WEBVTT

00:00:00.000 --> 00:00:03.120
This is Evolutionary Psychology at the podcast.

00:00:03.440 --> 00:00:09.120
Actual Evolutionary Psychology by Actual Evolutionary Psychologists.

00:00:28.310 --> 00:00:31.750
Today we have a repeat customer, Dan Conroy Beam.

00:00:32.150 --> 00:00:33.750
Our first repeat customer.

00:00:33.990 --> 00:00:36.150
I believe the first guest that we've had on for a second time.

00:00:36.230 --> 00:00:36.310
Yeah.

00:00:36.550 --> 00:00:36.790
Is that right?

00:00:37.030 --> 00:00:40.630
Or no, I guess well, I guess we had uh Christina on as a co-host.

00:00:41.030 --> 00:00:42.870
Yeah, we had Christina on as a co-host.

00:00:43.030 --> 00:00:43.190
Yeah.

00:00:43.350 --> 00:00:45.190
And we will have the Zach Garfield.

00:00:45.350 --> 00:00:48.950
Uh Zach Garfield episode hasn't been the Ray Hames one isn't out yet.

00:00:49.030 --> 00:00:51.040
Or or it might have just come out.

00:00:51.440 --> 00:00:54.160
But this is our first repeat guest in the guest.

00:00:54.640 --> 00:00:55.680
Substantive, yes, exactly.

00:00:55.840 --> 00:00:56.080
Yeah.

00:00:56.240 --> 00:01:01.440
And no one better than Dan Conroy Beam because uh Dan is deriving

00:01:02.080 --> 00:01:07.120
from scratch, he is uh god among um uh

00:01:07.600 --> 00:01:13.200
silicone based agents, recreating the evolution of sex, sex roles,

00:01:13.440 --> 00:01:14.560
and gender.

00:01:14.850 --> 00:01:15.250
Gender.

00:01:15.410 --> 00:01:15.650
Yeah.

00:01:15.890 --> 00:01:17.410
So this is all about gender.

00:01:17.650 --> 00:01:19.650
Not all about, but half about.

00:01:20.210 --> 00:01:25.890
Or the complex interrelations between sex, sex roles, and

00:01:25.890 --> 00:01:26.450
gender.

00:01:26.690 --> 00:01:30.450
And I found it just really um, I don't know.

00:01:30.610 --> 00:01:36.290
Like I I knew a lot of the stuff, you know, reading Dawkins and uh the evolutionary bios

00:01:36.370 --> 00:01:41.810
stuff, but it was great to just hear Dan synthesize it and review it and

00:01:42.320 --> 00:01:48.160
show the elegance of these ideas and how they can be cashed out mathematically,

00:01:48.320 --> 00:01:54.160
uh, and review just the linkages between you know the evolutionary biology of sex and

00:01:54.160 --> 00:01:59.680
sex roles and how that manifests in gender in a complex uh

00:01:59.680 --> 00:02:01.760
cultural species such as Homo sapiens.

00:02:01.920 --> 00:02:05.360
I I found it to be really uh enlightening and enriching.

00:02:05.520 --> 00:02:06.400
Um, yeah.

00:02:06.690 --> 00:02:06.930
Yeah.

00:02:07.010 --> 00:02:11.490
And I think like, you know, pinning biology versus culture, we always know that that's

00:02:11.490 --> 00:02:11.890
wrong.

00:02:12.210 --> 00:02:18.210
But even going through this in as an exercise in working out the details as carefully

00:02:18.450 --> 00:02:23.970
and frankly in an unbiased way as one can, as Dan did,

00:02:24.290 --> 00:02:27.170
it was even for me sort of a check on my own.

00:02:27.330 --> 00:02:31.090
Uh um, you know, I was still sort of pitting biology against culture.

00:02:31.170 --> 00:02:36.800
And I don't think uh, you know, there's obviously lots of heat and temperature

00:02:36.960 --> 00:02:40.160
cultural issue wise around gender, especially now.

00:02:40.400 --> 00:02:45.760
I don't think any I think this trans um uh this is uh

00:02:45.760 --> 00:02:51.440
orthogonal to all of that, or at least it it it um it rises above all of that because it

00:02:51.440 --> 00:02:57.200
really talks about how there is learning and there are socials and it is dynamic, but it's

00:02:57.200 --> 00:03:00.960
also very much grounded in um uh sex.

00:03:01.490 --> 00:03:07.410
but what sex is is more abstract, even if it's biological and it's it's it's more weird.

00:03:07.570 --> 00:03:09.330
It's making the familiar strange.

00:03:09.490 --> 00:03:15.330
And even both sides in the culture war, well I think what Dan is describing

00:03:15.330 --> 00:03:20.930
is a far stranger, weirder world than either s either sides um would

00:03:21.250 --> 00:03:22.210
would imagine.

00:03:22.850 --> 00:03:27.600
I think John Tuby once said that scientists sort of think that, like, you know, reality is

00:03:27.600 --> 00:03:31.680
like one side or the other for any at any moment in time in a scientific debate.

00:03:31.920 --> 00:03:37.760
But reality is far weirder and far far far way further out than even what either

00:03:37.760 --> 00:03:42.400
side is arguing typically, um, if you look across the history of science.

00:03:42.800 --> 00:03:43.280
For sure.

00:03:43.440 --> 00:03:48.160
And because this is such a hot button topic, I feel like Dan has done such a good job of

00:03:48.160 --> 00:03:53.090
just providing a level head and a North Star through which to navigate these complicated

00:03:53.090 --> 00:03:54.130
and messy ideas.

00:03:54.290 --> 00:03:57.170
And I've I just thought it was a really fun and interesting conversation.

00:03:57.250 --> 00:03:58.050
I'm sure you'll enjoy it.

00:03:58.290 --> 00:03:59.250
Yeah, I I loved it.

00:03:59.330 --> 00:04:04.130
And there's so much to I found it inspiring to just be motivated to think about new things

00:04:04.130 --> 00:04:04.770
to think about.

00:04:04.850 --> 00:04:10.690
And it made my life as a kid growing up and just who I thought was a role model just

00:04:10.690 --> 00:04:11.890
make more sense too.

00:04:12.220 --> 00:04:13.100
Yeah, totally.

00:04:13.420 --> 00:04:13.820
Enjoy.

00:04:13.980 --> 00:04:14.380
All right.

00:04:14.540 --> 00:04:15.340
Enjoy.

00:04:19.420 --> 00:04:22.300
Uh Dan, we've already had you on.

00:04:22.540 --> 00:04:24.220
We're not gonna do any intros.

00:04:24.380 --> 00:04:25.900
You're well, okay.

00:04:26.060 --> 00:04:31.820
You're doing you do Asian-based modeling and empirical work based on that

00:04:31.820 --> 00:04:32.620
modeling.

00:04:32.940 --> 00:04:37.970
You're doing a new area, uh, you're you're investigating a new thing.

00:04:38.210 --> 00:04:40.450
I don't know, maybe you disagree that it's new.

00:04:40.690 --> 00:04:42.050
Uh what are you doing?

00:04:42.530 --> 00:04:48.290
Um can you either talk about what you're doing now and why you got into it and

00:04:48.530 --> 00:04:52.450
or what agent-based modeling is for people who might not know?

00:04:53.970 --> 00:04:55.970
Um choosing adventure.

00:04:57.160 --> 00:04:59.000
Uh well and thanks for having me back.

00:04:59.080 --> 00:04:59.960
Hey guys, good to see you.

00:05:00.280 --> 00:05:00.440
Welcome.

00:05:00.840 --> 00:05:03.160
Um really been enjoying a pod.

00:05:04.680 --> 00:05:08.040
I think it's only two episodes that I are still on my to watch list.

00:05:08.280 --> 00:05:09.240
Wow, that's good.

00:05:09.400 --> 00:05:09.720
Nice.

00:05:09.880 --> 00:05:10.680
We got a super fam.

00:05:10.840 --> 00:05:11.160
That's cool.

00:05:11.320 --> 00:05:11.960
Oh yeah, that's great.

00:05:12.120 --> 00:05:14.440
We gotta give you we gotta give you a sticker.

00:05:14.680 --> 00:05:19.320
Uh David, you actually this is we're we'll cut this out, but we need to order stickers for

00:05:19.320 --> 00:05:19.960
HPus.

00:05:20.290 --> 00:05:20.610
Oh yeah.

00:05:21.010 --> 00:05:21.410
Yes you do.

00:05:21.730 --> 00:05:22.210
Yes you do.

00:05:22.770 --> 00:05:26.530
I'm actually I'm excited to see yeah just from hungry people at conferences and like visits

00:05:26.530 --> 00:05:31.330
and stuff like you have you have a listener base and I'm excited to see like at HPES to

00:05:31.330 --> 00:05:33.410
talk to people see what see what they're thinking about it.

00:05:33.650 --> 00:05:38.530
Yeah but this is a horrible can't believe I can't believe Dan said that thing he said.

00:05:40.610 --> 00:05:46.340
But anyway yes enough enough of our um uh sticker procurement um side

00:05:46.500 --> 00:05:47.380
sidebar.

00:05:47.540 --> 00:05:48.420
Apologies, Dan.

00:05:48.580 --> 00:05:52.500
So yeah, either agent based modeling and or what are you doing now?

00:05:53.300 --> 00:05:57.860
Um I mean, I guess we uh we could start with agent based modeling, I guess.

00:05:57.940 --> 00:05:59.460
I mean, like the bigger picture thing.

00:05:59.540 --> 00:06:01.700
So I mean, you say it like I'm doing a new thing.

00:06:01.780 --> 00:06:04.020
I don't really feel like I'm doing a new thing.

00:06:04.180 --> 00:06:08.740
Uh I'm doing a thing that maybe people haven't done enough, but I'm definitely like

00:06:08.740 --> 00:06:10.740
standing on the shoulders of giants.

00:06:10.980 --> 00:06:16.030
Um I mean, agent based modeling is just one technique for

00:06:17.470 --> 00:06:21.470
formally modeling psychological hypotheses.

00:06:21.630 --> 00:06:26.750
It's a computer simulation technique where what you do is you simulate

00:06:27.310 --> 00:06:32.030
populations of autonomous entities, which we call agents.

00:06:32.190 --> 00:06:37.950
Autonomous just meaning they execute behaviors in some fashion.

00:06:38.110 --> 00:06:44.100
They do so according to decision rules that are inside their own head, uh and they execute

00:06:44.100 --> 00:06:47.060
those based on information that they capture from their own environment.

00:06:47.300 --> 00:06:52.820
So you just basically simulate a little kind of proto person with a little simple

00:06:52.820 --> 00:06:58.740
brain, put them in some kind of environment and set them free to behave however they are

00:06:58.740 --> 00:06:59.700
going to behave.

00:07:00.020 --> 00:07:03.780
And this is really good for modeling all kinds of systems.

00:07:03.940 --> 00:07:06.420
It's especially good for modeling social systems.

00:07:06.500 --> 00:07:12.450
So situations where you think you have either some understanding of some bit

00:07:12.450 --> 00:07:16.770
of psychology and you want to know, you know, if you put a hundred people together that

00:07:16.770 --> 00:07:21.170
have this kind of mind, what's the sort of social phenomenon that's going to come out of

00:07:21.170 --> 00:07:21.410
that?

00:07:21.650 --> 00:07:26.130
Or a situation where you think you have a good understanding of the social phenomenon, but

00:07:26.130 --> 00:07:30.530
you're trying to reverse engineer what individual level psychologies are sufficient to

00:07:30.530 --> 00:07:31.010
produce that.

00:07:31.090 --> 00:07:35.250
So you can, you know, create a little silicone labor laboratory, right?

00:07:35.570 --> 00:07:40.900
Um And test out a bunch of different individual-level psychologies and see which of them

00:07:40.900 --> 00:07:44.980
are sufficient to produce that social level phenomenon you're interested in.

00:07:45.380 --> 00:07:47.860
I'm not the first person to do this.

00:07:47.940 --> 00:07:52.260
I mean, I like I have certainly been inspired.

00:07:52.340 --> 00:07:55.940
Like, I do this in the context of largely mating decisions, although, as you know, Dave,

00:07:56.100 --> 00:08:01.230
have been branching out into other kinds of social, social partner decisions, things like

00:08:01.230 --> 00:08:02.910
group formation, friend choice.

00:08:03.230 --> 00:08:08.350
Uh, but you know, there have been lots of people in the evolution and behavior space that

00:08:08.350 --> 00:08:10.510
who have used similar kinds of models.

00:08:10.590 --> 00:08:14.670
I mean, you know, I was super inspired as an undergrad, as we all probably were, by like,

00:08:14.750 --> 00:08:17.950
you know, Axle Rod's prisoners dilemma tournaments.

00:08:18.110 --> 00:08:22.200
I was really inspired by um, you know the blind watchmaker.

00:08:22.360 --> 00:08:23.640
It's blind watchmaker, right?

00:08:23.720 --> 00:08:25.320
That was one of my favorite Dawkins books.

00:08:25.400 --> 00:08:29.240
And that's where he has the you know simulations where he's like evolving the different

00:08:29.240 --> 00:08:31.080
shapes to look like different animals.

00:08:33.000 --> 00:08:37.240
You know, the work of people like Jeffrey Miller and Peter Todd back in the day when they

00:08:37.240 --> 00:08:39.880
were, they were doing agent-based models of great choice.

00:08:40.600 --> 00:08:44.370
And when I came across that I was like, oh, this is this is how we should have been

00:08:44.370 --> 00:08:45.650
studying mating all along.

00:08:45.810 --> 00:08:49.970
Uh normally has done this similar work to, you know, of course like Paul Smaldino, who I

00:08:49.970 --> 00:08:50.850
know you've had on the pod.

00:08:51.170 --> 00:08:54.850
Um so there's lots of people over time who have done this.

00:08:55.010 --> 00:09:00.370
It it just like hasn't maybe caught on to quite the degree that I think it should.

00:09:01.490 --> 00:09:06.860
And AJ based modeling it's really good for math

00:09:07.260 --> 00:09:13.260
uh is hard to represent lots of interactions and lots

00:09:13.260 --> 00:09:14.140
of factors.

00:09:14.460 --> 00:09:19.980
My understanding is uh in that with an agent-based model,

00:09:20.460 --> 00:09:26.380
to some degree you can sort of look at the complex emergent things that

00:09:26.380 --> 00:09:31.910
you wouldn't be able to tell by looking at, you know, you you you stipulate the rules in

00:09:31.910 --> 00:09:37.910
each individual agent, but the properties that what ends up happening is not trivial

00:09:38.230 --> 00:09:44.230
or even predictable ahead of time, which is part of why you do it, is would be

00:09:44.230 --> 00:09:46.150
my understanding in a way.

00:09:46.790 --> 00:09:47.110
Yeah.

00:09:47.270 --> 00:09:49.190
I mean, I I would say a couple of things.

00:09:49.350 --> 00:09:54.550
One, agent-based modeling is a really good technique if you are interested in building

00:09:54.580 --> 00:09:59.140
formal models, but like me are not particularly skilled at math.

00:09:59.620 --> 00:10:05.060
So, you know, you get to instead of having to like, you know,

00:10:05.380 --> 00:10:10.660
set up and solve like a series of partial differential equations or something like that,

00:10:10.820 --> 00:10:15.460
you get to just sort of like, you just have to specialize specify the decision rules inside

00:10:15.460 --> 00:10:16.260
the agent's head.

00:10:16.340 --> 00:10:18.340
And then the computer handles the rest, right?

00:10:18.420 --> 00:10:21.460
Because it's it's all actual interactions between agents.

00:10:21.860 --> 00:10:26.970
And so a lot of the stuff that like a lot of the techniques that you would need for like uh

00:10:27.290 --> 00:10:32.970
analytic models um you just sort of like offload to the computer solve it by simulation

00:10:33.130 --> 00:10:38.090
right um so that's part of why i got into it originally because i was like i want to do

00:10:38.090 --> 00:10:43.690
these kinds of things but like i never took you know beyond high school calculus

00:10:43.930 --> 00:10:49.180
so um At the time when I was a grad student, this was like the easiest way in.

00:10:50.700 --> 00:10:53.420
But yeah, I do think there's virtue in that.

00:10:53.500 --> 00:10:59.260
Yeah, when you when you're trying to model uh systems that you think are the result of

00:10:59.580 --> 00:11:05.420
partially random, uh large numbers of partially random interactions between

00:11:05.420 --> 00:11:09.890
lots of parts, You can solve those things with math, but you have to make a lot of

00:11:09.890 --> 00:11:15.890
simplifying assumptions that uh sort of push you further

00:11:15.890 --> 00:11:17.730
and further away from what you're actually trying to model.

00:11:17.810 --> 00:11:21.410
And I think in some senses, you know, depending on your question, an agent-based model can

00:11:21.410 --> 00:11:26.050
be a more realistic representation of the system you're trying to study.

00:11:26.210 --> 00:11:29.010
That's not always a virtue, but but sometimes I think it is.

00:11:29.400 --> 00:11:29.560
Yeah.

00:11:29.880 --> 00:11:32.760
And it forces you to be explicit about things.

00:11:32.840 --> 00:11:38.840
Like I imagine as you're having a student, for example, come on, they um I imagine

00:11:38.920 --> 00:11:43.080
it's a sort of thing of like, oh, I realize I have to specify this, I have to specify this.

00:11:43.240 --> 00:11:44.920
Things that I didn't even really think about.

00:11:45.080 --> 00:11:47.800
I have to have an idea about how it works.

00:11:48.360 --> 00:11:51.240
Yeah, I think that's a major virtue of formal modeling, period.

00:11:51.320 --> 00:11:55.790
And I think agent-based modeling is like, you know no different from other modeling

00:11:55.790 --> 00:11:58.030
techniques in that it really draws these things out of you.

00:11:58.350 --> 00:12:02.350
And you know, David, I know you've done uh also a lot of like formal we were just talking

00:12:02.350 --> 00:12:05.550
about your hand paper, like a lot of formal modeling as well.

00:12:06.110 --> 00:12:12.110
I think this is a a huge virtue of trying to formalize your hypotheses,

00:12:12.270 --> 00:12:13.630
your theories, period.

00:12:13.870 --> 00:12:19.200
I mean I always tell students, yeah, like I've never sat down to write an

00:12:19.200 --> 00:12:24.560
agent-based model and not realized an assumption I didn't realize I was making.

00:12:24.800 --> 00:12:28.160
Um uh this is like just a universal process.

00:12:28.320 --> 00:12:32.720
And I think, you know, it depends, it depends on how far you want to get into this, but

00:12:32.800 --> 00:12:37.360
like so much of our theorizing in psychology is purely verbal.

00:12:37.680 --> 00:12:39.680
Um, and that's useful.

00:12:40.000 --> 00:12:45.650
Um It's, you know, honestly, like we're humans, is how we communicate at some level.

00:12:45.810 --> 00:12:48.690
Our our hypothesizing needs to be verbal.

00:12:50.210 --> 00:12:56.130
And you can get pretty quickly, you can get much farther into a complicated

00:12:56.130 --> 00:13:00.530
idea when you're working with it purely verbally than if you try to formalize it at every

00:13:00.530 --> 00:13:01.010
step.

