other selection pressures for the kinds of things that you're talking about, David,

788
00:46:48,900 --> 00:46:52,500
which themselves shape other aspects of phenotype that we should care about, including as

789
00:46:52,500 --> 00:46:53,140
psychologists.

790
00:46:53,300 --> 00:46:54,900
So those are kind of the subsequent models.

791
00:46:54,980 --> 00:46:59,380
So we start with okay, we had evolution of sexual reproduction, we move on to evolution of

792
00:46:59,380 --> 00:46:59,620
sex.

793
00:46:59,780 --> 00:47:04,660
We do produce these like gonochoric uh agents that I hope that's the right word, uh, that

794
00:47:04,660 --> 00:47:10,550
are um uh producing either really large or really small gametes, um, then I take those same

795
00:47:10,550 --> 00:47:13,030
populations and start introducing new selection pressures.

796
00:47:13,190 --> 00:47:19,030
Uh so now, you know, the the first one I do uh I think is um, you know,

797
00:47:19,110 --> 00:47:24,070
we can introduce a gene that regulates like investment in sort of costly mate competition,

798
00:47:24,150 --> 00:47:25,350
you can think of it as.

799
00:47:25,510 --> 00:47:31,040
Uh so if you're high on this particular gene, then what happens is like you're more

800
00:47:31,040 --> 00:47:33,840
likely to die before you get to produce gametes.

801
00:47:34,640 --> 00:47:39,440
But if you do produce gametes, like your gametes are basically at the front of the line

802
00:47:39,440 --> 00:47:40,960
when it comes for fertilization.

803
00:47:41,120 --> 00:47:45,120
So you get earlier reproductive opportunity, you're less likely to reproduce, but if you do

804
00:47:45,120 --> 00:47:47,520
get to reproduce, you get to reproduce first, right?

805
00:47:48,800 --> 00:47:53,600
That's distributed uniformly throughout the population, independent of gamete size.

806
00:47:53,680 --> 00:47:57,840
But we can ask like, well, what would selection do with that in a world where there are

807
00:47:57,840 --> 00:48:02,410
some individuals that are specializing in producing large numbers of small gametes and

808
00:48:02,410 --> 00:48:05,450
other individuals that are specializing in producing small numbers of large gametes.

809
00:48:05,690 --> 00:48:10,810
Well, if you're producing small numbers of large gametes, you know, the sex ratio is in

810
00:48:10,810 --> 00:48:11,290
your favor.

811
00:48:11,450 --> 00:48:13,050
There's lots of small gametes out there.

812
00:48:13,130 --> 00:48:17,210
You're virtually guaranteed that like all of your gametes are going to get fertilized, uh,

813
00:48:17,450 --> 00:48:20,650
assuming you know an equal ratio of those two morphs.

814
00:48:22,000 --> 00:48:27,120
And so, you know, dying before you get to reproduce, just to make sure that you get to

815
00:48:27,120 --> 00:48:29,520
reproduce first, like that doesn't have any benefit to you.

816
00:48:29,840 --> 00:48:35,120
And so selection should really strongly select against that kind of like risky, costly make

817
00:48:35,280 --> 00:48:36,800
competition among those individuals.

818
00:48:36,960 --> 00:48:42,400
But if you're a small gamete producer, right, you're there's lots and lots of small gametes

819
00:48:42,400 --> 00:48:42,640
out there.

820
00:48:42,720 --> 00:48:45,360
They're all competing over a smaller number of large gametes.

821
00:48:45,770 --> 00:48:51,610
So making sure that your gametes get to the large gametes first, like that's a huge

822
00:48:51,610 --> 00:48:53,690
bottleneck in terms of your reproduction.

823
00:48:53,770 --> 00:48:57,690
And so it's worth trading off a little bit of your survivability to make sure that if you

824
00:48:57,690 --> 00:49:01,770
do get to produce gametes, your gametes actually get to develop into an offspring.

825
00:49:01,850 --> 00:49:05,690
And so selection should favor those kinds of costly competition genes in the small gamete

826
00:49:05,850 --> 00:49:06,170
producers.

827
00:49:06,250 --> 00:49:08,090
And that's exactly what happens in the models, right?

828
00:49:08,170 --> 00:49:12,090
You can show, you can start off these populations where these competition genes are

829
00:49:12,090 --> 00:49:17,140
uniformly distributed independent of uh gamete production strategy, and they very quickly

830
00:49:17,140 --> 00:49:22,340
evolve into these, like this sort of sex differentiated mating strategy, where small gamete

831
00:49:22,420 --> 00:49:26,740
producers are investing in costly mate competition, large gamete producers are not.

832
00:49:27,140 --> 00:49:28,580
You can do the reverse.

833
00:49:28,820 --> 00:49:32,500
So at the same time, introduce what I'm calling like a parenting gene.

834
00:49:32,740 --> 00:49:38,670
This parenting gene, if you're high on it, uh, it costs you some of the energy that

835
00:49:38,670 --> 00:49:39,950
you use to produce gametes.

836
00:49:40,030 --> 00:49:42,990
So you can't make as many gametes if you're high on this parenting gene.

837
00:49:43,070 --> 00:49:47,710
Uh, but it increases the probability that if you produce a gamete, that gamete actually

838
00:49:47,710 --> 00:49:48,750
survives, right?

839
00:49:48,910 --> 00:49:52,990
And so we can again distribute that uniformly throughout the population and we can ask,

840
00:49:53,070 --> 00:49:54,430
well, what would selection do with that?

841
00:49:54,590 --> 00:50:00,380
Well, you know, if you reason it through, if you're a large gamete producer, right, you

842
00:50:00,380 --> 00:50:02,940
have relatively few reproductive opportunities.

843
00:50:03,180 --> 00:50:05,820
The population can only support so many offspring.

844
00:50:05,980 --> 00:50:07,900
We stipulate that as an assumption.

845
00:50:08,460 --> 00:50:13,500
So if one of your offspring, you know, uh dies because you're you know over carrying

846
00:50:13,500 --> 00:50:16,460
capacity or something, like that's a huge chunk of your reproductive success.

847
00:50:16,620 --> 00:50:20,620
It's probably worth producing slightly fewer offspring in order to make sure that all of

848
00:50:20,620 --> 00:50:23,660
your offspring uh actually survive to the next generation.

849
00:50:23,820 --> 00:50:27,610
Uh, whereas if you're a small gamete producer, like you don't even know if you're going to

850
00:50:27,610 --> 00:50:29,610
get to produce an offspring at all, right?

851
00:50:29,690 --> 00:50:32,570
Uh, because you're the the sex ratio is not in your favor.

852
00:50:32,970 --> 00:50:35,130
And if you do, you're probably going to produce a lot of them.

853
00:50:35,290 --> 00:50:38,970
So any one offspring is probably a relatively small chunk of your overall reproductive

854
00:50:38,970 --> 00:50:39,370
success.

855
00:50:39,610 --> 00:50:44,250
So it probably makes sense to just focus on buying as many lottery tickets as you can and

856
00:50:44,250 --> 00:50:48,570
just hoping they all work out rather than you know limiting your reproductive opportunities

857
00:50:49,100 --> 00:50:50,620
and keeping those alive.

858
00:50:50,780 --> 00:50:52,860
And that's exactly what happens in the model, right?

859
00:50:53,100 --> 00:50:57,500
At the same time that selection is favoring mate competition in the small gamete producers,

860
00:50:57,580 --> 00:51:01,100
it favors the evolution of these like costly parenting adaptations in the large gamete

861
00:51:01,180 --> 00:51:01,740
producers.

862
00:51:02,140 --> 00:51:06,620
So these are things that I think have been argued like kind of intuitively.

863
00:51:07,820 --> 00:51:11,340
And they've been formally modeled before too, but like we're showing in sort of one model

864
00:51:11,340 --> 00:51:15,340
system now, we're starting with the evolution of sex, getting to sexes, and now showing

865
00:51:15,340 --> 00:51:19,830
like sex differences in these other like mating and parenting phenotypes that follow

866
00:51:19,830 --> 00:51:23,670
directly from this bimodality in gamete production.

867
00:51:24,230 --> 00:51:25,110
Yeah, it's amazing.

868
00:51:25,190 --> 00:51:29,350
It's a good example too of people always, you know, accuse, well, not always, that's

869
00:51:29,350 --> 00:51:32,630
hyperbole, but often accuse just so stories, right?

870
00:51:32,710 --> 00:51:35,510
Where it's sort of like, well, it could have gone either way, but then there's just a post

871
00:51:35,670 --> 00:51:36,390
hoc story.

872
00:51:36,550 --> 00:51:42,280
And it's a lovely example of you literally are specifying the world as equal, and it's

873
00:51:42,280 --> 00:51:46,520
showing that you know, given there's there's deductive logic to differential reproductive

874
00:51:46,520 --> 00:51:46,920
success.

875
00:51:47,000 --> 00:51:47,800
So yeah.

876
00:51:48,360 --> 00:51:53,320
Yeah, and like these things are, you know, I think part of what these models illustrate is

877
00:51:53,480 --> 00:51:55,800
these things are really contingent on the assumptions that you make.

878
00:51:55,880 --> 00:52:01,640
So I don't wanna um I don't wanna like uh give the impression that like

879
00:52:01,880 --> 00:52:07,730
these sex differences are like the inevitable consequence of having sexes, like these

880
00:52:07,730 --> 00:52:11,170
kinds of things are like the essence of maleness or the essence of femaleness.

881
00:52:11,250 --> 00:52:14,690
Uh like there are species out there that don't show these patterns, right?

882
00:52:14,770 --> 00:52:17,010
Like phalaropes are the the classic example.

883
00:52:17,090 --> 00:52:20,610
There are these birds where like it's the females that are big and colorful and compete

884
00:52:20,610 --> 00:52:22,050
over access to the males.

885
00:52:22,210 --> 00:52:27,090
Um but you can explain those kinds of outcomes within the same sort of model system.

886
00:52:27,170 --> 00:52:27,410
Right.

887
00:52:27,570 --> 00:52:33,520
So you know, one of the key variables that regulates uh the evolution of these traits that

888
00:52:33,520 --> 00:52:35,840
we've already been talking about is the sex ratio.

889
00:52:36,000 --> 00:52:40,320
So the way I'd set up these models is that there's an equal sex ratio, right?

890
00:52:40,400 --> 00:52:43,440
There's an equal number of large gamete producers and small gamete producers.

891
00:52:43,840 --> 00:52:47,840
But if you change that assumption, if you change the environment, so like I have a version

892
00:52:47,840 --> 00:52:51,040
of the model where, like, you know, you can assume this does happen.

893
00:52:51,120 --> 00:52:57,010
Like, let's assume for whatever reason the large gamete producers are like really broadly

894
00:52:57,010 --> 00:52:58,450
dispersed throughout the environment.

895
00:52:58,530 --> 00:53:02,690
Like resources are really patchy, let's say, uh, they're really sparse and patchy.

896
00:53:02,770 --> 00:53:07,490
And so the large gamete producers are like spread out over like a really large uh, you

897
00:53:07,490 --> 00:53:09,170
know, physical space.

898
00:53:09,330 --> 00:53:12,770
So if you're a small gamete producer and you want to reproduce, like you've got to travel

899
00:53:12,770 --> 00:53:16,540
really long distances between these patches, that's going to slow down your reproductive

900
00:53:16,540 --> 00:53:16,940
rate, right?

901
00:53:17,020 --> 00:53:20,860
Even though even if there's an equal number of large and small gamete producers, if it just

902
00:53:20,860 --> 00:53:25,580
takes a long time to travel between patches of potential mates, that's going to necessarily

903
00:53:25,580 --> 00:53:27,100
slow down your reproductive rate.

904
00:53:27,340 --> 00:53:32,140
If you model that kind of system where, yeah, if you're a small gamete producer, you can

905
00:53:32,140 --> 00:53:35,980
produce a lot of gametes, but you can't necessarily use all of them just because of travel

906
00:53:35,980 --> 00:53:40,770
time, then you can actually see the reversal of that like competition difference.

907
00:53:40,930 --> 00:53:45,330
You can see evolution favoring high competition in the large gamete producers, higher

908
00:53:45,330 --> 00:53:47,970
competition in the large gamete producers and the small gamete producers.

909
00:53:48,130 --> 00:53:52,290
So it's not like these things, that's why earlier David, I was saying like I don't think I

910
00:53:52,290 --> 00:53:56,130
don't think these things like differences in parental investment, they aren't sex in and of

911
00:53:56,130 --> 00:53:56,770
themselves.

912
00:53:56,930 --> 00:53:58,370
They're things that follow from sex.

913
00:53:58,450 --> 00:54:03,650
So like what sex is doing is it's acting as kind of like a moderator variable that is

914
00:54:03,650 --> 00:54:08,260
shaping like how does your ecology lead to particular phenotypes?

915
00:54:08,900 --> 00:54:09,300
Cool.

916
00:54:10,180 --> 00:54:10,820
Interesting.

917
00:54:10,980 --> 00:54:15,860
I mean, it's it's uh the phalarope example is a is a is a lovely example, uh, you know,

918
00:54:16,100 --> 00:54:18,740
with you know an exception that proves the rule.

919
00:54:18,980 --> 00:54:24,660
Um But it's it's interesting because you see as what what's called a sex role reversal.

920
00:54:24,980 --> 00:54:29,780
Um do you think w is it does it make sense to think of

921
00:54:30,330 --> 00:54:35,450
phalaropes uh their different parental investment strategies, are is that sex?

922
00:54:35,930 --> 00:54:41,130
Like given that the the males are investing a lot and the females are competing over the

923
00:54:41,130 --> 00:54:46,010
males, should we flip it around and say that the males are females in some sense?

924
00:54:47,290 --> 00:54:48,410
It's a good question.

925
00:54:48,650 --> 00:54:54,090
Uh and this is um this is like kind of where I lost the Center for Evolutionary Psychology.

926
00:54:54,170 --> 00:54:59,080
It's like, you know, w what are the implications for all of this in terms of how we should

927
00:54:59,080 --> 00:55:00,600
think about like what sex is?

928
00:55:00,760 --> 00:55:05,240
I mean, I should say there's another model, it doesn't super matter for this conversation,

929
00:55:05,320 --> 00:55:09,640
but there's another model after this where I'm modeling like sex differences in mating

930
00:55:09,640 --> 00:55:09,960
strategies.

931
00:55:10,120 --> 00:55:14,520
So we add like another gene that's kind of like a mating parenting trade-off gene that

932
00:55:14,520 --> 00:55:19,080
regulates like, do you pursue lots of mating opportunities and do not very much parenting?

933
00:55:19,160 --> 00:55:23,320
Or do you do like more, do you spend more of your time on parenting and less of your time

934
00:55:23,320 --> 00:55:27,890
pursuing mating opportunities and you know the the sex difference you expect does evolve in

935
00:55:27,890 --> 00:55:31,810
this kind of context, assuming you know relatively equal sex ratio.

936
00:55:31,970 --> 00:55:36,690
But then if you crank up the the strength of selection on a parental investment, if you're

937
00:55:36,690 --> 00:55:41,170
a species like us that produces like highly altruitial offspring that really benefit from

938
00:55:41,170 --> 00:55:45,490
having biparental care, then what you see is selection favors sort of lots of parenting and

939
00:55:45,490 --> 00:55:48,450
not very much mating in both the large and small gamete producers.

940
00:55:48,530 --> 00:55:52,690
And so that's how you can get like the evolution of like pair bonding and biparental care.

941
00:55:53,940 --> 00:55:58,900
So yeah, the point is like sex shapes these phenotypes, but it doesn't inevitably lead to

942
00:55:58,900 --> 00:56:00,420
particular phenotypes, right?

943
00:56:00,580 --> 00:56:03,380
Um, so yeah, what does that mean for how we should think about sex?

944
00:56:03,540 --> 00:56:07,380
Yeah, should we call like the big colorful phalaropes the males?

945
00:56:07,460 --> 00:56:11,940
Uh or should we stick to like gametes as defining what the sexes are?

946
00:56:12,180 --> 00:56:17,700
I think we should stick to gametes because I think if you want to try to define sex in

947
00:56:17,700 --> 00:56:21,650
terms of these manifest phenotypes, like there's all kinds of phenotypes out there.

948
00:56:21,730 --> 00:56:26,370
It's like there's not going to be like what is universal across all these species is gamete

949
00:56:26,530 --> 00:56:27,330
production, right?

950
00:56:28,050 --> 00:56:33,250
These particular phenotypes are really particular to this species ecology and

951
00:56:33,490 --> 00:56:35,330
phylogenetic history and life history.

952
00:56:35,490 --> 00:56:40,290
And so if we want to be making like bespoke definitions of sex based on phenotypes, like

953
00:56:40,370 --> 00:56:43,570
we're going to need lots of really complicated definitions.

954
00:56:43,810 --> 00:56:49,580
Whereas, you know, I think gametizes is like a clean, universal um uh universal,

955
00:56:49,740 --> 00:56:51,500
universally applicable definition.

