1
00:00:00,160 --> 00:00:01,920
Here we are again for another episode.

2
00:00:02,000 --> 00:00:03,040
I'm David Pinsov.

3
00:00:03,200 --> 00:00:03,520
Who are you?

4
00:00:03,680 --> 00:00:05,200
I'm Dave Petrazuki.

5
00:00:05,680 --> 00:00:11,200
And this is uh Evolutionary Psychology, the podcast, Actual Evolutionary Psychology by

6
00:00:11,520 --> 00:00:13,840
Actual Evolutionary Psychologist.

7
00:00:14,320 --> 00:00:14,960
That's right.

8
00:00:15,120 --> 00:00:16,080
Uh keep listening.

9
00:00:16,320 --> 00:00:17,120
Enjoy.

10
00:00:27,410 --> 00:00:32,290
David, today we have uh uh uh we're talking to somebody in Germany.

11
00:00:32,770 --> 00:00:33,250
That's right.

12
00:00:33,330 --> 00:00:35,170
His name is Brett Beheim.

13
00:00:35,250 --> 00:00:38,130
He is from the Max Planck Institute in Germany.

14
00:00:38,210 --> 00:00:40,530
Uh he is a brilliant thinker and scientist.

15
00:00:40,930 --> 00:00:41,570
Oh I have to interrupt you.

16
00:00:41,650 --> 00:00:45,250
It's the Max Planck Institute for Evolutionary Anthropology, because there's tons of Max

17
00:00:45,250 --> 00:00:45,970
Planck Institutes.

18
00:00:46,050 --> 00:00:47,330
Yes, the one in Leipzig.

19
00:00:57,240 --> 00:01:01,560
How it evolves, how it spreads, how it diffuses, which is one of the things we talk about.

20
00:01:01,640 --> 00:01:06,280
Uh we talk about cultural distance, how we can actually quantify how different one cultural

21
00:01:06,280 --> 00:01:10,840
is from another and how that affects the way we we interact with people, whether we're more

22
00:01:10,840 --> 00:01:14,470
likely to help or less likely to help someone from a distant culture.

23
00:01:14,550 --> 00:01:15,910
Um, what else did we talk about, Dave?

24
00:01:16,310 --> 00:01:20,790
How genes are smooth and culture is crunchy, which is a really nice evocative image.

25
00:01:21,030 --> 00:01:24,150
Brett has an amazing whiteboard uh during this interview.

26
00:01:24,230 --> 00:01:28,470
So if you're not watching on a visual format, go check out our YouTube video for a second.

27
00:01:28,710 --> 00:01:34,470
And uh we even talk about um things like do you wait at a stop sign when nobody's around or

28
00:01:34,470 --> 00:01:35,350
the game of go.

29
00:01:35,430 --> 00:01:38,630
Like lots of fun, wonderful things to talk about with Brett.

30
00:01:38,710 --> 00:01:40,950
The the two hours went by so quick.

31
00:01:41,240 --> 00:01:41,480
Yeah.

32
00:01:41,640 --> 00:01:43,160
Had an absolute blast talking to Brett.

33
00:01:43,240 --> 00:01:44,600
Uh it was a really fun conversation.

34
00:01:44,760 --> 00:01:46,120
I think you'll have a fun time listening to it.

35
00:01:46,440 --> 00:01:46,600
Okay.

36
00:01:46,760 --> 00:01:47,240
Enjoy.

37
00:01:48,120 --> 00:01:48,600
All right.

38
00:01:48,840 --> 00:01:54,840
Um today we have Brett Beheim, who is it's nighttime in Germany, but

39
00:01:54,840 --> 00:02:00,200
because of the latitude in Germany in Leipzig, uh it's it's like, what is it,

40
00:02:00,360 --> 00:02:00,920
nine?

41
00:02:01,160 --> 00:02:02,840
Uh what time is it there?

42
00:02:03,160 --> 00:02:04,120
It is six.

43
00:02:04,440 --> 00:02:04,840
Six.

44
00:02:05,490 --> 00:02:06,770
We'll edit that out.

45
00:02:06,850 --> 00:02:09,170
Uh I didn't look at the clock.

46
00:02:09,250 --> 00:02:10,850
Um yeah, it's six o'clock at night.

47
00:02:10,930 --> 00:02:16,450
Yeah, but um, but it's almost never uh actually nighttime that latitude um because of the

48
00:02:16,610 --> 00:02:20,770
we're gonna cut all this up because it's like Yukon latitude, because I remember I moved to

49
00:02:20,770 --> 00:02:25,250
Berlin and during the summer it's always a little bit light out, and I looked it up and

50
00:02:25,250 --> 00:02:31,080
technically it's an nautical twilight, And it's because the latitude is so high that it

51
00:02:31,080 --> 00:02:34,280
never gets completely dark, which is kind of great in the summer.

52
00:02:34,360 --> 00:02:35,560
You know, like the days are really long.

53
00:02:35,640 --> 00:02:36,360
Do you like that, Brett?

54
00:02:36,440 --> 00:02:38,760
Or do you have to like, does it affect your sleeping?

55
00:02:39,160 --> 00:02:40,040
No, not at all.

56
00:02:40,520 --> 00:02:43,720
I mean, as you probably know, the shutters tend to be really good here.

57
00:02:43,880 --> 00:02:48,760
So uh you can kind of get a blackout in your in your uh in your room.

58
00:02:49,080 --> 00:02:52,670
Uh So yeah, I I like it quite a bit actually.

59
00:02:53,230 --> 00:02:58,110
For those of for those who are listening and not watching, um Brett we're gonna describe a

60
00:02:58,110 --> 00:02:58,750
scene for you here.

61
00:02:58,990 --> 00:03:04,830
Brett Brett is is sitting in front of a chalkboard full of mathematical

62
00:03:04,830 --> 00:03:06,510
equations and graphs.

63
00:03:06,750 --> 00:03:10,350
It looks like we're talking it looks like he's working on the Manhattan Project.

64
00:03:10,760 --> 00:03:14,120
Brett Brett, what what is on that trunk for boy.

65
00:03:14,280 --> 00:03:16,040
Uh that that is from a discussion earlier.

66
00:03:16,600 --> 00:03:17,400
Well how much time do we have?

67
00:03:17,480 --> 00:03:17,640
Yeah.

68
00:03:17,800 --> 00:03:19,240
So start from the top left.

69
00:03:19,320 --> 00:03:19,480
Okay.

70
00:03:20,440 --> 00:03:21,160
Yeah, hold on.

71
00:03:21,240 --> 00:03:24,440
Can I move the camera to you know pull it closer?

72
00:03:24,680 --> 00:03:29,240
No, we were we were working on something earlier today and uh uh uh sort of stepping

73
00:03:29,240 --> 00:03:34,490
through uh a particular technique uh that's used called network-based diffusion

74
00:03:34,490 --> 00:03:35,370
analysis.

75
00:03:35,690 --> 00:03:41,290
Um and it has a uh uh I think a very uh nice literature in

76
00:03:41,290 --> 00:03:47,210
animal behavior, uh looking at cultural diffusion processes inside uh uh many animal

77
00:03:47,210 --> 00:03:47,770
societies.

78
00:03:47,930 --> 00:03:49,370
And we're working on a project right now.

79
00:03:49,690 --> 00:03:49,850
Yeah.

80
00:03:50,090 --> 00:03:51,210
So what is cultural diffusion?

81
00:03:51,290 --> 00:03:52,410
What is network diffusion?

82
00:03:52,490 --> 00:03:53,850
What are what are we talking about?

83
00:03:54,460 --> 00:04:00,220
Uh so if you're looking at some kind of a measurable quantifiable

84
00:04:00,540 --> 00:04:06,220
cultural thing, uh give some concrete examples canoe design, right?

85
00:04:06,540 --> 00:04:11,900
Yeah, whether whether the well specifically whether the canoe has a certain motif or it's

86
00:04:11,980 --> 00:04:15,820
you know employs like a a a specific feature like an outrigger.

87
00:04:16,460 --> 00:04:22,010
Um if uh an animal is able to open a

88
00:04:22,010 --> 00:04:26,810
particular piece of food that's kind of hard to open using a specific technique.

89
00:04:27,130 --> 00:04:30,730
Um coconuts are really hard to open.

90
00:04:31,050 --> 00:04:31,690
Yeah, yeah.

91
00:04:31,770 --> 00:04:36,410
Um I spent like two hours once on a beach trying to hit it with rocks and there's some

92
00:04:36,810 --> 00:04:36,970
anyway.

93
00:04:37,130 --> 00:04:38,090
Christina can tell you about that.

94
00:04:38,250 --> 00:04:39,850
Yeah, so there's there's an art to it, right?

95
00:04:40,010 --> 00:04:43,850
And you know sometimes there's alternative solutions to the same problem.