00:13:01.570 --> 00:13:06.940
But part of the trade-off with that is like, yeah, you're sort of relying on a lot of your

00:13:06.940 --> 00:13:11.100
intuitive machinery in ways that you don't necessarily realize you're doing.

00:13:11.180 --> 00:13:17.020
Uh, and so it's easy to bake in assumptions that you're not realizing you're

00:13:17.020 --> 00:13:22.940
making, uh, or uh be overly vague and broad

00:13:23.020 --> 00:13:28.700
uh in ways that give you lots of degrees of freedom for your hypothesis to

00:13:28.700 --> 00:13:31.500
appear as though it's explaining a lot of things.

00:13:31.740 --> 00:13:37.710
Um and when you sit down and you have to say exactly what is the information that

00:13:37.710 --> 00:13:43.630
is available, exactly how is that processed into exactly what outputs, uh, there

00:13:43.630 --> 00:13:45.230
aren't as many shortcuts.

00:13:45.390 --> 00:13:49.070
Um and so you sort of realize, I mean, uh I might have mentioned this last time.

00:13:49.230 --> 00:13:51.470
Did I talk about this last time I was on the podcast?

00:13:51.630 --> 00:13:52.350
I don't remember.

00:13:52.510 --> 00:13:54.350
Um I don't know, I don't listen to the podcast.

00:13:54.430 --> 00:13:57.870
No, I don't remember what time you were the first guest in, I think.

00:13:58.110 --> 00:13:58.700
Is that right?

00:13:59.020 --> 00:14:00.540
It was Jamie, Jamie or you?

00:14:00.620 --> 00:14:01.180
I forget.

00:14:01.340 --> 00:14:03.420
Sorry, Jamie, or you better not have Jamie.

00:14:03.660 --> 00:14:04.620
I think I think it was Dan.

00:14:04.860 --> 00:14:05.260
I think it was Dan.

00:14:05.420 --> 00:14:07.980
Yeah, I think we're and we're getting up on a year.

00:14:08.140 --> 00:14:12.300
So this is maybe maybe maybe you should be our one year um episode.

00:14:12.380 --> 00:14:13.340
We'll see how it works out.

00:14:13.420 --> 00:14:13.740
I don't know.

00:14:13.900 --> 00:14:14.700
I'll take that.

00:14:15.420 --> 00:14:20.730
I mean part part of my like whole entryway into doing all this stuff was you know, I was I

00:14:20.730 --> 00:14:24.090
was at the University of Texas, I was interested in dating psychology.

00:14:24.330 --> 00:14:30.330
Um, there was some work coming out around the time, including from uh Paul Eastwick,

00:14:30.410 --> 00:14:32.570
who was also at the University of Texas at the time.

00:14:32.730 --> 00:14:34.890
He was like the floor below us.

00:14:35.050 --> 00:14:39.370
Uh, so we were having these kind of like occasional interactions in the building.

00:14:39.610 --> 00:14:44.940
Um, but you know, there were these papers coming out that were uh we talked about the speed

00:14:44.940 --> 00:14:49.340
dating stuff last time, um, these speed dating papers that were like, can we predict what

00:14:49.340 --> 00:14:52.460
mate choices people will make in a live in-person interaction?

00:14:52.620 --> 00:14:53.900
And the answer was kind of no.

00:14:54.060 --> 00:14:58.860
And so there's all this like hullabaloo, like, maybe our understanding of mating psychology

00:14:58.860 --> 00:14:59.660
is like totally wrong.

00:14:59.740 --> 00:15:01.420
We need to upend everything we know.

00:15:01.660 --> 00:15:06.300
Um and I was sort of like skeptical that that was true.

00:15:06.380 --> 00:15:12.010
And I was like, there's maybe simpler explanations for why we struggle to predict people's

00:15:12.010 --> 00:15:14.090
choices in live interactions.

00:15:14.330 --> 00:15:18.490
And I was like, maybe, you know, one explanation is just that

00:15:21.610 --> 00:15:23.210
I don't even remember where I started.

00:15:23.290 --> 00:15:26.890
But you know, I was like, maybe one explanation is just that like people want lots of

00:15:26.890 --> 00:15:28.090
different things, right?

00:15:28.730 --> 00:15:31.290
Have multiple different preferences, they've got to balance against them.

00:15:31.370 --> 00:15:36.810
And so maybe the person you met at the speed dating event who best met your

00:15:36.810 --> 00:15:41.360
kindness preferences was really far off your intelligence preferences, right?

00:15:41.520 --> 00:15:46.960
Um, and so if we only look at like how much did the partner you choose you chose

00:15:47.280 --> 00:15:50.800
match this one preference, we're missing those like trade-offs that people are making

00:15:50.800 --> 00:15:51.920
across dimensions.

00:15:52.080 --> 00:15:54.400
Um maybe that's what's going on.

00:15:54.640 --> 00:15:59.680
Um that feels intuitively true to me, but like, is that really sufficient to produce the

00:15:59.680 --> 00:16:02.000
kind of data that we're seeing in these speed dating studies?

00:16:02.370 --> 00:16:07.650
I just finished taking this like Monte Carlo analysis class that I had to take because it

00:16:07.650 --> 00:16:09.090
was like total serendipity.

00:16:09.170 --> 00:16:11.650
I like forgot to register for classes one semester.

00:16:11.890 --> 00:16:15.810
So the required stats class I needed to take filled up, but they wouldn't make an extra

00:16:15.810 --> 00:16:16.290
spot for me.

00:16:16.370 --> 00:16:20.290
And so I ended up taking this other stats class and that like totally changed my life.

00:16:20.530 --> 00:16:25.810
Um but I had just taken this like Monte Carlo analysis class and I was like, all right, I

00:16:25.810 --> 00:16:28.690
bet I like I could do one of the models that we do for that class.

00:16:28.930 --> 00:16:33.860
Like I could just like you know make up some data that looks kind of like make choice data.

00:16:34.020 --> 00:16:38.180
I could like simulate a process that looks kind of like what I'm thinking, like you know,

00:16:38.260 --> 00:16:40.660
people making choices across multiple dimensions.

00:16:40.900 --> 00:16:44.500
I could see what kind of data that makes across a large number of samples, and that would

00:16:44.500 --> 00:16:48.900
tell me, you know, is this sort of process actually capable of producing this kind of data?

00:16:49.060 --> 00:16:52.500
I didn't even know, I had never heard the word agent-based modeling at that time.

00:16:52.580 --> 00:16:54.260
Like I didn't know that this was a thing.

00:16:54.340 --> 00:16:56.980
I thought I was just kind of like hacking this together.

00:16:57.300 --> 00:17:02.490
Um So I started to try it out and I was like, okay, so like I'll simulate some data that

00:17:02.490 --> 00:17:03.850
looks kind of like made choice data.

00:17:03.930 --> 00:17:06.090
I sort of know what that looks like from the lab.

00:17:06.490 --> 00:17:09.290
Um and then you know, I need to have these agents.

00:17:09.450 --> 00:17:11.370
I didn't I didn't know they were called agents at the time.

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

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

00:17:21.050 --> 00:17:25.520
order to like make a decision that they want to see each other again.

00:17:26.080 --> 00:17:30.400
So, oh shoot, like, okay, I can give them preferences, I can give them traits, but like,

00:17:30.480 --> 00:17:34.000
how do I make them decide from that how attractive they are to one another?

00:17:34.240 --> 00:17:37.440
Actually, like crap, there's a lot of different ways you could do that.

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

00:17:42.800 --> 00:17:44.560
threshold, like then you're attractive.

00:17:44.640 --> 00:17:46.320
That's how Peter Todd said it works.

00:17:46.480 --> 00:17:50.560
But maybe I can make it like a linear model where the preferences are like weights and they

00:17:50.560 --> 00:17:51.920
sort of like like a regression.

00:17:52.000 --> 00:17:55.670
They like, you know, multiply the trait by the preference and then add it all up.

00:17:55.750 --> 00:17:59.030
That's sort of like how David is, David Buss has talked about it before.

00:17:59.270 --> 00:18:03.110
Oh man, like there's lots of different ways this could happen.

00:18:03.430 --> 00:18:09.030
I'd never thought about like this is an important part of the process that we

00:18:09.030 --> 00:18:12.710
haven't actually described how it works.

00:18:12.870 --> 00:18:17.750
Uh, and it wasn't until like I had to sit down and like make it happen in a computer that I

00:18:17.750 --> 00:18:23.440
realized, like, oh, this is a linchpin assumption in how mate choice works, that

00:18:23.600 --> 00:18:26.880
the entire field is just like mouth studying.

00:18:27.600 --> 00:18:32.240
So I think that's the that's a key virtue of like forcing yourself to do these things.

00:18:32.400 --> 00:18:38.320
Uh as you know, Dave, I now have in my lab, sort of like above the main meeting

00:18:38.320 --> 00:18:38.960
table.

00:18:39.200 --> 00:18:44.640
Um, I have a there's a famous quote, uh Richard Feynman from his chalkboard at the time of

00:18:44.640 --> 00:18:46.720
his death, What I cannot create, I do not understand.

00:18:47.120 --> 00:18:49.750
And that's sort of like a core lab ethos.

00:18:50.070 --> 00:18:54.710
Like if you understand how some system works, you should be able to build a copy of it in a

00:18:54.710 --> 00:18:55.190
computer.

00:19:00.550 --> 00:19:02.070
David, you were gonna say something.

00:19:02.550 --> 00:19:05.830
No, I mean it's just um very, very cool stuff.

00:19:05.990 --> 00:19:11.960
So I'm curious Now that we have this background of uh agent-based modeling and

00:19:11.960 --> 00:19:17.800
we know your methodology, uh what kind of new stuff do you have cooking

00:19:17.960 --> 00:19:21.320
in the kitchen with this methodology?

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

00:19:28.520 --> 00:19:28.840
hope.

00:19:29.320 --> 00:19:30.120
Well, yeah.

00:19:30.280 --> 00:19:33.080
Um I feel like I'm being pushed in a certain direction.

00:19:33.480 --> 00:19:33.880
Exactly.

00:19:35.480 --> 00:19:38.070
Um well, I mean, so you know, Dave knows.

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

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

00:19:50.150 --> 00:19:52.550
on the evolution of sex and gender.

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.

00:19:59.350 --> 00:20:03.320
Um just being very unsatisfied with

00:20:05.400 --> 00:20:11.400
not really knowing how to think about these concepts and not really feeling like there was

00:20:11.720 --> 00:20:15.080
clear guidance, at least that I'm aware of, in the literature.

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

00:20:20.520 --> 00:20:20.920
concepts.

00:20:21.000 --> 00:20:24.120
And then hopefully other people also find it useful and interesting.

00:20:24.360 --> 00:20:25.240
We'll see.

00:20:25.590 --> 00:20:30.870
Um but yeah, I mean I'm on I'm on sabbatical this year.

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

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.

00:20:41.590 --> 00:20:44.070
I mean I use it as podcasts, but go ahead.

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

00:20:49.110 --> 00:20:50.310
things or as interacting things?

00:20:50.390 --> 00:20:52.550
So how what are your what are your thoughts there?

00:20:53.420 --> 00:20:55.020
I was gonna ask you guys what your thoughts were.

00:20:55.340 --> 00:20:55.980
Yeah, I love this.

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

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

00:21:03.660 --> 00:21:04.380
think gender is?

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

00:21:08.620 --> 00:21:12.860
lovely scientific method of just like how would we define gender or is it different than

00:21:12.860 --> 00:21:13.100
sex?

00:21:13.180 --> 00:21:13.900
Yeah, what is it?

00:21:13.980 --> 00:21:14.300
Yeah.

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

00:21:18.710 --> 00:21:19.590
for basically every project.

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

00:21:23.670 --> 00:21:26.950
bar I'll just start asking people, What do you think about X?

00:21:28.630 --> 00:21:30.310
That's a fantastic way to do it.

00:21:30.390 --> 00:21:30.790
I love that.

00:21:30.870 --> 00:21:32.630
Did you when did you start doing that?

00:21:32.950 --> 00:21:33.510
I don't know.

00:21:33.670 --> 00:21:35.270
I think it's just uh I don't know.

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

00:21:39.030 --> 00:21:39.270
write.

00:21:39.590 --> 00:21:40.470
Yes, exactly.

00:21:41.030 --> 00:21:41.670
Your favorite.

00:21:43.440 --> 00:21:46.960
I remember Dan Dan in the hallway being like, what does the fundamental attribution error

00:21:46.960 --> 00:21:47.280
predict?

00:21:47.360 --> 00:21:53.280
Does it predict that um internal versus external uh uh will be higher um then

00:21:53.360 --> 00:21:54.240
like ordinally?

00:21:54.320 --> 00:21:55.840
Like what's the relationship between those two?

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

00:22:01.840 --> 00:22:02.400
for yourself?

00:22:02.480 --> 00:22:06.160
But it doesn't matter what the absolute magnitude is between external and internal.

00:22:06.560 --> 00:22:08.480
Great questions, but anyway.

00:22:09.440 --> 00:22:11.930
I mean, uh, let me throw out a wrong idea.

00:22:12.090 --> 00:22:13.690
You can tell me w why it's wrong.

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

00:22:18.490 --> 00:22:21.290
of evolutionary models on the evolution of sex.

00:22:21.450 --> 00:22:26.250
Uh, you know, it's about, you know, uh differences in um parental investment, operational

00:22:26.250 --> 00:22:30.570
sex ratio, potential reproductive rate, and these things lead to you know the different sex

00:22:30.570 --> 00:22:32.570
roles we see throughout the animal kingdom.

00:22:33.280 --> 00:22:36.960
Gender is an interesting thing because um it's a cultural thing.

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,

00:22:42.960 --> 00:22:47.120
which represents these, you know, a parental investment reproductive strategies.

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

00:22:53.280 --> 00:22:57.120
surface for other people to know, like uh necessarily.

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

00:23:03.710 --> 00:23:09.630
equilibrium um it could be a kind of yeah maybe let me let me

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

00:23:13.870 --> 00:23:17.870
straight to signaling that would be I would

00:23:19.870 --> 00:23:23.840
uh I like that idea and that I think that's actually pretty dang clear.

00:23:23.920 --> 00:23:26.320
I think you just got like two thirds of the paper.

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.

00:23:30.400 --> 00:23:31.840
I mean, I've asked David this before, so it's not fair.

00:23:31.920 --> 00:23:33.200
So maybe I'm asking just David.

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?

00:23:38.640 --> 00:23:41.920
Because you just said like, yeah, we have all these like evolutionary biological models,

00:23:42.080 --> 00:23:45.360
but like, do you think psychologists are aware of those?

00:23:47.990 --> 00:23:49.830
Probably most psychologists are.

00:23:49.990 --> 00:23:50.710
I mean, it depends.

00:23:50.790 --> 00:23:51.510
I don't know.

00:23:51.750 --> 00:23:55.350
It depends on how familiar they are with evolutionary psychology in general.

00:23:55.590 --> 00:23:57.190
And I'll at the end of the day.

00:24:02.230 --> 00:24:05.910
I think most psychologists would probably go like genitalia.

00:24:06.710 --> 00:24:12.550
Some would probably go genes, and a much smaller percentage would do gamete investment.

00:24:13.040 --> 00:24:13.760
Yeah, yeah.

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

00:24:19.840 --> 00:24:21.120
kind of shifted things a little bit.

00:24:21.200 --> 00:24:25.040
I was thinking about this project before that executive order and that executive order

00:24:25.200 --> 00:24:28.800
really blew a hole in in a lot of my thinking.

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

00:24:34.960 --> 00:24:35.200
at all.

00:24:38.000 --> 00:24:38.080
Okay.

00:24:38.400 --> 00:24:42.170
It doesn't quite say this, but it tries to erase gender as a concept entirely.

00:24:42.330 --> 00:24:43.450
That's my read of it at least.

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

00:24:49.290 --> 00:24:51.370
defines sex as being something about gamete size.

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

00:24:55.770 --> 00:24:57.130
been before that executive work.

00:24:57.450 --> 00:24:57.770
Yeah.

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.

00:25:02.490 --> 00:25:06.410
Obviously, sex and gender are like really important concepts when you're thinking about you

00:25:06.410 --> 00:25:07.530
know human relationships.

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

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

00:25:15.840 --> 00:25:21.040
psychologist you're sort of aware there are these concepts sex and gender they're supposed

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

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

00:25:30.720 --> 00:25:36.210
is sex itself and what is gender and why are those different things I just I

00:25:36.210 --> 00:25:40.850
personally have never deeply understood what I was supposed to be talking about.

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

00:25:45.170 --> 00:25:49.730
definitions or what are our models of these things, I find them really unsatisfying.

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

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

00:25:59.410 --> 00:26:04.100
like, you know, sex is a person's biological status.

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

00:26:10.260 --> 00:26:10.900
means.

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

00:26:16.980 --> 00:26:22.500
that's like genitalia, gonads, you know, sex chromosomes, hormones.

00:26:22.690 --> 00:26:27.090
they list off a number of indicators of sex, which I think is the correct.

00:26:27.170 --> 00:26:28.130
Those things are indicators.

00:26:28.210 --> 00:26:29.490
They're not sex in and of itself.

00:26:29.650 --> 00:26:34.850
A lot of other psychologists will define sex as being like what chromosomes you have, which

00:26:34.850 --> 00:26:36.770
I think is crazy.

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

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

00:26:47.420 --> 00:26:48.060
right either.

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.

00:26:53.580 --> 00:26:58.060
Like I have lots of statuses as a biological organism.

00:26:58.220 --> 00:27:00.940
Like, what what are we talking about when we talk about sex?

00:27:01.260 --> 00:27:03.180
Why are those things the indicators of sex?

00:27:03.260 --> 00:27:05.020
And they don't mention gamete size at all.

00:27:05.180 --> 00:27:09.260
The APA's definition of sex does not reference gametes at all.

00:27:09.420 --> 00:27:12.770
Uh and I think that's I think that's a problem.

00:27:13.010 --> 00:27:18.690
Uh one, because a lot of the things that psychologists focus on as

00:27:18.690 --> 00:27:24.610
being indicators of or definitions of sex are things that only apply to humans, right?

00:27:24.690 --> 00:27:29.970
Like sex chromosomes, they focus a lot on do you have X or Y chromosomes?

00:27:30.050 --> 00:27:34.930
Do you have certain like you know chromosomal disorders that lead to uh you know different

00:27:34.930 --> 00:27:36.610
sex development disorders?

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

00:27:41.980 --> 00:27:46.140
chromosomes, that's not definitive of sex across the diversity of life on this planet,

00:27:46.220 --> 00:27:46.380
right?

00:27:46.460 --> 00:27:51.100
Like that's how mammals do sex determination, but like birds don't have XY chromosomes,

00:27:51.180 --> 00:27:52.300
they have ZW chromosomes.

00:27:52.380 --> 00:27:55.260
Like lots of reptile reptiles don't have sex chromosomes at all.

00:27:55.340 --> 00:27:58.060
Like they do sex determination just by temperature, right?

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

00:28:03.690 --> 00:28:04.090
chromosomes.