956
00:56:51,820 --> 00:56:57,820
Uh but I think if we want to do that, we need to like, there are some things we're gonna

957
00:56:57,820 --> 00:57:02,460
have to give up, uh, which is I I think if we want to be serious about

958
00:57:03,740 --> 00:57:09,500
uh gamete production as being core to sex, I I think the argument I want to make is that

959
00:57:09,580 --> 00:57:15,530
like what sex is, is it's not really a variable that properly

960
00:57:15,530 --> 00:57:18,010
exists at like approximate level of analysis.

961
00:57:18,170 --> 00:57:20,970
It's not something that's in the organism itself.

962
00:57:21,130 --> 00:57:24,570
Uh, what sex is, it's it's a set of reproductive strategies, right?

963
00:57:24,650 --> 00:57:29,930
It's these optimal gamete production strategies that selection is always pushing organisms

964
00:57:29,930 --> 00:57:30,570
toward.

965
00:57:30,650 --> 00:57:35,700
Uh, but any individual organism is always going to be some kind of noisy approximation of

966
00:57:35,700 --> 00:57:36,500
those optima.

967
00:57:37,220 --> 00:57:41,940
And those optima are going to have lots of consequences for the kinds of phenotypes that

968
00:57:41,940 --> 00:57:46,900
evolve, but there's no one element in that phenotype that is the organism sex.

969
00:57:47,220 --> 00:57:53,140
So I think like we find it very useful to perceive the world

970
00:57:53,140 --> 00:57:57,220
in a way that like there are males and there are females, and that organism is a male, and

971
00:57:57,220 --> 00:57:58,820
that organism is a female.

972
00:57:59,530 --> 00:58:03,770
Because sex does exist, it does have these effects on phenotypes.

973
00:58:03,850 --> 00:58:08,650
It does structure the covariance matrix of phenotypes such that certain bundles of

974
00:58:08,650 --> 00:58:14,250
adaptations tend to go with some organisms and other bundles of adaptations tend to go with

975
00:58:14,250 --> 00:58:14,650
others.

976
00:58:15,050 --> 00:58:19,850
So it's like a useful hallucination to see sex in the real world, but like I think it's a

977
00:58:19,850 --> 00:58:21,050
hallucination, right?

978
00:58:21,130 --> 00:58:25,560
I think if we really want a proper scientific understanding of what sex is, we need to

979
00:58:25,560 --> 00:58:28,680
understand that like it's these abstract conceptual optima.

980
00:58:29,000 --> 00:58:31,640
It's it's the designs that selection is pushing towards.

981
00:58:31,800 --> 00:58:35,560
It's not anything that's in the physical organism's body.

982
00:58:37,960 --> 00:58:38,280
Interesting.

983
00:58:38,680 --> 00:58:41,480
That's where like people are like no, it's great.

984
00:58:41,560 --> 00:58:41,800
Yeah.

985
00:58:41,960 --> 00:58:45,800
I mean I think it's all I mean be I mean it sounds like it's something similar.

986
00:58:45,880 --> 00:58:50,430
I was thinking while you're talking like other analogies, excuse me.

987
00:58:51,150 --> 00:58:56,590
And things like risk-taking or being not risk-taking are also sort of these abstractions.

988
00:58:57,790 --> 00:59:01,870
But then again, of course, like risk-taking can vary by situation.

989
00:59:02,190 --> 00:59:07,550
And something like sex, there are the chemaphroditic species where it's sort of, you know,

990
00:59:07,790 --> 00:59:13,000
both, but it's not as facultative or situational specific, it seems.

991
00:59:13,080 --> 00:59:19,080
I mean, you know, so I think it's it's also a trick of our evolved psychology

992
00:59:19,240 --> 00:59:25,000
probably having strong biases about um having entries for sex, basically.

993
00:59:25,080 --> 00:59:29,080
Um, I think is probably part of why we're having trouble as scientists thinking about it

994
00:59:29,080 --> 00:59:34,680
the right way, as this abstraction or in this in design space, or sort of a set of dynamics

995
00:59:34,760 --> 00:59:36,120
in design space.

996
00:59:36,850 --> 00:59:37,170
Yeah.

997
00:59:37,730 --> 00:59:42,370
And I think there's really good reasons why we intuitively feel that way, which is part of

998
00:59:42,370 --> 00:59:43,810
like the gender stuff.

999
00:59:44,050 --> 00:59:48,850
Um, but yeah, I think we need to like sort of fight our intuitions.

1000
00:59:49,010 --> 00:59:51,410
Uh I and I think it's fine to have those intuitions.

1001
00:59:51,490 --> 00:59:55,810
I'm not saying like you in your everyday life need to like stop thinking about people as

1002
00:59:55,810 --> 01:00:00,420
having sexes, but I'm just saying, like, as scientists, if we want to have like an accurate

1003
01:00:00,420 --> 01:00:04,500
model of these things, we need to understand that like our intuitions are sort of leading

1004
01:00:04,500 --> 01:00:08,900
us in the wrong direction, and that like really what's going on, you know, I think we

1005
01:00:08,900 --> 01:00:13,060
should think about sex more in the same way as we think about like something like species.

1006
01:00:13,220 --> 01:00:17,940
Like it's useful to categorize organisms into species.

1007
01:00:18,100 --> 01:00:23,700
Like there are real evolutionary histories that cause you know humans to be different from

1008
01:00:23,700 --> 01:00:24,820
chimpanzees.

1009
01:00:24,980 --> 01:00:29,870
It's a consequence of like, you know, certain facts about the evolutionary history of those

1010
01:00:29,870 --> 01:00:30,830
organisms.

1011
01:00:31,870 --> 01:00:33,870
And it's a really useful concept.

1012
01:00:33,950 --> 01:00:37,710
Like, if I say that's a chimp, that tells you a lot about the kinds of things you can

1013
01:00:37,710 --> 01:00:40,910
expect to find in that organism, versus if I tell you that it's a human, but there's

1014
01:00:40,910 --> 01:00:43,630
nothing in a chimp that like is its chimpness, right?

1015
01:00:43,710 --> 01:00:49,440
There's no like one thing you could put your finger on that's like all chimps have this,

1016
01:00:49,600 --> 01:00:52,160
and everything that's not a chimp doesn't have this, right?

1017
01:00:52,240 --> 01:00:57,200
Like it's just a useful shorthand for saying like this is the evolutionary history of this

1018
01:00:57,200 --> 01:00:59,520
particular individual is X.

1019
01:00:59,760 --> 01:01:00,000
Right.

1020
01:01:00,240 --> 01:01:05,520
Uh you know, sex is Can take a stab at defining sex?

1021
01:01:05,840 --> 01:01:06,640
Yeah, yeah.

1022
01:01:06,880 --> 01:01:09,360
So please uh is this right?

1023
01:01:09,440 --> 01:01:14,720
It's the set of adaptations uh that tend to evolve as a function of gamete

1024
01:01:14,960 --> 01:01:15,520
size.

1025
01:01:16,070 --> 01:01:17,590
I wouldn't say it's the adaptations.

1026
01:01:18,230 --> 01:01:18,470
No.

1027
01:01:18,630 --> 01:01:18,790
Okay.

1028
01:01:19,270 --> 01:01:19,590
Yeah.

1029
01:01:19,830 --> 01:01:24,550
I I because then then I think you do get into like, well, phalarope sex is different than

1030
01:01:24,550 --> 01:01:25,510
like human sex.

1031
01:01:25,990 --> 01:01:26,950
That's why I said tend to.

1032
01:01:27,110 --> 01:01:30,150
Not necessarily I mean there are c there are exceptions like phalarop, but you instead of

1033
01:01:30,150 --> 01:01:33,350
actually patients that tend to evolve as a function gamete size.

1034
01:01:33,830 --> 01:01:36,950
I really want to avoid tend to oh okay all right.

1035
01:01:37,270 --> 01:01:40,660
How I Indicated by indicated by the case.

1036
01:01:40,740 --> 01:01:44,340
Well, I mean, too in most cases evolve, but you know, with some exceptions.

1037
01:01:44,740 --> 01:01:47,060
No, David, you good job taking a stab at this.

1038
01:01:47,140 --> 01:01:47,460
This is good.

1039
01:01:48,420 --> 01:01:52,980
We just we just I want to hear where you're going with that, but that that's part of why I

1040
01:01:52,980 --> 01:01:58,980
want to push towards like what sex is is these optimal trade off

1041
01:01:59,140 --> 01:02:03,540
points in the quality quantity trade off in gamete production.

1042
01:02:03,780 --> 01:02:04,100
Right?

1043
01:02:04,420 --> 01:02:09,270
Like no matter what kind of sexually reproducing species you are, you have to make some

1044
01:02:09,270 --> 01:02:12,710
sort of decision about how much you invest in each of your gametes.

1045
01:02:12,790 --> 01:02:17,350
That's going to have some costs and benefits in terms of sort of the quality versus

1046
01:02:17,510 --> 01:02:19,270
quantity of gametes you can produce.

1047
01:02:19,510 --> 01:02:25,510
There's going to be some set of optima, given your you know the details of

1048
01:02:25,510 --> 01:02:26,230
your species.

1049
01:02:26,950 --> 01:02:29,110
But it's often going to be one or two optima.

1050
01:02:29,430 --> 01:02:31,270
That just seems to be what happens.

1051
01:02:31,670 --> 01:02:36,880
Um So what sexes are, like the sexes are those optimal trade-off points.

1052
01:02:37,120 --> 01:02:37,760
So got it.

1053
01:02:37,920 --> 01:02:38,000
Okay.

1054
01:02:38,080 --> 01:02:40,080
So the sex is the get is the gametes.

1055
01:02:40,240 --> 01:02:43,280
And what you're either bigger or small gametes, basically.

1056
01:02:43,600 --> 01:02:45,040
Can I can I say can I poke?

1057
01:02:45,360 --> 01:02:50,160
I think it's what if Dan, it's that it's a sex is a set of selection pressures

1058
01:02:50,800 --> 01:02:56,800
given uh certain stipulations about the world in this

1059
01:02:56,800 --> 01:03:00,770
particular case, largely being gamete trade-off.

1060
01:03:00,930 --> 01:03:05,810
So it's it's the it's sort of it's a set of selection pressures uh

1061
01:03:06,290 --> 01:03:12,210
driving phenotypic driving phenotypes but it's it's in it's those set selection

1062
01:03:12,210 --> 01:03:17,330
pressures specifically predicated upon gamete trade-offs

1063
01:03:18,050 --> 01:03:21,010
I'm a little I I think that's I think that's a lot closer.

1064
01:03:21,090 --> 01:03:22,210
I'm and that might be right.

1065
01:03:25,680 --> 01:03:30,400
I think I want to say it's more it's the design that selection is pushing toward, which

1066
01:03:30,400 --> 01:03:31,200
might be equivalent.

1067
01:03:31,920 --> 01:03:35,600
But it it's it's the the optima toward which selection is pushing.

1068
01:03:35,760 --> 01:03:36,560
Those are the sexes.

1069
01:03:36,640 --> 01:03:37,520
That's what I'm saying.

1070
01:03:37,600 --> 01:03:37,840
Yeah.

1071
01:03:38,000 --> 01:03:38,720
Yeah, yeah, yeah.

1072
01:03:38,800 --> 01:03:39,040
Yeah.

1073
01:03:39,760 --> 01:03:45,760
Yeah, that is very uh uh it's like physics where you it's not

1074
01:03:45,760 --> 01:03:46,560
intuitive.

1075
01:03:46,800 --> 01:03:51,830
We don't have an entry uh intuitive entry for an object in that kind of model, right?

1076
01:03:51,990 --> 01:03:53,110
Like that's great.

1077
01:03:53,190 --> 01:03:53,350
Yeah.

1078
01:03:53,670 --> 01:03:58,630
Sex is the set of adaptations that specialize in either large or small gametes.

1079
01:04:01,910 --> 01:04:03,590
I'm a little bit nervous.

1080
01:04:03,750 --> 01:04:07,430
I mean, maybe I'm just a little bit nervous to define it in terms of the adaptations

1081
01:04:07,670 --> 01:04:12,710
because, like, how do you then define what those adaptations are,

1082
01:04:13,420 --> 01:04:16,780
especially across species that do it in lots of different ways.

1083
01:04:17,420 --> 01:04:22,940
Um the uh the specifically the gamete uh is the is the adaptation.

1084
01:04:23,100 --> 01:04:23,900
So the argument.

1085
01:04:25,020 --> 01:04:25,260
Yeah.

1086
01:04:25,580 --> 01:04:30,060
Or would it be the argument that there's evidence of special design that those phenotypic

1087
01:04:30,060 --> 01:04:34,940
features are the results of the gamete associated selection pressures, which is maybe just

1088
01:04:34,940 --> 01:04:36,700
a way of redescribing what you're saying, Dan?

1089
01:04:38,780 --> 01:04:40,370
Um it could be.

1090
01:04:40,530 --> 01:04:44,610
And then I think it gets really what is sex becomes super contentious.

1091
01:04:44,770 --> 01:04:50,530
Um it becomes like super contingent on like states of evidence for like special design for

1092
01:04:50,530 --> 01:04:51,250
certain things.

1093
01:04:51,490 --> 01:04:56,450
Um are you thinking of it just the simplest possible way as just big or small gamutes?

1094
01:04:58,450 --> 01:04:59,410
Um kind of.

1095
01:04:59,730 --> 01:05:02,930
I mean, I I I do want to be open to the possibility that it's not just big or small.

1096
01:05:03,010 --> 01:05:07,100
Like I do think it's it seems to be very often those are the kind of like most stable

1097
01:05:07,100 --> 01:05:07,420
optima.

1098
01:05:07,660 --> 01:05:10,940
And certainly there are isogamous species where it's like all one kind.

1099
01:05:11,100 --> 01:05:14,940
But in principle, like we can imagine we go to like an alien planet, and for whatever

1100
01:05:14,940 --> 01:05:19,500
reason, you know, the optima are like three there's three gamete sizes that kind of work

1101
01:05:19,500 --> 01:05:21,260
out, or four gamete sizes that kind of work out.

1102
01:05:21,340 --> 01:05:21,900
I don't know.

1103
01:05:22,060 --> 01:05:26,700
Um or maybe cheap, cheap or expensive gametes.

1104
01:05:27,820 --> 01:05:31,260
I I you know, I think that's how it seems to often work.

1105
01:05:31,580 --> 01:05:36,950
Um but that's why I'm trying to like push it out to like it's not a particular sex is not a

1106
01:05:36,950 --> 01:05:38,230
particular manifestation, right?

1107
01:05:38,310 --> 01:05:43,830
Like what sex is is just like whatever are the optima for this particular species, right?

1108
01:05:43,990 --> 01:05:49,350
Like and very often the optima are yeah, cheap or expensive or big or small.

1109
01:05:49,590 --> 01:05:53,350
Um, but in principle it could be something else.

1110
01:05:53,720 --> 01:05:57,720
So it's the equilibrium for selection on gamete size.

1111
01:05:57,960 --> 01:05:58,360
Exactly.

1112
01:05:58,520 --> 01:05:58,840
Exactly.

1113
01:05:59,400 --> 01:06:01,480
So if it was a yeah, so yeah, that's great.

1114
01:06:01,560 --> 01:06:07,320
But so if there was a species that was produced big uh gametes or small

1115
01:06:07,320 --> 01:06:11,320
gametes facultatively or every other day of the week, it would be one sex.

1116
01:06:12,840 --> 01:06:18,770
Because that's the universal um you're talking about like a

1117
01:06:18,770 --> 01:06:20,450
sequentially hermaphroditic species.

1118
01:06:20,610 --> 01:06:22,130
I think I would say there's two sexes there.

1119
01:06:22,210 --> 01:06:25,250
It's just you like individuals alternate between the sexes.

1120
01:06:25,490 --> 01:06:28,290
But that's like that's maybe really getting into semantics.

1121
01:06:28,450 --> 01:06:29,090
Like I don't know.

1122
01:06:29,490 --> 01:06:33,970
I should have I we added out my last comment because David had a nice wrap to wrap that up

1123
01:06:33,970 --> 01:06:34,130
nicely.

1124
01:06:34,370 --> 01:06:35,170
David, can you repeat that?

1125
01:06:35,250 --> 01:06:35,730
What was that again?

1126
01:06:35,970 --> 01:06:36,370
Sure, sure.

1127
01:06:36,770 --> 01:06:42,610
I believe um how I framed it's the it's the equilibrium uh of

1128
01:06:42,610 --> 01:06:45,090
the selection pressures on gamete size.

1129
01:06:45,940 --> 01:06:46,500
Yeah.

1130
01:06:46,660 --> 01:06:46,980
Yeah.

1131
01:06:47,140 --> 01:06:47,380
Yeah.

1132
01:06:47,540 --> 01:06:50,660
It's there's that is specific to the equilibrium.

1133
01:06:50,980 --> 01:06:51,220
Yeah.

1134
01:06:51,300 --> 01:06:54,020
It is the speci yes that is specific to a species.

1135
01:06:54,340 --> 01:06:54,580
Great.

1136
01:06:54,740 --> 01:06:56,260
Put that on a hallmark card.

1137
01:06:56,980 --> 01:06:57,220
Okay.

1138
01:06:57,300 --> 01:06:58,980
So now we've figured out sex.

1139
01:06:59,380 --> 01:07:00,020
Okay, hold on.