96
00:04:44,330 --> 00:04:49,940
Um so one of the starting places for a lot of this work is to imagine that you can

97
00:04:50,580 --> 00:04:52,020
uh discretize this.

98
00:04:52,100 --> 00:04:57,380
You can say whether the technique was present or absent, or the technology, the item, uh

99
00:04:57,460 --> 00:05:03,060
the marker, the variance was present or absent in a particular uh observation.

100
00:05:03,780 --> 00:05:09,700
Um and then the uh the analysis for for looking at a diffusion

101
00:05:09,700 --> 00:05:15,270
process uh might concentrate on the timing of different individuals in a

102
00:05:15,270 --> 00:05:20,630
population showing that particular technique or incorporating that

103
00:05:20,630 --> 00:05:26,310
particular uh uh item or or motif uh into their

104
00:05:26,310 --> 00:05:27,270
repertoire.

105
00:05:27,430 --> 00:05:32,230
Um and then you put that into like a time series and you can see it spreading through the

106
00:05:32,230 --> 00:05:33,830
population somehow.

107
00:05:34,960 --> 00:05:39,680
And so, I mean, this works really well in uh experimental setups with animals.

108
00:05:39,760 --> 00:05:45,600
Like if you intentionally, you know, give them a problem that requires coming up with

109
00:05:45,600 --> 00:05:51,360
a very discrete and obvious solution, like you know, opening a puzzle box in a certain

110
00:05:51,360 --> 00:05:57,200
way, um, then mapping how that spreads among individuals in the

111
00:05:57,200 --> 00:06:01,050
population becomes the basis for the analysis.

112
00:06:01,210 --> 00:06:03,290
Like, how do we how do we describe that process?

113
00:06:03,450 --> 00:06:05,610
Like, what is the speed at which it goes?

114
00:06:05,770 --> 00:06:10,810
Um, can we figure out how specific individuals might be influencing other individuals in

115
00:06:10,810 --> 00:06:11,930
the acquisition?

116
00:06:12,330 --> 00:06:18,250
Um and that's where the the board That's where all the math comes from.

117
00:06:18,570 --> 00:06:19,130
So exactly.

118
00:06:19,370 --> 00:06:22,890
So it's it's basically so you're referring to it as diffusion because you're drawing an

119
00:06:22,890 --> 00:06:27,360
analogy, presumably, from the diffusion, say, of putting a bit of creamer in a and some

120
00:06:27,360 --> 00:06:30,720
coffee and the cream will slowly diffuse and blend with the coffee.

121
00:06:30,880 --> 00:06:35,600
If you pump cold air into a room, the cold air and the warmer will will gradually you know

122
00:06:35,680 --> 00:06:37,040
the cold air will diffuse.

123
00:06:37,120 --> 00:06:42,720
Um if you cough during a pandemic, the pandemic will spread in the

124
00:06:44,320 --> 00:06:46,960
what you're interested in is the cultural version of that.

125
00:06:47,120 --> 00:06:51,870
So if I start tap dancing, how quickly are people going to imitate me and start tapping?

126
00:06:52,510 --> 00:06:53,470
Not quickly at all, David.

127
00:06:53,790 --> 00:06:53,870
No.

128
00:06:54,110 --> 00:06:54,190
No.

129
00:06:54,430 --> 00:06:55,870
Uh you're insulting me too.

130
00:06:56,590 --> 00:06:57,150
Actually, I don't know.

131
00:06:57,230 --> 00:06:58,190
It's an empirical question.

132
00:06:58,350 --> 00:07:00,510
Brett can record and we can actually measure this.

133
00:07:01,150 --> 00:07:05,150
Yeah, stuff stuff can uh die out too if they're not as popular as they used to be in the

134
00:07:05,150 --> 00:07:05,630
nineteen twenties.

135
00:07:07,070 --> 00:07:08,190
I'll edit that out.

136
00:07:08,270 --> 00:07:09,150
I'm sorry.

137
00:07:09,470 --> 00:07:13,710
Are are you studying how cultural diffusion can die out as well as spread?

138
00:07:13,870 --> 00:07:19,700
And what what are the factors that oh con that's the that's the whole

139
00:07:19,780 --> 00:07:25,140
uh uh you know the whole story basically is what's the you know what's the story for the

140
00:07:25,140 --> 00:07:31,140
rise and fall of particular trends um over uh long periods

141
00:07:31,140 --> 00:07:33,380
of time, multi-generational time spans?

142
00:07:33,620 --> 00:07:38,340
You know, do we see interesting changes in people's cultural repertoires or beliefs?

143
00:07:38,500 --> 00:07:41,450
Um but then also things that occur very quickly.

144
00:07:41,530 --> 00:07:47,530
Uh, you know, as we see the arrival of new technology, I mean, uh, I think all of us are

145
00:07:47,530 --> 00:07:52,410
sort of grappling with uh incorporating things like uh

146
00:07:52,650 --> 00:07:55,690
LLMs into our repertoires right now.

147
00:07:55,770 --> 00:07:59,770
Like what's the right way to sort of, you know, have this in the classroom or have this in

148
00:07:59,770 --> 00:08:01,850
the, you know, the workplace?

149
00:08:02,080 --> 00:08:07,120
Uh this is not a multi-generational process, like this is happening right now to everyone

150
00:08:07,120 --> 00:08:08,320
simultaneously.

151
00:08:08,480 --> 00:08:12,800
Uh just like, you know, I mean I remember when cell phones first became a thing.

152
00:08:12,960 --> 00:08:13,840
That was amazing.

153
00:08:13,920 --> 00:08:18,720
It's like I could, you know, call my parents and I I wasn't home uh and I didn't have to

154
00:08:18,720 --> 00:08:20,640
find like a payphone or something.

155
00:08:20,880 --> 00:08:22,960
Um smartphones too.

156
00:08:23,040 --> 00:08:27,710
That's another one I think that you know is quite quite culturally significant and is a

157
00:08:27,710 --> 00:08:32,270
great motivating example, you know, how rapidly things have changed since then.

158
00:08:32,430 --> 00:08:38,270
Like I I remember um if you watch this video of uh I think it was the

159
00:08:38,270 --> 00:08:43,230
first um debut of the iPhone back in like 2006 or something.

160
00:08:43,390 --> 00:08:45,150
Um with Steve Jobs.

161
00:08:45,950 --> 00:08:46,510
Crazy recently.

162
00:08:46,910 --> 00:08:47,470
Exactly, right?

163
00:08:47,550 --> 00:08:52,430
And you you watch this video, you know, and uh at the time, you know, the the like cutting

164
00:08:52,430 --> 00:08:55,140
edge was like Blackberries, right?

165
00:08:55,220 --> 00:09:00,740
These sort of very large uh brick like phones that had keypad keypads on them, like

166
00:09:00,740 --> 00:09:01,940
physical buttons.

167
00:09:02,260 --> 00:09:04,820
And it was like a miniature, miniature keyboard.

168
00:09:04,900 --> 00:09:09,380
I mean, like, I don't know who yeah, it was like it was like left in the dryer, and it was

169
00:09:09,380 --> 00:09:10,900
just lunked down.

170
00:09:11,380 --> 00:09:11,620
Yeah.

171
00:09:11,940 --> 00:09:15,620
And there was this sort of a push to be like, well, maybe we can make the keyboard like

172
00:09:15,620 --> 00:09:19,700
fold open, and then you can have this like full size keyboard that you're tapping on, uh,

173
00:09:19,780 --> 00:09:22,740
or there's like all sorts of complicated sliding gimmicks.

174
00:09:22,990 --> 00:09:28,510
Um and then at like the Mac developer conference, uh Steve Jobs, you know, famously is

175
00:09:28,510 --> 00:09:30,990
pulling out this tiny little device.

176
00:09:31,470 --> 00:09:36,590
Um and we're looking at, you know, something now that is just ubiquitous worldwide.

177
00:09:36,670 --> 00:09:40,590
Like, you know, so many people engage with this technology, and you can see basically when

178
00:09:40,590 --> 00:09:42,670
it first really became visible.

179
00:09:42,830 --> 00:09:43,630
I mean, it's incredible.

180
00:09:43,710 --> 00:09:46,430
Like you watch this and he's, you know, he's got this little gizmo.

181
00:09:46,670 --> 00:09:47,950
Actually, can I show it to you?

182
00:09:48,110 --> 00:09:49,630
I have I have like please.

183
00:09:49,870 --> 00:09:51,150
I wonder if I have Russia.

184
00:09:51,550 --> 00:09:53,680
Um Maybe I don't.

185
00:09:53,840 --> 00:09:55,200
You're gonna have to edit this out too.

186
00:09:55,440 --> 00:09:56,000
That's okay.

187
00:09:56,640 --> 00:10:00,400
Yeah, I have it somewhere, but we're giving you a better view of the board.

188
00:10:00,560 --> 00:10:03,360
Um oh here it is.

189
00:10:03,440 --> 00:10:04,080
Yeah, okay.