00:28:04.170 --> 00:28:05.850
You wanted to define it in terms of genitalia.

00:28:05.930 --> 00:28:07.930
There's a whole diversity of genitalia out there, right?

00:28:08.010 --> 00:28:11.210
The way that we do genitalia is not the only way to do genitalia.

00:28:11.610 --> 00:28:13.210
You want to define it in terms of hormones?

00:28:13.370 --> 00:28:18.730
Like plants have sexes, but don't have, you know, at least hormones like we have.

00:28:18.810 --> 00:28:21.290
You could argue they have their own kind of hormones, but like they don't have

00:28:21.290 --> 00:28:23.370
testosterone, they don't have estradyl.

00:28:24.440 --> 00:28:29.960
so the definitions of sex that like psychologists use are really, really

00:28:29.960 --> 00:28:35.880
anthropocentric and don't capture like the concept of sex more broadly, which I

00:28:35.880 --> 00:28:36.440
think is a problem.

00:28:36.680 --> 00:28:40.520
Psychologists might think it's fine, but I I think that's really unsatisfying.

00:28:40.840 --> 00:28:46.360
And then you think about gender, um, like what are definitions of gender?

00:28:46.440 --> 00:28:48.450
They're also just really vague.

00:28:48.530 --> 00:28:53.650
Like, you know, they'll say things, people will say things like sex is biological and

00:28:53.650 --> 00:28:55.730
gender is like cultural.

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

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.

00:29:07.970 --> 00:29:12.290
Uh, but two, it also irritates me because, and you know, people will recognize this, like

00:29:12.370 --> 00:29:14.930
it's not clear where that boundary actually is.

00:29:15.090 --> 00:29:20.940
Like there are many things that uh there are ways in which things you

00:29:20.940 --> 00:29:24.620
might want to call cultural intersect with things you might want to call biological.

00:29:24.860 --> 00:29:28.380
And so, where should you like delineate the boundary between what sex and gender becomes

00:29:28.380 --> 00:29:28.940
really unclear?

00:29:29.100 --> 00:29:31.420
And it's also just like not clear what gender is supposed to be.

00:29:31.500 --> 00:29:37.180
Like people will offer definitions of gender that are literally like, you know, aspects of

00:29:37.180 --> 00:29:37.980
femininity.

00:29:38.140 --> 00:29:40.140
And it's like, what does that mean?

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.

00:29:44.730 --> 00:29:47.370
Like, how do I build a robot that has aspects of femininity?

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

00:29:52.250 --> 00:29:52.970
totally circular.

00:29:53.050 --> 00:29:56.410
Like, there's no information in that definition as to what gender actually is.

00:29:56.650 --> 00:30:02.650
Uh, so I've been trying to sketch out sort of models, agent-based models, uh,

00:30:02.730 --> 00:30:07.820
but also you know, agent-based models as representations of like theoretical ideas for like

00:30:08.060 --> 00:30:11.980
how should we as evolutionary psychologists think about these concepts?

00:30:12.140 --> 00:30:12.940
Are these different things?

00:30:13.020 --> 00:30:14.460
And if so, what are they?

00:30:14.620 --> 00:30:19.020
Um and hopefully that's also useful to like other psychologists more broadly.

00:30:19.900 --> 00:30:22.220
So I'm I've been writing this like law on paper.

00:30:22.300 --> 00:30:23.420
It's way too long right now.

00:30:23.500 --> 00:30:26.940
I I just finished like the very rough first draft on Tuesday.

00:30:27.820 --> 00:30:30.220
These ideas are still like very actively cooking.

00:30:31.210 --> 00:30:31.530
cool.

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

00:30:36.650 --> 00:30:37.690
and gender in the first place?

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

00:30:41.770 --> 00:30:43.610
kind of sketching how I think we should think about gender.

00:30:43.930 --> 00:30:44.090
Yeah.

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,

00:30:50.330 --> 00:30:56.240
in addition to reinventing agent-based modeling when you were um back in Texas, Uh in

00:30:56.240 --> 00:31:02.240
your model, I if I followed you um sort of re-derived the evolution of

00:31:02.240 --> 00:31:03.840
sex in your model, which was kind of cool.

00:31:04.000 --> 00:31:06.800
You started very much from like first principles.

00:31:07.600 --> 00:31:07.840
Yeah.

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,

00:31:14.080 --> 00:31:16.640
which is partly contributing to its excessive length.

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.

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

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

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

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

00:31:40.590 --> 00:31:41.070
for it to run.

00:31:41.150 --> 00:31:46.270
So you pause it, you have your little population, you analyze them, you report some figures

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,

00:31:50.980 --> 00:31:55.300
put them on a shelf and like never really worry about them ever again.

00:31:55.540 --> 00:32:01.460
Um in this project, I'm trying to uh sort of work with a set

00:32:01.460 --> 00:32:06.180
of populations of agents throughout uh a variety of different models.

00:32:06.260 --> 00:32:08.900
Uh and again, yeah, starting from first principles.

00:32:08.980 --> 00:32:12.100
So like the first question is like why do we even have sex?

00:32:13.300 --> 00:32:18.570
So a build an agent-based model that is a uh sort of

00:32:18.570 --> 00:32:22.250
recapitulation of John Tuby's model of the evolution of sex.

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

00:32:26.890 --> 00:32:29.050
a super satisfying answer to.

00:32:29.210 --> 00:32:31.290
Like, why do we have sex in the first place?

00:32:31.450 --> 00:32:34.090
Why are we not all asexually reproducing organisms?

00:32:34.410 --> 00:32:37.770
Asexual reproduction just seems like the better way to reproduce.

00:32:37.930 --> 00:32:40.970
Like you don't have to pay all the costs of searching for a mate.

00:32:41.140 --> 00:32:44.180
you certainly don't have to pay all the costs of like establishing and maintaining a

00:32:44.180 --> 00:32:45.460
relationship like we do.

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

00:32:51.220 --> 00:32:57.140
you know, suppose your organism produces two offspring per generation, you know you're

00:32:57.140 --> 00:32:59.460
getting two copies of yourself per generation, right?

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

00:33:04.020 --> 00:33:06.740
of your offspring only have a 50% chance of inheriting you.

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

00:33:12.810 --> 00:33:13.290
halved, right?

00:33:13.610 --> 00:33:14.970
Like, why would you do that?

00:33:15.290 --> 00:33:20.330
Um, so there are some hypotheses out there.

00:33:20.410 --> 00:33:22.890
I think this might have been John Tiwee's first paper.

00:33:23.050 --> 00:33:26.250
Uh I actually I keep saying that, but I need to verify that it's true.

00:33:26.410 --> 00:33:32.300
But like he offered uh a model that at

00:33:32.300 --> 00:33:33.260
the time was novel.

00:33:33.660 --> 00:33:34.700
I think he got kind of unfortunate.

00:33:34.940 --> 00:33:37.100
He was a little bit unlucky in that W.D.

00:33:37.260 --> 00:33:40.460
Hamilton proposed a very similar model at the same time.

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

00:33:45.100 --> 00:33:47.660
it to John because he independently came up with it.

00:33:47.900 --> 00:33:51.660
Um John's idea was that it has to do with pathogens, right?

00:33:51.740 --> 00:33:57.130
Like so we're all in this long standing coevolutionary arms race with pathogens.

00:33:57.290 --> 00:34:02.090
They want to invade our bodies, you know, consume our bodies to produce copies of

00:34:02.090 --> 00:34:02.650
themselves.

00:34:02.810 --> 00:34:04.250
That's good for their reproduction.

00:34:04.330 --> 00:34:05.770
It's bad for our reproduction.

00:34:05.930 --> 00:34:08.490
So we evolve defenses to try to keep them out.

00:34:08.650 --> 00:34:11.370
They evolve counter defenses to try to get in.

00:34:11.690 --> 00:34:13.210
You know, that goes on forever.

00:34:13.450 --> 00:34:18.900
The problem for us is, you know, as large-bodied, long-lived, you know, slow reproducing

00:34:18.900 --> 00:34:22.100
organisms, pathogens reproduce much more quickly than we do, right?

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

00:34:27.220 --> 00:34:33.060
generations to adapt to our defenses than we have to evolve, you know,

00:34:33.220 --> 00:34:34.420
new defenses.

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

00:34:39.620 --> 00:34:45.610
reproducing organism, you're invaded by some pathogen, it gets many generations to

00:34:45.610 --> 00:34:48.970
evolve unique counteroffenses to your defenses.

00:34:49.050 --> 00:34:51.530
So it's really good at exploiting your physiology.

00:34:51.690 --> 00:34:56.090
Then you produce offspring, which are genetic clones of yourself, but to the extent that

00:34:56.090 --> 00:35:01.050
your defenses are heritable, the pathogens that are well adapted to you are immediately

00:35:01.050 --> 00:35:03.210
well adapted to infecting your offspring.

00:35:03.290 --> 00:35:04.890
And so you're never going to shake them off.

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,

00:35:10.480 --> 00:35:13.600
randomly scrambling them with half of David's genes.

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

00:35:18.480 --> 00:35:19.600
genetically unique, right?

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

00:35:24.800 --> 00:35:27.760
perfectly adapted to invading your offspring.

00:35:27.920 --> 00:35:33.230
Um, and so that, John argued, could be a sort of constant selection pressure that would

00:35:33.230 --> 00:35:37.390
favor the evolution of sexual reproduction over and above its sort of baseline costs.

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

00:35:41.630 --> 00:35:46.670
agents that are infected by a pathogen agent, they can reproduce asexually or sexually, and

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.

00:35:51.230 --> 00:35:54.910
There is a point eventually where selection will favor sexual reproduction over asexual

00:35:54.910 --> 00:35:55.870
reproduction.

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

00:36:02.200 --> 00:36:06.280
wanted to build an agent-based model of that idea because I love that idea.

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

00:36:11.400 --> 00:36:13.960
m mysteries of biology is why we sexually reproduce.

00:36:14.040 --> 00:36:18.040
And it's a particularly, I think particularly satisfying solution to that problem.

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.

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.

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

00:36:34.430 --> 00:36:37.550
just assuming, well, of course there's sex and just taking it for granted.

00:36:37.790 --> 00:36:37.950
Yeah.

00:36:38.110 --> 00:36:38.590
Um yeah.

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

00:36:44.510 --> 00:36:50.280
seem like such a window dressing thing from like a modern everyday perspective.

00:36:50.360 --> 00:36:51.800
Like that's what Lysol's for.

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

00:36:57.240 --> 00:36:58.760
modern uh intuition.

00:36:58.840 --> 00:37:00.680
It feels like, oh, pathogens, whatever.

00:37:00.760 --> 00:37:02.040
That's like the stuff in the carpet.

00:37:02.120 --> 00:37:04.120
I do, you know, I build rockets.

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

00:37:09.080 --> 00:37:15.070
take um the uh the world is much more powerful, even the microscopic world is much

00:37:15.070 --> 00:37:16.670
more powerful than you might think.

00:37:18.270 --> 00:37:18.510
Yeah.

00:37:18.670 --> 00:37:19.230
Yeah.

00:37:19.630 --> 00:37:22.910
So anyway, were you going to continue with the with the journey?

00:37:23.070 --> 00:37:25.630
So we we got the evolution of sex comes from viruses.

00:37:25.870 --> 00:37:26.910
What's the next step here?

00:37:27.630 --> 00:37:28.110
Yeah, yeah, yeah.

00:37:28.350 --> 00:37:30.510
So I mean that gives us sexual reproduction.

00:37:30.590 --> 00:37:30.830
Right.

00:37:30.990 --> 00:37:34.830
So then what I do is I take those agents that have now evolved sexual reproduction.

00:37:35.070 --> 00:37:38.030
And we ask, well, where does where do sexes come from?

00:37:38.110 --> 00:37:38.270
Right?

00:37:38.350 --> 00:37:39.470
Why, why would there be sexes?

00:37:39.630 --> 00:37:41.230
You have another sexual reproduction.

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

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

00:37:52.660 --> 00:37:54.180
and simplified way.

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

00:38:00.020 --> 00:38:03.300
Parker and you know, again, Tube and Cosmics.

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

00:38:08.420 --> 00:38:11.650
presented to the CEP, but it'll make it into the paper.

00:38:11.810 --> 00:38:14.770
Um in the supplement, probably, but it'll be there.

00:38:14.930 --> 00:38:18.930
Uh so you know the question is why do we have sexes?

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

00:38:23.170 --> 00:38:25.810
parental investment and operational sex ratio and stuff like that.

00:38:25.970 --> 00:38:27.970
I don't think that stuff is actually sex either.

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

00:38:33.970 --> 00:38:35.890
definitive of what sex is.

00:38:36.290 --> 00:38:42.180
Um and I think like biologists have roughly known this answer for a

00:38:42.180 --> 00:38:44.420
long time, but psychologists just don't know about it.

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

00:38:49.380 --> 00:38:50.180
psychologists.

00:38:50.340 --> 00:38:52.100
Uh where does where does sex come from?

00:38:52.260 --> 00:38:56.820
Well, if you reproduce sexually, the way you do it is you produce gametes, sex cells,

00:38:56.900 --> 00:38:57.060
right?

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

00:39:01.860 --> 00:39:07.690
offspring to be constructed from, as well as some just like general cellular machinery

00:39:07.770 --> 00:39:11.930
and like energy and nutrient stores that like allow the cell to function.

00:39:12.170 --> 00:39:17.770
Those gametes fuse together and you know if that fusion is successful, if there's enough

00:39:17.770 --> 00:39:22.010
energy and nutrient stores, if there's like the correct kind of machinery, then that fused

00:39:22.010 --> 00:39:24.010
zygote can develop into an offspring.

00:39:24.940 --> 00:39:30.940
So there's some probably like minimal investment that each uh

00:39:30.940 --> 00:39:33.420
sort of parent organism needs to make.

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.

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,

00:39:43.180 --> 00:39:45.340
they have some probability of success, right?

00:39:45.570 --> 00:39:48.690
Um, the way the model works is sort of built off of that kind of assumption.

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

00:39:53.730 --> 00:39:58.450
needs uh to be able to develop successfully into an organism when two gametes fuse, right?

00:39:58.850 --> 00:40:04.770
Um that said, uh as long as both parents put in that sort of

00:40:04.770 --> 00:40:10.260
50% minimum, there's you know some probability of zygote success, but there's inevitably

00:40:10.260 --> 00:40:14.500
going to be variation in how much parent organisms invest in their gametes, right?

00:40:14.660 --> 00:40:17.540
And that variation is going to have consequences, right?

00:40:17.620 --> 00:40:22.980
There's going to be an inevitable sort of quality-quantity trade-off in gamete production.

00:40:23.060 --> 00:40:28.900
Uh, so I can put more uh energy and nutrients into each of my gametes

00:40:29.150 --> 00:40:34.750
that is probably going to increase the likely success of those gametes in developing into

00:40:34.750 --> 00:40:35.950
successful offspring.

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

00:40:40.750 --> 00:40:43.310
many of them given a fixed energy budget, right?

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

00:40:49.310 --> 00:40:51.150
to have as many reproductive opportunities.

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.

00:40:57.420 --> 00:41:01.980
Uh, they're gonna be less likely to develop into successful offspring when they fuse with

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

00:41:06.700 --> 00:41:08.460
make more of them with a fixed budget, right?

00:41:08.620 --> 00:41:12.460
Uh this is again like very inspired by Dawkins, right?

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

00:41:16.220 --> 00:41:17.020
exactly this logic.

00:41:17.100 --> 00:41:20.220
He was also sort of basing this off of like Parker's ideas.

00:41:22.050 --> 00:41:27.730
So, you know, you could think you know, one potentially optimal strategy is everybody just

00:41:27.730 --> 00:41:28.930
invests 50%.

00:41:30.290 --> 00:41:36.050
But another potentially optimal strategy is you can make lots of really

00:41:36.050 --> 00:41:41.890
small gametes, which provide very little investment into the resulting offspring, but

00:41:41.890 --> 00:41:44.370
give you lots and lots of opportunities to produce offspring.

00:41:44.610 --> 00:41:48.500
And you can make those gametes such that they specifically target relatively large

00:41:48.500 --> 00:41:48.980
partners.

00:41:49.060 --> 00:41:53.380
And that sort of balances the cost and benefits of both extremes of that investment

00:41:53.380 --> 00:41:54.340
trade-off, right?

00:41:54.500 --> 00:41:58.980
If you make lots of small gametes that specifically target large gametes, then your

00:41:59.140 --> 00:42:04.020
offspring are relatively likely to be successful because they're targeting gametes that

00:42:04.020 --> 00:42:08.290
provide the energy and nutrients that you're not providing, but you get lots of

00:42:08.290 --> 00:42:09.650
reproductive opportunities.

00:42:10.130 --> 00:42:14.690
So you can set up, you know, we take those agents that have involved sexual reproduction.

00:42:14.850 --> 00:42:16.050
Now they make gametes.

00:42:16.130 --> 00:42:18.530
They have a gene that regulates do you make big or small ones?

00:42:18.690 --> 00:42:20.210
They have a fixed energy budget.

00:42:20.290 --> 00:42:25.410
And they have another gene that regulates like what size of gamete do your gametes target.

00:42:25.650 --> 00:42:30.130
And you can show, you can start off as population where there's like uniform, whatever

00:42:30.130 --> 00:42:33.090
initial distribution of gamete production you want, but you can start it off where there's

00:42:33.090 --> 00:42:37.320
like a uniform distribution of gamete size, kick that off.

00:42:37.480 --> 00:42:42.040
And very rapidly, what you see is the evolution of a bimodal distribution in gamete size,

00:42:42.120 --> 00:42:47.960
where there's some individuals that specialize in producing as small of gametes as they can

00:42:48.040 --> 00:42:50.680
that target partners that are as large as they can.

00:42:50.760 --> 00:42:54.920
And then there's other individuals that specialize in producing relatively large gametes,

00:42:55.000 --> 00:42:57.000
and there's not very much selection on their preferences.

00:42:57.270 --> 00:42:59.910
And so that that is the evolution of sexes, right?

00:43:00.310 --> 00:43:02.870
And I think that's how we need to think about sexes.

00:43:03.270 --> 00:43:09.190
Um, what sex is, is there is this inevitable quality-quantity trade-off

00:43:09.190 --> 00:43:10.230
in gamete production.

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

00:43:13.510 --> 00:43:14.070
argument.

00:43:14.150 --> 00:43:18.790
Uh, but there is this inevitable quality-quantity trade-off in gamete production.

00:43:19.280 --> 00:43:25.040
There is, given the details of a particular species like life history and

00:43:25.200 --> 00:43:31.200
ecology, there are going to be some optima in terms of where you

00:43:31.200 --> 00:43:33.760
fall on that trade off distribution.

00:43:33.920 --> 00:43:36.160
So there are isogamous species out there.

00:43:36.240 --> 00:43:40.320
There are species where the optimum is everybody contributes the same amount to their

00:43:40.320 --> 00:43:40.960
gametes.

00:43:41.040 --> 00:43:42.480
All gametes are equally sized.

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.

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

00:43:51.250 --> 00:43:52.850
of that trade-off distribution, right?