1140
01:07:00,100 --> 01:07:04,340
I I think there's one more because what I was thinking, so there's also sex roles, which is

1141
01:07:04,420 --> 01:07:06,100
I think different from gender.

1142
01:07:06,310 --> 01:07:11,110
So what I was thinking of in terms of you know potential reproductive rate, uh, you know,

1143
01:07:11,190 --> 01:07:16,870
uh parental investment, stuff like that, that tends to follow from uh gamet

1144
01:07:17,190 --> 01:07:20,390
size, but doesn't always, as in the case of the fowler opes.

1145
01:07:20,790 --> 01:07:25,430
Um and so you have sex, which is the equilibrium selection pressure on gamete size, and

1146
01:07:25,430 --> 01:07:30,020
then you have the sex roles, which tend to follow from that equilibrium, but do not always

1147
01:07:30,020 --> 01:07:30,420
follow.

1148
01:07:30,660 --> 01:07:35,060
And and sex roles would be something like parental investment, intersexual competition,

1149
01:07:35,220 --> 01:07:36,020
stuff like that.

1150
01:07:36,660 --> 01:07:37,380
I think that's right.

1151
01:07:37,460 --> 01:07:37,700
Yeah.

1152
01:07:37,940 --> 01:07:38,100
Okay.

1153
01:07:38,340 --> 01:07:39,780
And I I think they do follow.

1154
01:07:39,940 --> 01:07:45,860
It's just um it's not a one-to-one mapping of like gamete size to a particular role.

1155
01:07:46,020 --> 01:07:51,330
It's just gamete size dictates it's very likely that there will be some kind of sex role.

1156
01:07:51,410 --> 01:07:56,930
And gamete size dictates is one force that dictates what the sex roles will will look like.

1157
01:07:57,250 --> 01:07:57,890
Got it.

1158
01:07:58,130 --> 01:07:58,450
Okay.

1159
01:07:58,530 --> 01:07:59,410
So we got sex.

1160
01:07:59,490 --> 01:08:00,530
We got sex role.

1161
01:08:00,690 --> 01:08:02,930
Now let's move on to gender.

1162
01:08:03,410 --> 01:08:03,730
Yeah.

1163
01:08:03,810 --> 01:08:06,290
So why what is gender and why do we have it?

1164
01:08:06,450 --> 01:08:10,610
This is like so that whole part, the whole first part is just like recapitulating what

1165
01:08:10,610 --> 01:08:12,930
evolutionary biologists have already said for the most part.

1166
01:08:13,090 --> 01:08:17,250
Maybe that equilibrium thing is like that's my like tinfoil hat addition.

1167
01:08:19,180 --> 01:08:22,860
But gender, I think, is something that has been way under theorized.

1168
01:08:23,180 --> 01:08:25,820
In part because it turns out it's like a really new concept.

1169
01:08:26,220 --> 01:08:27,660
I was not really aware of this.

1170
01:08:27,820 --> 01:08:33,740
Like people, the like the first reference to gender as a concept separate

1171
01:08:33,740 --> 01:08:38,700
from sex was from a like a psychiatry paper in like 1955 or something like that.

1172
01:08:38,860 --> 01:08:43,340
Like people have not really been thinking about these, at least in technical ways, people

1173
01:08:43,340 --> 01:08:46,620
have not been thinking about these as separate concepts for all that long.

1174
01:08:47,330 --> 01:08:51,810
And, you know, like I said, the definitions are really vague and I think unhelpful.

1175
01:08:53,010 --> 01:08:58,610
So the definition I've been trying to sketch out for gender is like what gender is, is

1176
01:08:58,850 --> 01:09:04,850
a sort of set of folk theories about how sex manifests in

1177
01:09:04,850 --> 01:09:06,450
your particular ecology.

1178
01:09:07,490 --> 01:09:10,780
So why do we need these folk theories?

1179
01:09:11,420 --> 01:09:15,980
Why can't we just know, like, if sex leads to sex roles, why can't we just be like born

1180
01:09:16,140 --> 01:09:18,300
knowing, like, okay, these are human sex roles?

1181
01:09:18,540 --> 01:09:21,260
This is my sex, I'm in an activist role.

1182
01:09:21,500 --> 01:09:27,100
Um, I think it has something to do with like our species' unique

1183
01:09:27,260 --> 01:09:29,820
distribution and foraging niche, right?

1184
01:09:29,980 --> 01:09:35,830
Like, so we are really widely dispersed hyper generalist

1185
01:09:35,830 --> 01:09:36,790
foragers, right?

1186
01:09:36,870 --> 01:09:42,790
Like humans live in all you know inhabitable ecologies on this

1187
01:09:42,790 --> 01:09:43,990
planet plus Canada.

1188
01:09:44,550 --> 01:09:45,270
And New Jersey.

1189
01:09:45,350 --> 01:09:46,150
And New Jersey.

1190
01:09:46,470 --> 01:09:47,350
And New Jersey.

1191
01:09:47,910 --> 01:09:53,670
And we, you know, wherever we are born, wherever we migrate, we just like figure

1192
01:09:53,670 --> 01:09:57,670
out how to extract food from that ecology.

1193
01:09:58,870 --> 01:10:04,000
And we tend to specialize in the things that are hardest to extract, right?

1194
01:10:04,080 --> 01:10:09,680
We tend to specialize in things like, you know, big game, which is very, you know, calorie

1195
01:10:09,680 --> 01:10:13,120
and nutrient dense, but really doesn't want to provide those calories and nutrients.

1196
01:10:13,200 --> 01:10:16,240
And so it's uh very difficult to extract.

1197
01:10:16,480 --> 01:10:20,640
Or we, you know, rely on these like difficult to extract gathered goods, like you know,

1198
01:10:20,720 --> 01:10:26,630
like tubers and honey and stuff like that, that requires like great skill and technology

1199
01:10:26,630 --> 01:10:30,790
and time to extract, and also potentially great skill, technology, and time to process into

1200
01:10:30,790 --> 01:10:31,670
something that we can eat.

1201
01:10:31,750 --> 01:10:37,430
But in exchange, you know, we get these like huge energy packages that you know

1202
01:10:37,590 --> 01:10:41,110
both require but also feed this like big expensive brain that we have, right?

1203
01:10:41,270 --> 01:10:43,910
So this is all like you know, known.

1204
01:10:44,070 --> 01:10:48,120
I'm like standing on the shoulders of like anthropological giants here.

1205
01:10:48,200 --> 01:10:52,760
Like this is all very much inspired by like you know, Kaplan and Lancaster and Gillener

1206
01:10:52,920 --> 01:10:54,040
Toto and stuff like that.

1207
01:10:54,280 --> 01:11:00,280
Um so I think we have this unique foraging niche where you know if you're a gene

1208
01:11:00,280 --> 01:11:03,880
inside of a human infant body, right?

1209
01:11:04,120 --> 01:11:07,560
Like your organism opens its eyes for the first time.

1210
01:11:07,720 --> 01:11:13,430
It doesn't know where on this planet it's been born, It doesn't know like what kind of

1211
01:11:13,590 --> 01:11:17,990
like, you know, how do my people make their way in this world, right?

1212
01:11:18,070 --> 01:11:20,550
Like what kind of food are we primarily fishers?

1213
01:11:20,710 --> 01:11:22,630
Are we primarily large game hunters?

1214
01:11:22,950 --> 01:11:25,830
Are we, you know, industrialized Americans?

1215
01:11:25,990 --> 01:11:28,230
Are we primarily eat like processed fast food?

1216
01:11:28,390 --> 01:11:30,070
Like, where does my food come from?

1217
01:11:30,230 --> 01:11:31,350
I think is a major question.

1218
01:11:31,510 --> 01:11:35,270
And I think that's a major question because it that shapes all kinds of other aspects of

1219
01:11:35,270 --> 01:11:35,910
life, right?

1220
01:11:35,990 --> 01:11:41,800
Like how you subsist in the ecology where you've been born, like that shapes how you

1221
01:11:41,800 --> 01:11:42,680
spend your time, right?

1222
01:11:42,760 --> 01:11:45,560
It shapes what technologies you invest time learning into.

1223
01:11:45,800 --> 01:11:51,080
Um, it invests like what skills do you uh try to build up over a lifetime?

1224
01:11:51,240 --> 01:11:53,400
It shapes, you know, how do you live?

1225
01:11:53,480 --> 01:11:54,600
Like, are you sedentary?

1226
01:11:54,680 --> 01:11:55,640
Are you very mobile?

1227
01:11:55,800 --> 01:11:57,560
What kinds of relationships do you have with others?

1228
01:11:57,720 --> 01:12:03,630
Do you have like you know, risk pool sharing, or do you have like uh just like you

1229
01:12:03,630 --> 01:12:05,390
know individual reciprocity?

1230
01:12:05,550 --> 01:12:10,590
Like all of these things are shaped by the ecology that you have been born into, heavily by

1231
01:12:10,590 --> 01:12:14,430
like the ways that that ecology you know affords you getting your food.

1232
01:12:14,670 --> 01:12:19,150
Um and so humans have to be able to learn how to do all those things.

1233
01:12:19,230 --> 01:12:23,390
They have to be able to learn like what do I eat in this ecology and how does that shape uh

1234
01:12:23,390 --> 01:12:24,270
all the rest of it?

1235
01:12:24,430 --> 01:12:29,480
Uh from the Martin Daly interview you guys did, uh He was talking about like one of his

1236
01:12:29,480 --> 01:12:34,280
first uh postdocs was in a lab that was that was like a socioecology lab.

1237
01:12:34,760 --> 01:12:39,480
Um which I don't think I'd actually heard the name of that discipline before, but I looked

1238
01:12:39,480 --> 01:12:44,120
it up and it's like you know the study of how like ecology shapes you know social

1239
01:12:44,120 --> 01:12:44,440
structures.

1240
01:12:44,520 --> 01:12:45,960
And I thought like that's perfect.

1241
01:12:46,040 --> 01:12:52,030
Like I think like because of our unique foraging need niche, Like humans basically have to

1242
01:12:52,030 --> 01:12:54,270
be like intuitive socioecologists, right?

1243
01:12:54,350 --> 01:12:57,470
You need to be able to like look around in the place you've been born and ask yourself

1244
01:12:57,550 --> 01:13:01,950
like, what's my ecology and how does that shape how people live their lives here?

1245
01:13:02,110 --> 01:13:05,150
Um, I think that's just a question that humans need to ask.

1246
01:13:05,470 --> 01:13:08,430
Um go ahead.

1247
01:13:08,670 --> 01:13:10,910
So just uh sorry, it's an interesting thought you just had.

1248
01:13:10,990 --> 01:13:16,830
Um so socioecology tends to study the relationships between ecology and social

1249
01:13:16,830 --> 01:13:19,390
structure that emerge over evolutionary time.

1250
01:13:19,640 --> 01:13:25,480
But what you're saying is that um those relationships between ecology and social structure

1251
01:13:25,640 --> 01:13:31,400
um in humans uh emerge over the span of uh you know a few generations rather than

1252
01:13:31,400 --> 01:13:35,720
evolutionary time because humans learn how to adjust their social structure to their

1253
01:13:35,720 --> 01:13:36,600
ecology.

1254
01:13:36,760 --> 01:13:41,480
Um so it it's a similar discipline in that it's still mapping ecology to social structure,

1255
01:13:41,560 --> 01:13:45,880
but the mapping arises over ontogenetic time as opposed to evolutionary time.

1256
01:13:46,280 --> 01:13:47,800
Yeah, yeah, that's exactly right.

1257
01:13:47,880 --> 01:13:48,120
Yeah.

1258
01:13:48,290 --> 01:13:52,370
And I, you know, I think like this is what, you know, forager studies is all about.

1259
01:13:52,450 --> 01:13:54,850
This is so much of what like cultural evolution is all about.

1260
01:13:55,010 --> 01:13:58,690
Uh, I think like these things are well established and it's like good literature to draw in

1261
01:13:58,690 --> 01:14:03,730
there that like, yeah, like the ways in which people tend to live their lives are heavily

1262
01:14:03,730 --> 01:14:07,810
structured by in large part the ways that they get their food, which is structured by the

1263
01:14:07,890 --> 01:14:09,490
like the apologies they happen to live in.

1264
01:14:09,640 --> 01:14:13,960
And there are, you know, I think the adaptations, the psychological adaptations underlying

1265
01:14:13,960 --> 01:14:17,240
these things, this is where evolutionary psychologists could do better, like mapping out

1266
01:14:17,240 --> 01:14:18,680
how those adaptations actually work.

1267
01:14:18,840 --> 01:14:24,120
But there are clearly adaptations for like, you know, learning how to produce uh you know

1268
01:14:24,360 --> 01:14:28,840
technologies that are going to be useful in your particular ecology, or you know, learning

1269
01:14:28,840 --> 01:14:33,080
how to flexibly deploy uh you know different kinds of foraging strategies or different

1270
01:14:33,080 --> 01:14:37,080
kinds of cooperation strategies depending on you know the structure of the environment you

1271
01:14:37,080 --> 01:14:38,280
happen to live in.

1272
01:14:39,010 --> 01:14:43,490
So I think, you know, the variations in our ecologies generate this variation.

1273
01:14:43,650 --> 01:14:45,090
So now there's an adaptive problem.

1274
01:14:45,170 --> 01:14:50,690
If you're a human, you need to figure out like, what is my, you know, what does life look

1275
01:14:50,690 --> 01:14:52,930
like in my in my population?

1276
01:14:53,170 --> 01:14:56,210
This is something you can maybe help me like workshop this, because I've been trying to

1277
01:14:56,210 --> 01:14:57,330
figure out a way to describe this.

1278
01:14:57,410 --> 01:15:01,600
Because I feel like as I describe this to people, what they take away is like, oh, so

1279
01:15:01,600 --> 01:15:03,760
you're saying gender is about learning how to forage.

1280
01:15:03,920 --> 01:15:05,600
That's not really the argument I'm trying to make.

1281
01:15:05,680 --> 01:15:10,160
What I'm trying the argument I'm trying to make is variability in foraging generates

1282
01:15:10,240 --> 01:15:12,880
variability in all these other aspects of life.

1283
01:15:13,760 --> 01:15:14,960
That's what you need to learn.

1284
01:15:15,120 --> 01:15:20,400
Like you need to learn how to live your life given you know the way that uh foraging

1285
01:15:20,400 --> 01:15:21,200
generates variation.

1286
01:15:21,360 --> 01:15:25,990
So I'm kind of been trying to think of a name for like what's that set of things that like

1287
01:15:26,230 --> 01:15:29,430
vary potentially as a function of like your ecology or foraging.

1288
01:15:29,590 --> 01:15:34,390
I'm currently thinking like life way as sort of like an homage to like Kelly's foraging

1289
01:15:34,390 --> 01:15:36,470
spectrum, the life way of hunter gatherers.

1290
01:15:37,110 --> 01:15:42,150
Like as a baby human, like a pressing problem for you is like you need to learn like what

1291
01:15:42,150 --> 01:15:44,710
is the life way for people like me?

1292
01:15:44,870 --> 01:15:46,470
Like what foods do I eat?

1293
01:15:46,630 --> 01:15:48,340
What technologies do I need to master?

1294
01:15:48,420 --> 01:15:49,620
What skills do I need to master?

1295
01:15:49,780 --> 01:15:54,660
How do I need to relate to other people so that I can you know successfully capture energy

1296
01:15:54,660 --> 01:15:57,300
from this environment and turn it into offspring, basically?

1297
01:15:58,180 --> 01:16:03,540
Dan, can I ask uh this is uh uh a question that's also though maybe uh

1298
01:16:04,020 --> 01:16:06,500
helping maybe in that articulation.

1299
01:16:06,660 --> 01:16:11,780
Um is one way of putting it, because David, I always think about David's notion of status

1300
01:16:11,780 --> 01:16:17,490
games, is one way of thinking about it is that the status games that are in a

1301
01:16:17,490 --> 01:16:23,090
community are not arbitrary, but they're sort of tethered

1302
01:16:24,050 --> 01:16:30,050
to um resources and therefore the ecology.

1303
01:16:30,610 --> 01:16:36,290
So that so that's that that's another way I think of putting the same thing, right?

1304
01:16:36,450 --> 01:16:40,930
Like that's sort of there's this tethering between the status games that can exist, what

1305
01:16:40,930 --> 01:16:43,890
counts as being good or bad, or what the games are.

1306
01:16:44,450 --> 01:16:50,400
There can be some proliferation, some elaboration, but ultimately there's some feedback

1307
01:16:50,400 --> 01:16:56,080
from the ecology and the resources because ultimately that's really where the rubber hits

1308
01:16:56,080 --> 01:16:56,880
the road.

1309
01:16:57,280 --> 01:16:58,320
I think that's exactly right.

1310
01:16:58,480 --> 01:16:58,720
Yeah.

1311
01:16:58,800 --> 01:17:03,600
And I think importantly, you know, we could be the kind of species, we could be multiple

1312
01:17:03,600 --> 01:17:04,640
different species, right?

1313
01:17:04,800 --> 01:17:07,280
And I think you know that's what racists think, right?