190
00:10:04,320 --> 00:10:06,720
So uh check this out.

191
00:10:06,800 --> 00:10:08,640
I got this in my technology museum here.

192
00:10:08,720 --> 00:10:09,440
So there it is.

193
00:10:09,600 --> 00:10:10,960
Um wow.

194
00:10:11,520 --> 00:10:15,490
And uh he's up there holding this device.

195
00:10:16,850 --> 00:10:17,970
That's the first iPhone.

196
00:10:18,210 --> 00:10:23,330
Uh technically it's an iPod, but that was when they were switching the iPods to be looking

197
00:10:23,330 --> 00:10:24,290
like uh iPhones.

198
00:10:24,370 --> 00:10:25,650
It's the same form factor, I think.

199
00:10:25,890 --> 00:10:26,450
Okay, yeah.

200
00:10:26,690 --> 00:10:29,250
Um and a deck of cards.

201
00:10:29,570 --> 00:10:30,130
Yeah, right.

202
00:10:30,290 --> 00:10:34,610
And it's you know, compared to modern phones, it's this tiny little thing, too.

203
00:10:35,090 --> 00:10:39,100
Um but he's showing you know all these things you can do with it.

204
00:10:39,180 --> 00:10:44,460
Like they've you know, they ordered pizza live on stage for everybody, and he's showing all

205
00:10:44,460 --> 00:10:47,020
these different apps, and people are like amazed.

206
00:10:47,100 --> 00:10:50,940
It's like, oh, I can open up the music app now and you know and play it like an iPod.

207
00:10:51,100 --> 00:10:55,420
And he's like, Oh, you know, I I really should like you know go to a new song.

208
00:10:55,500 --> 00:10:56,300
How do I do that?

209
00:10:56,460 --> 00:11:02,060
And he flicks his finger on the screen, and it has that sort of inertial scrolling

210
00:11:02,140 --> 00:11:08,050
mechanic that we use every day, and the audience gasps at watching this.

211
00:11:08,210 --> 00:11:14,210
It's really remarkable to see, you know, something that is such a you know basic part

212
00:11:14,210 --> 00:11:20,050
of our daily lives now uh being shockingly new and amazing to

213
00:11:20,050 --> 00:11:22,850
people uh not even two decades ago.

214
00:11:23,010 --> 00:11:23,410
Yeah.

215
00:11:23,650 --> 00:11:29,340
Um so that that's you know, that's a a a big part, I think, of of what I'm interested to is

216
00:11:29,420 --> 00:11:34,060
I mean, you have a lot of very slow cultural change processes that you know you can look at

217
00:11:34,060 --> 00:11:38,540
over the course of many generations, but you also have very rapid diffusion type processes.

218
00:11:38,860 --> 00:11:44,620
Um and David, to your point, I think that the um the reason that we use

219
00:11:44,780 --> 00:11:49,390
the you know the the phrase diffusion for that, I mean, of course, there's, you know,

220
00:11:49,470 --> 00:11:53,470
there's a there's a lot of literature on this, you know, in in many different um social

221
00:11:53,710 --> 00:11:54,670
sciences.

222
00:11:54,910 --> 00:11:59,230
Um, like there's the famous Rogers book, The The Diffusion of Innovations.

223
00:12:00,190 --> 00:12:06,190
Um, but uh I think that the primary reason why the uh

224
00:12:06,190 --> 00:12:11,900
analogy is so firm is because there's a lot of similar models for describing these

225
00:12:11,900 --> 00:12:12,300
processes.

226
00:12:12,380 --> 00:12:16,300
There's sort of these uh symmetries in the way that we describe these different physical

227
00:12:16,300 --> 00:12:16,620
systems.

228
00:12:16,780 --> 00:12:22,620
You know, you got physical stuff like the diffusion of um heat uh uh in a in a

229
00:12:22,780 --> 00:12:26,860
in a physical uh medium of some kind, like a block of iron.

230
00:12:27,100 --> 00:12:31,980
Um, and you've got the diffusion of genes in a population, uh, which I think is the

231
00:12:31,980 --> 00:12:37,260
immediate place where diffusion uh modeling comes in uh to cultural uh

232
00:12:37,500 --> 00:12:38,220
topics.

233
00:12:38,350 --> 00:12:43,630
And then you have epidemiological work, right, too, which is looking at the spread of

234
00:12:43,630 --> 00:12:46,670
particular contagions.

235
00:12:47,150 --> 00:12:53,070
And a lot of the math is very similar, if not exactly the same, which is really neat

236
00:12:53,150 --> 00:12:58,990
that you can effectively model so many different processes, phenomena, using

237
00:12:59,470 --> 00:13:04,590
these kinds of toy models that capture a particular phenomenon.

238
00:13:05,620 --> 00:13:09,380
And so that I think is is really one of the cool sort of starting places.

239
00:13:09,540 --> 00:13:14,100
Does it say what are the sort of symmetries that we can see in different topics or

240
00:13:14,100 --> 00:13:15,540
different study systems?

241
00:13:15,860 --> 00:13:19,460
And what does that help us maybe understand the topic a little bit more?

242
00:13:19,780 --> 00:13:20,020
Yeah.

243
00:13:20,180 --> 00:13:24,500
What when I looked at your website, I looked, uh I'm looking right now at what your working

244
00:13:24,500 --> 00:13:26,980
papers are, which means like the most recent ones.

245
00:13:27,140 --> 00:13:28,180
And there's two.

246
00:13:28,340 --> 00:13:32,340
One is called the development of risk attitudes in their cultural transmission.

247
00:13:32,570 --> 00:13:37,370
And the second one is called a model of coronary

248
00:13:37,370 --> 00:13:39,210
artherosclerosis.

249
00:13:39,450 --> 00:13:43,530
I can't say that can you say yeah artherosclerosis.

250
00:13:43,690 --> 00:13:44,410
Yes, thank you.

251
00:13:44,650 --> 00:13:50,490
Um uh and that to me is like so cool because it's sort of like on your website

252
00:13:50,570 --> 00:13:55,290
it would be the equivalent of like studying I'm studying you know um um

253
00:13:56,660 --> 00:13:59,700
Like a galaxy and bicycle tires.

254
00:13:59,860 --> 00:14:03,940
You know, like if you don't take a scientific approach, like what does a galaxy and a

255
00:14:03,940 --> 00:14:05,140
bicycle tire have in common?

256
00:14:05,300 --> 00:14:05,620
Nothing.

257
00:14:05,780 --> 00:14:10,740
And I think one of the things that's like magical about science is that you can have a tool

258
00:14:10,740 --> 00:14:15,780
or a way of looking at a galaxy that's the same way of thinking about a bicycle tire.

259
00:14:15,860 --> 00:14:21,540
And I think one of the amazing things for me in whenever I see you give a talk,

260
00:14:21,700 --> 00:14:27,610
um is that uh it's this taking this approach that's sort of for me, as

261
00:14:27,610 --> 00:14:31,370
like a psychologist or like somebody who just sort of naively thinks about, like, well,

262
00:14:31,450 --> 00:14:33,610
yeah, galaxies and bike tires are different.

263
00:14:33,770 --> 00:14:38,970
Um, there are all these tools in the evolutionary behavioral sciences for

264
00:14:39,290 --> 00:14:41,290
adopting more stringent models.

265
00:14:41,370 --> 00:14:45,130
And maybe we can talk about why that is, and maybe a little bit about like the backstory.

266
00:14:45,210 --> 00:14:48,730
Because so far we've talked a lot about psychology and individual behavior and

267
00:14:48,730 --> 00:14:52,200
methodological individualism, but we haven't really talked about why would you want to

268
00:14:52,200 --> 00:14:53,480
model these things precisely?

269
00:14:54,040 --> 00:14:59,480
But yeah, I just like I want to make the comment that like it's so obvious and

270
00:14:59,480 --> 00:15:00,120
cool.

271
00:15:00,280 --> 00:15:02,520
Um, you know, I think there's different kinds of sciences.

272
00:15:02,680 --> 00:15:06,360
One is sort of like, you know, I study snakes, and this is this snake, this is another

273
00:15:06,360 --> 00:15:08,280
snake, and that's cool because snakes are cool.

274
00:15:08,360 --> 00:15:12,600
But there's another version of science where you unite something like a bicycle tire and a

275
00:15:12,600 --> 00:15:13,160
galaxy.

276
00:15:13,240 --> 00:15:15,240
And I think what you're doing is sort of like that.

277
00:15:15,320 --> 00:15:16,600
It's sort of a superpower.

278
00:15:17,640 --> 00:15:18,360
Thank you.

279
00:15:18,600 --> 00:15:18,920
Yeah.

280
00:15:19,250 --> 00:15:25,170
Um so uh so can we get a little bit into then uh why

281
00:15:25,170 --> 00:15:30,130
do we want a precise model and what the heck does evolution have to do with any of this?