00:43:53.010 --> 00:43:57.810
Like one optimum is produce gametes basically as small as you can, the other optimum is

00:43:57.810 --> 00:43:59.730
produce gametes basically as large as you can.

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

00:44:04.690 --> 00:44:05.330
species.

00:44:07.360 --> 00:44:13.360
Dan, can I ask from an intuitive perspective, like already

00:44:13.360 --> 00:44:19.360
existing as a like a huge human, it feels like the investment in gametes

00:44:19.360 --> 00:44:22.720
is trivial, even when they're um large.

00:44:23.040 --> 00:44:23.200
Yeah.

00:44:23.680 --> 00:44:28.800
Is the idea that phylogenetically, i.e., over evolutionary time,

00:44:29.790 --> 00:44:35.790
for even small critters, that was that really was a big investment or a

00:44:35.790 --> 00:44:37.390
big investment difference.

00:44:37.710 --> 00:44:43.550
And then there's a bunch of concomitant differences that follow from

00:44:43.870 --> 00:44:49.630
that change in investment, um, such that, you know, the generally speaking, there's

00:44:49.630 --> 00:44:53.550
obligate investment on the larger gamete producer.

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

00:44:59.600 --> 00:45:05.200
gamete difference came first and then the the big macroscopic

00:45:06.160 --> 00:45:12.080
sex differences or dimorphisms came later because from the again from like the the

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

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

00:45:22.080 --> 00:45:27.070
if it's this enormous difference but I think that might be a bias perspective.

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

00:45:30.590 --> 00:45:30.990
question.

00:45:31.150 --> 00:45:33.390
I think people might be wondering when they're hearing that.

00:45:33.470 --> 00:45:35.070
And I'm I'm wondering it too.

00:45:35.150 --> 00:45:35.790
Yeah.

00:45:36.270 --> 00:45:36.510
Yeah.

00:45:36.590 --> 00:45:39.470
And I think I think a lot of psychologists are going to wonder that.

00:45:39.550 --> 00:45:44.030
Like I I want to communicate this to like a broader psychological audience than just

00:45:44.030 --> 00:45:45.070
evolutionary psychologists.

00:45:45.150 --> 00:45:48.590
So I do think that's like um that will be an issue.

00:45:48.750 --> 00:45:54.620
Um and I mean we're also like immediately falling into assuming

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

00:46:00.540 --> 00:46:02.380
as an individual you specialize in one.

00:46:02.780 --> 00:46:03.340
Yeah, and that's right.

00:46:03.420 --> 00:46:05.660
Yeah, no, totally totally that need not be the case.

00:46:05.740 --> 00:46:06.780
There's lots of things.

00:46:07.020 --> 00:46:07.340
Yeah, yeah.

00:46:09.020 --> 00:46:09.660
Yeah, exactly.

00:46:09.740 --> 00:46:10.460
Yeah, yeah, yeah.

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

00:46:14.700 --> 00:46:14.860
of time.

00:46:16.890 --> 00:46:18.410
My bias, my bias is showing.

00:46:18.570 --> 00:46:19.050
My species.

00:46:20.970 --> 00:46:21.210
Okay.

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

00:46:27.770 --> 00:46:28.410
of sex.

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

00:46:33.530 --> 00:46:34.570
energy budget.

00:46:34.810 --> 00:46:39.780
Um and I think what happens is that the reason why this

00:46:39.780 --> 00:46:45.700
bimodality in uh gamete production matters is because that does set

00:46:45.700 --> 00:46:48.740
up other selection pressures for the kinds of things that you're talking about, David,

00:46:48.900 --> 00:46:52.500
which themselves shape other aspects of phenotype that we should care about, including as

00:46:52.500 --> 00:46:53.140
psychologists.

00:46:53.300 --> 00:46:54.900
So those are kind of the subsequent models.

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

00:46:59.380 --> 00:46:59.620
sex.

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

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

00:47:10.550 --> 00:47:13.030
populations and start introducing new selection pressures.

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,

00:47:19.110 --> 00:47:24.070
we can introduce a gene that regulates like investment in sort of costly mate competition,

00:47:24.150 --> 00:47:25.350
you can think of it as.

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

00:47:31.040 --> 00:47:33.840
likely to die before you get to produce gametes.

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

00:47:39.440 --> 00:47:40.960
when it comes for fertilization.

00:47:41.120 --> 00:47:45.120
So you get earlier reproductive opportunity, you're less likely to reproduce, but if you do

00:47:45.120 --> 00:47:47.520
get to reproduce, you get to reproduce first, right?

00:47:48.800 --> 00:47:53.600
That's distributed uniformly throughout the population, independent of gamete size.

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

00:47:57.840 --> 00:48:02.410
some individuals that are specializing in producing large numbers of small gametes and

00:48:02.410 --> 00:48:05.450
other individuals that are specializing in producing small numbers of large gametes.

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

00:48:10.810 --> 00:48:11.290
your favor.

00:48:11.450 --> 00:48:13.050
There's lots of small gametes out there.

00:48:13.130 --> 00:48:17.210
You're virtually guaranteed that like all of your gametes are going to get fertilized, uh,

00:48:17.450 --> 00:48:20.650
assuming you know an equal ratio of those two morphs.

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

00:48:27.120 --> 00:48:29.520
reproduce first, like that doesn't have any benefit to you.

00:48:29.840 --> 00:48:35.120
And so selection should really strongly select against that kind of like risky, costly make

00:48:35.280 --> 00:48:36.800
competition among those individuals.

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

00:48:42.400 --> 00:48:42.640
out there.

00:48:42.720 --> 00:48:45.360
They're all competing over a smaller number of large gametes.

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

00:48:51.610 --> 00:48:53.690
bottleneck in terms of your reproduction.

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

00:48:57.690 --> 00:49:01.770
do get to produce gametes, your gametes actually get to develop into an offspring.

00:49:01.850 --> 00:49:05.690
And so selection should favor those kinds of costly competition genes in the small gamete

00:49:05.850 --> 00:49:06.170
producers.

00:49:06.250 --> 00:49:08.090
And that's exactly what happens in the models, right?

00:49:08.170 --> 00:49:12.090
You can show, you can start off these populations where these competition genes are

00:49:12.090 --> 00:49:17.140
uniformly distributed independent of uh gamete production strategy, and they very quickly

00:49:17.140 --> 00:49:22.340
evolve into these, like this sort of sex differentiated mating strategy, where small gamete

00:49:22.420 --> 00:49:26.740
producers are investing in costly mate competition, large gamete producers are not.

00:49:27.140 --> 00:49:28.580
You can do the reverse.

00:49:28.820 --> 00:49:32.500
So at the same time, introduce what I'm calling like a parenting gene.

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

00:49:38.670 --> 00:49:39.950
you use to produce gametes.

00:49:40.030 --> 00:49:42.990
So you can't make as many gametes if you're high on this parenting gene.

00:49:43.070 --> 00:49:47.710
Uh, but it increases the probability that if you produce a gamete, that gamete actually

00:49:47.710 --> 00:49:48.750
survives, right?

00:49:48.910 --> 00:49:52.990
And so we can again distribute that uniformly throughout the population and we can ask,

00:49:53.070 --> 00:49:54.430
well, what would selection do with that?

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

00:50:00.380 --> 00:50:02.940
have relatively few reproductive opportunities.

00:50:03.180 --> 00:50:05.820
The population can only support so many offspring.

00:50:05.980 --> 00:50:07.900
We stipulate that as an assumption.

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

00:50:13.500 --> 00:50:16.460
capacity or something, like that's a huge chunk of your reproductive success.

00:50:16.620 --> 00:50:20.620
It's probably worth producing slightly fewer offspring in order to make sure that all of

00:50:20.620 --> 00:50:23.660
your offspring uh actually survive to the next generation.

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

00:50:27.610 --> 00:50:29.610
get to produce an offspring at all, right?

00:50:29.690 --> 00:50:32.570
Uh, because you're the the sex ratio is not in your favor.

00:50:32.970 --> 00:50:35.130
And if you do, you're probably going to produce a lot of them.

00:50:35.290 --> 00:50:38.970
So any one offspring is probably a relatively small chunk of your overall reproductive

00:50:38.970 --> 00:50:39.370
success.

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

00:50:44.250 --> 00:50:48.570
just hoping they all work out rather than you know limiting your reproductive opportunities

00:50:49.100 --> 00:50:50.620
and keeping those alive.

00:50:50.780 --> 00:50:52.860
And that's exactly what happens in the model, right?

00:50:53.100 --> 00:50:57.500
At the same time that selection is favoring mate competition in the small gamete producers,

00:50:57.580 --> 00:51:01.100
it favors the evolution of these like costly parenting adaptations in the large gamete

00:51:01.180 --> 00:51:01.740
producers.

00:51:02.140 --> 00:51:06.620
So these are things that I think have been argued like kind of intuitively.

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

00:51:11.340 --> 00:51:15.340
system now, we're starting with the evolution of sex, getting to sexes, and now showing

00:51:15.340 --> 00:51:19.830
like sex differences in these other like mating and parenting phenotypes that follow

00:51:19.830 --> 00:51:23.670
directly from this bimodality in gamete production.

00:51:24.230 --> 00:51:25.110
Yeah, it's amazing.

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

00:51:29.350 --> 00:51:32.630
hyperbole, but often accuse just so stories, right?

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

00:51:35.670 --> 00:51:36.390
hoc story.

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

00:51:42.280 --> 00:51:46.520
showing that you know, given there's there's deductive logic to differential reproductive

00:51:46.520 --> 00:51:46.920
success.

00:51:47.000 --> 00:51:47.800
So yeah.

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

00:51:53.480 --> 00:51:55.800
these things are really contingent on the assumptions that you make.

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

00:52:01.880 --> 00:52:07.730
these sex differences are like the inevitable consequence of having sexes, like these

00:52:07.730 --> 00:52:11.170
kinds of things are like the essence of maleness or the essence of femaleness.

00:52:11.250 --> 00:52:14.690
Uh like there are species out there that don't show these patterns, right?

00:52:14.770 --> 00:52:17.010
Like phalaropes are the the classic example.

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

00:52:20.610 --> 00:52:22.050
over access to the males.

00:52:22.210 --> 00:52:27.090
Um but you can explain those kinds of outcomes within the same sort of model system.

00:52:27.170 --> 00:52:27.410
Right.

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

00:52:33.520 --> 00:52:35.840
we've already been talking about is the sex ratio.

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?

00:52:40.400 --> 00:52:43.440
There's an equal number of large gamete producers and small gamete producers.

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

00:52:47.840 --> 00:52:51.040
of the model where, like, you know, you can assume this does happen.

00:52:51.120 --> 00:52:57.010
Like, let's assume for whatever reason the large gamete producers are like really broadly

00:52:57.010 --> 00:52:58.450
dispersed throughout the environment.

00:52:58.530 --> 00:53:02.690
Like resources are really patchy, let's say, uh, they're really sparse and patchy.

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

00:53:07.490 --> 00:53:09.170
know, physical space.

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

00:53:12.770 --> 00:53:16.540
really long distances between these patches, that's going to slow down your reproductive

00:53:16.540 --> 00:53:16.940
rate, right?

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

00:53:20.860 --> 00:53:25.580
takes a long time to travel between patches of potential mates, that's going to necessarily

00:53:25.580 --> 00:53:27.100
slow down your reproductive rate.

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

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

00:53:35.980 --> 00:53:40.770
time, then you can actually see the reversal of that like competition difference.

00:53:40.930 --> 00:53:45.330
You can see evolution favoring high competition in the large gamete producers, higher

00:53:45.330 --> 00:53:47.970
competition in the large gamete producers and the small gamete producers.

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

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

00:53:56.130 --> 00:53:56.770
themselves.

00:53:56.930 --> 00:53:58.370
They're things that follow from sex.

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

00:54:03.650 --> 00:54:08.260
shaping like how does your ecology lead to particular phenotypes?

00:54:08.900 --> 00:54:09.300
Cool.

00:54:10.180 --> 00:54:10.820
Interesting.

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,

00:54:16.100 --> 00:54:18.740
with you know an exception that proves the rule.

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.

00:54:24.980 --> 00:54:29.780
Um do you think w is it does it make sense to think of

00:54:30.330 --> 00:54:35.450
phalaropes uh their different parental investment strategies, are is that sex?

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

00:54:41.130 --> 00:54:46.010
males, should we flip it around and say that the males are females in some sense?

00:54:47.290 --> 00:54:48.410
It's a good question.

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.

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

00:54:59.080 --> 00:55:00.600
think about like what sex is?

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,

00:55:05.320 --> 00:55:09.640
but there's another model after this where I'm modeling like sex differences in mating

00:55:09.640 --> 00:55:09.960
strategies.

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

00:55:14.520 --> 00:55:19.080
regulates like, do you pursue lots of mating opportunities and do not very much parenting?

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

00:55:23.320 --> 00:55:27.890
pursuing mating opportunities and you know the the sex difference you expect does evolve in

00:55:27.890 --> 00:55:31.810
this kind of context, assuming you know relatively equal sex ratio.

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

00:55:36.690 --> 00:55:41.170
a species like us that produces like highly altruitial offspring that really benefit from

00:55:41.170 --> 00:55:45.490
having biparental care, then what you see is selection favors sort of lots of parenting and

00:55:45.490 --> 00:55:48.450
not very much mating in both the large and small gamete producers.

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.

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

00:55:58.900 --> 00:56:00.420
particular phenotypes, right?

00:56:00.580 --> 00:56:03.380
Um, so yeah, what does that mean for how we should think about sex?

00:56:03.540 --> 00:56:07.380
Yeah, should we call like the big colorful phalaropes the males?

00:56:07.460 --> 00:56:11.940
Uh or should we stick to like gametes as defining what the sexes are?

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

00:56:17.700 --> 00:56:21.650
terms of these manifest phenotypes, like there's all kinds of phenotypes out there.

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

00:56:26.530 --> 00:56:27.330
production, right?

00:56:28.050 --> 00:56:33.250
These particular phenotypes are really particular to this species ecology and

00:56:33.490 --> 00:56:35.330
phylogenetic history and life history.

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

00:56:40.370 --> 00:56:43.570
we're going to need lots of really complicated definitions.

00:56:43.810 --> 00:56:49.580
Whereas, you know, I think gametizes is like a clean, universal um uh universal,

00:56:49.740 --> 00:56:51.500
universally applicable definition.

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

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

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

00:57:09.580 --> 00:57:15.530
like what sex is, is it's not really a variable that properly

00:57:15.530 --> 00:57:18.010
exists at like approximate level of analysis.

00:57:18.170 --> 00:57:20.970
It's not something that's in the organism itself.

00:57:21.130 --> 00:57:24.570
Uh, what sex is, it's it's a set of reproductive strategies, right?

00:57:24.650 --> 00:57:29.930
It's these optimal gamete production strategies that selection is always pushing organisms

00:57:29.930 --> 00:57:30.570
toward.

00:57:30.650 --> 00:57:35.700
Uh, but any individual organism is always going to be some kind of noisy approximation of

00:57:35.700 --> 00:57:36.500
those optima.

00:57:37.220 --> 00:57:41.940
And those optima are going to have lots of consequences for the kinds of phenotypes that

00:57:41.940 --> 00:57:46.900
evolve, but there's no one element in that phenotype that is the organism sex.

00:57:47.220 --> 00:57:53.140
So I think like we find it very useful to perceive the world

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

00:57:57.220 --> 00:57:58.820
that organism is a female.

00:57:59.530 --> 00:58:03.770
Because sex does exist, it does have these effects on phenotypes.

00:58:03.850 --> 00:58:08.650
It does structure the covariance matrix of phenotypes such that certain bundles of

00:58:08.650 --> 00:58:14.250
adaptations tend to go with some organisms and other bundles of adaptations tend to go with

00:58:14.250 --> 00:58:14.650
others.

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

00:58:19.850 --> 00:58:21.050
hallucination, right?

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

00:58:25.560 --> 00:58:28.680
understand that like it's these abstract conceptual optima.

00:58:29.000 --> 00:58:31.640
It's it's the designs that selection is pushing towards.

00:58:31.800 --> 00:58:35.560
It's not anything that's in the physical organism's body.

00:58:37.960 --> 00:58:38.280
Interesting.

00:58:38.680 --> 00:58:41.480
That's where like people are like no, it's great.

00:58:41.560 --> 00:58:41.800
Yeah.

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.

00:58:45.880 --> 00:58:50.430
I was thinking while you're talking like other analogies, excuse me.

00:58:51.150 --> 00:58:56.590
And things like risk-taking or being not risk-taking are also sort of these abstractions.

00:58:57.790 --> 00:59:01.870
But then again, of course, like risk-taking can vary by situation.

00:59:02.190 --> 00:59:07.550
And something like sex, there are the chemaphroditic species where it's sort of, you know,

00:59:07.790 --> 00:59:13.000
both, but it's not as facultative or situational specific, it seems.

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

00:59:19.240 --> 00:59:25.000
probably having strong biases about um having entries for sex, basically.

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

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

00:59:34.760 --> 00:59:36.120
in design space.

00:59:36.850 --> 00:59:37.170
Yeah.

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

00:59:42.370 --> 00:59:43.810
like the gender stuff.

00:59:44.050 --> 00:59:48.850
Um, but yeah, I think we need to like sort of fight our intuitions.

00:59:49.010 --> 00:59:51.410
Uh I and I think it's fine to have those intuitions.

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

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

01:00:00.420 --> 01:00:04.500
model of these things, we need to understand that like our intuitions are sort of leading

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

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.

01:00:13.220 --> 01:00:17.940
Like it's useful to categorize organisms into species.

01:00:18.100 --> 01:00:23.700
Like there are real evolutionary histories that cause you know humans to be different from

01:00:23.700 --> 01:00:24.820
chimpanzees.

01:00:24.980 --> 01:00:29.870
It's a consequence of like, you know, certain facts about the evolutionary history of those

01:00:29.870 --> 01:00:30.830
organisms.

01:00:31.870 --> 01:00:33.870
And it's a really useful concept.

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

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

01:00:40.910 --> 01:00:43.630
nothing in a chimp that like is its chimpness, right?

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,

01:00:49.600 --> 01:00:52.160
and everything that's not a chimp doesn't have this, right?

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

01:00:57.200 --> 01:00:59.520
particular individual is X.

01:00:59.760 --> 01:01:00.000
Right.

01:01:00.240 --> 01:01:05.520
Uh you know, sex is Can take a stab at defining sex?

01:01:05.840 --> 01:01:06.640
Yeah, yeah.

01:01:06.880 --> 01:01:09.360
So please uh is this right?

01:01:09.440 --> 01:01:14.720
It's the set of adaptations uh that tend to evolve as a function of gamete

01:01:14.960 --> 01:01:15.520
size.

01:01:16.070 --> 01:01:17.590
I wouldn't say it's the adaptations.

01:01:18.230 --> 01:01:18.470
No.

01:01:18.630 --> 01:01:18.790
Okay.

01:01:19.270 --> 01:01:19.590
Yeah.

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

01:01:24.550 --> 01:01:25.510
like human sex.

01:01:25.990 --> 01:01:26.950
That's why I said tend to.