1314
01:17:07,600 --> 01:17:12,270
Like we could do like there's an African morph and there's an Asian morph, and like, you

1315
01:17:12,270 --> 01:17:17,230
know, uh, it could be that like you're just bored knowing, like, oh, I'm Asian, therefore,

1316
01:17:17,310 --> 01:17:20,750
I eat these kinds of things, therefore, these are the sort of status games, but that's just

1317
01:17:20,750 --> 01:17:22,190
not how human genetics works, right?

1318
01:17:22,270 --> 01:17:24,830
Like, there's huge gene flow across human populations.

1319
01:17:25,070 --> 01:17:29,310
And so there is this variation that's tethered to ecology, but it's also importantly,

1320
01:17:29,550 --> 01:17:33,070
because there's constant interaction between different human populations, there's constant

1321
01:17:33,070 --> 01:17:37,630
gene flow across different populations, the that variation is unpredictable from the genes

1322
01:17:37,790 --> 01:17:38,270
I view.

1323
01:17:38,420 --> 01:17:40,260
It's again, I'm going back to Dawkins.

1324
01:17:40,420 --> 01:17:43,940
It's like it's the Abra Andromeda problem that he talks about in Sylvis Gene, right?

1325
01:17:44,020 --> 01:17:48,820
Like the genes need to build one psychology that can work in any kind of ecology.

1326
01:17:48,980 --> 01:17:51,300
It doesn't know what that ecology is going to look like in advance.

1327
01:17:51,380 --> 01:17:56,340
And so it needs to build a learning program that can like look around in its ecology and

1328
01:17:56,340 --> 01:17:58,660
say, well, this is how my people seem to live.

1329
01:17:58,820 --> 01:18:01,460
So I'm gonna do the same.

1330
01:18:02,070 --> 01:18:03,030
Yeah, that's amazing.

1331
01:18:03,190 --> 01:18:07,990
Yeah, and that really suggests like a very rich um, this is very much about the develop

1332
01:18:08,230 --> 01:18:09,030
development then.

1333
01:18:09,110 --> 01:18:14,470
And it in and it's um and it really suggests like a very rich, unexplored

1334
01:18:14,470 --> 01:18:20,310
psychology of resources, uh, ways of making a living,

1335
01:18:20,550 --> 01:18:21,190
status.

1336
01:18:22,150 --> 01:18:25,830
And these are not the right words because we don't have the the words in our natural

1337
01:18:25,830 --> 01:18:30,480
language, but it it suggests that um, and I think some developmental psychologists have

1338
01:18:30,480 --> 01:18:31,760
studied these kinds of things.

1339
01:18:31,920 --> 01:18:36,880
But yeah, but I think um this is amazing because yeah, it sounds like it's really opening

1340
01:18:36,880 --> 01:18:40,320
up this idea of yeah, it's intuitive.

1341
01:18:40,400 --> 01:18:43,520
I I hear what you're saying, that it you're sort of looking at like what's the way of

1342
01:18:43,520 --> 01:18:45,600
making a living, broadly speaking.

1343
01:18:45,760 --> 01:18:49,920
Um, but fleshing that out in a way that you could implement it in a little critter, uh

1344
01:18:50,080 --> 01:18:52,720
software critter, is uh that's cool.

1345
01:18:52,880 --> 01:18:55,010
That yeah, that's that's a hard, yeah.

1346
01:18:55,170 --> 01:18:57,090
But a good it's fruitful.

1347
01:18:57,170 --> 01:18:59,970
Like you I can I can start imagining ways of doing that.

1348
01:19:00,050 --> 01:19:00,450
Yeah.

1349
01:19:00,850 --> 01:19:02,450
Yeah, I wouldn't say it's unexplored.

1350
01:19:02,530 --> 01:19:05,490
Like I feel like developmental psychologists have been studying this and anthropologists

1351
01:19:05,490 --> 01:19:11,090
have been studying this for a long time, but maybe just not organized as like this is the

1352
01:19:11,090 --> 01:19:15,330
sort of unified, this is the elephant that we're all trying to describe, you know.

1353
01:19:15,490 --> 01:19:15,810
Right.

1354
01:19:16,050 --> 01:19:18,880
Um so I mean, where does gender come from in this though?

1355
01:19:18,960 --> 01:19:21,920
Like I'm just talking about like, you know, learning life wave.

1356
01:19:22,160 --> 01:19:28,000
I think um because of sex, because sex does shape phenotypes in ways that we've

1357
01:19:28,000 --> 01:19:32,160
already talked about, uh, those phenotypes that follow from sex are going to have

1358
01:19:32,160 --> 01:19:33,840
implications for that life way, right?

1359
01:19:33,920 --> 01:19:36,560
And we know this from lots of anthropological research, right?

1360
01:19:36,640 --> 01:19:42,470
Like one major aspect of this is like some individuals have high levels of obligate

1361
01:19:42,470 --> 01:19:47,190
childcare in the form of internal gestation and uh and lactation.

1362
01:19:47,910 --> 01:19:53,350
That's going to make certain forms of foraging more difficult in some ecologies, right?

1363
01:19:53,430 --> 01:19:59,350
It's like difficult and dangerous to large game hunting in some ecologies if you have

1364
01:19:59,350 --> 01:20:01,910
to breastfeed an infant on demand.

1365
01:20:02,800 --> 01:20:07,520
And, you know, Kaplan and Lancaster and colleagues, they have like these really nice models

1366
01:20:07,520 --> 01:20:12,880
that show like because large game hunting is so skill intensive, like and because of the

1367
01:20:12,880 --> 01:20:18,640
amount of time that like, you know, a sort of like natural fertility uh

1368
01:20:18,640 --> 01:20:24,160
woman would spend uh, you know, pregnant or breastfeeding, um, like

1369
01:20:24,240 --> 01:20:29,990
it's just not worth the investment to try to build up the skills to do

1370
01:20:29,990 --> 01:20:30,870
large game hunting.

1371
01:20:31,030 --> 01:20:35,670
Like it's it's a much better use of your time to focus on other resources uh than it is to

1372
01:20:35,670 --> 01:20:37,750
try to uh invest in those skills.

1373
01:20:37,910 --> 01:20:42,310
And so they argue like this is why you get sexual divisions of labor that you see across,

1374
01:20:42,390 --> 01:20:43,910
you know, many foraging societies.

1375
01:20:43,990 --> 01:20:49,700
Like it's it's just a more efficient arrangement for everybody if you males

1376
01:20:49,700 --> 01:20:54,020
focus on these activities that are not very compatible with childcare, because they have

1377
01:20:54,020 --> 01:20:57,940
lots of time to build up the skills to make that a worthwhile investment.

1378
01:20:58,260 --> 01:21:03,140
And females invest more in other activities

1379
01:21:04,980 --> 01:21:07,380
that are more compatible with child care demands.

1380
01:21:10,670 --> 01:21:16,430
sex is going to shape optimal lifeways, but it's going to do so in unpredictable ways

1381
01:21:16,430 --> 01:21:17,390
across ecologies, right?

1382
01:21:17,470 --> 01:21:19,550
In some ecologies, maybe sex won't matter that much, right?

1383
01:21:19,710 --> 01:21:25,310
Like if you're primarily a fishing population, like anybody can fish a lot of the time.

1384
01:21:25,710 --> 01:21:28,270
You know, it depends on the details of your ecology, is the point.

1385
01:21:28,350 --> 01:21:34,190
So it's very likely if you're, you know, uh an infant human, it's very likely that you live

1386
01:21:34,190 --> 01:21:38,590
in a place where there's sex differences in your this kind of like life way.

1387
01:21:38,720 --> 01:21:42,080
uh, but the nature of those sex differences is going to be opaque to you.

1388
01:21:42,160 --> 01:21:47,920
Uh, so part of learning this life way is my argument is like probably a more efficient way

1389
01:21:47,920 --> 01:21:51,920
of learning this life way is if you do have this kind of conceptual slot already.

1390
01:21:52,000 --> 01:21:57,120
If you're born with an expectation that there are probably different lifeways based on your

1391
01:21:57,120 --> 01:22:02,050
gamete production strategy, um and you can learn different lifeways from different models,

1392
01:22:02,130 --> 01:22:04,770
then you can deploy a life way that like works best for you.

1393
01:22:04,930 --> 01:22:10,530
And so I think what gender is is like your models of like what is the life way in my

1394
01:22:10,530 --> 01:22:14,050
particular ecology separated by you know sex.

1395
01:22:16,850 --> 01:22:17,250
Awesome.

1396
01:22:17,490 --> 01:22:18,290
And we can model this.

1397
01:22:18,610 --> 01:22:19,090
Yeah, yeah.

1398
01:22:19,170 --> 01:22:19,730
Interesting.

1399
01:22:19,890 --> 01:22:25,500
Um think presumably there's also um a a psychological

1400
01:22:25,500 --> 01:22:29,500
dimension to the downstream consequences of of gamete size.

1401
01:22:29,580 --> 01:22:33,340
You you mentioned that, you know, among males, they might accept a a lower survival

1402
01:22:33,340 --> 01:22:37,500
probability in exchange for greater reproductive uh uh potential.

1403
01:22:37,660 --> 01:22:42,140
Um and so that might manifest at the level of psychology with greater appetite for

1404
01:22:42,140 --> 01:22:45,980
risk-taking and dangerous activities among men, that they're willing to trade off some

1405
01:22:45,980 --> 01:22:51,150
survival risk for a large reproductive payoff potentially in status or whatever.

1406
01:22:51,870 --> 01:22:55,470
And so that could be another variable that affects these life ways.

1407
01:22:55,630 --> 01:23:00,510
And so it's not merely that men are, you know, don't have the physical gestational costs

1408
01:23:00,510 --> 01:23:02,830
that allow them to hunt big game.

1409
01:23:02,990 --> 01:23:06,830
It might also be that they just have more of a tolerance for taking dangerous risks.

1410
01:23:06,910 --> 01:23:12,190
And women just are not as enticed by the opportunity of hunting a dangerous animal as a man

1411
01:23:12,190 --> 01:23:12,590
would be.

1412
01:23:12,670 --> 01:23:18,360
And so there are psychological Predispositions that cause certain cultures to

1413
01:23:18,360 --> 01:23:23,000
develop lifeways as well as morphological.

1414
01:23:23,640 --> 01:23:24,440
I think that's right.

1415
01:23:26,680 --> 01:23:31,400
So certainly I don't take me to be arguing like selection stops the neck, right?

1416
01:23:31,480 --> 01:23:36,840
Like this isn't uh this is honestly I've sort of backed into social role theory in the

1417
01:23:36,840 --> 01:23:40,760
process of doing all this, but like this is totally different from social role theory.

1418
01:23:40,840 --> 01:23:44,990
But I do think sex does structure psychological adaptations.

1419
01:23:45,150 --> 01:23:48,670
And those psychological adaptations, in addition to the physiological adaptations, are

1420
01:23:48,670 --> 01:23:52,110
going to be part of what shapes sex differences in these kinds of lifeways.

1421
01:23:52,270 --> 01:23:56,750
So I'm focusing on like foraging and like gestation just because I think those are like

1422
01:23:56,750 --> 01:23:57,630
really clear.

1423
01:23:57,790 --> 01:24:03,550
Like we definitely know those are uncontroversially exist and uncontroversially shape these

1424
01:24:03,550 --> 01:24:06,910
aspects of lifeways that have important consequences for other things.

1425
01:24:07,070 --> 01:24:11,940
But yeah, like I'm confident that there are psychological adaptations that follow from sex

1426
01:24:11,940 --> 01:24:14,340
that also play into these kinds of processes as well.

1427
01:24:14,660 --> 01:24:14,900
Right.

1428
01:24:15,140 --> 01:24:17,300
So in the Oh, sorry, Dan, go ahead.

1429
01:24:17,540 --> 01:24:17,700
Go ahead.

1430
01:24:18,260 --> 01:24:22,580
I was just gonna say, so like from a learning perspective, I for me it's helpful to think

1431
01:24:22,580 --> 01:24:24,420
about the ways to get a robot wrong.

1432
01:24:24,660 --> 01:24:30,660
So it'd be something where, like, if you built um a learning robot that was like,

1433
01:24:30,740 --> 01:24:36,350
you know, I'm a baby, look around at the other babies, and um figure out the baby that like

1434
01:24:36,430 --> 01:24:37,870
gets the most attention.

1435
01:24:37,950 --> 01:24:39,870
And that's like what I want to grow up to be.

1436
01:24:40,030 --> 01:24:41,550
Like that's a bad design, right?

1437
01:24:41,630 --> 01:24:46,030
So you have to like build in that there's like life or like there's there's uh

1438
01:24:46,030 --> 01:24:47,390
developmental morphs.

1439
01:24:47,630 --> 01:24:52,510
And so part of you know, maybe I should compare myself to other babies too, for the

1440
01:24:52,510 --> 01:24:57,780
purposes of currently being a baby, but you can't design me to sort of aspire to be the

1441
01:24:57,780 --> 01:25:00,980
best baby 40 years from now that I can possibly be, right?

1442
01:25:01,060 --> 01:25:06,740
Like that's that's a that's the wrong sort of social learning target to be.

1443
01:25:06,820 --> 01:25:10,580
So you have to build somewhere de facto into the system like age morphs too.

1444
01:25:10,740 --> 01:25:16,180
And the argument is just like age morphs, then it's also sex morphs to the extent that

1445
01:25:16,180 --> 01:25:20,500
there are that there are differences in the ecology or in your psychology.

1446
01:25:20,580 --> 01:25:26,450
So that if you built a robot that didn't see sex or gender, you might sort of

1447
01:25:26,450 --> 01:25:30,050
mash things up and you in and it wouldn't work right.

1448
01:25:30,370 --> 01:25:31,730
Maybe I think that's exactly right.

1449
01:25:31,810 --> 01:25:35,010
So I mean it might be helpful to talk a little bit about like what the models look like

1450
01:25:35,010 --> 01:25:35,490
right now.

1451
01:25:36,450 --> 01:25:40,290
So you know the way this the way I've been doing this, you know, we it was take that

1452
01:25:40,290 --> 01:25:45,410
population of agents where we evolved like uh pair bonding and biparental care.

1453
01:25:45,860 --> 01:25:50,660
So now like these agents are forming into monogamous pairs.

1454
01:25:51,620 --> 01:25:56,260
And what I then expose the agents to is basically like they have like a household that they

1455
01:25:56,260 --> 01:25:56,820
need to manage.

1456
01:25:56,900 --> 01:26:02,340
Like they need to gather a certain amount of resources and they reproduce

1457
01:26:02,340 --> 01:26:06,740
together proportional to how successful they are at gathering those resources.

1458
01:26:06,980 --> 01:26:10,850
The resources they need to gather, I'm just focusing for simplicity on like three things.

1459
01:26:11,010 --> 01:26:15,410
They need to gather, like, there's two nutrients that, like, I'm calling them like nutrient

1460
01:26:15,410 --> 01:26:17,650
P and nutrient M for like plant and meat.

1461
01:26:17,810 --> 01:26:19,970
And then they need to do a certain amount of parenting.

1462
01:26:20,130 --> 01:26:24,850
And they have a fixed time budget that they can use to like gather those resources.

1463
01:26:25,010 --> 01:26:28,930
At the end of that time budget, they reproduce based on how successful they've been.

1464
01:26:29,280 --> 01:26:30,320
They can get those.

1465
01:26:30,400 --> 01:26:32,320
It's like the model's complicated.

1466
01:26:32,400 --> 01:26:33,840
I'm not going to be able to describe it today.

1467
01:26:34,080 --> 01:26:35,600
You can read the paper when it comes out.

1468
01:26:35,680 --> 01:26:40,800
Uh, but it's a Markov decision process where like there's a bunch of states the agents can

1469
01:26:40,800 --> 01:26:42,960
be in, there's a bunch of actions they can choose.

1470
01:26:43,120 --> 01:26:45,840
Those actions probabilistically generate those resources.

1471
01:26:46,000 --> 01:26:49,280
The important thing is like there's 11 actions they can take.

1472
01:26:49,440 --> 01:26:51,680
One of them is like parent your offspring.

1473
01:26:51,920 --> 01:26:56,000
The other 10 are just different food resources that the agents can gather.

1474
01:26:56,270 --> 01:27:00,990
Those food resources generate a certain amount of those of those nutrients that they need.

1475
01:27:01,070 --> 01:27:06,990
And so they need to learn like what's the right balance of parenting and gathering these

1476
01:27:06,990 --> 01:27:12,830
other food resources to like maximize my household's sort of store of resources.

1477
01:27:13,230 --> 01:27:17,470
And so we can create different environments for the agents by manipulating properties of

1478
01:27:17,470 --> 01:27:18,350
those food resources.

1479
01:27:18,430 --> 01:27:23,100
So we can make some of them like in this ecology, you know, meat number one is super

1480
01:27:23,100 --> 01:27:25,500
productive, meat number two is not very productive.

1481
01:27:25,660 --> 01:27:29,260
But in that ecology, it's meat number two that's super productive, and it's meat number one

1482
01:27:29,260 --> 01:27:30,140
that's not very productive.

1483
01:27:30,380 --> 01:27:32,460
We can manipulate like, are these dangerous?