282
00:15:31,250 --> 00:15:32,930
Oh, that's a great question.

283
00:15:33,250 --> 00:15:39,250
Um well uh as you may have talked about in the past, I don't

284
00:15:39,250 --> 00:15:39,490
know.

285
00:15:39,650 --> 00:15:45,090
Um, you know, evolution as a topic, of course, didn't start as a like a mathematical field,

286
00:15:45,170 --> 00:15:45,410
right?

287
00:15:45,570 --> 00:15:51,080
I I don't think like Charles Darwin was doing any math, you know, in his various work,

288
00:15:51,640 --> 00:15:53,320
at least not very formal math.

289
00:15:55,000 --> 00:16:00,360
Simultaneously, there was work being done, you know, by Gregor Mendel and his successors

290
00:16:00,440 --> 00:16:06,360
that was very quantitative, and it was trying to carefully measure uh inheritance um in

291
00:16:06,680 --> 00:16:11,240
in ways that you know lend themselves to uh statistical analysis.

292
00:16:12,370 --> 00:16:17,250
But it took a while for those two branches, I think, to intersect.

293
00:16:17,570 --> 00:16:22,930
It wasn't until like the early 20th century where you really had a lot of work actually

294
00:16:23,250 --> 00:16:24,690
uniting the two.

295
00:16:24,770 --> 00:16:27,570
I mean, that's what we refer to as the modern synthesis, right?

296
00:16:27,730 --> 00:16:33,010
Was the initial kind of wave of mathematical models that were taking

297
00:16:33,010 --> 00:16:38,660
Darwinian ideas and coupling them with what we knew about Mendelian inheritance

298
00:16:38,820 --> 00:16:43,220
and showing that they were completely compatible, which I think was actually a source of

299
00:16:43,220 --> 00:16:48,660
controversy in the late 1800s, was this view that Darwinian

300
00:16:48,980 --> 00:16:53,780
evolution uh requires things to be a little smoother than they are with Mendel's

301
00:16:54,260 --> 00:16:56,690
discrete inheritance mechanisms.

302
00:16:57,010 --> 00:16:58,850
And so these are incompatible frameworks.

303
00:16:59,010 --> 00:17:03,010
And people like Ronald Fisher and Sewell Wright show up and they're like, well, actually,

304
00:17:03,250 --> 00:17:09,010
uh you can make mathematical models that show that account for the kinds of you know uh uh

305
00:17:09,250 --> 00:17:14,370
distributions of diversity we see using discrete inheritance mechanisms.

306
00:17:14,930 --> 00:17:20,380
Um and I mean that it was so successful that we don't even really

307
00:17:20,380 --> 00:17:22,140
consciously think about that now.

308
00:17:22,300 --> 00:17:26,540
I think that these at one point were almost like rival paradigms for understanding the

309
00:17:26,540 --> 00:17:27,820
origin of life.

310
00:17:28,060 --> 00:17:32,780
Um and uh the modeling in that case did a lot of the heavy lifting, I think.

311
00:17:32,860 --> 00:17:36,220
It wasn't necessarily, I mean, these were very forceful personalities as well.

312
00:17:36,300 --> 00:17:39,740
If you know the history of statistics, you know, Ronald Fisher and Sewell Wright.

313
00:17:39,980 --> 00:17:45,750
Um But uh I think that what really drove home the uh

314
00:17:46,390 --> 00:17:52,310
the value of it was that we had these models we could fall back on and use them as a

315
00:17:52,310 --> 00:17:54,550
starting place to think about process.

316
00:17:55,350 --> 00:18:01,190
Um and uh that's I think very rhetorically useful, like to sort of say,

317
00:18:01,270 --> 00:18:06,820
well, we're not just you know claiming that these are the different forces we see in

318
00:18:07,300 --> 00:18:08,980
the evolutionary process.

319
00:18:09,380 --> 00:18:15,300
We're giving you examples of how we think they might work in very controlled, sort of uh

320
00:18:15,540 --> 00:18:17,380
idealized circumstances.

321
00:18:17,540 --> 00:18:21,780
And then we're sort of imagining how each one of them relate to each other, how you might

322
00:18:21,780 --> 00:18:22,980
turn one on, turn it off.

323
00:18:23,140 --> 00:18:27,460
If you turn them all off, you get like you know, Harvey Weinberg equilibrium, right?

324
00:18:27,940 --> 00:18:31,950
Um and that is what is Harvey Weinberg you could be able to do.

325
00:18:32,110 --> 00:18:35,230
Oh man, I I don't know if I can I don't know if I can give that a good addition.

326
00:18:35,710 --> 00:18:36,910
New board, new board, new porn.

327
00:18:37,150 --> 00:18:38,030
Yeah, exactly.

328
00:18:38,270 --> 00:18:41,790
But the uh book just swipe the board.

329
00:18:44,110 --> 00:18:49,950
Um so uh the the question here, I think, uh from what I

330
00:18:49,950 --> 00:18:55,700
remember from Population Genetics is um what happens if you turn off every force that we

331
00:18:55,700 --> 00:19:01,700
think of as kind of a directional force in acting on a

332
00:19:02,020 --> 00:19:04,180
population of genes.

333
00:19:04,740 --> 00:19:05,460
So there's no selection.

334
00:19:07,300 --> 00:19:13,220
So this idea of, you know, like as you said, we have discrete genes sort of underlying

335
00:19:13,220 --> 00:19:13,620
traits.

336
00:19:13,700 --> 00:19:17,140
And there's just sort of a one-to-one mapping, which we talked about as sort of the

337
00:19:17,140 --> 00:19:23,140
phenotypic gambit before, and some assumption about how they combine and sort of how

338
00:19:23,140 --> 00:19:24,180
they get inherited.

339
00:19:24,340 --> 00:19:28,310
And it can be simplified, but still simplified enough to be sort of accurate, right?

340
00:19:28,390 --> 00:19:31,270
Like eye color or some any trait, right?

341
00:19:31,670 --> 00:19:37,510
And then um and then the forces are if you just sort of let the

342
00:19:37,510 --> 00:19:43,190
model run, there's ideas about, well, some of the traits are favored more than

343
00:19:43,190 --> 00:19:46,950
others by the environment, for example, but the and that's an example of a force.

344
00:19:47,030 --> 00:19:51,940
So to what extent does the environment favor, for example, you know, like having

345
00:19:51,940 --> 00:19:55,140
photosynthesis if you're a plant versus not, you know.

346
00:19:55,300 --> 00:19:59,940
And so like you could just model that and say, does photosynthesis sort of diffuse

347
00:20:00,180 --> 00:20:01,940
throughout the population over time?

348
00:20:02,100 --> 00:20:03,700
So sorry, just to give that sort of background.

349
00:20:04,020 --> 00:20:05,220
Yeah, that's exactly it.

350
00:20:05,380 --> 00:20:10,820
And uh you could think of migration as a force if there's like a systematic movement of

351
00:20:10,820 --> 00:20:15,830
organisms between you know, islands is the classic uh sort of dynamic here.

352
00:20:15,910 --> 00:20:19,830
Like if you've got a mainland and you've got islands, there's sort of a regular flow rate

353
00:20:19,830 --> 00:20:22,390
that's occurring between each uh place.

354
00:20:23,270 --> 00:20:25,990
Um drift is also a force.

355
00:20:26,150 --> 00:20:31,430
This is something that uh is maybe not as talked about in

356
00:20:31,670 --> 00:20:36,390
evolutionary models, but some people have suggested that it's it's quite a powerful force

357
00:20:36,470 --> 00:20:41,180
actually for understanding how um distributions change over time, you know, if they're

358
00:20:41,180 --> 00:20:45,420
genetic distributions or behavioral distributions, cultural traits.

359
00:20:46,380 --> 00:20:51,420
And that's uh in the context of the equilibrium model, uh,

360
00:20:51,660 --> 00:20:55,900
it's uh a powerful force when you have small populations.

361
00:20:56,380 --> 00:21:01,580
Um so individual stochastic events can really shift things around.

362
00:21:03,130 --> 00:21:08,890
But if you have a very, very large population, then you know uh Laval's numbers

363
00:21:08,890 --> 00:21:11,930
tends to move things towards averages.

364
00:21:12,170 --> 00:21:17,450
Uh and so if you have stochastic events, they're occurring at such a small rate, they're

365
00:21:17,450 --> 00:21:19,610
not really modifying the distribution meaningfully.

366
00:21:19,770 --> 00:21:23,660
I mean, you know, an infinitely sized population has no drift, basically.

367
00:21:23,900 --> 00:21:27,020
Um, and so that's like all the idealized circumstances.

368
00:21:27,100 --> 00:21:29,820
Like you're just going through it and you're like, okay, so imagine a population where

369
00:21:29,820 --> 00:21:35,020
there's, you know, there's no environmental favoring of specific uh variants in the

370
00:21:35,020 --> 00:21:35,660
population.