01:01:27.110 --> 01:01:30.150
Not necessarily I mean there are c there are exceptions like phalarop, but you instead of

01:01:30.150 --> 01:01:33.350
actually patients that tend to evolve as a function gamete size.

01:01:33.830 --> 01:01:36.950
I really want to avoid tend to oh okay all right.

01:01:37.270 --> 01:01:40.660
How I Indicated by indicated by the case.

01:01:40.740 --> 01:01:44.340
Well, I mean, too in most cases evolve, but you know, with some exceptions.

01:01:44.740 --> 01:01:47.060
No, David, you good job taking a stab at this.

01:01:47.140 --> 01:01:47.460
This is good.

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

01:01:52.980 --> 01:01:58.980
want to push towards like what sex is is these optimal trade off

01:01:59.140 --> 01:02:03.540
points in the quality quantity trade off in gamete production.

01:02:03.780 --> 01:02:04.100
Right?

01:02:04.420 --> 01:02:09.270
Like no matter what kind of sexually reproducing species you are, you have to make some

01:02:09.270 --> 01:02:12.710
sort of decision about how much you invest in each of your gametes.

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

01:02:17.510 --> 01:02:19.270
quantity of gametes you can produce.

01:02:19.510 --> 01:02:25.510
There's going to be some set of optima, given your you know the details of

01:02:25.510 --> 01:02:26.230
your species.

01:02:26.950 --> 01:02:29.110
But it's often going to be one or two optima.

01:02:29.430 --> 01:02:31.270
That just seems to be what happens.

01:02:31.670 --> 01:02:36.880
Um So what sexes are, like the sexes are those optimal trade-off points.

01:02:37.120 --> 01:02:37.760
So got it.

01:02:37.920 --> 01:02:38.000
Okay.

01:02:38.080 --> 01:02:40.080
So the sex is the get is the gametes.

01:02:40.240 --> 01:02:43.280
And what you're either bigger or small gametes, basically.

01:02:43.600 --> 01:02:45.040
Can I can I say can I poke?

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

01:02:50.800 --> 01:02:56.800
given uh certain stipulations about the world in this

01:02:56.800 --> 01:03:00.770
particular case, largely being gamete trade-off.

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

01:03:06.290 --> 01:03:12.210
driving phenotypic driving phenotypes but it's it's in it's those set selection

01:03:12.210 --> 01:03:17.330
pressures specifically predicated upon gamete trade-offs

01:03:18.050 --> 01:03:21.010
I'm a little I I think that's I think that's a lot closer.

01:03:21.090 --> 01:03:22.210
I'm and that might be right.

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

01:03:30.400 --> 01:03:31.200
might be equivalent.

01:03:31.920 --> 01:03:35.600
But it it's it's the the optima toward which selection is pushing.

01:03:35.760 --> 01:03:36.560
Those are the sexes.

01:03:36.640 --> 01:03:37.520
That's what I'm saying.

01:03:37.600 --> 01:03:37.840
Yeah.

01:03:38.000 --> 01:03:38.720
Yeah, yeah, yeah.

01:03:38.800 --> 01:03:39.040
Yeah.

01:03:39.760 --> 01:03:45.760
Yeah, that is very uh uh it's like physics where you it's not

01:03:45.760 --> 01:03:46.560
intuitive.

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?

01:03:51.990 --> 01:03:53.110
Like that's great.

01:03:53.190 --> 01:03:53.350
Yeah.

01:03:53.670 --> 01:03:58.630
Sex is the set of adaptations that specialize in either large or small gametes.

01:04:01.910 --> 01:04:03.590
I'm a little bit nervous.

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

01:04:07.670 --> 01:04:12.710
because, like, how do you then define what those adaptations are,

01:04:13.420 --> 01:04:16.780
especially across species that do it in lots of different ways.

01:04:17.420 --> 01:04:22.940
Um the uh the specifically the gamete uh is the is the adaptation.

01:04:23.100 --> 01:04:23.900
So the argument.

01:04:25.020 --> 01:04:25.260
Yeah.

01:04:25.580 --> 01:04:30.060
Or would it be the argument that there's evidence of special design that those phenotypic

01:04:30.060 --> 01:04:34.940
features are the results of the gamete associated selection pressures, which is maybe just

01:04:34.940 --> 01:04:36.700
a way of redescribing what you're saying, Dan?

01:04:38.780 --> 01:04:40.370
Um it could be.

01:04:40.530 --> 01:04:44.610
And then I think it gets really what is sex becomes super contentious.

01:04:44.770 --> 01:04:50.530
Um it becomes like super contingent on like states of evidence for like special design for

01:04:50.530 --> 01:04:51.250
certain things.

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?

01:04:58.450 --> 01:04:59.410
Um kind of.

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.

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

01:05:07.100 --> 01:05:07.420
optima.

01:05:07.660 --> 01:05:10.940
And certainly there are isogamous species where it's like all one kind.

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

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

01:05:19.500 --> 01:05:21.260
out, or four gamete sizes that kind of work out.

01:05:21.340 --> 01:05:21.900
I don't know.

01:05:22.060 --> 01:05:26.700
Um or maybe cheap, cheap or expensive gametes.

01:05:27.820 --> 01:05:31.260
I I you know, I think that's how it seems to often work.

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

01:05:36.950 --> 01:05:38.230
particular manifestation, right?

01:05:38.310 --> 01:05:43.830
Like what sex is is just like whatever are the optima for this particular species, right?

01:05:43.990 --> 01:05:49.350
Like and very often the optima are yeah, cheap or expensive or big or small.

01:05:49.590 --> 01:05:53.350
Um, but in principle it could be something else.

01:05:53.720 --> 01:05:57.720
So it's the equilibrium for selection on gamete size.

01:05:57.960 --> 01:05:58.360
Exactly.

01:05:58.520 --> 01:05:58.840
Exactly.

01:05:59.400 --> 01:06:01.480
So if it was a yeah, so yeah, that's great.

01:06:01.560 --> 01:06:07.320
But so if there was a species that was produced big uh gametes or small

01:06:07.320 --> 01:06:11.320
gametes facultatively or every other day of the week, it would be one sex.

01:06:12.840 --> 01:06:18.770
Because that's the universal um you're talking about like a

01:06:18.770 --> 01:06:20.450
sequentially hermaphroditic species.

01:06:20.610 --> 01:06:22.130
I think I would say there's two sexes there.

01:06:22.210 --> 01:06:25.250
It's just you like individuals alternate between the sexes.

01:06:25.490 --> 01:06:28.290
But that's like that's maybe really getting into semantics.

01:06:28.450 --> 01:06:29.090
Like I don't know.

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

01:06:33.970 --> 01:06:34.130
nicely.

01:06:34.370 --> 01:06:35.170
David, can you repeat that?

01:06:35.250 --> 01:06:35.730
What was that again?

01:06:35.970 --> 01:06:36.370
Sure, sure.

01:06:36.770 --> 01:06:42.610
I believe um how I framed it's the it's the equilibrium uh of

01:06:42.610 --> 01:06:45.090
the selection pressures on gamete size.

01:06:45.940 --> 01:06:46.500
Yeah.

01:06:46.660 --> 01:06:46.980
Yeah.

01:06:47.140 --> 01:06:47.380
Yeah.

01:06:47.540 --> 01:06:50.660
It's there's that is specific to the equilibrium.

01:06:50.980 --> 01:06:51.220
Yeah.

01:06:51.300 --> 01:06:54.020
It is the speci yes that is specific to a species.

01:06:54.340 --> 01:06:54.580
Great.

01:06:54.740 --> 01:06:56.260
Put that on a hallmark card.

01:06:56.980 --> 01:06:57.220
Okay.

01:06:57.300 --> 01:06:58.980
So now we've figured out sex.

01:06:59.380 --> 01:07:00.020
Okay, hold on.

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

01:07:04.420 --> 01:07:06.100
I think different from gender.

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,

01:07:11.190 --> 01:07:16.870
uh parental investment, stuff like that, that tends to follow from uh gamet

01:07:17.190 --> 01:07:20.390
size, but doesn't always, as in the case of the fowler opes.

01:07:20.790 --> 01:07:25.430
Um and so you have sex, which is the equilibrium selection pressure on gamete size, and

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

01:07:30.020 --> 01:07:30.420
follow.

01:07:30.660 --> 01:07:35.060
And and sex roles would be something like parental investment, intersexual competition,

01:07:35.220 --> 01:07:36.020
stuff like that.

01:07:36.660 --> 01:07:37.380
I think that's right.

01:07:37.460 --> 01:07:37.700
Yeah.

01:07:37.940 --> 01:07:38.100
Okay.

01:07:38.340 --> 01:07:39.780
And I I think they do follow.

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.

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.

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.

01:07:57.250 --> 01:07:57.890
Got it.

01:07:58.130 --> 01:07:58.450
Okay.

01:07:58.530 --> 01:07:59.410
So we got sex.

01:07:59.490 --> 01:08:00.530
We got sex role.

01:08:00.690 --> 01:08:02.930
Now let's move on to gender.

01:08:03.410 --> 01:08:03.730
Yeah.

01:08:03.810 --> 01:08:06.290
So why what is gender and why do we have it?

01:08:06.450 --> 01:08:10.610
This is like so that whole part, the whole first part is just like recapitulating what

01:08:10.610 --> 01:08:12.930
evolutionary biologists have already said for the most part.

01:08:13.090 --> 01:08:17.250
Maybe that equilibrium thing is like that's my like tinfoil hat addition.

01:08:19.180 --> 01:08:22.860
But gender, I think, is something that has been way under theorized.

01:08:23.180 --> 01:08:25.820
In part because it turns out it's like a really new concept.

01:08:26.220 --> 01:08:27.660
I was not really aware of this.

01:08:27.820 --> 01:08:33.740
Like people, the like the first reference to gender as a concept separate

01:08:33.740 --> 01:08:38.700
from sex was from a like a psychiatry paper in like 1955 or something like that.

01:08:38.860 --> 01:08:43.340
Like people have not really been thinking about these, at least in technical ways, people

01:08:43.340 --> 01:08:46.620
have not been thinking about these as separate concepts for all that long.

01:08:47.330 --> 01:08:51.810
And, you know, like I said, the definitions are really vague and I think unhelpful.

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

01:08:58.850 --> 01:09:04.850
a sort of set of folk theories about how sex manifests in

01:09:04.850 --> 01:09:06.450
your particular ecology.

01:09:07.490 --> 01:09:10.780
So why do we need these folk theories?

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

01:09:16.140 --> 01:09:18.300
knowing, like, okay, these are human sex roles?

01:09:18.540 --> 01:09:21.260
This is my sex, I'm in an activist role.

01:09:21.500 --> 01:09:27.100
Um, I think it has something to do with like our species' unique

01:09:27.260 --> 01:09:29.820
distribution and foraging niche, right?

01:09:29.980 --> 01:09:35.830
Like, so we are really widely dispersed hyper generalist

01:09:35.830 --> 01:09:36.790
foragers, right?

01:09:36.870 --> 01:09:42.790
Like humans live in all you know inhabitable ecologies on this

01:09:42.790 --> 01:09:43.990
planet plus Canada.

01:09:44.550 --> 01:09:45.270
And New Jersey.

01:09:45.350 --> 01:09:46.150
And New Jersey.

01:09:46.470 --> 01:09:47.350
And New Jersey.

01:09:47.910 --> 01:09:53.670
And we, you know, wherever we are born, wherever we migrate, we just like figure

01:09:53.670 --> 01:09:57.670
out how to extract food from that ecology.

01:09:58.870 --> 01:10:04.000
And we tend to specialize in the things that are hardest to extract, right?

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

01:10:09.680 --> 01:10:13.120
and nutrient dense, but really doesn't want to provide those calories and nutrients.

01:10:13.200 --> 01:10:16.240
And so it's uh very difficult to extract.

01:10:16.480 --> 01:10:20.640
Or we, you know, rely on these like difficult to extract gathered goods, like you know,

01:10:20.720 --> 01:10:26.630
like tubers and honey and stuff like that, that requires like great skill and technology

01:10:26.630 --> 01:10:30.790
and time to extract, and also potentially great skill, technology, and time to process into

01:10:30.790 --> 01:10:31.670
something that we can eat.

01:10:31.750 --> 01:10:37.430
But in exchange, you know, we get these like huge energy packages that you know

01:10:37.590 --> 01:10:41.110
both require but also feed this like big expensive brain that we have, right?

01:10:41.270 --> 01:10:43.910
So this is all like you know, known.

01:10:44.070 --> 01:10:48.120
I'm like standing on the shoulders of like anthropological giants here.

01:10:48.200 --> 01:10:52.760
Like this is all very much inspired by like you know, Kaplan and Lancaster and Gillener

01:10:52.920 --> 01:10:54.040
Toto and stuff like that.

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

01:11:00.280 --> 01:11:03.880
inside of a human infant body, right?

01:11:04.120 --> 01:11:07.560
Like your organism opens its eyes for the first time.

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

01:11:13.590 --> 01:11:17.990
like, you know, how do my people make their way in this world, right?

01:11:18.070 --> 01:11:20.550
Like what kind of food are we primarily fishers?

01:11:20.710 --> 01:11:22.630
Are we primarily large game hunters?

01:11:22.950 --> 01:11:25.830
Are we, you know, industrialized Americans?

01:11:25.990 --> 01:11:28.230
Are we primarily eat like processed fast food?

01:11:28.390 --> 01:11:30.070
Like, where does my food come from?

01:11:30.230 --> 01:11:31.350
I think is a major question.

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

01:11:35.270 --> 01:11:35.910
life, right?

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

01:11:41.800 --> 01:11:42.680
spend your time, right?

01:11:42.760 --> 01:11:45.560
It shapes what technologies you invest time learning into.

01:11:45.800 --> 01:11:51.080
Um, it invests like what skills do you uh try to build up over a lifetime?

01:11:51.240 --> 01:11:53.400
It shapes, you know, how do you live?

01:11:53.480 --> 01:11:54.600
Like, are you sedentary?

01:11:54.680 --> 01:11:55.640
Are you very mobile?

01:11:55.800 --> 01:11:57.560
What kinds of relationships do you have with others?

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

01:12:03.630 --> 01:12:05.390
know individual reciprocity?

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

01:12:10.590 --> 01:12:14.430
like the ways that that ecology you know affords you getting your food.

01:12:14.670 --> 01:12:19.150
Um and so humans have to be able to learn how to do all those things.

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

01:12:23.390 --> 01:12:24.270
all the rest of it?

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

01:12:29.480 --> 01:12:34.280
first uh postdocs was in a lab that was that was like a socioecology lab.

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

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

01:12:44.120 --> 01:12:44.440
structures.

01:12:44.520 --> 01:12:45.960
And I thought like that's perfect.

01:12:46.040 --> 01:12:52.030
Like I think like because of our unique foraging need niche, Like humans basically have to

01:12:52.030 --> 01:12:54.270
be like intuitive socioecologists, right?

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

01:12:57.550 --> 01:13:01.950
like, what's my ecology and how does that shape how people live their lives here?

01:13:02.110 --> 01:13:05.150
Um, I think that's just a question that humans need to ask.

01:13:05.470 --> 01:13:08.430
Um go ahead.

01:13:08.670 --> 01:13:10.910
So just uh sorry, it's an interesting thought you just had.

01:13:10.990 --> 01:13:16.830
Um so socioecology tends to study the relationships between ecology and social

01:13:16.830 --> 01:13:19.390
structure that emerge over evolutionary time.

01:13:19.640 --> 01:13:25.480
But what you're saying is that um those relationships between ecology and social structure

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

01:13:31.400 --> 01:13:35.720
evolutionary time because humans learn how to adjust their social structure to their

01:13:35.720 --> 01:13:36.600
ecology.

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,

01:13:41.560 --> 01:13:45.880
but the mapping arises over ontogenetic time as opposed to evolutionary time.

01:13:46.280 --> 01:13:47.800
Yeah, yeah, that's exactly right.

01:13:47.880 --> 01:13:48.120
Yeah.

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.

01:13:52.450 --> 01:13:54.850
This is so much of what like cultural evolution is all about.

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

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

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

01:14:07.890 --> 01:14:09.490
like the apologies they happen to live in.

01:14:09.640 --> 01:14:13.960
And there are, you know, I think the adaptations, the psychological adaptations underlying

01:14:13.960 --> 01:14:17.240
these things, this is where evolutionary psychologists could do better, like mapping out

01:14:17.240 --> 01:14:18.680
how those adaptations actually work.

01:14:18.840 --> 01:14:24.120
But there are clearly adaptations for like, you know, learning how to produce uh you know

01:14:24.360 --> 01:14:28.840
technologies that are going to be useful in your particular ecology, or you know, learning

01:14:28.840 --> 01:14:33.080
how to flexibly deploy uh you know different kinds of foraging strategies or different

01:14:33.080 --> 01:14:37.080
kinds of cooperation strategies depending on you know the structure of the environment you

01:14:37.080 --> 01:14:38.280
happen to live in.

01:14:39.010 --> 01:14:43.490
So I think, you know, the variations in our ecologies generate this variation.

01:14:43.650 --> 01:14:45.090
So now there's an adaptive problem.

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

01:14:50.690 --> 01:14:52.930
like in my in my population?

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

01:14:56.210 --> 01:14:57.330
figure out a way to describe this.

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

01:15:01.600 --> 01:15:03.760
you're saying gender is about learning how to forage.

01:15:03.920 --> 01:15:05.600
That's not really the argument I'm trying to make.

01:15:05.680 --> 01:15:10.160
What I'm trying the argument I'm trying to make is variability in foraging generates

01:15:10.240 --> 01:15:12.880
variability in all these other aspects of life.

01:15:13.760 --> 01:15:14.960
That's what you need to learn.

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

01:15:20.400 --> 01:15:21.200
generates variation.

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

01:15:26.230 --> 01:15:29.430
vary potentially as a function of like your ecology or foraging.

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

01:15:34.390 --> 01:15:36.470
spectrum, the life way of hunter gatherers.

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

01:15:42.150 --> 01:15:44.710
is the life way for people like me?

01:15:44.870 --> 01:15:46.470
Like what foods do I eat?

01:15:46.630 --> 01:15:48.340
What technologies do I need to master?

01:15:48.420 --> 01:15:49.620
What skills do I need to master?

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

01:15:54.660 --> 01:15:57.300
from this environment and turn it into offspring, basically?

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

01:16:04.020 --> 01:16:06.500
helping maybe in that articulation.

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

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

01:16:17.490 --> 01:16:23.090
community are not arbitrary, but they're sort of tethered

01:16:24.050 --> 01:16:30.050
to um resources and therefore the ecology.

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?

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

01:16:40.930 --> 01:16:43.890
counts as being good or bad, or what the games are.

01:16:44.450 --> 01:16:50.400
There can be some proliferation, some elaboration, but ultimately there's some feedback

01:16:50.400 --> 01:16:56.080
from the ecology and the resources because ultimately that's really where the rubber hits

01:16:56.080 --> 01:16:56.880
the road.

01:16:57.280 --> 01:16:58.320
I think that's exactly right.

01:16:58.480 --> 01:16:58.720
Yeah.

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

01:17:03.600 --> 01:17:04.640
different species, right?