1484
01:27:32,620 --> 01:27:36,620
Like is there some probability of injuring yourself if you pursue this resource versus that

1485
01:27:36,620 --> 01:27:37,100
resource?

1486
01:27:37,260 --> 01:27:40,060
Um we can manipulate like what's the success rate?

1487
01:27:40,220 --> 01:27:43,740
Like is this a really risky resource like large game hunting or is it a really stable

1488
01:27:43,740 --> 01:27:46,540
resource like you know like lots of gathered resources?

1489
01:27:46,850 --> 01:27:52,370
And then the critical one is we can manipulate is foraging this resource compatible with

1490
01:27:52,370 --> 01:27:53,170
childcare or not?

1491
01:27:53,410 --> 01:27:57,890
So in some ecologies, maybe all the resources are pretty compatible with childcare.

1492
01:27:57,970 --> 01:28:00,370
Like anybody can forage anything relatively easy.

1493
01:28:00,450 --> 01:28:03,890
And so you know the sex differences in life ways are going to be relatively small.

1494
01:28:04,050 --> 01:28:08,610
But in other ecologies, like maybe one of the most important resources you need is really

1495
01:28:08,610 --> 01:28:09,970
not compatible with childcare.

1496
01:28:10,130 --> 01:28:12,850
That would be like a sort of large game hunting kind of situation.

1497
01:28:13,090 --> 01:28:17,940
And in that case, like the optimal life way is for small gamete producers to focus on

1498
01:28:17,940 --> 01:28:21,860
foraging that resource and large gamete resource producers to specialize in producing other

1499
01:28:21,860 --> 01:28:24,420
resources, there should be a sexual division of labor.

1500
01:28:25,140 --> 01:28:30,660
So we like throw these agents into ecologies that vary unpredictably on these different

1501
01:28:30,660 --> 01:28:31,460
dimensions.

1502
01:28:32,020 --> 01:28:37,540
And then we give them different ways of evolving, sort of like I'm calling them foraging

1503
01:28:37,540 --> 01:28:37,940
strategies.

1504
01:28:38,100 --> 01:28:39,780
Maybe I should call them like life ways.

1505
01:28:40,570 --> 01:28:43,770
But basically there's like three kinds of agents in the first model.

1506
01:28:43,930 --> 01:28:47,050
There's ones that just have like a genetically encoded foraging strategy.

1507
01:28:47,130 --> 01:28:50,570
Like they're just born like, I like meat one, I don't like meat two.

1508
01:28:50,810 --> 01:28:53,130
And that doesn't change throughout their lifetime.

1509
01:28:53,930 --> 01:28:58,410
There's other agents that like have a psychology that does reinforcement learning.

1510
01:28:58,890 --> 01:29:04,650
They pay attention to how successful have I been at pursuing this resource in the past

1511
01:29:04,730 --> 01:29:07,290
and that adjusts how they behave in the future.

1512
01:29:07,700 --> 01:29:13,700
And what you show is what you can show what I find in the models is if the ecologies are

1513
01:29:13,700 --> 01:29:14,660
really predictable, right?

1514
01:29:14,740 --> 01:29:18,820
So like there's no gene flow, there's like different environments, but like offspring are

1515
01:29:18,820 --> 01:29:21,060
always born into the exact same environments as the parents.

1516
01:29:21,220 --> 01:29:22,980
The environments never change over time.

1517
01:29:23,460 --> 01:29:27,460
So this is sort of like the racist model of the world where there's just like, you know,

1518
01:29:27,700 --> 01:29:29,140
stably different kinds of humans.

1519
01:29:29,220 --> 01:29:31,700
You can know what sort of environment you're going to be born into.

1520
01:29:31,780 --> 01:29:36,290
And In that kind of environment, selection favors a genetically encoded foraging strategy.

1521
01:29:36,370 --> 01:29:41,650
Like it just makes sense to be born knowing like my people eat fish, I'm going to get ready

1522
01:29:41,650 --> 01:29:44,210
to forage for fish.

1523
01:29:44,610 --> 01:29:47,970
But if environments are unpredictable, if there's some amount of gene flow, like there's

1524
01:29:47,970 --> 01:29:50,930
some probability you might be born in a different environment than the ones that your

1525
01:29:50,930 --> 01:29:55,010
parents were born into, then selection no longer favors that genetically encoded strategy.

1526
01:29:55,090 --> 01:29:57,170
It favors a psychology of reinforcement learning.

1527
01:29:57,250 --> 01:30:02,900
It favors a psychology that tries to figure out um uh you know how to make a way

1528
01:30:03,140 --> 01:30:06,500
how to make a a living in the ecology where you happen to have been born.

1529
01:30:06,660 --> 01:30:12,340
And it specifically favors uh a psychology that engages in what's called what's called

1530
01:30:12,340 --> 01:30:14,180
model-based reinforcement learning.

1531
01:30:14,340 --> 01:30:20,020
Um so these agents, they not only pay attention to like, you know, how much reward did I

1532
01:30:20,020 --> 01:30:24,750
get when I tried that action last time, but they also slowly build up a model of how the

1533
01:30:24,750 --> 01:30:25,390
environment works.

1534
01:30:25,470 --> 01:30:29,630
So they can make guesses like if I do this action, this is what's going to happen next.

1535
01:30:29,790 --> 01:30:35,390
And so they can use that to like make even more accurate predictions about how to uh how to

1536
01:30:35,390 --> 01:30:35,790
behave.

1537
01:30:35,950 --> 01:30:41,390
And I think it's that model, uh, that model of how does my ecology work, that I think is

1538
01:30:41,390 --> 01:30:44,220
where gender really exists.

1539
01:30:44,700 --> 01:30:49,740
So that like that first model is just showing like you know, variability in foraging will

1540
01:30:49,740 --> 01:30:53,420
generate selection favoring this kind of learning psychology.

1541
01:30:53,820 --> 01:30:59,740
The next model is okay, we have if we set up environments

1542
01:30:59,740 --> 01:31:05,260
such that like they're random, but there's some probability that a sexual division of labor

1543
01:31:05,260 --> 01:31:10,190
is going to be optimal, like there's some important resource that is not compatible with

1544
01:31:10,190 --> 01:31:11,310
childcare.

1545
01:31:12,510 --> 01:31:17,230
We have a population of agents who all do reinforcement learning, but now we add in a new

1546
01:31:17,230 --> 01:31:20,750
mutation that regulates whether they do social learning, right?

1547
01:31:21,070 --> 01:31:24,510
So the original agents are just learning based on their own experience.

1548
01:31:24,670 --> 01:31:29,150
But I mean, exactly as you were saying, Dave, now we have agents who, when they're born,

1549
01:31:29,230 --> 01:31:33,950
they can look at the adults in their ecology, they can observe their behavior.

1550
01:31:34,140 --> 01:31:37,980
And instead of just learning based on their own behavior, what they can do is they start to

1551
01:31:37,980 --> 01:31:42,460
form a model of the environment based on observing adult behavior.

1552
01:31:42,620 --> 01:31:48,460
So they get to, they're sort of like they're getting a head start on learning their life

1553
01:31:48,460 --> 01:31:52,380
way because they're using the information that already exists from the adults in their

1554
01:31:52,380 --> 01:31:53,260
ecology.

1555
01:31:53,660 --> 01:31:55,340
And you can do that in one of two ways.

1556
01:31:55,420 --> 01:31:57,830
Like you can do that ignorant to sex.

1557
01:31:57,910 --> 01:32:01,590
So you can just say, I'm going to look at any adult, any adult behavior is useful.

1558
01:32:01,750 --> 01:32:05,830
I'm going to use that to train my model, the environment, and then I'm going to use that

1559
01:32:05,830 --> 01:32:07,750
model to guide my own behavior.

1560
01:32:08,070 --> 01:32:13,990
Or you can do it in a sort of sex-aware way where you can train different models

1561
01:32:13,990 --> 01:32:15,430
for adults of different sexes.

1562
01:32:15,510 --> 01:32:19,350
And then you can use whichever model is applicable to your body.

1563
01:32:19,510 --> 01:32:19,670
Right.

1564
01:32:19,880 --> 01:32:25,720
And what you see is as long as uh at least some ecologies have a

1565
01:32:25,720 --> 01:32:31,080
sexual division of labor, at least in some ecologies, there are some resources that are not

1566
01:32:31,080 --> 01:32:32,520
compatible with childcare.

1567
01:32:32,600 --> 01:32:38,520
Uh selection favors the learning agents that learn from adults in a

1568
01:32:38,520 --> 01:32:39,480
sex differentiated way.

1569
01:32:39,640 --> 01:32:43,640
Like they're born with an expectation that there's some kind of sexual division of labor

1570
01:32:43,720 --> 01:32:49,530
and it's my job to figure out like what is my specialty in this, in this economy,

1571
01:32:49,770 --> 01:32:50,090
right?

1572
01:32:50,810 --> 01:32:54,890
And so I think that is how we should think about the psychology of gender, right?

1573
01:32:54,970 --> 01:33:00,890
Like what gender is is your model of how sex

1574
01:33:00,970 --> 01:33:04,410
manifests in the particular ecology where you happen to have been born.

1575
01:33:06,420 --> 01:33:06,900
Cool.

1576
01:33:07,140 --> 01:33:11,460
And it also suggests that there's a representation of what you are.

1577
01:33:11,860 --> 01:33:17,300
That that that is uh almost by definition not the

1578
01:33:17,300 --> 01:33:19,380
adult version yet.

1579
01:33:19,620 --> 01:33:20,180
Right.

1580
01:33:20,740 --> 01:33:24,340
Yeah, I mean, so it implies a lot, it implies there's a representation of what you are,

1581
01:33:24,420 --> 01:33:27,300
which I think is what people often talk about as gender identity.

1582
01:33:27,910 --> 01:33:31,990
To your point earlier, David, there has to be a representation of what other people are,

1583
01:33:32,310 --> 01:33:34,790
which will involve probably some amount of signaling.

1584
01:33:34,870 --> 01:33:37,830
And I think that's what people often talk about as gender expression.

1585
01:33:38,150 --> 01:33:41,350
In the model, those are always perfectly aligned, but of course, in the real world, they're

1586
01:33:41,350 --> 01:33:41,910
not going to be.

1587
01:33:42,070 --> 01:33:46,550
There are going to be people who, you know, identify with a sex that is different from what

1588
01:33:46,550 --> 01:33:47,750
their physiology is.

1589
01:33:47,910 --> 01:33:50,710
And so I think that gives you a model of like how can you start to think about like

1590
01:33:50,710 --> 01:33:52,950
transgender identities, for example.

1591
01:33:54,520 --> 01:33:59,400
So I like, I think you get a lot of things that we think intuitively are involved in the

1592
01:33:59,400 --> 01:34:04,920
concepts of sex and gender out of this, as well as, you know, a model of like what is it

1593
01:34:04,920 --> 01:34:07,960
that kids are actually trying to learn and how are they trying to learn it?

1594
01:34:08,040 --> 01:34:08,200
Right.

1595
01:34:08,440 --> 01:34:12,280
And I, you know, it's like to your earlier point, like these ideas do exist out there,

1596
01:34:12,360 --> 01:34:12,520
right?

1597
01:34:12,600 --> 01:34:16,510
Like there's lots of work in developmental psychology on like gender essentialism.

1598
01:34:16,590 --> 01:34:20,430
Like kids, you know, essentialize gender as this category and they like attach all kinds of

1599
01:34:20,430 --> 01:34:21,070
ideas to it.

1600
01:34:21,230 --> 01:34:23,390
There's like developmental intergroup theory, right?

1601
01:34:23,470 --> 01:34:28,350
Which suggests that like kids learn from adult behaviors about like what categories there

1602
01:34:28,350 --> 01:34:28,830
are in the world.

1603
01:34:28,910 --> 01:34:34,670
So because adults treat gender as a category that matters, kids learn to do the

1604
01:34:34,670 --> 01:34:34,990
same.

1605
01:34:35,150 --> 01:34:39,470
And I think like it's it's similar to those in that like it's saying, yes, that it's right,

1606
01:34:39,550 --> 01:34:44,270
but it's not just like an arbitrary categorization that kids are like picking out of the

1607
01:34:44,270 --> 01:34:44,430
world.

1608
01:34:44,510 --> 01:34:50,280
Like, no, like kids for very good reason are born with an expectation that gender is

1609
01:34:50,280 --> 01:34:52,440
probably a thing that shapes lifeways.

1610
01:34:52,600 --> 01:34:57,880
And their job as kids is to figure out like in my particular ecology, how does how does sex

1611
01:34:57,880 --> 01:34:58,280
manifest?

1612
01:34:58,360 --> 01:35:02,680
Like what is the right way to be male in this place I happen to have been born?

1613
01:35:03,000 --> 01:35:07,470
So I think it like aligns with those things, but it's sort of like it connects them with

1614
01:35:07,470 --> 01:35:11,630
what we know about our species' evolutionary history and it gives us a computational model

1615
01:35:11,630 --> 01:35:13,470
of how that psychology is actually working.

1616
01:35:13,790 --> 01:35:18,910
Yeah, and and like for my own expertise in thinking about alliances or even race, right?

1617
01:35:18,990 --> 01:35:22,590
People always talked about like, well, there's categories that are inferentially rich, like

1618
01:35:22,590 --> 01:35:25,070
age, sex, race, slash, alliance.

1619
01:35:25,470 --> 01:35:31,360
But people always acknowledge that learning about alliances and or or race

1620
01:35:31,440 --> 01:35:36,880
or whatever arbitrary sets, as Jim Sidanius called it, was different than gender.

1621
01:35:37,360 --> 01:35:39,760
Um, but in but nobody there was no theory of that.

1622
01:35:39,840 --> 01:35:42,880
It was just sort of like we all have that intuition, that's what the data shows.

1623
01:35:43,040 --> 01:35:48,800
And I like that this gives teeth to that, which is it's the difference between there's a

1624
01:35:48,800 --> 01:35:54,510
difference between alliances and the which which

1625
01:35:54,830 --> 01:35:58,750
morphs or morphs or ways of making a living, to what degree are those different?

1626
01:35:58,910 --> 01:36:00,670
Those are different features of the world, right?

1627
01:36:00,830 --> 01:36:06,830
Like the particular alliances, um, and who's on whose side is different than this is

1628
01:36:06,830 --> 01:36:11,470
the way you kind of make a living given the the nature of the ecology or the environment,

1629
01:36:11,950 --> 01:36:13,630
which again is different than age.

1630
01:36:13,790 --> 01:36:15,620
They all interact, right?

1631
01:36:15,780 --> 01:36:17,060
Um to some degree.

1632
01:36:17,140 --> 01:36:21,380
But I I really like that, yeah, that it it it gives it computational teeth and you could

1633
01:36:21,380 --> 01:36:23,620
start to think about like how a robot would be built.

1634
01:36:24,020 --> 01:36:29,700
Um it it creates um a task, you could do a task analysis of

1635
01:36:29,780 --> 01:36:30,340
of this.

1636
01:36:30,500 --> 01:36:30,740
Yeah.

1637
01:36:31,060 --> 01:36:32,340
Yeah, yeah, yeah.

1638
01:36:32,900 --> 01:36:35,140
Um good.

1639
01:36:35,890 --> 01:36:41,810
Uh I I want to explore um how intrasexual competition

1640
01:36:41,810 --> 01:36:43,090
fits into this.

1641
01:36:43,570 --> 01:36:49,010
Because this is a I I feel like this is probably a really important uh process uh by which

1642
01:36:49,010 --> 01:36:51,170
sex and gender manifests.

1643
01:36:51,330 --> 01:36:57,250
Um so you know, this the sexes choose among the other sex whom whom

1644
01:36:57,250 --> 01:36:57,970
to mate with.

1645
01:36:58,130 --> 01:37:03,780
Uh within sexes, you know, uh people choose their friends and and allies and they compete

1646
01:37:03,780 --> 01:37:08,180
with other members of their sex for status, for access to to mates.

1647
01:37:08,580 --> 01:37:14,100
Um and I feel like this is probably playing a big role in in in gender expression,

1648
01:37:14,260 --> 01:37:15,460
gender signaling.

1649
01:37:15,700 --> 01:37:20,660
Um it seems to me that if if there's a particular sexual division of labor such that say

1650
01:37:20,900 --> 01:37:26,430
men invest in big game hunting and women invest in, say, gathered resources, um it

1651
01:37:26,670 --> 01:37:32,350
seems to me that women would have an interest in mating with the best big game hunters, and

1652
01:37:32,350 --> 01:37:35,630
men would have an interest in mating with the best gatherers.

1653
01:37:35,790 --> 01:37:41,070
And and there may even be some invariances as well, insofar as the small gamete sex is

1654
01:37:41,070 --> 01:37:45,950
going to uh invest more in dangerous risk taking and have a a lower um

1655
01:37:47,300 --> 01:37:50,020
or have a higher appetite for dangerous risks.

1656
01:37:51,460 --> 01:37:56,340
Or they might be bigger and stronger and more they might be more geared toward fighting

1657
01:37:57,060 --> 01:37:58,420
with other members of their sex.