371
00:21:35,980 --> 00:21:41,740
Uh, there's no drift at all because it's infinitely sized, there's no migration, uh, and

372
00:21:41,820 --> 00:21:45,130
there's um There's no mutation either.

373
00:21:45,290 --> 00:21:49,530
So there's no spontaneous change of one type into another type.

374
00:21:49,850 --> 00:21:51,050
I think that's all of them.

375
00:21:51,130 --> 00:21:52,010
I'm not sure.

376
00:21:52,810 --> 00:21:54,330
But that's a starting place.

377
00:21:54,410 --> 00:21:57,690
We're saying, okay, so we switched off all the different forces that could be acting.

378
00:21:58,010 --> 00:22:00,810
So then we would expect the population to be at equilibrium.

379
00:22:00,890 --> 00:22:03,850
Like it's not going to change over the course of time.

380
00:22:04,970 --> 00:22:07,210
But then, I mean, that's sort of a null condition, right?

381
00:22:07,370 --> 00:22:13,140
Then the question is, okay, like when we do see a change, can we switch on one

382
00:22:13,140 --> 00:22:15,220
force or the other in the modeling?

383
00:22:15,300 --> 00:22:18,820
And does it look kind of like what we're seeing in real life?

384
00:22:18,980 --> 00:22:21,220
Like in terms of the rate at which it's happening?

385
00:22:23,140 --> 00:22:27,860
Does the uh process of change kind of look like an R curve when you're graphing it over

386
00:22:27,860 --> 00:22:28,020
time?

387
00:22:28,180 --> 00:22:32,580
Or does it look more like an S curve is like a classic differentiation of the two?

388
00:22:32,740 --> 00:22:38,180
Um R curves are more consistent with like mutation, uh, and S curves are um

389
00:22:38,740 --> 00:22:44,650
uh I mean, the the classic selection signal, I think, is looking at like an S curve,

390
00:22:44,730 --> 00:22:45,930
a selective sweep.

391
00:22:46,730 --> 00:22:51,690
So it's basically it's similar to the idea of in the same way like gravity.

392
00:22:51,770 --> 00:22:56,330
If you have a model of sort of how gravity works, and then you add something like a

393
00:22:56,330 --> 00:23:00,010
coefficient of drag, and then you like measure leaf leaves falling.

394
00:23:00,090 --> 00:23:02,170
Like that was an example I used in an earlier episode.

395
00:23:02,330 --> 00:23:06,570
And so you can have a model of sort of, well, here's how the leaves are actually falling

396
00:23:06,570 --> 00:23:07,610
outside my window.

397
00:23:07,840 --> 00:23:11,200
What's the model look like when we only have gravity?

398
00:23:11,360 --> 00:23:14,240
What's the model look like when there's only coefficient of drag?

399
00:23:14,400 --> 00:23:16,720
What's the what does the model do when we combine both?

400
00:23:16,880 --> 00:23:19,680
And then how does that compare to the actually how the leaves fall?

401
00:23:19,760 --> 00:23:25,360
And what that allows you to do is ask the question: we have these models of this

402
00:23:25,360 --> 00:23:31,270
complicated thing, like how something behaves, and we're trying to capture um

403
00:23:31,750 --> 00:23:34,870
something more sophisticated than just down, right?

404
00:23:35,190 --> 00:23:39,670
Like if we want to have a model of like how does a leaf fall enough to like predict it or

405
00:23:39,670 --> 00:23:44,550
recreate it or whatever, then you need it better than just like an arrow down, right?

406
00:23:44,630 --> 00:23:45,510
Um maybe.

407
00:23:46,070 --> 00:23:51,670
And so the and that's the same thing with human behavior or traits of an organism, right?

408
00:23:51,830 --> 00:23:56,790
Like that's the idea that there's this sort of revolution in part of science, which is

409
00:23:56,790 --> 00:23:59,190
treating behavior or a trait.

410
00:23:59,350 --> 00:24:04,020
Uh and obviously there's differences between like, you know, hair color versus like what

411
00:24:04,020 --> 00:24:07,380
kind of music I listen to, whether like or the design of a canoe.

412
00:24:07,540 --> 00:24:11,380
But as you said, it's this principle of like galaxies and bike tires.

413
00:24:11,460 --> 00:24:17,460
There's there's a level at which the modeling at least can capture the changes

414
00:24:17,460 --> 00:24:17,860
over time.

415
00:24:17,940 --> 00:24:20,740
And that's the thing that like unifies what you're looking at.

416
00:24:21,460 --> 00:24:23,060
Yeah, I think that's exactly it.

417
00:24:23,300 --> 00:24:23,860
Yeah.

418
00:24:24,180 --> 00:24:25,460
It's crazy.

419
00:24:27,590 --> 00:24:31,590
So uh what is cultural distance?

420
00:24:33,590 --> 00:24:36,870
Uh well yeah, how how long how much time do we have?

421
00:24:36,950 --> 00:24:41,430
Uh Brett lives in Germany, he's gonna tell you all kinds of anecdotes about cultural.

422
00:24:41,830 --> 00:24:42,550
Yeah, exactly.

423
00:24:42,790 --> 00:24:48,470
Um this is a uh this is a particular

424
00:24:48,470 --> 00:24:54,070
concept that has been interesting to a number of scholars in cultural

425
00:24:54,070 --> 00:24:55,270
evolution recently.

426
00:24:55,430 --> 00:24:59,800
Um and so there's been a lot of modeling effort directed at this.

427
00:25:00,040 --> 00:25:05,800
Um the basic idea is that we are

428
00:25:05,800 --> 00:25:11,720
trying to come up with some kind of a quantification for uh kind of our sentiment that

429
00:25:11,800 --> 00:25:17,640
you know two um two cultures or two uh sets

430
00:25:17,640 --> 00:25:23,430
of norms uh are more similar to each other than uh uh a

431
00:25:23,430 --> 00:25:28,630
third that you're comparing them against um similar to what that means the plant thing

432
00:25:28,710 --> 00:25:32,550
right like just in the same way you can say like two plants now have photosynthesis and the

433
00:25:32,550 --> 00:25:37,190
third didn't that's right it's kind of capturing that but applying it to you now culture

434
00:25:37,350 --> 00:25:43,110
right that's right and just in concrete example you might intuitively think that America

435
00:25:43,110 --> 00:25:47,830
and the UK are closer to each other culturally than America is to China.

436
00:25:48,380 --> 00:25:49,980
Yeah, that's exactly the comparison.

437
00:25:50,300 --> 00:25:54,060
I used that that that those three actually in a recently.

438
00:25:55,500 --> 00:26:01,020
And then there's just, you know, uh various ways to try to quantify that somehow.

439
00:26:01,180 --> 00:26:06,540
Um so uh there's this particular data set called the World Values Survey, uh, which has

440
00:26:06,540 --> 00:26:12,530
been um a major focus for attention uh among uh sociologists uh

441
00:26:13,170 --> 00:26:19,090
for uh basically giving us uh a large number of questions that uh are asked around the

442
00:26:19,090 --> 00:26:24,370
world uh about uh you know large variety of different topics uh relating to people's

443
00:26:24,370 --> 00:26:25,010
values.

444
00:26:25,170 --> 00:26:30,690
And then uh there's a bunch of algorithms that people use.

445
00:26:30,850 --> 00:26:35,010
They they they calculate distance measures and they say these two places are more similar

446
00:26:35,010 --> 00:26:38,690
than these two places or somebody from this country is more similar to to somebody from

447
00:26:38,690 --> 00:26:39,730
this other country.

448
00:26:40,320 --> 00:26:42,560
I mean, this isn't just cultural evolution.

449
00:26:42,720 --> 00:26:47,600
Uh, sort of our uh uh contribution, the evolutionary sciences, I think, is to try to

450
00:26:47,600 --> 00:26:53,520
connect this um a little more formally to other kinds of distance measures in

451
00:26:53,520 --> 00:26:54,400
biology.

452
00:26:54,560 --> 00:27:00,560
Um, but outside of evolutionary theory, there's also a large number of different um

453
00:27:00,800 --> 00:27:04,080
distance measures that people have used or calculated.

454
00:27:04,320 --> 00:27:09,890
Um What what they mean exactly is kind of also a big issue.

455
00:27:09,970 --> 00:27:15,250
Like when you say, you know, I think, you know, intuitively that um, you know, two

456
00:27:15,490 --> 00:27:20,290
societies or two people might have similar cultural dispositions.

457
00:27:20,530 --> 00:27:25,970
Um, you know, okay, maybe you can quantify that somehow, but then, you know, what does that

458
00:27:25,970 --> 00:27:26,850
actually do for us?

459
00:27:26,930 --> 00:27:29,010
Like, why why would you want to know that?

460
00:27:29,250 --> 00:27:35,200
Uh and um I mean, I think it can go a lot of different ways at that point.