01:17:04.800 --> 01:17:07.280
And I think you know that's what racists think, right?

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

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,

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

01:17:20.750 --> 01:17:22.190
not how human genetics works, right?

01:17:22.270 --> 01:17:24.830
Like, there's huge gene flow across human populations.

01:17:25.070 --> 01:17:29.310
And so there is this variation that's tethered to ecology, but it's also importantly,

01:17:29.550 --> 01:17:33.070
because there's constant interaction between different human populations, there's constant

01:17:33.070 --> 01:17:37.630
gene flow across different populations, the that variation is unpredictable from the genes

01:17:37.790 --> 01:17:38.270
I view.

01:17:38.420 --> 01:17:40.260
It's again, I'm going back to Dawkins.

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?

01:17:44.020 --> 01:17:48.820
Like the genes need to build one psychology that can work in any kind of ecology.

01:17:48.980 --> 01:17:51.300
It doesn't know what that ecology is going to look like in advance.

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

01:17:56.340 --> 01:17:58.660
say, well, this is how my people seem to live.

01:17:58.820 --> 01:18:01.460
So I'm gonna do the same.

01:18:02.070 --> 01:18:03.030
Yeah, that's amazing.

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

01:18:08.230 --> 01:18:09.030
development then.

01:18:09.110 --> 01:18:14.470
And it in and it's um and it really suggests like a very rich, unexplored

01:18:14.470 --> 01:18:20.310
psychology of resources, uh, ways of making a living,

01:18:20.550 --> 01:18:21.190
status.

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

01:18:25.830 --> 01:18:30.480
language, but it it suggests that um, and I think some developmental psychologists have

01:18:30.480 --> 01:18:31.760
studied these kinds of things.

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

01:18:36.880 --> 01:18:40.320
up this idea of yeah, it's intuitive.

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

01:18:43.520 --> 01:18:45.600
making a living, broadly speaking.

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

01:18:50.080 --> 01:18:52.720
software critter, is uh that's cool.

01:18:52.880 --> 01:18:55.010
That yeah, that's that's a hard, yeah.

01:18:55.170 --> 01:18:57.090
But a good it's fruitful.

01:18:57.170 --> 01:18:59.970
Like you I can I can start imagining ways of doing that.

01:19:00.050 --> 01:19:00.450
Yeah.

01:19:00.850 --> 01:19:02.450
Yeah, I wouldn't say it's unexplored.

01:19:02.530 --> 01:19:05.490
Like I feel like developmental psychologists have been studying this and anthropologists

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

01:19:11.090 --> 01:19:15.330
sort of unified, this is the elephant that we're all trying to describe, you know.

01:19:15.490 --> 01:19:15.810
Right.

01:19:16.050 --> 01:19:18.880
Um so I mean, where does gender come from in this though?

01:19:18.960 --> 01:19:21.920
Like I'm just talking about like, you know, learning life wave.

01:19:22.160 --> 01:19:28.000
I think um because of sex, because sex does shape phenotypes in ways that we've

01:19:28.000 --> 01:19:32.160
already talked about, uh, those phenotypes that follow from sex are going to have

01:19:32.160 --> 01:19:33.840
implications for that life way, right?

01:19:33.920 --> 01:19:36.560
And we know this from lots of anthropological research, right?

01:19:36.640 --> 01:19:42.470
Like one major aspect of this is like some individuals have high levels of obligate

01:19:42.470 --> 01:19:47.190
childcare in the form of internal gestation and uh and lactation.

01:19:47.910 --> 01:19:53.350
That's going to make certain forms of foraging more difficult in some ecologies, right?

01:19:53.430 --> 01:19:59.350
It's like difficult and dangerous to large game hunting in some ecologies if you have

01:19:59.350 --> 01:20:01.910
to breastfeed an infant on demand.

01:20:02.800 --> 01:20:07.520
And, you know, Kaplan and Lancaster and colleagues, they have like these really nice models

01:20:07.520 --> 01:20:12.880
that show like because large game hunting is so skill intensive, like and because of the

01:20:12.880 --> 01:20:18.640
amount of time that like, you know, a sort of like natural fertility uh

01:20:18.640 --> 01:20:24.160
woman would spend uh, you know, pregnant or breastfeeding, um, like

01:20:24.240 --> 01:20:29.990
it's just not worth the investment to try to build up the skills to do

01:20:29.990 --> 01:20:30.870
large game hunting.

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

01:20:35.670 --> 01:20:37.750
try to uh invest in those skills.

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,

01:20:42.390 --> 01:20:43.910
you know, many foraging societies.

01:20:43.990 --> 01:20:49.700
Like it's it's just a more efficient arrangement for everybody if you males

01:20:49.700 --> 01:20:54.020
focus on these activities that are not very compatible with childcare, because they have

01:20:54.020 --> 01:20:57.940
lots of time to build up the skills to make that a worthwhile investment.

01:20:58.260 --> 01:21:03.140
And females invest more in other activities

01:21:04.980 --> 01:21:07.380
that are more compatible with child care demands.

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

01:21:16.430 --> 01:21:17.390
across ecologies, right?

01:21:17.470 --> 01:21:19.550
In some ecologies, maybe sex won't matter that much, right?

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.

01:21:25.710 --> 01:21:28.270
You know, it depends on the details of your ecology, is the point.

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

01:21:34.190 --> 01:21:38.590
in a place where there's sex differences in your this kind of like life way.

01:21:38.720 --> 01:21:42.080
uh, but the nature of those sex differences is going to be opaque to you.

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

01:21:47.920 --> 01:21:51.920
of learning this life way is if you do have this kind of conceptual slot already.

01:21:52.000 --> 01:21:57.120
If you're born with an expectation that there are probably different lifeways based on your

01:21:57.120 --> 01:22:02.050
gamete production strategy, um and you can learn different lifeways from different models,

01:22:02.130 --> 01:22:04.770
then you can deploy a life way that like works best for you.

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

01:22:10.530 --> 01:22:14.050
particular ecology separated by you know sex.

01:22:16.850 --> 01:22:17.250
Awesome.

01:22:17.490 --> 01:22:18.290
And we can model this.

01:22:18.610 --> 01:22:19.090
Yeah, yeah.

01:22:19.170 --> 01:22:19.730
Interesting.

01:22:19.890 --> 01:22:25.500
Um think presumably there's also um a a psychological

01:22:25.500 --> 01:22:29.500
dimension to the downstream consequences of of gamete size.

01:22:29.580 --> 01:22:33.340
You you mentioned that, you know, among males, they might accept a a lower survival

01:22:33.340 --> 01:22:37.500
probability in exchange for greater reproductive uh uh potential.

01:22:37.660 --> 01:22:42.140
Um and so that might manifest at the level of psychology with greater appetite for

01:22:42.140 --> 01:22:45.980
risk-taking and dangerous activities among men, that they're willing to trade off some

01:22:45.980 --> 01:22:51.150
survival risk for a large reproductive payoff potentially in status or whatever.

01:22:51.870 --> 01:22:55.470
And so that could be another variable that affects these life ways.

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

01:23:00.510 --> 01:23:02.830
that allow them to hunt big game.

01:23:02.990 --> 01:23:06.830
It might also be that they just have more of a tolerance for taking dangerous risks.

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

01:23:12.190 --> 01:23:12.590
would be.

01:23:12.670 --> 01:23:18.360
And so there are psychological Predispositions that cause certain cultures to

01:23:18.360 --> 01:23:23.000
develop lifeways as well as morphological.

01:23:23.640 --> 01:23:24.440
I think that's right.

01:23:26.680 --> 01:23:31.400
So certainly I don't take me to be arguing like selection stops the neck, right?

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

01:23:36.840 --> 01:23:40.760
process of doing all this, but like this is totally different from social role theory.

01:23:40.840 --> 01:23:44.990
But I do think sex does structure psychological adaptations.

01:23:45.150 --> 01:23:48.670
And those psychological adaptations, in addition to the physiological adaptations, are

01:23:48.670 --> 01:23:52.110
going to be part of what shapes sex differences in these kinds of lifeways.

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

01:23:56.750 --> 01:23:57.630
really clear.

01:23:57.790 --> 01:24:03.550
Like we definitely know those are uncontroversially exist and uncontroversially shape these

01:24:03.550 --> 01:24:06.910
aspects of lifeways that have important consequences for other things.

01:24:07.070 --> 01:24:11.940
But yeah, like I'm confident that there are psychological adaptations that follow from sex

01:24:11.940 --> 01:24:14.340
that also play into these kinds of processes as well.

01:24:14.660 --> 01:24:14.900
Right.

01:24:15.140 --> 01:24:17.300
So in the Oh, sorry, Dan, go ahead.

01:24:17.540 --> 01:24:17.700
Go ahead.

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

01:24:22.580 --> 01:24:24.420
about the ways to get a robot wrong.

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,

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

01:24:36.430 --> 01:24:37.870
gets the most attention.

01:24:37.950 --> 01:24:39.870
And that's like what I want to grow up to be.

01:24:40.030 --> 01:24:41.550
Like that's a bad design, right?

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

01:24:46.030 --> 01:24:47.390
developmental morphs.

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

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

01:24:57.780 --> 01:25:00.980
best baby 40 years from now that I can possibly be, right?

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.

01:25:06.820 --> 01:25:10.580
So you have to build somewhere de facto into the system like age morphs too.

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

01:25:16.180 --> 01:25:20.500
there are that there are differences in the ecology or in your psychology.

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

01:25:26.450 --> 01:25:30.050
mash things up and you in and it wouldn't work right.

01:25:30.370 --> 01:25:31.730
Maybe I think that's exactly right.

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

01:25:35.010 --> 01:25:35.490
right now.

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

01:25:40.290 --> 01:25:45.410
population of agents where we evolved like uh pair bonding and biparental care.

01:25:45.860 --> 01:25:50.660
So now like these agents are forming into monogamous pairs.

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

01:25:56.260 --> 01:25:56.820
need to manage.

01:25:56.900 --> 01:26:02.340
Like they need to gather a certain amount of resources and they reproduce

01:26:02.340 --> 01:26:06.740
together proportional to how successful they are at gathering those resources.

01:26:06.980 --> 01:26:10.850
The resources they need to gather, I'm just focusing for simplicity on like three things.

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

01:26:15.410 --> 01:26:17.650
P and nutrient M for like plant and meat.

01:26:17.810 --> 01:26:19.970
And then they need to do a certain amount of parenting.

01:26:20.130 --> 01:26:24.850
And they have a fixed time budget that they can use to like gather those resources.

01:26:25.010 --> 01:26:28.930
At the end of that time budget, they reproduce based on how successful they've been.

01:26:29.280 --> 01:26:30.320
They can get those.

01:26:30.400 --> 01:26:32.320
It's like the model's complicated.

01:26:32.400 --> 01:26:33.840
I'm not going to be able to describe it today.

01:26:34.080 --> 01:26:35.600
You can read the paper when it comes out.

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

01:26:40.800 --> 01:26:42.960
be in, there's a bunch of actions they can choose.

01:26:43.120 --> 01:26:45.840
Those actions probabilistically generate those resources.

01:26:46.000 --> 01:26:49.280
The important thing is like there's 11 actions they can take.

01:26:49.440 --> 01:26:51.680
One of them is like parent your offspring.

01:26:51.920 --> 01:26:56.000
The other 10 are just different food resources that the agents can gather.

01:26:56.270 --> 01:27:00.990
Those food resources generate a certain amount of those of those nutrients that they need.

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

01:27:06.990 --> 01:27:12.830
other food resources to like maximize my household's sort of store of resources.

01:27:13.230 --> 01:27:17.470
And so we can create different environments for the agents by manipulating properties of

01:27:17.470 --> 01:27:18.350
those food resources.

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

01:27:23.100 --> 01:27:25.500
productive, meat number two is not very productive.

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

01:27:29.260 --> 01:27:30.140
that's not very productive.

01:27:30.380 --> 01:27:32.460
We can manipulate like, are these dangerous?

01:27:32.620 --> 01:27:36.620
Like is there some probability of injuring yourself if you pursue this resource versus that

01:27:36.620 --> 01:27:37.100
resource?

01:27:37.260 --> 01:27:40.060
Um we can manipulate like what's the success rate?

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

01:27:43.740 --> 01:27:46.540
resource like you know like lots of gathered resources?

01:27:46.850 --> 01:27:52.370
And then the critical one is we can manipulate is foraging this resource compatible with

01:27:52.370 --> 01:27:53.170
childcare or not?

01:27:53.410 --> 01:27:57.890
So in some ecologies, maybe all the resources are pretty compatible with childcare.

01:27:57.970 --> 01:28:00.370
Like anybody can forage anything relatively easy.

01:28:00.450 --> 01:28:03.890
And so you know the sex differences in life ways are going to be relatively small.

01:28:04.050 --> 01:28:08.610
But in other ecologies, like maybe one of the most important resources you need is really

01:28:08.610 --> 01:28:09.970
not compatible with childcare.

01:28:10.130 --> 01:28:12.850
That would be like a sort of large game hunting kind of situation.

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

01:28:17.940 --> 01:28:21.860
foraging that resource and large gamete resource producers to specialize in producing other

01:28:21.860 --> 01:28:24.420
resources, there should be a sexual division of labor.

01:28:25.140 --> 01:28:30.660
So we like throw these agents into ecologies that vary unpredictably on these different

01:28:30.660 --> 01:28:31.460
dimensions.

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

01:28:37.540 --> 01:28:37.940
strategies.

01:28:38.100 --> 01:28:39.780
Maybe I should call them like life ways.

01:28:40.570 --> 01:28:43.770
But basically there's like three kinds of agents in the first model.

01:28:43.930 --> 01:28:47.050
There's ones that just have like a genetically encoded foraging strategy.

01:28:47.130 --> 01:28:50.570
Like they're just born like, I like meat one, I don't like meat two.

01:28:50.810 --> 01:28:53.130
And that doesn't change throughout their lifetime.

01:28:53.930 --> 01:28:58.410
There's other agents that like have a psychology that does reinforcement learning.

01:28:58.890 --> 01:29:04.650
They pay attention to how successful have I been at pursuing this resource in the past

01:29:04.730 --> 01:29:07.290
and that adjusts how they behave in the future.

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

01:29:13.700 --> 01:29:14.660
really predictable, right?

01:29:14.740 --> 01:29:18.820
So like there's no gene flow, there's like different environments, but like offspring are

01:29:18.820 --> 01:29:21.060
always born into the exact same environments as the parents.

01:29:21.220 --> 01:29:22.980
The environments never change over time.

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,

01:29:27.700 --> 01:29:29.140
stably different kinds of humans.

01:29:29.220 --> 01:29:31.700
You can know what sort of environment you're going to be born into.

01:29:31.780 --> 01:29:36.290
And In that kind of environment, selection favors a genetically encoded foraging strategy.

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

01:29:41.650 --> 01:29:44.210
to forage for fish.

01:29:44.610 --> 01:29:47.970
But if environments are unpredictable, if there's some amount of gene flow, like there's

01:29:47.970 --> 01:29:50.930
some probability you might be born in a different environment than the ones that your

01:29:50.930 --> 01:29:55.010
parents were born into, then selection no longer favors that genetically encoded strategy.

01:29:55.090 --> 01:29:57.170
It favors a psychology of reinforcement learning.

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

01:30:03.140 --> 01:30:06.500
how to make a a living in the ecology where you happen to have been born.

01:30:06.660 --> 01:30:12.340
And it specifically favors uh a psychology that engages in what's called what's called

01:30:12.340 --> 01:30:14.180
model-based reinforcement learning.

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

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

01:30:24.750 --> 01:30:25.390
environment works.

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.

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

01:30:35.390 --> 01:30:35.790
behave.

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

01:30:41.390 --> 01:30:44.220
where gender really exists.

01:30:44.700 --> 01:30:49.740
So that like that first model is just showing like you know, variability in foraging will

01:30:49.740 --> 01:30:53.420
generate selection favoring this kind of learning psychology.

01:30:53.820 --> 01:30:59.740
The next model is okay, we have if we set up environments

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

01:31:05.260 --> 01:31:10.190
is going to be optimal, like there's some important resource that is not compatible with

01:31:10.190 --> 01:31:11.310
childcare.

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

01:31:17.230 --> 01:31:20.750
mutation that regulates whether they do social learning, right?

01:31:21.070 --> 01:31:24.510
So the original agents are just learning based on their own experience.

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,

01:31:29.230 --> 01:31:33.950
they can look at the adults in their ecology, they can observe their behavior.

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

01:31:37.980 --> 01:31:42.460
form a model of the environment based on observing adult behavior.

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

01:31:48.460 --> 01:31:52.380
way because they're using the information that already exists from the adults in their

01:31:52.380 --> 01:31:53.260
ecology.

01:31:53.660 --> 01:31:55.340
And you can do that in one of two ways.

01:31:55.420 --> 01:31:57.830
Like you can do that ignorant to sex.

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.

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

01:32:05.830 --> 01:32:07.750
model to guide my own behavior.

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

01:32:13.990 --> 01:32:15.430
for adults of different sexes.

01:32:15.510 --> 01:32:19.350
And then you can use whichever model is applicable to your body.

01:32:19.510 --> 01:32:19.670
Right.

01:32:19.880 --> 01:32:25.720
And what you see is as long as uh at least some ecologies have a

01:32:25.720 --> 01:32:31.080
sexual division of labor, at least in some ecologies, there are some resources that are not

01:32:31.080 --> 01:32:32.520
compatible with childcare.

01:32:32.600 --> 01:32:38.520
Uh selection favors the learning agents that learn from adults in a

01:32:38.520 --> 01:32:39.480
sex differentiated way.

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

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,

01:32:49.770 --> 01:32:50.090
right?

01:32:50.810 --> 01:32:54.890
And so I think that is how we should think about the psychology of gender, right?

01:32:54.970 --> 01:33:00.890
Like what gender is is your model of how sex

01:33:00.970 --> 01:33:04.410
manifests in the particular ecology where you happen to have been born.

01:33:06.420 --> 01:33:06.900
Cool.

01:33:07.140 --> 01:33:11.460
And it also suggests that there's a representation of what you are.

01:33:11.860 --> 01:33:17.300
That that that is uh almost by definition not the

01:33:17.300 --> 01:33:19.380
adult version yet.

01:33:19.620 --> 01:33:20.180
Right.

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,

01:33:24.420 --> 01:33:27.300
which I think is what people often talk about as gender identity.

01:33:27.910 --> 01:33:31.990
To your point earlier, David, there has to be a representation of what other people are,

01:33:32.310 --> 01:33:34.790
which will involve probably some amount of signaling.

01:33:34.870 --> 01:33:37.830
And I think that's what people often talk about as gender expression.

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

01:33:41.350 --> 01:33:41.910
not going to be.

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

01:33:46.550 --> 01:33:47.750
their physiology is.

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

01:33:50.710 --> 01:33:52.950
transgender identities, for example.

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

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

01:34:04.920 --> 01:34:07.960
that kids are actually trying to learn and how are they trying to learn it?

01:34:08.040 --> 01:34:08.200
Right.

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,

01:34:12.360 --> 01:34:12.520
right?