1658
01:37:58,580 --> 01:38:02,180
You know, in cases where there are extreme sex roles, the females will prefer the biggest

1659
01:38:02,180 --> 01:38:06,580
and strongest males because they're going to give them the most successful offspring.

1660
01:38:07,270 --> 01:38:11,590
or in cases where there's more intersexual selection, they might prefer the the males with

1661
01:38:11,590 --> 01:38:17,270
the most colorful tailer tail feathers or the the most eye-catching song and dance routine.

1662
01:38:17,510 --> 01:38:23,510
Um males similarly might prefer the females who uh m uh uh have

1663
01:38:23,510 --> 01:38:28,420
cues of higher health or fertility or going to provide more investment for their offspring.

1664
01:38:28,580 --> 01:38:32,740
So it seems like there there is selection within each sex to signal a kind of

1665
01:38:32,740 --> 01:38:33,860
prototypicality.

1666
01:38:34,020 --> 01:38:38,420
Where if I'm a male, I want to signal that I am a pi I'm the biggest and strongest, most

1667
01:38:38,420 --> 01:38:42,660
competitive male, or the male with the the most colorful tail feathers.

1668
01:38:42,740 --> 01:38:46,820
If I'm a female, I w I might want to signal that uh I'm going to provide the best

1669
01:38:46,820 --> 01:38:49,300
investment and nourishment for uh offspring.

1670
01:38:49,540 --> 01:38:52,100
I'm the healthiest and the most fertile or or whatever.

1671
01:38:52,690 --> 01:38:57,410
That might be occurring at an abstract level across sex roles, across species, but you

1672
01:38:57,410 --> 01:39:02,530
know, it it'll obviously manifest differently uh in in a human setting with you know sexual

1673
01:39:02,530 --> 01:39:03,490
divisions of labor.

1674
01:39:03,650 --> 01:39:09,490
But it seems like sexual uh or gender prototypicality is is playing a big

1675
01:39:09,490 --> 01:39:15,010
role here and signaling that you know I'm a particularly good big game hunter, I'm a

1676
01:39:15,010 --> 01:39:21,010
particularly good at the at the tasks that that my sex uh pursues in this

1677
01:39:21,010 --> 01:39:21,810
life way.

1678
01:39:23,340 --> 01:39:24,780
I think that's right.

1679
01:39:25,100 --> 01:39:31,020
Um so I mean, uh, there's lots of things to pick up on there.

1680
01:39:31,180 --> 01:39:36,860
Um I I think one is um, so I've been talking about this in terms of like

1681
01:39:37,100 --> 01:39:42,860
what the gender concept is, is like you learning the best sort of life way for you.

1682
01:39:43,430 --> 01:39:48,230
It's like a very selfish, kind of individualistic way of thinking about it.

1683
01:39:48,390 --> 01:39:53,910
But yeah, like if we think that's gender, you could ask, well, why would we ever care about

1684
01:39:53,910 --> 01:39:55,190
other people's gender concepts?

1685
01:39:55,270 --> 01:39:56,470
Which we very clearly do, right?

1686
01:39:56,550 --> 01:40:02,550
Like so much of like sort of contemporary sort of social issues and so much of moralizing

1687
01:40:02,630 --> 01:40:06,870
is like moralizing how other people think about or enact gender.

1688
01:40:06,950 --> 01:40:11,140
And so, like, why would we do that if what gender is is just like you figuring out how to

1689
01:40:11,140 --> 01:40:12,820
live your life in your ecology.

1690
01:40:12,980 --> 01:40:17,620
Um, so I do have like the last model in the paper is sort of like starting to get into

1691
01:40:17,620 --> 01:40:18,500
those kinds of questions.

1692
01:40:18,580 --> 01:40:23,620
Like, why would we ever care or reason about other people's gender concepts?

1693
01:40:23,780 --> 01:40:27,300
And I think there's lots of reasons why we should, if that's what gender is, and you've

1694
01:40:27,300 --> 01:40:28,100
touched on some of them.

1695
01:40:28,180 --> 01:40:33,770
Like there are lots of cases where how other people think about gender is relevant to your

1696
01:40:33,770 --> 01:40:34,410
interests.

1697
01:40:34,970 --> 01:40:40,490
I think one is, yeah, that you hit on already is uh if we are, you know, we're sexually

1698
01:40:40,490 --> 01:40:45,290
reproducing species and we're also these like, you know, biparental, uh, you know,

1699
01:40:45,770 --> 01:40:51,530
relatively lifelong monogamous uh maters, like it really matters if there is

1700
01:40:51,610 --> 01:40:55,380
like a sexual division of labor, or just even if they're even if there's not, it just like

1701
01:40:55,380 --> 01:40:55,780
really matters.

1702
01:40:55,860 --> 01:41:00,820
If I'm going to be attaching my reproductive interests to one other person, uh, it really

1703
01:41:00,820 --> 01:41:05,700
matters that like we have compatible understandings of like what our roles are.

1704
01:41:05,860 --> 01:41:11,380
Um, so that we both are like, you know, investing in the right like skills and uh

1705
01:41:11,780 --> 01:41:16,980
um activities that are going to be like, you know, maximizing our our shared reproductive

1706
01:41:16,980 --> 01:41:17,380
interests.

1707
01:41:17,540 --> 01:41:22,170
And so I'm gonna have yeah, strong opinions that like whatever my concept of gender is, I'm

1708
01:41:22,170 --> 01:41:26,490
gonna wanna make sure that my mate has a concept of gender that like plays along with that

1709
01:41:26,490 --> 01:41:30,250
one and that like they're enacting the role that I think they should be enacting in a

1710
01:41:30,250 --> 01:41:34,570
relatively effective way so that we can maximize our shared reproductive interests.

1711
01:41:34,730 --> 01:41:35,770
So I think that's one case.

1712
01:41:35,850 --> 01:41:40,330
Like in the mating market, like you're gonna wanna make sure like whoever you mate with,

1713
01:41:40,410 --> 01:41:44,650
they're on the same page as you in terms of like how do you make a living in the psychology

1714
01:41:44,650 --> 01:41:46,490
that we that we that we live in.

1715
01:41:46,650 --> 01:41:51,620
Uh, I think there's another case where like you should really care about your

1716
01:41:51,620 --> 01:41:53,700
offspring's gender concepts, right?

1717
01:41:53,780 --> 01:41:59,460
Because if your gender concept is shaping your life way, like you as an adult invested a

1718
01:41:59,460 --> 01:42:03,300
lot of time and effort and energy learning a good life way.

1719
01:42:03,540 --> 01:42:09,220
Uh, like you want to make sure that like your kid does the same, because if you know

1720
01:42:09,380 --> 01:42:14,070
there's no value in you successfully reproducing if like your kid then learns like the

1721
01:42:14,070 --> 01:42:17,590
optimal thing to do is like eat rocks and hit my head hit my head with a hammer, right?

1722
01:42:17,670 --> 01:42:22,390
Like so you want to make sure that like they're learning a good lifeway as well.

1723
01:42:22,470 --> 01:42:26,870
And so it makes sense that parents would have strong feelings about like what is their

1724
01:42:27,030 --> 01:42:31,430
their children's models of their environment and therefore like what is their children's

1725
01:42:31,430 --> 01:42:32,310
concepts of gender.

1726
01:42:32,470 --> 01:42:36,790
And does that align with you know what you think is a correct life way for the ecology that

1727
01:42:36,790 --> 01:42:37,670
you live in.

1728
01:42:39,060 --> 01:42:42,020
and that I have a model of that, right?

1729
01:42:42,260 --> 01:42:48,100
So we can go into this population uh that has evolved this like sex differentiated

1730
01:42:48,100 --> 01:42:48,980
learning strategy.

1731
01:42:49,220 --> 01:42:53,860
We can manipulate like are environments relatively stable over time so that like you know,

1732
01:42:54,020 --> 01:42:57,220
the kinds of environments where parents tend to live in are similar to the kinds of

1733
01:42:57,220 --> 01:43:00,980
environments where offspring tend to live in, or do they change relatively quickly?

1734
01:43:01,140 --> 01:43:05,060
Like, you know, is the optimal foraging strategy pretty different from generation to

1735
01:43:05,060 --> 01:43:05,700
generation?

1736
01:43:05,940 --> 01:43:11,230
Um, and we introduced, I can introduce this gene that's basically like if you're high, I

1737
01:43:11,310 --> 01:43:14,750
call it like a gender policing gene, for lack of a better word.

1738
01:43:15,070 --> 01:43:20,190
We're basically like agents that are high on this gene, what they do is parent agents who

1739
01:43:20,190 --> 01:43:24,830
have already learned a good policy, what they can do is they can sort of intervene in their

1740
01:43:24,830 --> 01:43:25,870
offspring's behavior.

1741
01:43:26,030 --> 01:43:30,620
They can prevent the offspring from enacting their own policy and they can just force their

1742
01:43:30,620 --> 01:43:35,020
offspring to do what the parent would have done, which I think intuitively we feel like

1743
01:43:35,020 --> 01:43:37,100
parents often do to their children.

1744
01:43:37,900 --> 01:43:42,220
So you, if you're high on this gene, you sort of prevent your offspring from learning their

1745
01:43:42,220 --> 01:43:42,700
own policy.

1746
01:43:42,860 --> 01:43:44,300
They just implement your policy.

1747
01:43:44,540 --> 01:43:47,820
The advantage of that, if the environment is relatively similar, you've already learned a

1748
01:43:47,820 --> 01:43:48,300
good policy.

1749
01:43:48,380 --> 01:43:52,540
So your offspring are gonna, they're not gonna waste a lot of time sort of learning and

1750
01:43:52,540 --> 01:43:54,300
exploring and trying out things that don't work.

1751
01:43:54,460 --> 01:43:55,850
They get the benefit of your knowledge.

1752
01:43:55,930 --> 01:43:58,170
They're going to implement a good life way right from the beginning.

1753
01:43:58,330 --> 01:44:02,330
They're going to outcompete those other kids that are left to like try things out on their

1754
01:44:02,330 --> 01:44:02,730
own.

1755
01:44:02,890 --> 01:44:05,290
Um, that works if the environment is stable.

1756
01:44:05,530 --> 01:44:08,570
If the environment is unstable, though, that's a terrible strategy, right?

1757
01:44:08,650 --> 01:44:12,570
Because the environment your kid has inherited is different from the one where you trained

1758
01:44:12,570 --> 01:44:13,130
your policy.

1759
01:44:13,210 --> 01:44:16,650
And so letting them learn their own policy is probably going to be the more optimal thing

1760
01:44:16,650 --> 01:44:16,890
to do.

1761
01:44:17,050 --> 01:44:20,540
And yeah, you see, depending on the structure of the environment, when environments are

1762
01:44:20,540 --> 01:44:25,180
stable, selection favors this kind of like policing strategy of like impose your gender

1763
01:44:25,180 --> 01:44:26,940
concept onto your offspring.

1764
01:44:27,100 --> 01:44:32,380
But if environments are unstable, then selection favors parents that kind of back off and

1765
01:44:32,380 --> 01:44:34,780
let their children learn their own their own strategy.

1766
01:44:35,100 --> 01:44:40,780
So I think like these are the kinds of situations where agents would have strong feelings

1767
01:44:40,780 --> 01:44:45,980
about and would try to intervene on the ways that other agents think about and like express

1768
01:44:45,980 --> 01:44:47,100
their gender concepts.

1769
01:44:47,260 --> 01:44:52,210
And if you think about like if you take that for granted, right, okay, parents have an

1770
01:44:52,210 --> 01:44:56,530
interest in making sure their offspring develop good gender policies.

1771
01:44:56,690 --> 01:45:01,250
Well, then if you know that the way that your offspring are developing these gender

1772
01:45:01,250 --> 01:45:05,090
policies is by looking at adults and building a model based on adult behavior, well then

1773
01:45:05,090 --> 01:45:07,890
that gives you reason to care about what other adults are doing, right?

1774
01:45:07,970 --> 01:45:10,900
Because you want to make sure your kids have good targets around.

1775
01:45:11,060 --> 01:45:15,460
So that's why you would care about like the gender policy, even of like unrelated adult

1776
01:45:15,460 --> 01:45:16,180
strangers, right?

1777
01:45:16,260 --> 01:45:21,940
Like, I don't want you projecting a what I think is a bad life way, because my kid is going

1778
01:45:21,940 --> 01:45:23,780
to look at you and that's going to become part of their model.

1779
01:45:23,860 --> 01:45:26,100
And then they're going to start behaving in suboptimal ways.

1780
01:45:26,420 --> 01:45:32,420
And so all of this just creates grounds for like all kinds of conflict between individuals

1781
01:45:32,500 --> 01:45:37,570
about like what is the proper way to think about, you know, gender, think about life ways

1782
01:45:37,570 --> 01:45:39,250
in neurocology and to enact those things.

1783
01:45:39,330 --> 01:45:44,930
And I think that potentially explains like so much of like culture war about sex and gender

1784
01:45:44,930 --> 01:45:45,410
today.

1785
01:45:45,810 --> 01:45:46,530
Yeah, it's interesting.

1786
01:45:46,690 --> 01:45:50,130
I mean there's also um, you know, parent offspring conflict I bet plays a role.

1787
01:45:50,290 --> 01:45:55,250
So there's research showing that uh parents and children diverge in their uh preferences

1788
01:45:55,250 --> 01:45:57,320
for what for for mates for their children.

1789
01:45:57,720 --> 01:46:02,120
So uh child children tend to prefer uh tend to care more about physical attractiveness,

1790
01:46:02,200 --> 01:46:06,040
say, than their parents do uh when choosing mates for their for their children.

1791
01:46:06,200 --> 01:46:10,040
Um and parents care more about cultural similarity than the children do.

1792
01:46:10,280 --> 01:46:10,600
Yeah.

1793
01:46:11,640 --> 01:46:17,400
And you could uh imagine that you know cultural similarity is uh is

1794
01:46:17,400 --> 01:46:22,600
going to have better inclusive fitness benefits for the parent uh because that's going to

1795
01:46:22,600 --> 01:46:28,230
allow for uh more um for less investment, number one, less investment from them that's

1796
01:46:28,230 --> 01:46:33,350
going to be required because they're gonna be better at at investing and and pooling labor.

1797
01:46:33,670 --> 01:46:38,950
And number two, perhaps, uh uh better cooperative opportunities with with their kin.

1798
01:46:39,110 --> 01:46:42,390
Um and so parents want more cooperation among kin than the kin wants.

1799
01:46:42,470 --> 01:46:46,470
You know, the kin want twice as much stuff as their sorry, uh kids want twice as much stuff

1800
01:46:46,470 --> 01:46:50,360
as their sibling, but parents want to uh uh distribute investment equally.

1801
01:46:50,600 --> 01:46:55,640
So, all else equal, the parent is going to want um the kids to choose mates that are more

1802
01:46:55,640 --> 01:46:57,320
culturally similar than the kid will want.

1803
01:46:57,480 --> 01:47:02,440
And so that could play a role in here as well, where you know uh uh parents are going to

1804
01:47:02,440 --> 01:47:07,160
want um adherence to whatever the life way is uh among their kids more than their kids

1805
01:47:07,400 --> 01:47:07,720
will.

1806
01:47:08,280 --> 01:47:09,560
I think that's exactly right.

1807
01:47:09,640 --> 01:47:13,850
Um yeah, I think even in cases where you think the environment is the same, yeah, parents

1808
01:47:13,850 --> 01:47:15,530
and offering don't have overlapping interests.

1809
01:47:15,610 --> 01:47:19,210
And so there's room for conflict over like how you think about and manifest gender, even in

1810
01:47:19,210 --> 01:47:19,770
that scenario.

1811
01:47:20,090 --> 01:47:23,930
There's also like, I mean, so my former postdoc, Elizabeth Aggie, has like done a lot of

1812
01:47:23,930 --> 01:47:24,090
that work.

1813
01:47:24,170 --> 01:47:26,090
You should have her on the pod, she'd be a great guess.

1814
01:47:26,250 --> 01:47:29,050
Um uh, but I, you know, I I think that's exactly right.

1815
01:47:29,130 --> 01:47:30,330
I think it's a really good point.

1816
01:47:30,490 --> 01:47:35,930
Um, it's also like I just for simplicity, I've been modeling this as if like environments

1817
01:47:35,930 --> 01:47:37,530
are either stable or they're not stable.

1818
01:47:37,690 --> 01:47:41,700
But the reality is like environments vary in how stable there are.

1819
01:47:41,780 --> 01:47:43,940
And that's something you need to make an inference about, right?

1820
01:47:44,020 --> 01:47:49,780
Like when you're born, you need to ask yourself, like, is my ecology going to be the

1821
01:47:49,780 --> 01:47:52,260
same as the adults in my ecology?

1822
01:47:52,660 --> 01:47:56,740
And different people can make different inferences based on the based on the kinds of

1823
01:47:56,740 --> 01:47:59,860
information they're getting and also just like the kind of body they particularly have,

1824
01:47:59,940 --> 01:48:00,100
right?

1825
01:48:00,180 --> 01:48:05,450
Like it might be the environment is the same, but like what worked for my dad just won't

1826
01:48:05,450 --> 01:48:07,610
work for me because my dad and me are different people.