461
00:27:35,360 --> 00:27:41,200
Like, you know, people have used cultural compatibility as a uh, you know, like

462
00:27:41,280 --> 00:27:46,640
in the popular uh discourse as a a sort of a flashpoint for a lot of controversial

463
00:27:46,640 --> 00:27:47,520
opinions.

464
00:27:47,920 --> 00:27:53,680
Um I think you can flip it around and also think about this

465
00:27:53,680 --> 00:27:59,310
in terms of complementary skill sets or complementary uh

466
00:28:00,190 --> 00:28:01,390
interactive possibilities.

467
00:28:01,550 --> 00:28:07,150
I mean, one of the things we think of as a, you know, is as the basis for a lot of economic

468
00:28:07,150 --> 00:28:10,670
exchange is having comparative advantage.

469
00:28:10,830 --> 00:28:16,670
And if you have two different, you know, quote unquote skill sets

470
00:28:16,830 --> 00:28:20,750
or norms or something like that, it could be that they actually are quite uh beneficial

471
00:28:20,750 --> 00:28:22,510
when they interact with each other.

472
00:28:23,820 --> 00:28:28,620
So, I mean, it's not, I I think it's it's not a very well developed topic.

473
00:28:28,780 --> 00:28:32,620
That would be my general assessment of it, is that there's a lot of interest in trying to

474
00:28:32,620 --> 00:28:35,260
come up with quantifications of cultural distance.

475
00:28:35,900 --> 00:28:41,740
Um and I think that you can map those on to lots of interesting

476
00:28:41,740 --> 00:28:47,570
um uh predictions about things like how people will behave in cooperative, uh

477
00:28:47,730 --> 00:28:50,850
uh cooperative experiments, for example.

478
00:28:51,090 --> 00:28:56,530
Um but uh what what would you what would you predict there?

479
00:28:57,250 --> 00:28:58,530
Well I wouldn't predict anything.

480
00:28:58,690 --> 00:29:03,970
I mean I'm not uh I'm not actually publishing on this very much but uh there was um some

481
00:29:03,970 --> 00:29:09,580
work recently on using a measure called cultural FST to predict uh willingness to

482
00:29:09,580 --> 00:29:15,420
cooperate in um I believe they were in uh vignettes that resembled like

483
00:29:15,420 --> 00:29:19,900
a uh uh some kind of prisoner's dilemma type interaction, uh, where there was the

484
00:29:19,900 --> 00:29:23,020
possibility of giving an altruistic resource to somebody else.

485
00:29:23,260 --> 00:29:27,820
Um and prisoner's dilemma just uh because I don't think we mentioned this before, believe

486
00:29:27,820 --> 00:29:32,700
it or not, um, would be it's just the prisoner's dilemma of like both people are caught,

487
00:29:32,890 --> 00:29:36,650
they're interrogated by the police and they're interrogated separately.

488
00:29:36,890 --> 00:29:42,170
And uh if both of them stay quiet, they both are well off, right?

489
00:29:42,330 --> 00:29:47,850
But if one of them rats on the other, the one who rats is well off, and the one who's sort

490
00:29:47,850 --> 00:29:51,930
of the who doesn't rat but gets ratted on is sort of the sucker's payoff.

491
00:29:52,010 --> 00:29:57,450
And so it's sort of a distribution of where it benefits you to cooperate, but there's also

492
00:29:57,450 --> 00:29:59,290
a temptation to defect on the other.

493
00:29:59,370 --> 00:30:01,050
But if you both defect, you're both screwed.

494
00:30:01,130 --> 00:30:06,860
And so it's just mirroring the prisoner's dilemma as that toy example fits for a lot of

495
00:30:06,860 --> 00:30:12,220
things where it's sort of like, you know, um like parenting parenting off effort where

496
00:30:12,300 --> 00:30:17,100
like, you know, if one parent does everything and you're pl going off and doing fun things,

497
00:30:17,180 --> 00:30:19,340
um, then uh your kid's okay.

498
00:30:19,420 --> 00:30:21,900
But if you both go off and do fun things, your kid's not taken care of.

499
00:30:22,060 --> 00:30:24,300
So there's just lots of things in life that have that structure.

500
00:30:24,460 --> 00:30:25,420
Sorry to interrupt, but yeah.

501
00:30:25,750 --> 00:30:25,990
No worries.

502
00:30:26,070 --> 00:30:28,310
And and the and the key thing here is the ordering of the payoff.

503
00:30:28,470 --> 00:30:31,510
So for me, the best outcome is if I defect and you cooperate.

504
00:30:31,670 --> 00:30:33,350
Ha ha ha, I got one over on you.

505
00:30:33,750 --> 00:30:36,310
Uh the second best outcome is if we both cooperate.

506
00:30:36,550 --> 00:30:40,950
Uh the third best outcome is if we both defect.

507
00:30:41,190 --> 00:30:45,990
And then the worst outcome for me is if uh you defect while I cooperate.

508
00:30:46,150 --> 00:30:48,870
And so and and so that is the the the nature of the of the game.

509
00:30:51,020 --> 00:30:53,820
Yes, the suckers pay off is the would be the the worst outcome.

510
00:30:53,980 --> 00:30:57,180
So anyway, now that we know the prisoner's dilemma, continue with with what you were

511
00:30:57,180 --> 00:30:57,500
saying.

512
00:30:57,820 --> 00:30:58,060
Yeah.

513
00:30:58,140 --> 00:30:59,820
Well, I'm just thinking about this paper.

514
00:30:59,900 --> 00:31:04,620
Um, I think that the first author is Hanley, uh, and Sarah Matthew is the second author.

515
00:31:04,940 --> 00:31:07,260
Um and they were doing in a little bit, yeah.

516
00:31:07,500 --> 00:31:08,780
Or scheduling Sarah.

517
00:31:08,860 --> 00:31:08,940
Yeah.

518
00:31:09,020 --> 00:31:09,100
Yeah.

519
00:31:09,420 --> 00:31:09,580
Yeah.

520
00:31:09,660 --> 00:31:11,180
Well, okay, then maybe I shouldn't talk about it.

521
00:31:11,340 --> 00:31:12,140
No, no, no, go for it.

522
00:31:14,380 --> 00:31:17,100
Every guest we've had, we've been like, Well, I didn't get to X, Y, and Z.

523
00:31:17,180 --> 00:31:17,420
So yeah.

524
00:31:17,770 --> 00:31:17,930
Right.

525
00:31:18,490 --> 00:31:23,610
Well, uh, they were doing a study uh working with the field population that that that she's

526
00:31:23,610 --> 00:31:24,730
worked with for some time now.

527
00:31:24,810 --> 00:31:26,010
Uh I think the Turkana.

528
00:31:26,730 --> 00:31:32,490
Um and uh the Turkana are kind of notable for having quite a bit of violence uh

529
00:31:32,650 --> 00:31:38,490
in uh lots of cattle raiding taking place, um uh both within the Turkana

530
00:31:38,650 --> 00:31:42,010
and also between Turkana and uh neighboring ethnic groups.

531
00:31:42,170 --> 00:31:47,080
Um and they were very interested in seeing how people would behave um, or at least they

532
00:31:47,080 --> 00:31:50,920
would say they would behave in these uh vignette circumstances where they're asked to

533
00:31:50,920 --> 00:31:56,920
provide aid to um uh to some hypothetical

534
00:31:56,920 --> 00:32:02,680
person that you know may be part of uh uh of an in-group or maybe part of an out-group, uh,

535
00:32:02,920 --> 00:32:07,080
as measured by the uh cultural distance measure.

536
00:32:08,230 --> 00:32:13,910
And their basic argument is that this measure itself is highly predictive

537
00:32:14,150 --> 00:32:19,110
of people's cooperativeness in these kinds of vignette circumstances, where they're being

538
00:32:19,110 --> 00:32:23,590
asked to provide a resource with no possibility really of return on that resource.

539
00:32:23,990 --> 00:32:25,910
So it's it's kind of like a prisoner's dilemma.

540
00:32:25,990 --> 00:32:28,390
It has that kind of altruistic vibe to it.

541
00:32:29,590 --> 00:32:31,910
And uh they found a really strong effect

542
00:32:41,520 --> 00:32:47,440
That they're assessing on people and they're uh using uh kind of a a different battery

543
00:32:47,440 --> 00:32:52,240
of questions asking people about um uh uh a different set of topics.

544
00:32:52,850 --> 00:32:57,410
So so the the the dependent measure here was not cooperation in a prisoner's dilemma,

545
00:32:57,490 --> 00:32:57,810
correct?

546
00:32:57,890 --> 00:33:03,730
It w it was the the willingness to altruistically benefit the the target uh without any

547
00:33:03,730 --> 00:33:05,090
obvious return benefit.

548
00:33:05,170 --> 00:33:06,610
Is that is that correct?