01:34:12.600 --> 01:34:16.510
Like there's lots of work in developmental psychology on like gender essentialism.

01:34:16.590 --> 01:34:20.430
Like kids, you know, essentialize gender as this category and they like attach all kinds of

01:34:20.430 --> 01:34:21.070
ideas to it.

01:34:21.230 --> 01:34:23.390
There's like developmental intergroup theory, right?

01:34:23.470 --> 01:34:28.350
Which suggests that like kids learn from adult behaviors about like what categories there

01:34:28.350 --> 01:34:28.830
are in the world.

01:34:28.910 --> 01:34:34.670
So because adults treat gender as a category that matters, kids learn to do the

01:34:34.670 --> 01:34:34.990
same.

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,

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

01:34:44.270 --> 01:34:44.430
world.

01:34:44.510 --> 01:34:50.280
Like, no, like kids for very good reason are born with an expectation that gender is

01:34:50.280 --> 01:34:52.440
probably a thing that shapes lifeways.

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

01:34:57.880 --> 01:34:58.280
manifest?

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?

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

01:35:07.470 --> 01:35:11.630
what we know about our species' evolutionary history and it gives us a computational model

01:35:11.630 --> 01:35:13.470
of how that psychology is actually working.

01:35:13.790 --> 01:35:18.910
Yeah, and and like for my own expertise in thinking about alliances or even race, right?

01:35:18.990 --> 01:35:22.590
People always talked about like, well, there's categories that are inferentially rich, like

01:35:22.590 --> 01:35:25.070
age, sex, race, slash, alliance.

01:35:25.470 --> 01:35:31.360
But people always acknowledge that learning about alliances and or or race

01:35:31.440 --> 01:35:36.880
or whatever arbitrary sets, as Jim Sidanius called it, was different than gender.

01:35:37.360 --> 01:35:39.760
Um, but in but nobody there was no theory of that.

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.

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

01:35:48.800 --> 01:35:54.510
difference between alliances and the which which

01:35:54.830 --> 01:35:58.750
morphs or morphs or ways of making a living, to what degree are those different?

01:35:58.910 --> 01:36:00.670
Those are different features of the world, right?

01:36:00.830 --> 01:36:06.830
Like the particular alliances, um, and who's on whose side is different than this is

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,

01:36:11.950 --> 01:36:13.630
which again is different than age.

01:36:13.790 --> 01:36:15.620
They all interact, right?

01:36:15.780 --> 01:36:17.060
Um to some degree.

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

01:36:21.380 --> 01:36:23.620
start to think about like how a robot would be built.

01:36:24.020 --> 01:36:29.700
Um it it creates um a task, you could do a task analysis of

01:36:29.780 --> 01:36:30.340
of this.

01:36:30.500 --> 01:36:30.740
Yeah.

01:36:31.060 --> 01:36:32.340
Yeah, yeah, yeah.

01:36:32.900 --> 01:36:35.140
Um good.

01:36:35.890 --> 01:36:41.810
Uh I I want to explore um how intrasexual competition

01:36:41.810 --> 01:36:43.090
fits into this.

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

01:36:49.010 --> 01:36:51.170
sex and gender manifests.

01:36:51.330 --> 01:36:57.250
Um so you know, this the sexes choose among the other sex whom whom

01:36:57.250 --> 01:36:57.970
to mate with.

01:36:58.130 --> 01:37:03.780
Uh within sexes, you know, uh people choose their friends and and allies and they compete

01:37:03.780 --> 01:37:08.180
with other members of their sex for status, for access to to mates.

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,

01:37:14.260 --> 01:37:15.460
gender signaling.

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

01:37:20.900 --> 01:37:26.430
men invest in big game hunting and women invest in, say, gathered resources, um it

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

01:37:32.350 --> 01:37:35.630
men would have an interest in mating with the best gatherers.

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

01:37:41.070 --> 01:37:45.950
going to uh invest more in dangerous risk taking and have a a lower um

01:37:47.300 --> 01:37:50.020
or have a higher appetite for dangerous risks.

01:37:51.460 --> 01:37:56.340
Or they might be bigger and stronger and more they might be more geared toward fighting

01:37:57.060 --> 01:37:58.420
with other members of their sex.

01:37:58.580 --> 01:38:02.180
You know, in cases where there are extreme sex roles, the females will prefer the biggest

01:38:02.180 --> 01:38:06.580
and strongest males because they're going to give them the most successful offspring.

01:38:07.270 --> 01:38:11.590
or in cases where there's more intersexual selection, they might prefer the the males with

01:38:11.590 --> 01:38:17.270
the most colorful tailer tail feathers or the the most eye-catching song and dance routine.

01:38:17.510 --> 01:38:23.510
Um males similarly might prefer the females who uh m uh uh have

01:38:23.510 --> 01:38:28.420
cues of higher health or fertility or going to provide more investment for their offspring.

01:38:28.580 --> 01:38:32.740
So it seems like there there is selection within each sex to signal a kind of

01:38:32.740 --> 01:38:33.860
prototypicality.

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

01:38:38.420 --> 01:38:42.660
competitive male, or the male with the the most colorful tail feathers.

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

01:38:46.820 --> 01:38:49.300
investment and nourishment for uh offspring.

01:38:49.540 --> 01:38:52.100
I'm the healthiest and the most fertile or or whatever.

01:38:52.690 --> 01:38:57.410
That might be occurring at an abstract level across sex roles, across species, but you

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

01:39:02.530 --> 01:39:03.490
divisions of labor.

01:39:03.650 --> 01:39:09.490
But it seems like sexual uh or gender prototypicality is is playing a big

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

01:39:15.010 --> 01:39:21.010
particularly good at the at the tasks that that my sex uh pursues in this

01:39:21.010 --> 01:39:21.810
life way.

01:39:23.340 --> 01:39:24.780
I think that's right.

01:39:25.100 --> 01:39:31.020
Um so I mean, uh, there's lots of things to pick up on there.

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

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.

01:39:43.430 --> 01:39:48.230
It's like a very selfish, kind of individualistic way of thinking about it.

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

01:39:53.910 --> 01:39:55.190
other people's gender concepts?

01:39:55.270 --> 01:39:56.470
Which we very clearly do, right?

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

01:40:02.630 --> 01:40:06.870
is like moralizing how other people think about or enact gender.

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

01:40:11.140 --> 01:40:12.820
live your life in your ecology.

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

01:40:17.620 --> 01:40:18.500
those kinds of questions.

01:40:18.580 --> 01:40:23.620
Like, why would we ever care or reason about other people's gender concepts?

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

01:40:27.300 --> 01:40:28.100
touched on some of them.

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

01:40:33.770 --> 01:40:34.410
interests.

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

01:40:40.490 --> 01:40:45.290
reproducing species and we're also these like, you know, biparental, uh, you know,

01:40:45.770 --> 01:40:51.530
relatively lifelong monogamous uh maters, like it really matters if there is

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

01:40:55.380 --> 01:40:55.780
really matters.

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

01:41:00.820 --> 01:41:05.700
matters that like we have compatible understandings of like what our roles are.

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

01:41:11.780 --> 01:41:16.980
um activities that are going to be like, you know, maximizing our our shared reproductive

01:41:16.980 --> 01:41:17.380
interests.

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

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

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

01:41:30.250 --> 01:41:34.570
relatively effective way so that we can maximize our shared reproductive interests.

01:41:34.730 --> 01:41:35.770
So I think that's one case.

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,

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

01:41:44.650 --> 01:41:46.490
that we that we that we live in.

01:41:46.650 --> 01:41:51.620
Uh, I think there's another case where like you should really care about your

01:41:51.620 --> 01:41:53.700
offspring's gender concepts, right?

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

01:41:59.460 --> 01:42:03.300
lot of time and effort and energy learning a good life way.

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

01:42:09.380 --> 01:42:14.070
there's no value in you successfully reproducing if like your kid then learns like the

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?

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.

01:42:22.470 --> 01:42:26.870
And so it makes sense that parents would have strong feelings about like what is their

01:42:27.030 --> 01:42:31.430
their children's models of their environment and therefore like what is their children's

01:42:31.430 --> 01:42:32.310
concepts of gender.

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

01:42:36.790 --> 01:42:37.670
you live in.

01:42:39.060 --> 01:42:42.020
and that I have a model of that, right?

01:42:42.260 --> 01:42:48.100
So we can go into this population uh that has evolved this like sex differentiated

01:42:48.100 --> 01:42:48.980
learning strategy.

01:42:49.220 --> 01:42:53.860
We can manipulate like are environments relatively stable over time so that like you know,

01:42:54.020 --> 01:42:57.220
the kinds of environments where parents tend to live in are similar to the kinds of

01:42:57.220 --> 01:43:00.980
environments where offspring tend to live in, or do they change relatively quickly?

01:43:01.140 --> 01:43:05.060
Like, you know, is the optimal foraging strategy pretty different from generation to

01:43:05.060 --> 01:43:05.700
generation?

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

01:43:11.310 --> 01:43:14.750
call it like a gender policing gene, for lack of a better word.

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

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

01:43:24.830 --> 01:43:25.870
offspring's behavior.

01:43:26.030 --> 01:43:30.620
They can prevent the offspring from enacting their own policy and they can just force their

01:43:30.620 --> 01:43:35.020
offspring to do what the parent would have done, which I think intuitively we feel like

01:43:35.020 --> 01:43:37.100
parents often do to their children.

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

01:43:42.220 --> 01:43:42.700
own policy.

01:43:42.860 --> 01:43:44.300
They just implement your policy.

01:43:44.540 --> 01:43:47.820
The advantage of that, if the environment is relatively similar, you've already learned a

01:43:47.820 --> 01:43:48.300
good policy.

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

01:43:52.540 --> 01:43:54.300
exploring and trying out things that don't work.

01:43:54.460 --> 01:43:55.850
They get the benefit of your knowledge.

01:43:55.930 --> 01:43:58.170
They're going to implement a good life way right from the beginning.

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

01:44:02.330 --> 01:44:02.730
own.

01:44:02.890 --> 01:44:05.290
Um, that works if the environment is stable.

01:44:05.530 --> 01:44:08.570
If the environment is unstable, though, that's a terrible strategy, right?

01:44:08.650 --> 01:44:12.570
Because the environment your kid has inherited is different from the one where you trained

01:44:12.570 --> 01:44:13.130
your policy.

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

01:44:16.650 --> 01:44:16.890
to do.

01:44:17.050 --> 01:44:20.540
And yeah, you see, depending on the structure of the environment, when environments are

01:44:20.540 --> 01:44:25.180
stable, selection favors this kind of like policing strategy of like impose your gender

01:44:25.180 --> 01:44:26.940
concept onto your offspring.

01:44:27.100 --> 01:44:32.380
But if environments are unstable, then selection favors parents that kind of back off and

01:44:32.380 --> 01:44:34.780
let their children learn their own their own strategy.

01:44:35.100 --> 01:44:40.780
So I think like these are the kinds of situations where agents would have strong feelings

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

01:44:45.980 --> 01:44:47.100
their gender concepts.

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

01:44:52.210 --> 01:44:56.530
interest in making sure their offspring develop good gender policies.

01:44:56.690 --> 01:45:01.250
Well, then if you know that the way that your offspring are developing these gender

01:45:01.250 --> 01:45:05.090
policies is by looking at adults and building a model based on adult behavior, well then

01:45:05.090 --> 01:45:07.890
that gives you reason to care about what other adults are doing, right?

01:45:07.970 --> 01:45:10.900
Because you want to make sure your kids have good targets around.

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

01:45:15.460 --> 01:45:16.180
strangers, right?

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

01:45:21.940 --> 01:45:23.780
to look at you and that's going to become part of their model.

01:45:23.860 --> 01:45:26.100
And then they're going to start behaving in suboptimal ways.

01:45:26.420 --> 01:45:32.420
And so all of this just creates grounds for like all kinds of conflict between individuals

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

01:45:37.570 --> 01:45:39.250
in neurocology and to enact those things.

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

01:45:44.930 --> 01:45:45.410
today.

01:45:45.810 --> 01:45:46.530
Yeah, it's interesting.

01:45:46.690 --> 01:45:50.130
I mean there's also um, you know, parent offspring conflict I bet plays a role.

01:45:50.290 --> 01:45:55.250
So there's research showing that uh parents and children diverge in their uh preferences

01:45:55.250 --> 01:45:57.320
for what for for mates for their children.

01:45:57.720 --> 01:46:02.120
So uh child children tend to prefer uh tend to care more about physical attractiveness,

01:46:02.200 --> 01:46:06.040
say, than their parents do uh when choosing mates for their for their children.

01:46:06.200 --> 01:46:10.040
Um and parents care more about cultural similarity than the children do.

01:46:10.280 --> 01:46:10.600
Yeah.

01:46:11.640 --> 01:46:17.400
And you could uh imagine that you know cultural similarity is uh is

01:46:17.400 --> 01:46:22.600
going to have better inclusive fitness benefits for the parent uh because that's going to

01:46:22.600 --> 01:46:28.230
allow for uh more um for less investment, number one, less investment from them that's

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.

01:46:33.670 --> 01:46:38.950
And number two, perhaps, uh uh better cooperative opportunities with with their kin.

01:46:39.110 --> 01:46:42.390
Um and so parents want more cooperation among kin than the kin wants.

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

01:46:46.470 --> 01:46:50.360
as their sibling, but parents want to uh uh distribute investment equally.

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

01:46:55.640 --> 01:46:57.320
culturally similar than the kid will want.

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

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

01:47:07.400 --> 01:47:07.720
will.

01:47:08.280 --> 01:47:09.560
I think that's exactly right.

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

01:47:13.850 --> 01:47:15.530
and offering don't have overlapping interests.

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

01:47:19.210 --> 01:47:19.770
that scenario.

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

01:47:23.930 --> 01:47:24.090
that work.

01:47:24.170 --> 01:47:26.090
You should have her on the pod, she'd be a great guess.

01:47:26.250 --> 01:47:29.050
Um uh, but I, you know, I I think that's exactly right.

01:47:29.130 --> 01:47:30.330
I think it's a really good point.

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

01:47:35.930 --> 01:47:37.530
are either stable or they're not stable.

01:47:37.690 --> 01:47:41.700
But the reality is like environments vary in how stable there are.

01:47:41.780 --> 01:47:43.940
And that's something you need to make an inference about, right?

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

01:47:49.780 --> 01:47:52.260
same as the adults in my ecology?

01:47:52.660 --> 01:47:56.740
And different people can make different inferences based on the based on the kinds of

01:47:56.740 --> 01:47:59.860
information they're getting and also just like the kind of body they particularly have,

01:47:59.940 --> 01:48:00.100
right?

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

01:48:05.450 --> 01:48:07.610
work for me because my dad and me are different people.

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

01:48:11.130 --> 01:48:11.770
good for my son.

01:48:11.850 --> 01:48:12.970
He needs to behave this way.

01:48:13.130 --> 01:48:16.090
And I'm getting information, like, nope, I gotta chart a different path.

01:48:16.170 --> 01:48:18.250
Like, what worked for him is not gonna work out for me.

01:48:18.410 --> 01:48:23.210
And so that can generate sort of intergenerational conflict, even in the same ecology, just

01:48:23.210 --> 01:48:27.760
because like there's no uh there's no way to know who's right, right?

01:48:27.920 --> 01:48:31.520
Both agents are having to make inferences based on uncertain information, and they might

01:48:31.520 --> 01:48:35.280
just make different inferences about the best way to go, and that's gonna cause conflict.

01:48:35.360 --> 01:48:39.040
And there will be adaptations, there will be selection for adaptations that sort of tip

01:48:39.200 --> 01:48:40.560
that conflict in either direction.

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

01:48:45.360 --> 01:48:48.390
good hunter didn't want to do that.

01:48:48.550 --> 01:48:49.750
I think it was a Hunter.

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

01:48:55.430 --> 01:48:57.110
early that he didn't want to do that.

01:48:57.190 --> 01:48:57.990
Wasn't his life way.

01:48:58.070 --> 01:49:00.630
And this is not even per se a gendered one.

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,

01:49:04.950 --> 01:49:07.030
this was not, I'm not built.

01:49:07.350 --> 01:49:12.960
Um, it it I'm not built to do the trajectory that I see my successful

01:49:12.960 --> 01:49:14.400
dad pursuing.

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

01:49:20.240 --> 01:49:26.080
construction that the whether it's gendered or not, there's also this

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.

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

01:49:36.810 --> 01:49:42.650
a lot of different reasons, like parenting, status schemes, resources, um, and

01:49:42.650 --> 01:49:46.890
thinking about like what what am I particularly good at, you know, and figuring that out.

01:49:46.970 --> 01:49:50.010
And I that's really cool that it hooks up to that too.

01:49:51.850 --> 01:49:53.610
Yeah, I think that's exactly right.

01:49:53.850 --> 01:49:59.360
Um yeah, and that's there's a yeah, there's an individual difference angle

01:50:00.240 --> 01:50:02.480
that I'm also sort of like under exploring.

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

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

01:50:12.880 --> 01:50:13.760
be optimal for you, right?

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

01:50:16.960 --> 01:50:22.000
particular, and that's gonna differ from how everyone else sees this just because of you

01:50:22.000 --> 01:50:23.590
know reactive inheritability, right?

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

01:50:29.190 --> 01:50:29.830
for you.

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

01:50:35.510 --> 01:50:40.870
like gamete based personality where it's sort of like you know it's like

01:50:41.270 --> 01:50:46.220
yeah yeah I like that way of dis of of describing it and thinking about it.

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

01:50:50.940 --> 01:50:53.980
different socio-ecological niches that can be filled.

01:50:54.220 --> 01:50:55.900
Um that's interesting.

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

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

01:51:05.020 --> 01:51:07.820
think of women who are more or less feminine.

01:51:08.060 --> 01:51:12.530
Um and to some extent that is a pref a mate preference.

01:51:12.610 --> 01:51:16.690
You know, men might prefer more feminine women, and women might prefer more masculine men.

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

01:51:23.570 --> 01:51:29.010
femininity and how that might play into mate preferences, status games,

01:51:29.410 --> 01:51:31.730
uh uh, etc.?

01:51:32.840 --> 01:51:35.080
I so it's a great, it's a great question.

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

01:51:40.280 --> 01:51:43.240
super clear about either is, and this comes out of the models as well.

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

01:51:49.480 --> 01:51:51.240
theories about how sex manifests.

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

01:51:57.230 --> 01:51:58.830
what sex is or these optima.

01:51:59.070 --> 01:52:02.030
And in our species, there tends to be, there seems to be two.

01:52:02.190 --> 01:52:05.870
Uh, but that doesn't mean that gender roles themselves need to be binary.

01:52:06.190 --> 01:52:11.390
Um and you can have, depending on the structure of the ecology, like there can be

01:52:11.390 --> 01:52:16.190
multiple good strategies for a large gamete producer, or there can be multiple good

01:52:16.190 --> 01:52:18.510
strategies for a small gamete producer.

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

01:52:22.430 --> 01:52:23.230
being a woman.

01:52:23.390 --> 01:52:26.510
Um, so you don't need to think about gender as being binary.