1827
01:48:07,850 --> 01:48:11,130
And so my dad might be getting information, like, my policy is still going to be really

1828
01:48:11,130 --> 01:48:11,770
good for my son.

1829
01:48:11,850 --> 01:48:12,970
He needs to behave this way.

1830
01:48:13,130 --> 01:48:16,090
And I'm getting information, like, nope, I gotta chart a different path.

1831
01:48:16,170 --> 01:48:18,250
Like, what worked for him is not gonna work out for me.

1832
01:48:18,410 --> 01:48:23,210
And so that can generate sort of intergenerational conflict, even in the same ecology, just

1833
01:48:23,210 --> 01:48:27,760
because like there's no uh there's no way to know who's right, right?

1834
01:48:27,920 --> 01:48:31,520
Both agents are having to make inferences based on uncertain information, and they might

1835
01:48:31,520 --> 01:48:35,280
just make different inferences about the best way to go, and that's gonna cause conflict.

1836
01:48:35,360 --> 01:48:39,040
And there will be adaptations, there will be selection for adaptations that sort of tip

1837
01:48:39,200 --> 01:48:40,560
that conflict in either direction.

1838
01:48:41,120 --> 01:48:45,360
That reminds me, Zach, we had Zach Garfield on, and he talked about how the son of a really

1839
01:48:45,360 --> 01:48:48,390
good hunter didn't want to do that.

1840
01:48:48,550 --> 01:48:49,750
I think it was a Hunter.

1841
01:48:49,830 --> 01:48:55,430
Um, and and he just and Zach characterized it as the kid seemed to know soon and

1842
01:48:55,430 --> 01:48:57,110
early that he didn't want to do that.

1843
01:48:57,190 --> 01:48:57,990
Wasn't his life way.

1844
01:48:58,070 --> 01:49:00,630
And this is not even per se a gendered one.

1845
01:49:00,710 --> 01:49:04,870
Uh I mean, it might, it might be also, but but it was more just sort of that, you know,

1846
01:49:04,950 --> 01:49:07,030
this was not, I'm not built.

1847
01:49:07,350 --> 01:49:12,960
Um, it it I'm not built to do the trajectory that I see my successful

1848
01:49:12,960 --> 01:49:14,400
dad pursuing.

1849
01:49:14,640 --> 01:49:20,080
Um, and I love that about this too, is it hooks up to this sort of niche

1850
01:49:20,240 --> 01:49:26,080
construction that the whether it's gendered or not, there's also this

1851
01:49:26,080 --> 01:49:31,930
thing of looking out of the world and saying, How am I gonna make a living and succeed.

1852
01:49:32,250 --> 01:49:36,810
We've talked about that a lot in a lot of different ways with a lot of different people for

1853
01:49:36,810 --> 01:49:42,650
a lot of different reasons, like parenting, status schemes, resources, um, and

1854
01:49:42,650 --> 01:49:46,890
thinking about like what what am I particularly good at, you know, and figuring that out.

1855
01:49:46,970 --> 01:49:50,010
And I that's really cool that it hooks up to that too.

1856
01:49:51,850 --> 01:49:53,610
Yeah, I think that's exactly right.

1857
01:49:53,850 --> 01:49:59,360
Um yeah, and that's there's a yeah, there's an individual difference angle

1858
01:50:00,240 --> 01:50:02,480
that I'm also sort of like under exploring.

1859
01:50:02,640 --> 01:50:07,760
But yeah, there's just like there's a you can think about like what is the kind of optimal

1860
01:50:07,760 --> 01:50:12,880
set of life ways in your ecology, but like those are not those are almost guaranteed to not

1861
01:50:12,880 --> 01:50:13,760
be optimal for you, right?

1862
01:50:13,840 --> 01:50:16,960
Like at the end of the day, you're still gonna have to tailor your life way to you in

1863
01:50:16,960 --> 01:50:22,000
particular, and that's gonna differ from how everyone else sees this just because of you

1864
01:50:22,000 --> 01:50:23,590
know reactive inheritability, right?

1865
01:50:23,670 --> 01:50:28,950
Like the body you happen to inherit is gonna shape what is the the right way to do things

1866
01:50:29,190 --> 01:50:29,830
for you.

1867
01:50:30,310 --> 01:50:35,510
It's almost like oh sorry David, I was just gonna say um it's almost like gender is almost

1868
01:50:35,510 --> 01:50:40,870
like gamete based personality where it's sort of like you know it's like

1869
01:50:41,270 --> 01:50:46,220
yeah yeah I like that way of dis of of describing it and thinking about it.

1870
01:50:46,300 --> 01:50:50,940
And and of course, your personality is is affected by life way and and you know the

1871
01:50:50,940 --> 01:50:53,980
different socio-ecological niches that can be filled.

1872
01:50:54,220 --> 01:50:55,900
Um that's interesting.

1873
01:50:56,060 --> 01:51:00,460
I I feel like there's there's something missing here in that gender is not merely a

1874
01:51:00,460 --> 01:51:05,020
dichotomy but a continuum, in that I can think of men who are more or less masculine, I can

1875
01:51:05,020 --> 01:51:07,820
think of women who are more or less feminine.

1876
01:51:08,060 --> 01:51:12,530
Um and to some extent that is a pref a mate preference.

1877
01:51:12,610 --> 01:51:16,690
You know, men might prefer more feminine women, and women might prefer more masculine men.

1878
01:51:17,650 --> 01:51:23,570
And so how do you how are you thinking about the uh the continuum of masculinity or

1879
01:51:23,570 --> 01:51:29,010
femininity and how that might play into mate preferences, status games,

1880
01:51:29,410 --> 01:51:31,730
uh uh, etc.?

1881
01:51:32,840 --> 01:51:35,080
I so it's a great, it's a great question.

1882
01:51:35,160 --> 01:51:40,280
And I think one sort of upshot of thinking about gender in this way that I haven't been

1883
01:51:40,280 --> 01:51:43,240
super clear about either is, and this comes out of the models as well.

1884
01:51:43,400 --> 01:51:49,400
Like uh gender, yes, like I think we should think about gender as being these kinds of folk

1885
01:51:49,480 --> 01:51:51,240
theories about how sex manifests.

1886
01:51:51,400 --> 01:51:56,760
And yes, sex tends to be, you know, binary if we think about it as like there are these

1887
01:51:57,230 --> 01:51:58,830
what sex is or these optima.

1888
01:51:59,070 --> 01:52:02,030
And in our species, there tends to be, there seems to be two.

1889
01:52:02,190 --> 01:52:05,870
Uh, but that doesn't mean that gender roles themselves need to be binary.

1890
01:52:06,190 --> 01:52:11,390
Um and you can have, depending on the structure of the ecology, like there can be

1891
01:52:11,390 --> 01:52:16,190
multiple good strategies for a large gamete producer, or there can be multiple good

1892
01:52:16,190 --> 01:52:18,510
strategies for a small gamete producer.

1893
01:52:18,590 --> 01:52:22,430
Um, and people can specialize in like different ways of being a man or different ways of

1894
01:52:22,430 --> 01:52:23,230
being a woman.

1895
01:52:23,390 --> 01:52:26,510
Um, so you don't need to think about gender as being binary.

1896
01:52:27,220 --> 01:52:31,700
it thinking about gender as being folk theories of sex does not imply that gender itself

1897
01:52:31,700 --> 01:52:32,820
has to be binary.

1898
01:52:33,060 --> 01:52:38,580
And even if it were the case that, like in a particular ecology, you know, there's just one

1899
01:52:38,580 --> 01:52:44,500
sort of relatively good strategy for being a male, like because of how all this

1900
01:52:44,500 --> 01:52:49,780
works, like every individual is going to learn a slightly different, you know, perspective

1901
01:52:49,780 --> 01:52:50,820
on what that is, right?

1902
01:52:50,900 --> 01:52:56,150
Because every individual, they, you know, they're forming their model based on, you know,

1903
01:52:56,310 --> 01:52:59,590
somewhat random observations of adults in their environments.

1904
01:52:59,670 --> 01:53:03,350
So all of their models are going to be slightly different variations on that same thing.

1905
01:53:03,510 --> 01:53:08,230
Um, you know, part of doing reinforcement learning is like there is randomness inherent in

1906
01:53:08,230 --> 01:53:10,630
the learning because you have to try out different things.

1907
01:53:10,790 --> 01:53:14,470
And you're going to, there's going to be randomness in terms of the reward those actions

1908
01:53:14,470 --> 01:53:14,870
give you.

1909
01:53:15,110 --> 01:53:18,870
So some agents are just going to, you know, there's going to be some variation in how

1910
01:53:18,870 --> 01:53:20,790
rewarding they find different actions.

1911
01:53:20,950 --> 01:53:24,340
And so there can be multiple optimal strategies.

1912
01:53:24,420 --> 01:53:28,260
And for any given optimal strategy, any agent's sort of like assessment of what that

1913
01:53:28,260 --> 01:53:30,340
strategy looks like is going to vary to some degree.

1914
01:53:30,500 --> 01:53:34,420
And that variation is also going to feed forward into like the role models that those

1915
01:53:34,420 --> 01:53:36,340
agents provide for the next generation.

1916
01:53:36,980 --> 01:53:42,980
So I think thinking about gender in this way like naturally gives you all

1917
01:53:42,980 --> 01:53:45,380
of this variation in gender concepts.

1918
01:53:45,550 --> 01:53:49,230
uh, you don't have to think about these things and they don't emerge as being sort of like

1919
01:53:49,230 --> 01:53:52,510
strict binary, like this is what a man is and this is what a woman is.

1920
01:53:52,670 --> 01:53:55,550
And then, of course, these things are all ecologically specific.

1921
01:53:55,630 --> 01:53:59,790
So, like, what is a man in this environment is going to differ from what is a man in that

1922
01:53:59,790 --> 01:54:03,710
environment, because it's all about learning how to sex manifest in this particular

1923
01:54:03,710 --> 01:54:04,350
ecology.

1924
01:54:04,510 --> 01:54:08,830
Uh, so there's, you know, it also handles and also gives you this like cross-cultural

1925
01:54:08,830 --> 01:54:11,950
variation in like what is masculinity, right?

1926
01:54:12,780 --> 01:54:16,860
Yeah, and I think the non-binary part also would explain the continuum, right?

1927
01:54:16,940 --> 01:54:22,940
Because if, for example, let's just say risk taking, if there's uh uh just because of

1928
01:54:22,940 --> 01:54:28,300
the the gamete selection pressures, uh there are uh

1929
01:54:28,620 --> 01:54:33,180
versions of stable equilibria of male, we'll just call them male and female for exposition,

1930
01:54:33,420 --> 01:54:38,620
male uh type stable equilibria of ways of making a living.

1931
01:54:39,290 --> 01:54:43,050
on average, some of those are going to be really high risk taking, but maybe not everyone.

1932
01:54:43,210 --> 01:54:46,890
Maybe there's a lower risk-taking male stable equilibrium.

1933
01:54:47,050 --> 01:54:52,010
But so therefore, you're going to get a continuum of risk taking among men, and same for

1934
01:54:52,010 --> 01:54:52,250
women.

1935
01:54:52,410 --> 01:54:56,650
It might just be that there's a really low risk-taking stable equilibrium for women, but

1936
01:54:56,650 --> 01:55:00,650
there's another stable equilibrium for women that's not as averse to risk taking.

1937
01:55:00,940 --> 01:55:04,940
And so consequently, because it's not binary, but there's actually multiple stable

1938
01:55:04,940 --> 01:55:10,700
equilibria within each uh class, that's exactly what what you would perceive as a

1939
01:55:10,700 --> 01:55:11,740
continuum, right?

1940
01:55:12,060 --> 01:55:14,220
Yeah, yeah, yeah, that's exactly right.

1941
01:55:14,380 --> 01:55:16,780
And yeah, you we can we can model this if you want.

1942
01:55:16,940 --> 01:55:22,940
Like you I've set up models where um, you know, let's say there's two resources

1943
01:55:22,940 --> 01:55:27,340
that are compatible with childcare, but they both are really skill intensive, like they

1944
01:55:27,340 --> 01:55:32,590
both require a lot of time invested to get good at foraging them.

1945
01:55:32,670 --> 01:55:35,070
And so you can't specialize in both of them.

1946
01:55:35,790 --> 01:55:39,790
You set that up for these agents, they naturally like pick one or the other to specialize.

1947
01:55:39,870 --> 01:55:45,630
So you get sort of two kinds of life ways that emerge within a given uh a given sex.

1948
01:55:45,870 --> 01:55:49,870
And you can imagine building more complicated versions of that exactly along those lines.

1949
01:55:50,030 --> 01:55:54,180
Like, you know, one of the dimensions is like how dangerous is this food to forage?

1950
01:55:54,660 --> 01:55:59,380
Maybe how dangerous it is depends on some aspect of your phenotype that varies relatively

1951
01:55:59,380 --> 01:56:00,100
continuously.

1952
01:56:00,260 --> 01:56:04,740
So yeah, maybe some individuals are willing or able to take on those risks.

1953
01:56:04,820 --> 01:56:07,300
And so that becomes like a viable life way for them.

1954
01:56:07,460 --> 01:56:09,540
Others are not willing or able to take on that risk.

1955
01:56:09,620 --> 01:56:11,380
So that becomes a viable life way.

1956
01:56:11,460 --> 01:56:15,700
And yeah, so there's like a, you know, maybe spectrum of how much people are investing in

1957
01:56:16,100 --> 01:56:18,730
those uh uh those particular lifeways.

1958
01:56:18,810 --> 01:56:21,050
Or maybe there's like a balance that you can do.

1959
01:56:21,370 --> 01:56:26,090
Um, there's a range of different lifeways based on you know how you mix that activity into

1960
01:56:26,090 --> 01:56:28,570
your sort of portfolio of overall activities.

1961
01:56:28,730 --> 01:56:33,450
Um so yeah, these things don't need to be like rigid categories.

1962
01:56:33,530 --> 01:56:39,530
Uh they can emerge as as uh uh more variable, the more variable structures

1963
01:56:39,530 --> 01:56:40,730
that they actually are.

1964
01:56:41,020 --> 01:56:41,260
Yeah.

1965
01:56:41,340 --> 01:56:45,100
And it suggests, like, I mean, you mentioned you alluded this to the mating thing, but I

1966
01:56:45,100 --> 01:56:50,060
think a lot of partner choice, whether it's like choosing a mentor or a friend, like I

1967
01:56:50,060 --> 01:56:53,740
think we've all had, I've had the experience of you just meet somebody, and there's

1968
01:56:53,740 --> 01:56:58,220
something about the way they're constituent, they're they're they're made up of that just

1969
01:56:58,220 --> 01:56:59,900
really it's very like compelling.

1970
01:56:59,980 --> 01:57:03,500
And you're just like, oh, I want to really learn from this person, or like this is makes a

1971
01:57:03,500 --> 01:57:04,460
really good friend.

1972
01:57:04,690 --> 01:57:07,810
And I and sometimes it's maybe that you're the same.

1973
01:57:07,970 --> 01:57:12,370
You know, like you detect that there's something weird about me and I don't know how to

1974
01:57:12,370 --> 01:57:15,650
make this weird thing in me work as to make a living.

1975
01:57:15,730 --> 01:57:18,530
And then you find somebody who has that same thing in you.

1976
01:57:18,690 --> 01:57:20,850
And then you you can sort of learn from them.

1977
01:57:21,010 --> 01:57:25,490
You know, like I think that that's like a lot of the tropes of mentorship or like I found,

1978
01:57:25,650 --> 01:57:29,640
I found my tribe, you know, is sort of the the the the that trope.

1979
01:57:29,720 --> 01:57:32,840
And then there's also just like the complementary, complementariness too.

1980
01:57:32,920 --> 01:57:35,480
You know, somebody else is good at this thing that I'm bad in.

1981
01:57:35,640 --> 01:57:41,560
Um, and this suggests that both for like finding mentors and friends and mates, um,

1982
01:57:41,640 --> 01:57:47,240
it suggests there's this very rich psychological like landscape of these folk

1983
01:57:47,400 --> 01:57:51,160
representations that we might not there might be literature on that maybe not be framed

1984
01:57:51,160 --> 01:57:55,730
this way, but it really suggests that we probably like a lot of friendship choice, like if

1985
01:57:55,730 --> 01:57:59,410
the friendship literature, a lot of it is sort of like similarity writ large.

1986
01:57:59,570 --> 01:58:03,170
But I suspect it's actually a lot of like what we're talking about, but what you're talking

1987
01:58:03,170 --> 01:58:03,570
about here.

1988
01:58:03,650 --> 01:58:07,490
It's about like ways of making a living status game kind of things.

1989
01:58:07,570 --> 01:58:07,810
Yeah.

1990
01:58:08,050 --> 01:58:08,210
Yeah.

1991
01:58:08,450 --> 01:58:09,090
Yeah.

1992
01:58:09,810 --> 01:58:10,530
I think you're right.

1993
01:58:10,610 --> 01:58:12,210
The paper's already like 15,000 words.

1994
01:58:12,290 --> 01:58:15,010
So this is and this is definitely a BBS paper.