549
00:33:07,250 --> 00:33:07,810
Uh that's right.

550
00:33:07,890 --> 00:33:08,050
Yeah.

551
00:33:08,130 --> 00:33:11,970
So it wasn't exactly a prisoner's dilemma where it's simultaneous choice, you know, and

552
00:33:11,970 --> 00:33:17,740
then you benefit or or or or Aaron Powell So here the result is

553
00:33:17,820 --> 00:33:23,660
um if we're more culturally distant, I'm less likely to altruistically help you

554
00:33:23,740 --> 00:33:27,900
uh without any kind of expectation of of a return benefit.

555
00:33:28,140 --> 00:33:32,940
Um and so I would expect that result to be highly con uh conditional on the pat on that

556
00:33:32,940 --> 00:33:37,610
particular game where I help you uh just out of the goodness of my heart.

557
00:33:37,770 --> 00:33:42,490
Um, there could be other instances where, say, we're trading, and maybe um your cultural

558
00:33:42,490 --> 00:33:45,690
distance might increase the likelihood that I want to trade with you because you have

559
00:33:45,690 --> 00:33:49,930
different stuff and you do different things and you have a comparative advantage uh uh in

560
00:33:49,930 --> 00:33:51,130
some skills rather than others.

561
00:33:51,290 --> 00:33:56,890
So I would so um uh has there been work on that or or or is it yeah um uh so my colleague

562
00:33:57,050 --> 00:34:02,900
Ann Piser actually, this is one of the things she focuses on the most is um long distance

563
00:34:02,900 --> 00:34:08,100
interactions between individuals who aren't part of the same ethnic group potentially as a

564
00:34:08,100 --> 00:34:13,620
desired outcome, like you know, people consciously pursuing uh long-distance ties because

565
00:34:13,620 --> 00:34:19,300
they have exactly, as you say, uh the potential for providing unique resources or unique

566
00:34:19,300 --> 00:34:20,660
interactive uh abilities.

567
00:34:20,820 --> 00:34:26,470
And I think that, you know, it kind of works as a nice complement to uh the sort of

568
00:34:26,470 --> 00:34:31,750
altruism work, uh, where you know it really does depend on the interaction structures, uh,

569
00:34:31,830 --> 00:34:33,430
the kinds of games people are playing.

570
00:34:33,590 --> 00:34:38,150
In some circumstances, you can see cultural uh distance, you know, big cultural distances

571
00:34:38,150 --> 00:34:40,790
is like a big barrier for getting people organized.

572
00:34:41,190 --> 00:34:46,790
Uh but in a different circumstance, you know, cultural distance could also indicate a lot

573
00:34:46,790 --> 00:34:52,580
of potential for mutually beneficial exchange, because you have division of labor, you

574
00:34:52,580 --> 00:34:57,140
have uh comparative advantage, you have all these nice things that uh uh tend to that

575
00:34:57,140 --> 00:35:00,660
humans tend to uh figure out how to do pretty well, actually.

576
00:35:00,820 --> 00:35:05,780
Uh and uh that you know that's sort of my interest, is thinking about how the game

577
00:35:05,780 --> 00:35:09,780
structures affect our interpretations of things like cultural distance.

578
00:35:10,100 --> 00:35:14,180
Um, you know, how in some circumstances it might seem like it's a bad thing, in other

579
00:35:14,180 --> 00:35:19,620
circumstances uh it could potentially be uh highly advantageous to have a you know

580
00:35:20,010 --> 00:35:24,810
a lot of distance, cultural distance between people in the same uh same team.

581
00:35:25,850 --> 00:35:27,850
So w why do you think that is?

582
00:35:27,930 --> 00:35:33,290
So so why is it that um if you're culturally distant from me, I'm less likely to just help

583
00:35:33,290 --> 00:35:34,970
you out of the goodness of my heart?

584
00:35:35,290 --> 00:35:40,410
But if you're more culturally distant than me, I might be more interested in trading with

585
00:35:40,410 --> 00:35:43,690
you uh to get some of the stuff you have for for the stuff that I have.

586
00:35:43,930 --> 00:35:47,530
So wha what explains that difference in in in my in my behaviors there?

587
00:35:47,610 --> 00:35:49,930
What what do you think is going on psychologically?

588
00:35:51,440 --> 00:35:53,200
Psychologically, oh man.

589
00:35:56,880 --> 00:35:59,280
I I couldn't even hazard a guess actually.

590
00:35:59,760 --> 00:36:01,920
I mean that's sort of outside my wheelhouse.

591
00:36:02,240 --> 00:36:08,080
I would I would much rather uh uh ask that to Dave uh given your work in in in group

592
00:36:08,080 --> 00:36:13,910
structuring uh I mean it's uh i i it it certainly seems like

593
00:36:13,910 --> 00:36:15,350
it's an important force, right?

594
00:36:15,430 --> 00:36:19,750
That's my my general understanding from reading this literature is that um humans are

595
00:36:19,750 --> 00:36:20,790
really groupy.

596
00:36:21,030 --> 00:36:26,550
Um it's a it's a major structuring process in our behavior and understanding, you know, uh

597
00:36:26,870 --> 00:36:32,150
how things like opinions form uh is heavily influenced by uh our group

598
00:36:32,150 --> 00:36:33,030
affiliations.

599
00:36:33,670 --> 00:36:39,340
Um but uh yeah, I couldn't I couldn't uh go further than that in terms of

600
00:36:39,340 --> 00:36:40,540
speculation, to be honest.

601
00:36:40,940 --> 00:36:42,140
Me, me neither, actually.

602
00:36:42,300 --> 00:36:46,620
So but but actually that answer brings up one other question or one other thing to say,

603
00:36:46,700 --> 00:36:51,420
which is like, you know, one thing about uh our science is everybody's sort of doing a

604
00:36:51,420 --> 00:36:52,460
different job.

605
00:36:52,700 --> 00:36:58,410
And um, you know, one of the things that uh we've talked to a lot of anthropologists on and

606
00:36:58,410 --> 00:37:02,330
I always talk about like as an experimental psychologist, you know, I can control the

607
00:37:02,330 --> 00:37:04,570
variables that I show to participants.

608
00:37:04,650 --> 00:37:10,330
And I can have a really good idea about like what's the software inside the mind, you know,

609
00:37:10,410 --> 00:37:15,370
that's causing it, you know, so like I can really experimentally control like what I show

610
00:37:15,370 --> 00:37:17,370
people, what they remember, what they respond.

611
00:37:17,530 --> 00:37:19,050
I can even measure like the timing.

612
00:37:19,210 --> 00:37:23,540
But what I don't, what I can't do is like measure real world stuff, right?

613
00:37:23,620 --> 00:37:27,940
So I have to sort of like recapture real world dynamics in the lab.

614
00:37:28,100 --> 00:37:31,060
And it's sort of lame, like, you know, on the computer.

615
00:37:31,300 --> 00:37:35,380
And so I always want to talk to anthropologists to sort of keep me honest.

616
00:37:35,780 --> 00:37:41,460
And it sounds like, you know, in addition to being in part anthropologist,

617
00:37:41,540 --> 00:37:46,980
it's also there, it's a similar thing of, you know, psychology

618
00:37:47,650 --> 00:37:51,890
is interested in psychology, like the individual like wiring in the brain.

619
00:37:51,970 --> 00:37:57,650
And so, like, this question of like, why would we care about um uh helping people

620
00:37:57,650 --> 00:38:01,410
altruistically who are closer to us but trading more distantly?

621
00:38:01,650 --> 00:38:06,290
But we can't even ask that question if we don't know that that's actually what's happening,

622
00:38:06,450 --> 00:38:06,690
right?

623
00:38:06,770 --> 00:38:07,890
Out in the real world.

624
00:38:08,130 --> 00:38:13,760
So it's sort of like the data that we actually have to like collect to know

625
00:38:13,840 --> 00:38:19,520
even what question to even ask is part of like this team effort, right?

626
00:38:19,600 --> 00:38:25,600
And so it sounds like one of the things you're doing is in the old days, our ability to

627
00:38:25,600 --> 00:38:30,000
sort of describe behavior was sort of like just write down some stuff people do.

628
00:38:30,160 --> 00:38:33,520
And it wasn't very mathematical and it wasn't very precise.

629
00:38:33,760 --> 00:38:38,830
Um, And the problem was you could write down the same behavior in different ways, and it

630
00:38:38,830 --> 00:38:40,030
wasn't always clear it was true.

631
00:38:40,350 --> 00:38:44,830
Sort of a replication crisis of just documenting what are the things we even have to

632
00:38:44,830 --> 00:38:45,550
explain.

633
00:38:45,790 --> 00:38:51,230
And so the way I think about it, and you can tell me if this is correct, is that you and

634
00:38:51,310 --> 00:38:57,150
like the people you work with are sort of in a wave of science

635
00:38:57,470 --> 00:39:03,160
where you are sort of keeping all the rest of us honest by coming up with better

636
00:39:03,160 --> 00:39:08,760
ways to quantify and measure real-world like phenomena, right?