01:52:27.220 --> 01:52:31.700
it thinking about gender as being folk theories of sex does not imply that gender itself

01:52:31.700 --> 01:52:32.820
has to be binary.

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

01:52:38.580 --> 01:52:44.500
sort of relatively good strategy for being a male, like because of how all this

01:52:44.500 --> 01:52:49.780
works, like every individual is going to learn a slightly different, you know, perspective

01:52:49.780 --> 01:52:50.820
on what that is, right?

01:52:50.900 --> 01:52:56.150
Because every individual, they, you know, they're forming their model based on, you know,

01:52:56.310 --> 01:52:59.590
somewhat random observations of adults in their environments.

01:52:59.670 --> 01:53:03.350
So all of their models are going to be slightly different variations on that same thing.

01:53:03.510 --> 01:53:08.230
Um, you know, part of doing reinforcement learning is like there is randomness inherent in

01:53:08.230 --> 01:53:10.630
the learning because you have to try out different things.

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

01:53:14.470 --> 01:53:14.870
give you.

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

01:53:18.870 --> 01:53:20.790
rewarding they find different actions.

01:53:20.950 --> 01:53:24.340
And so there can be multiple optimal strategies.

01:53:24.420 --> 01:53:28.260
And for any given optimal strategy, any agent's sort of like assessment of what that

01:53:28.260 --> 01:53:30.340
strategy looks like is going to vary to some degree.

01:53:30.500 --> 01:53:34.420
And that variation is also going to feed forward into like the role models that those

01:53:34.420 --> 01:53:36.340
agents provide for the next generation.

01:53:36.980 --> 01:53:42.980
So I think thinking about gender in this way like naturally gives you all

01:53:42.980 --> 01:53:45.380
of this variation in gender concepts.

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

01:53:49.230 --> 01:53:52.510
strict binary, like this is what a man is and this is what a woman is.

01:53:52.670 --> 01:53:55.550
And then, of course, these things are all ecologically specific.

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

01:53:59.790 --> 01:54:03.710
environment, because it's all about learning how to sex manifest in this particular

01:54:03.710 --> 01:54:04.350
ecology.

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

01:54:08.830 --> 01:54:11.950
variation in like what is masculinity, right?

01:54:12.780 --> 01:54:16.860
Yeah, and I think the non-binary part also would explain the continuum, right?

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

01:54:22.940 --> 01:54:28.300
the the gamete selection pressures, uh there are uh

01:54:28.620 --> 01:54:33.180
versions of stable equilibria of male, we'll just call them male and female for exposition,

01:54:33.420 --> 01:54:38.620
male uh type stable equilibria of ways of making a living.

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.

01:54:43.210 --> 01:54:46.890
Maybe there's a lower risk-taking male stable equilibrium.

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

01:54:52.010 --> 01:54:52.250
women.

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

01:54:56.650 --> 01:55:00.650
there's another stable equilibrium for women that's not as averse to risk taking.

01:55:00.940 --> 01:55:04.940
And so consequently, because it's not binary, but there's actually multiple stable

01:55:04.940 --> 01:55:10.700
equilibria within each uh class, that's exactly what what you would perceive as a

01:55:10.700 --> 01:55:11.740
continuum, right?

01:55:12.060 --> 01:55:14.220
Yeah, yeah, yeah, that's exactly right.

01:55:14.380 --> 01:55:16.780
And yeah, you we can we can model this if you want.

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

01:55:22.940 --> 01:55:27.340
that are compatible with childcare, but they both are really skill intensive, like they

01:55:27.340 --> 01:55:32.590
both require a lot of time invested to get good at foraging them.

01:55:32.670 --> 01:55:35.070
And so you can't specialize in both of them.

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.

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.

01:55:45.870 --> 01:55:49.870
And you can imagine building more complicated versions of that exactly along those lines.

01:55:50.030 --> 01:55:54.180
Like, you know, one of the dimensions is like how dangerous is this food to forage?

01:55:54.660 --> 01:55:59.380
Maybe how dangerous it is depends on some aspect of your phenotype that varies relatively

01:55:59.380 --> 01:56:00.100
continuously.

01:56:00.260 --> 01:56:04.740
So yeah, maybe some individuals are willing or able to take on those risks.

01:56:04.820 --> 01:56:07.300
And so that becomes like a viable life way for them.

01:56:07.460 --> 01:56:09.540
Others are not willing or able to take on that risk.

01:56:09.620 --> 01:56:11.380
So that becomes a viable life way.

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

01:56:16.100 --> 01:56:18.730
those uh uh those particular lifeways.

01:56:18.810 --> 01:56:21.050
Or maybe there's like a balance that you can do.

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

01:56:26.090 --> 01:56:28.570
your sort of portfolio of overall activities.

01:56:28.730 --> 01:56:33.450
Um so yeah, these things don't need to be like rigid categories.

01:56:33.530 --> 01:56:39.530
Uh they can emerge as as uh uh more variable, the more variable structures

01:56:39.530 --> 01:56:40.730
that they actually are.

01:56:41.020 --> 01:56:41.260
Yeah.

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

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

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

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

01:56:58.220 --> 01:56:59.900
really it's very like compelling.

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

01:57:03.500 --> 01:57:04.460
really good friend.

01:57:04.690 --> 01:57:07.810
And I and sometimes it's maybe that you're the same.

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

01:57:12.370 --> 01:57:15.650
make this weird thing in me work as to make a living.

01:57:15.730 --> 01:57:18.530
And then you find somebody who has that same thing in you.

01:57:18.690 --> 01:57:20.850
And then you you can sort of learn from them.

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,

01:57:25.650 --> 01:57:29.640
I found my tribe, you know, is sort of the the the the that trope.

01:57:29.720 --> 01:57:32.840
And then there's also just like the complementary, complementariness too.

01:57:32.920 --> 01:57:35.480
You know, somebody else is good at this thing that I'm bad in.

01:57:35.640 --> 01:57:41.560
Um, and this suggests that both for like finding mentors and friends and mates, um,

01:57:41.640 --> 01:57:47.240
it suggests there's this very rich psychological like landscape of these folk

01:57:47.400 --> 01:57:51.160
representations that we might not there might be literature on that maybe not be framed

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

01:57:55.730 --> 01:57:59.410
the friendship literature, a lot of it is sort of like similarity writ large.

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

01:58:03.170 --> 01:58:03.570
about here.

01:58:03.650 --> 01:58:07.490
It's about like ways of making a living status game kind of things.

01:58:07.570 --> 01:58:07.810
Yeah.

01:58:08.050 --> 01:58:08.210
Yeah.

01:58:08.450 --> 01:58:09.090
Yeah.

01:58:09.810 --> 01:58:10.530
I think you're right.

01:58:10.610 --> 01:58:12.210
The paper's already like 15,000 words.

01:58:12.290 --> 01:58:15.010
So this is and this is definitely a BBS paper.

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

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

01:58:24.860 --> 01:58:29.580
development partial developmental psychologist I find this like it connects so much area so

01:58:29.580 --> 01:58:33.340
many areas of developmental psychology that have been unconnected with so many other areas

01:58:33.340 --> 01:58:38.380
that are not developmental psychology like mating or parenting or friendship yeah good i'm

01:58:38.380 --> 01:58:42.270
glad I'm glad to hear that Ten out of ten.

01:58:42.590 --> 01:58:43.710
Well yeah.

01:58:44.670 --> 01:58:46.990
Looking forward to the BBS paper.

01:58:47.150 --> 01:58:50.910
Um from your lips to Paul Bloom's ears.

01:58:51.310 --> 01:58:54.590
Yes, if you're listening, Paul Bloom, you gotta you gotta accept this one.

01:58:55.150 --> 01:58:58.830
Um Pins off twenty twenty six.

01:59:01.390 --> 01:59:01.860
All right.

01:59:01.940 --> 01:59:04.900
Well, I mean, we're we're uh about two hours now.

01:59:05.060 --> 01:59:05.140
Yeah.

01:59:05.940 --> 01:59:10.020
Um is there anything we we should have asked you that we that we missed, Dan?

01:59:10.660 --> 01:59:11.300
Oh shoot.

01:59:11.380 --> 01:59:14.580
I I knew that question was coming and I still failed the preparedness.

01:59:15.380 --> 01:59:17.540
We don't maybe we don't have to ask it for repeat.

01:59:17.780 --> 01:59:18.340
I don't know.

01:59:18.660 --> 01:59:19.700
Should we have asked you that?

01:59:19.860 --> 01:59:21.540
Should we Oh should should we have asked you that?

01:59:21.700 --> 01:59:22.260
Maybe a better question.

01:59:22.580 --> 01:59:23.380
Maybe a better question.

01:59:23.620 --> 01:59:25.140
Is there anything we asked you that we shouldn't have?

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

01:59:30.810 --> 01:59:31.610
asked you about?

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

01:59:36.810 --> 01:59:38.730
consider whether we should continue that tradition or not.

01:59:38.970 --> 01:59:39.610
Yes, exactly.

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.

01:59:45.610 --> 01:59:48.890
Like, how many guests are you gonna be able to do that's true completely something?

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.

01:59:53.450 --> 01:59:54.170
That's I like this.

01:59:54.250 --> 01:59:56.010
This is like the signal detection thing.

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

02:00:00.560 --> 02:00:02.400
should have Or what's what's the dumbest thing we said?

02:00:02.640 --> 02:00:04.480
What's the thing you least like about this?

02:00:04.880 --> 02:00:06.880
Who's the dumbest guest we've had on, Dan?

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

02:00:11.200 --> 02:00:11.600
hosts.

02:00:11.680 --> 02:00:16.640
That's the Uh I'll tell you my answer offline.

02:00:17.040 --> 02:00:17.680
I think it's me.

02:00:17.920 --> 02:00:18.720
I think it's me.

02:00:19.040 --> 02:00:19.440
Yeah.

02:00:20.880 --> 02:00:22.240
Well this is yeah.

02:00:22.480 --> 02:00:24.560
Uh do you have an answer?

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

02:00:29.370 --> 02:00:32.570
try to like air all these things out as I'm trying to write this paper.

02:00:32.730 --> 02:00:34.490
So uh I've got things to think about.

02:00:34.890 --> 02:00:35.130
Awesome.

02:00:35.610 --> 02:00:35.770
Yeah.

02:00:35.930 --> 02:00:36.170
Yeah.

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

02:00:39.930 --> 02:00:40.970
to do on this too.

02:00:41.050 --> 02:00:43.370
So it's just I I this is super exciting.

02:00:43.450 --> 02:00:44.490
I just this is awesome.

02:00:44.570 --> 02:00:44.810
Yeah.

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

02:00:50.440 --> 02:00:51.720
we've been saying this all along.

02:00:51.960 --> 02:00:53.560
Like, why are you just coming around to this?

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

02:00:58.920 --> 02:01:02.040
your stream rather than kind of like steal your thunder.

02:01:02.360 --> 02:01:02.520
Yeah.

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

02:01:06.840 --> 02:01:07.880
think it's self-indulgent.

02:01:07.960 --> 02:01:09.640
Uh like my will editorialize for a second.

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

02:01:13.880 --> 02:01:19.470
think it's crucial in the sense of you're doing this form like really

02:01:19.470 --> 02:01:21.870
methodically, logically.

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

02:01:27.150 --> 02:01:28.750
like, I'm checking my assumptions.

02:01:28.910 --> 02:01:30.110
Does this follow?

02:01:30.350 --> 02:01:32.830
Without sort of grasping at some phenomena.

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

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

02:01:41.380 --> 02:01:42.900
accommodate this known phenomena.

02:01:43.060 --> 02:01:45.860
You're like deriving it from first principles.

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.

02:01:50.660 --> 02:01:50.820
Right.

02:01:50.900 --> 02:01:52.580
So I think I don't think it's self-intentional.

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

02:01:58.660 --> 02:01:59.780
different pieces.

02:02:00.420 --> 02:02:00.660
Yeah.

02:02:00.890 --> 02:02:01.770
It's a synthesis.

02:02:01.850 --> 02:02:02.330
It's a helpful.

02:02:02.730 --> 02:02:04.090
I think that might be one way to frame it.

02:02:04.250 --> 02:02:04.650
Yeah.

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

02:02:08.890 --> 02:02:10.010
in one place before.

02:02:10.170 --> 02:02:12.170
And I think that is is doing a service to the field.

02:02:12.410 --> 02:02:12.890
Yeah, I like that.

02:02:13.050 --> 02:02:14.170
A synthesis of gender.

02:02:14.250 --> 02:02:16.490
That's actually a lovely way of putting what you're doing.

02:02:16.730 --> 02:02:17.130
Yeah.

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

02:02:22.730 --> 02:02:23.610
fine by me.

02:02:25.100 --> 02:02:29.740
Or or a robot factory that's gonna build a robots with gender.

02:02:29.820 --> 02:02:30.380
Yeah.

02:02:31.740 --> 02:02:33.260
Uh no, I that's helpful.

02:02:33.340 --> 02:02:35.020
I need to I need to remember to frame it that way.

02:02:35.100 --> 02:02:37.420
Maybe that'll be the sub subtitle of the paper.

02:02:37.740 --> 02:02:38.700
Synthesis.

02:02:39.900 --> 02:02:40.780
There you go.

02:02:41.020 --> 02:02:41.660
Awesome.

02:02:42.300 --> 02:02:45.100
Uh well, um on that note.

02:02:45.500 --> 02:02:46.620
A sexy synthesis.

02:02:50.380 --> 02:02:50.970
I will.

02:02:53.610 --> 02:02:54.890
All right, Dan, awesome.

02:02:55.370 --> 02:02:56.170
Good to see you.

02:02:56.330 --> 02:02:58.410
Um and uh good luck.

02:02:58.490 --> 02:03:00.410
We look forward to hearing more about this.

02:03:00.730 --> 02:03:01.130
Thank you.

02:03:01.210 --> 02:03:02.730
Thank you guys for having me back.

02:03:06.570 --> 02:03:09.850
David, um stickers.

02:03:10.700 --> 02:03:13.180
Oh yeah, we gotta print out stickers for HBES.

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?

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.

02:03:23.980 --> 02:03:25.820
Uh uh a fanny pack.

02:03:26.540 --> 02:03:26.860
Yeah.

02:03:27.180 --> 02:03:27.900
Not an animal.

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,

02:03:32.140 --> 02:03:33.500
which is the the downside of a pin.

02:03:33.820 --> 02:03:35.420
Were you thinking of lapel pin?

02:03:35.740 --> 02:03:38.140
Uh uh just throwing out possible options.

02:03:38.220 --> 02:03:40.140
Um That's pretty highbrow.

02:03:40.470 --> 02:03:42.150
Yeah, it is a little highbrow.

02:03:42.230 --> 02:03:42.710
Yeah.

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

02:03:47.590 --> 02:03:49.270
all the kids are hanging off their backpacks?

02:03:49.350 --> 02:03:50.950
It's little boo-boos or something like that.

02:03:51.190 --> 02:03:51.590
Oh yeah.

02:03:51.750 --> 02:03:55.110
Kids are into the should we do evolutionary psychology like boo-boos?

02:03:55.430 --> 02:03:56.470
I don't even know what that would be like.

02:03:56.870 --> 02:03:58.390
I don't even know how what that would look like.

02:03:58.550 --> 02:03:59.830
I'm imagining a care bear.

02:03:59.990 --> 02:04:02.710
Do you remember the care bears where they had like symbols on their bellies?

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.

02:04:07.910 --> 02:04:08.950
Maybe it would be a brain.

02:04:09.110 --> 02:04:09.430
I don't know.

02:04:09.670 --> 02:04:09.830
Yeah.

02:04:10.140 --> 02:04:10.540
Not the best.

02:04:11.740 --> 02:04:14.380
I feel like that would take a lot of funding to uh to figure out.

02:04:14.860 --> 02:04:16.460
A lot of time and effort.

02:04:16.700 --> 02:04:19.660
We're we're we're worried about the ability to get stickers done.

02:04:19.740 --> 02:04:21.580
So yeah, that might be yeah.

02:04:21.660 --> 02:04:21.900
Yeah.

02:04:22.220 --> 02:04:22.540
Okay.

02:04:22.940 --> 02:04:24.700
Uh anyway, on that note.

02:04:25.580 --> 02:04:26.460
Uh yeah.

02:04:26.860 --> 02:04:27.500
Support us.

02:04:27.660 --> 02:04:31.020
If you want those laboooboos, you gotta pony up on Patreon.

02:04:31.420 --> 02:04:33.820
I think we'll I think this will get posted before HBest.

02:04:33.980 --> 02:04:35.740
So we look forward to seeing everybody at HBest.

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

02:04:41.350 --> 02:04:42.550
recordings at HBus.

02:04:42.950 --> 02:04:43.830
Oh yeah, that'd be fun.

02:04:43.910 --> 02:04:44.310
Oh yeah.

02:04:44.390 --> 02:04:46.150
So sort of like man on the street kind of thing.

02:04:46.310 --> 02:04:46.870
Yeah, exactly.

02:04:46.950 --> 02:04:47.430
Yeah, exactly.

02:04:48.950 --> 02:04:51.030
Assault people with a microphone and ask them.

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

02:04:54.710 --> 02:04:55.030
States?

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

02:04:58.950 --> 02:05:00.550
like Conan or J Leno or somebody?

02:05:00.870 --> 02:05:01.910
Oh yeah, classic bit.

02:05:01.990 --> 02:05:02.150
Yeah.

02:05:02.310 --> 02:05:02.470
Yeah.

02:05:02.550 --> 02:05:02.630
Yeah.

02:05:02.870 --> 02:05:05.510
Should we should we ask gotcha questions to everyone at HBS?

02:05:05.960 --> 02:05:09.240
That seems like a really um mean thing to do.

02:05:09.320 --> 02:05:10.600
So probably yes.

02:05:10.760 --> 02:05:11.480
No, I don't know.

02:05:11.880 --> 02:05:12.760
I was I was joking.

02:05:13.640 --> 02:05:14.680
No, I don't I don't know.

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

02:05:20.440 --> 02:05:22.520
diversity of people attending HBus.

02:05:22.760 --> 02:05:23.400
I don't know.

02:05:23.640 --> 02:05:23.960
Yeah.

02:05:24.040 --> 02:05:24.920
No, it'd be fun.

02:05:25.080 --> 02:05:25.240
Yeah.

02:05:25.400 --> 02:05:25.800
We'll see.

02:05:26.120 --> 02:05:26.520
Good idea.

02:05:26.600 --> 02:05:26.760
Yeah.

02:05:26.920 --> 02:05:27.080
All right.

02:05:27.480 --> 02:05:30.440
We'll probably edit this out because we won't well, we might do it.

02:05:30.530 --> 02:05:31.170
Yeah.

02:05:31.730 --> 02:05:32.130
All right.

02:05:32.210 --> 02:05:33.890
Uh thank you everybody for listening.

02:05:34.050 --> 02:05:36.610
Um, and uh we'll see you next week.

02:05:36.850 --> 02:05:37.730
Yep, see ya.

02:06:00.620 --> 02:06:06.540
Pontum Pont, Pantum Pum two, Pant, Pontum Pan P, Pontem

02:06:06.780 --> 02:06:07.820
T, Canton.