1995
01:58:15,170 --> 01:58:19,180
It would be a sh a sin if this doesn't get into BBS for people who don't know because it

1996
01:58:19,180 --> 01:58:24,860
gives people it gives people a uh a venue many many areas to comment on because i as a

1997
01:58:24,860 --> 01:58:29,580
development partial developmental psychologist I find this like it connects so much area so

1998
01:58:29,580 --> 01:58:33,340
many areas of developmental psychology that have been unconnected with so many other areas

1999
01:58:33,340 --> 01:58:38,380
that are not developmental psychology like mating or parenting or friendship yeah good i'm

2000
01:58:38,380 --> 01:58:42,270
glad I'm glad to hear that Ten out of ten.

2001
01:58:42,590 --> 01:58:43,710
Well yeah.

2002
01:58:44,670 --> 01:58:46,990
Looking forward to the BBS paper.

2003
01:58:47,150 --> 01:58:50,910
Um from your lips to Paul Bloom's ears.

2004
01:58:51,310 --> 01:58:54,590
Yes, if you're listening, Paul Bloom, you gotta you gotta accept this one.

2005
01:58:55,150 --> 01:58:58,830
Um Pins off twenty twenty six.

2006
01:59:01,390 --> 01:59:01,860
All right.

2007
01:59:01,940 --> 01:59:04,900
Well, I mean, we're we're uh about two hours now.

2008
01:59:05,060 --> 01:59:05,140
Yeah.

2009
01:59:05,940 --> 01:59:10,020
Um is there anything we we should have asked you that we that we missed, Dan?

2010
01:59:10,660 --> 01:59:11,300
Oh shoot.

2011
01:59:11,380 --> 01:59:14,580
I I knew that question was coming and I still failed the preparedness.

2012
01:59:15,380 --> 01:59:17,540
We don't maybe we don't have to ask it for repeat.

2013
01:59:17,780 --> 01:59:18,340
I don't know.

2014
01:59:18,660 --> 01:59:19,700
Should we have asked you that?

2015
01:59:19,860 --> 01:59:21,540
Should we Oh should should we have asked you that?

2016
01:59:21,700 --> 01:59:22,260
Maybe a better question.

2017
01:59:22,580 --> 01:59:23,380
Maybe a better question.

2018
01:59:23,620 --> 01:59:25,140
Is there anything we asked you that we shouldn't have?

2019
01:59:25,300 --> 01:59:30,810
That should be that should be for repeat, repeat guess we should what shouldn't we have

2020
01:59:30,810 --> 01:59:31,610
asked you about?

2021
01:59:32,330 --> 01:59:36,810
I mean I since we're cut we know we got our one year anniversary, like maybe we should

2022
01:59:36,810 --> 01:59:38,730
consider whether we should continue that tradition or not.

2023
01:59:38,970 --> 01:59:39,610
Yes, exactly.

2024
01:59:39,850 --> 01:59:45,530
Oh you know, I do like the tradition, but I do feel like it's got a low hit rate.

2025
01:59:45,610 --> 01:59:48,890
Like, how many guests are you gonna be able to do that's true completely something?

2026
01:59:49,050 --> 01:59:53,290
I mean, I like the idea of you have to pick something we shouldn't have asked you.

2027
01:59:53,450 --> 01:59:54,170
That's I like this.

2028
01:59:54,250 --> 01:59:56,010
This is like the signal detection thing.

2029
01:59:56,170 --> 02:00:00,560
We shouldn't just err on the side of the things we missed, the hits there or the things we

2030
02:00:00,560 --> 02:00:02,400
should have Or what's what's the dumbest thing we said?

2031
02:00:02,640 --> 02:00:04,480
What's the thing you least like about this?

2032
02:00:04,880 --> 02:00:06,880
Who's the dumbest guest we've had on, Dan?

2033
02:00:07,040 --> 02:00:11,200
And and you can who's the dumbest person on this podcast that we've and it can include the

2034
02:00:11,200 --> 02:00:11,600
hosts.

2035
02:00:11,680 --> 02:00:16,640
That's the Uh I'll tell you my answer offline.

2036
02:00:17,040 --> 02:00:17,680
I think it's me.

2037
02:00:17,920 --> 02:00:18,720
I think it's me.

2038
02:00:19,040 --> 02:00:19,440
Yeah.

2039
02:00:20,880 --> 02:00:22,240
Well this is yeah.

2040
02:00:22,480 --> 02:00:24,560
Uh do you have an answer?

2041
02:00:25,290 --> 02:00:29,370
No, and I I think this has been a it's been a really helpful conversation for me just to

2042
02:00:29,370 --> 02:00:32,570
try to like air all these things out as I'm trying to write this paper.

2043
02:00:32,730 --> 02:00:34,490
So uh I've got things to think about.

2044
02:00:34,890 --> 02:00:35,130
Awesome.

2045
02:00:35,610 --> 02:00:35,770
Yeah.

2046
02:00:35,930 --> 02:00:36,170
Yeah.

2047
02:00:36,250 --> 02:00:39,930
Every time and Dan, every time I hear this, I think there's so much developmental uh stuff

2048
02:00:39,930 --> 02:00:40,970
to do on this too.

2049
02:00:41,050 --> 02:00:43,370
So it's just I I this is super exciting.

2050
02:00:43,450 --> 02:00:44,490
I just this is awesome.

2051
02:00:44,570 --> 02:00:44,810
Yeah.

2052
02:00:44,970 --> 02:00:50,170
I think I hope I hope people see it as like yeah, I I I'm worried it will come off as like

2053
02:00:50,440 --> 02:00:51,720
we've been saying this all along.

2054
02:00:51,960 --> 02:00:53,560
Like, why are you just coming around to this?

2055
02:00:53,720 --> 02:00:58,840
Uh I need to figure out how to pitch it as like, yeah, like I we're trying to sort of join

2056
02:00:58,920 --> 02:01:02,040
your stream rather than kind of like steal your thunder.

2057
02:01:02,360 --> 02:01:02,520
Yeah.

2058
02:01:02,600 --> 02:01:06,840
Well, I think connecting it up from like a your I mean, and what I like, I don't I don't

2059
02:01:06,840 --> 02:01:07,880
think it's self-indulgent.

2060
02:01:07,960 --> 02:01:09,640
Uh like my will editorialize for a second.

2061
02:01:09,720 --> 02:01:13,880
You said earlier it's self-indulgent to start at like the evolution of sex, but I actually

2062
02:01:13,880 --> 02:01:19,470
think it's crucial in the sense of you're doing this form like really

2063
02:01:19,470 --> 02:01:21,870
methodically, logically.

2064
02:01:22,030 --> 02:01:27,150
And if there's one thing evolutionary behavioral sciences is about is sort of you're just

2065
02:01:27,150 --> 02:01:28,750
like, I'm checking my assumptions.

2066
02:01:28,910 --> 02:01:30,110
Does this follow?

2067
02:01:30,350 --> 02:01:32,830
Without sort of grasping at some phenomena.

2068
02:01:32,990 --> 02:01:36,670
Like obviously the phenomena is a thing to be explained, but it's not like all of a sudden

2069
02:01:36,750 --> 02:01:41,380
you're like, well, we gotta, we gotta like kind of, you know, round up and round down to

2070
02:01:41,380 --> 02:01:42,900
accommodate this known phenomena.

2071
02:01:43,060 --> 02:01:45,860
You're like deriving it from first principles.

2072
02:01:45,940 --> 02:01:50,580
And of course, the fact that you're recovering it, that's a virtue, not a vice.

2073
02:01:50,660 --> 02:01:50,820
Right.

2074
02:01:50,900 --> 02:01:52,580
So I think I don't think it's self-intentional.

2075
02:01:52,820 --> 02:01:58,660
And I do think it's a contribution to provide a causal framework attached to all these

2076
02:01:58,660 --> 02:01:59,780
different pieces.

2077
02:02:00,420 --> 02:02:00,660
Yeah.

2078
02:02:00,890 --> 02:02:01,770
It's a synthesis.

2079
02:02:01,850 --> 02:02:02,330
It's a helpful.

2080
02:02:02,730 --> 02:02:04,090
I think that might be one way to frame it.

2081
02:02:04,250 --> 02:02:04,650
Yeah.

2082
02:02:04,890 --> 02:02:08,890
Um you're you're bringing all this stuff together in one place when it hasn't been together

2083
02:02:08,890 --> 02:02:10,010
in one place before.

2084
02:02:10,170 --> 02:02:12,170
And I think that is is doing a service to the field.

2085
02:02:12,410 --> 02:02:12,890
Yeah, I like that.

2086
02:02:13,050 --> 02:02:14,170
A synthesis of gender.

2087
02:02:14,250 --> 02:02:16,490
That's actually a lovely way of putting what you're doing.

2088
02:02:16,730 --> 02:02:17,130
Yeah.

2089
02:02:17,530 --> 02:02:22,730
Uh yeah it sounds like uh the name of a book I might find in a humanities section, which is

2090
02:02:22,730 --> 02:02:23,610
fine by me.

2091
02:02:25,100 --> 02:02:29,740
Or or a robot factory that's gonna build a robots with gender.

2092
02:02:29,820 --> 02:02:30,380
Yeah.

2093
02:02:31,740 --> 02:02:33,260
Uh no, I that's helpful.

2094
02:02:33,340 --> 02:02:35,020
I need to I need to remember to frame it that way.

2095
02:02:35,100 --> 02:02:37,420
Maybe that'll be the sub subtitle of the paper.

2096
02:02:37,740 --> 02:02:38,700
Synthesis.

2097
02:02:39,900 --> 02:02:40,780
There you go.

2098
02:02:41,020 --> 02:02:41,660
Awesome.

2099
02:02:42,300 --> 02:02:45,100
Uh well, um on that note.

2100
02:02:45,500 --> 02:02:46,620
A sexy synthesis.

2101
02:02:50,380 --> 02:02:50,970
I will.

2102
02:02:53,610 --> 02:02:54,890
All right, Dan, awesome.

2103
02:02:55,370 --> 02:02:56,170
Good to see you.

2104
02:02:56,330 --> 02:02:58,410
Um and uh good luck.

2105
02:02:58,490 --> 02:03:00,410
We look forward to hearing more about this.

2106
02:03:00,730 --> 02:03:01,130
Thank you.

2107
02:03:01,210 --> 02:03:02,730
Thank you guys for having me back.

2108
02:03:06,570 --> 02:03:09,850
David, um stickers.

2109
02:03:10,700 --> 02:03:13,180
Oh yeah, we gotta print out stickers for HBES.

2110
02:03:13,420 --> 02:03:17,180
Do you you think stickers is the way to go in terms of the the stuff to hand out?

2111
02:03:18,220 --> 02:03:23,740
Yeah, because you can put in stickers on anything like a a mug or um That's true.

2112
02:03:23,980 --> 02:03:25,820
Uh uh a fanny pack.

2113
02:03:26,540 --> 02:03:26,860
Yeah.

2114
02:03:27,180 --> 02:03:27,900
Not an animal.

2115
02:03:27,980 --> 02:03:31,980
You wouldn't want to put it a lapel pin can s is sort of limited to clothing, I guess,

2116
02:03:32,140 --> 02:03:33,500
which is the the downside of a pin.

2117
02:03:33,820 --> 02:03:35,420
Were you thinking of lapel pin?

2118
02:03:35,740 --> 02:03:38,140
Uh uh just throwing out possible options.

2119
02:03:38,220 --> 02:03:40,140
Um That's pretty highbrow.

2120
02:03:40,470 --> 02:03:42,150
Yeah, it is a little highbrow.

2121
02:03:42,230 --> 02:03:42,710
Yeah.

2122
02:03:43,030 --> 02:03:47,590
Although you could use a um what are those things that are like the stuffed animals that

2123
02:03:47,590 --> 02:03:49,270
all the kids are hanging off their backpacks?

2124
02:03:49,350 --> 02:03:50,950
It's little boo-boos or something like that.

2125
02:03:51,190 --> 02:03:51,590
Oh yeah.

2126
02:03:51,750 --> 02:03:55,110
Kids are into the should we do evolutionary psychology like boo-boos?

2127
02:03:55,430 --> 02:03:56,470
I don't even know what that would be like.

2128
02:03:56,870 --> 02:03:58,390
I don't even know how what that would look like.

2129
02:03:58,550 --> 02:03:59,830
I'm imagining a care bear.

2130
02:03:59,990 --> 02:04:02,710
Do you remember the care bears where they had like symbols on their bellies?

2131
02:04:02,790 --> 02:04:07,830
I'm imagining a a care bear with like a brain and the logo on its belly, but I don't know.

2132
02:04:07,910 --> 02:04:08,950
Maybe it would be a brain.

2133
02:04:09,110 --> 02:04:09,430
I don't know.

2134
02:04:09,670 --> 02:04:09,830
Yeah.

2135
02:04:10,140 --> 02:04:10,540
Not the best.

2136
02:04:11,740 --> 02:04:14,380
I feel like that would take a lot of funding to uh to figure out.

2137
02:04:14,860 --> 02:04:16,460
A lot of time and effort.

2138
02:04:16,700 --> 02:04:19,660
We're we're we're worried about the ability to get stickers done.

2139
02:04:19,740 --> 02:04:21,580
So yeah, that might be yeah.

2140
02:04:21,660 --> 02:04:21,900
Yeah.

2141
02:04:22,220 --> 02:04:22,540
Okay.

2142
02:04:22,940 --> 02:04:24,700
Uh anyway, on that note.

2143
02:04:25,580 --> 02:04:26,460
Uh yeah.

2144
02:04:26,860 --> 02:04:27,500
Support us.

2145
02:04:27,660 --> 02:04:31,020
If you want those laboooboos, you gotta pony up on Patreon.

2146
02:04:31,420 --> 02:04:33,820
I think we'll I think this will get posted before HBest.

2147
02:04:33,980 --> 02:04:35,740
So we look forward to seeing everybody at HBest.

2148
02:04:35,980 --> 02:04:41,350
And um yeah, maybe we'll um maybe I'll bring a lavalier mic and we can do a few like short

2149
02:04:41,350 --> 02:04:42,550
recordings at HBus.

2150
02:04:42,950 --> 02:04:43,830
Oh yeah, that'd be fun.

2151
02:04:43,910 --> 02:04:44,310
Oh yeah.

2152
02:04:44,390 --> 02:04:46,150
So sort of like man on the street kind of thing.

2153
02:04:46,310 --> 02:04:46,870
Yeah, exactly.

2154
02:04:46,950 --> 02:04:47,430
Yeah, exactly.

2155
02:04:48,950 --> 02:04:51,030
Assault people with a microphone and ask them.

2156
02:04:51,510 --> 02:04:54,710
Wasn't there a shtick like people would ask people like what's the capital of the United

2157
02:04:54,710 --> 02:04:55,030
States?

2158
02:04:55,110 --> 02:04:58,790
And people would like, I don't know, Albany, you know, like like wasn't that a thing it was

2159
02:04:58,950 --> 02:05:00,550
like Conan or J Leno or somebody?

2160
02:05:00,870 --> 02:05:01,910
Oh yeah, classic bit.

2161
02:05:01,990 --> 02:05:02,150
Yeah.

2162
02:05:02,310 --> 02:05:02,470
Yeah.

2163
02:05:02,550 --> 02:05:02,630
Yeah.

2164
02:05:02,870 --> 02:05:05,510
Should we should we ask gotcha questions to everyone at HBS?

2165
02:05:05,960 --> 02:05:09,240
That seems like a really um mean thing to do.

2166
02:05:09,320 --> 02:05:10,600
So probably yes.

2167
02:05:10,760 --> 02:05:11,480
No, I don't know.

2168
02:05:11,880 --> 02:05:12,760
I was I was joking.

2169
02:05:13,640 --> 02:05:14,680
No, I don't I don't know.

2170
02:05:14,760 --> 02:05:20,200
Yeah, but we can um it might be a nice way to like feature students or just like the uh

2171
02:05:20,440 --> 02:05:22,520
diversity of people attending HBus.

2172
02:05:22,760 --> 02:05:23,400
I don't know.

2173
02:05:23,640 --> 02:05:23,960
Yeah.

2174
02:05:24,040 --> 02:05:24,920
No, it'd be fun.

2175
02:05:25,080 --> 02:05:25,240
Yeah.

2176
02:05:25,400 --> 02:05:25,800
We'll see.

2177
02:05:26,120 --> 02:05:26,520
Good idea.

2178
02:05:26,600 --> 02:05:26,760
Yeah.

2179
02:05:26,920 --> 02:05:27,080
All right.

2180
02:05:27,480 --> 02:05:30,440
We'll probably edit this out because we won't well, we might do it.

2181
02:05:30,530 --> 02:05:31,170
Yeah.

2182
02:05:31,730 --> 02:05:32,130
All right.

2183
02:05:32,210 --> 02:05:33,890
Uh thank you everybody for listening.

2184
02:05:34,050 --> 02:05:36,610
Um, and uh we'll see you next week.

2185
02:05:36,850 --> 02:05:37,730
Yep, see ya.

2186
02:06:00,620 --> 02:06:06,540
Pontum Pont, Pantum Pum two, Pant, Pontum Pan P, Pontem

2187
02:06:06,780 --> 02:06:07,820
T, Canton.