637
00:39:08,920 --> 00:39:14,680
And so it's sort of you're developing a toolkit for the rest of us to use and for us to be

638
00:39:14,680 --> 00:39:19,000
more confident in like we can be sure that this thing is actually happening.

639
00:39:19,160 --> 00:39:20,920
Is that sort of fair?

640
00:39:22,850 --> 00:39:24,530
Uh yeah, I think so.

641
00:39:24,690 --> 00:39:27,570
Um I mean the the way I think about it is a little different.

642
00:39:27,730 --> 00:39:32,850
That uh I mean there's just too much to observe, like in a given empirical circumstance.

643
00:39:33,010 --> 00:39:37,170
Like there's just an infinite number of details that could be potentially recorded.

644
00:39:37,410 --> 00:39:43,090
Um and one of the values of having theory or sort of formal

645
00:39:43,090 --> 00:39:49,010
theory is that it focuses things a bit and says, well, some details might be irrelevant

646
00:39:49,010 --> 00:39:53,700
for a particular question, uh, even though they might seem like they might be important,

647
00:39:53,940 --> 00:39:56,980
uh, other details might be really important.

648
00:39:57,060 --> 00:39:58,980
And so you should focus in on those.

649
00:39:59,140 --> 00:40:04,180
Uh and I think that there's kind of this back and forth, and as a result, like we get um,

650
00:40:04,580 --> 00:40:09,780
you know, theoretical, theoretical guidance for looking, you know, in the sea of infinite

651
00:40:09,780 --> 00:40:11,860
observation possibilities to get data.

652
00:40:12,070 --> 00:40:13,510
Uh, the data is interesting.

653
00:40:13,590 --> 00:40:16,630
It it builds on the theory and motivates new theory.

654
00:40:16,710 --> 00:40:21,430
And then the theory goes back and you know sharpens the focus a little bit empirically.

655
00:40:21,910 --> 00:40:22,070
Yeah.

656
00:40:22,310 --> 00:40:25,350
And a good example might be like, and I don't know what the state of the art is, but when

657
00:40:25,350 --> 00:40:29,350
we talk to Chris von Ruden, we talked about like warfare and rates of conflict.

658
00:40:29,510 --> 00:40:33,670
And you could have a naive view, which is sort of like, well, warfare is a consequence of

659
00:40:33,670 --> 00:40:35,190
like how belligerent people are.

660
00:40:35,350 --> 00:40:38,790
And there's just sort of like sometimes people are grumpy and other times not.

661
00:40:39,030 --> 00:40:45,000
But then there's also like evidence that it's also uh what what correlates or tracks

662
00:40:45,000 --> 00:40:50,920
with uh conflict is also the harshness, for example, of the circumstances for

663
00:40:50,920 --> 00:40:52,040
like having enough stuff.

664
00:40:52,200 --> 00:40:58,120
So if you're agrarian climate, and like if if climate becomes bad and inhospitable

665
00:40:58,120 --> 00:41:02,680
to having enough stuff, food, for example, then conflict goes up.

666
00:41:02,840 --> 00:41:08,360
And so one model, which is sort of just like based on just sort of random, like, do people

667
00:41:08,360 --> 00:41:13,610
talk about conflict, is con contrasted with a model that

668
00:41:13,610 --> 00:41:19,050
incorporates like, you know, resource scarcity, but then you need a model of resource

669
00:41:19,050 --> 00:41:20,650
scarcity and a way to quantify that.

670
00:41:20,730 --> 00:41:23,370
So you can actually test between those two models, right?

671
00:41:23,770 --> 00:41:24,250
Mm-hmm.

672
00:41:26,810 --> 00:41:32,520
Um I mean my my intuition as as a psychologist for what's going on there is

673
00:41:32,680 --> 00:41:38,040
uh again, just to bring it back to the um altruism toward culturally distant

674
00:41:38,040 --> 00:41:43,640
outgroups being low, but exchange with culturally distant outgroups in some cases being

675
00:41:43,640 --> 00:41:49,080
high, is that uh the selection pressures that gave rise to altruism in the first place

676
00:41:49,400 --> 00:41:55,160
um usually come from the expectation that the altruism will lead to a long-term

677
00:41:55,240 --> 00:41:58,050
mutually beneficial cooperative relationship.

678
00:41:58,210 --> 00:41:58,530
Right.

679
00:41:58,610 --> 00:42:03,410
So if I am, you know, helping people, providing a large benefit to them at low cost to

680
00:42:03,410 --> 00:42:08,210
myself, the odds that that benefit will return to me at some point, maybe they'll

681
00:42:08,210 --> 00:42:08,850
reciprocate.

682
00:42:09,010 --> 00:42:13,090
Maybe someone will see me helping them and view me as a good cooperative partner and be

683
00:42:13,090 --> 00:42:14,050
more likely to help me.

684
00:42:14,210 --> 00:42:20,180
These kinds of return benefits are likely going to be higher if I if I deliver

685
00:42:20,180 --> 00:42:23,060
them to someone who is culturally similar to me.

686
00:42:23,540 --> 00:42:27,060
If I'm in a different culture from someone, if I help that person, it's pretty pretty low

687
00:42:27,060 --> 00:42:29,780
likely that they're going to come back and reciprocate at some point.

688
00:42:29,860 --> 00:42:34,180
Or because we're so culturally distant, the probability that I'm going to form a mutually

689
00:42:34,180 --> 00:42:37,460
beneficial relationship with the person I'm helping is just so low.

690
00:42:37,620 --> 00:42:42,100
Just because it's just so hard for us to have a mutually beneficial relationship.

691
00:42:42,580 --> 00:42:45,300
If we speak a different language, that's going to be a huge barrier to that.

692
00:42:45,540 --> 00:42:51,350
So I think perhaps some part of the mind is estimating the likelihood that this

693
00:42:51,350 --> 00:42:55,830
act of helping will result in a long-term mutually beneficial relationship that will make

694
00:42:55,830 --> 00:42:59,750
the altruism worth it, that will make the altruism pay off.

695
00:43:00,470 --> 00:43:06,070
And so that might be shaping the low willingness to help as a result of

696
00:43:06,070 --> 00:43:07,030
cultural distance.

697
00:43:07,190 --> 00:43:12,260
But if the cooperation is already established and there already is an exchange relationship

698
00:43:12,340 --> 00:43:17,540
and you can already be confident that uh uh giving that person something is going to result

699
00:43:17,540 --> 00:43:22,180
in something beneficial coming back to you, well then all that goes out the window and you

700
00:43:22,180 --> 00:43:26,980
can just uh ask the question of how valuable is their stuff uh to me, uh how how valuable

701
00:43:26,980 --> 00:43:28,420
is my stuff to them, and so forth.

702
00:43:28,580 --> 00:43:31,780
And so that that I think might be explaining what's going on.

703
00:43:31,940 --> 00:43:33,620
Do you do you buy that story?

704
00:43:35,130 --> 00:43:37,770
Um yes and no.

705
00:43:38,010 --> 00:43:42,810
Um I think that the the the thing I would add to it is uh

706
00:43:43,770 --> 00:43:49,690
that it's often the case that we don't know this kind of

707
00:43:49,690 --> 00:43:49,930
thing.

708
00:43:50,090 --> 00:43:56,010
Like we we don't know how uh how likely we are to see a return on

709
00:43:56,010 --> 00:43:57,690
a particular interaction.

710
00:43:57,930 --> 00:44:03,640
Uh and that constraint is itself important, like how people get around that constraint or

711
00:44:03,640 --> 00:44:09,480
how they use uh information around them to try to fill in the gaps that they that they

712
00:44:09,480 --> 00:44:09,800
have.

713
00:44:10,040 --> 00:44:14,600
Um so I mean that's uh I would say probably one of the most interesting things about

714
00:44:14,600 --> 00:44:20,360
cultural evolution as a as a subfield is that one of the main focuses is how do

715
00:44:20,360 --> 00:44:26,230
individuals uh or how do organisms figure

716
00:44:26,230 --> 00:44:29,990
out things that they would need to know that do those kinds of calculations.

717
00:44:32,070 --> 00:44:34,390
How do they know how variable the environment is?

718
00:44:34,550 --> 00:44:39,830
That itself is actually really hard because if it's a really variable environment, then

719
00:44:40,390 --> 00:44:45,670
you might not have many examples around to teach that to you because they're all dead.

720
00:44:46,710 --> 00:44:50,470
And so it's now it becomes like an information problem, right?

721
00:44:50,630 --> 00:44:55,280
Is you know, given the information that I have available, you know, what kind of cognition

722
00:44:55,280 --> 00:44:59,840
would you expect to develop that's trying to basically mine

723
00:45:00,800 --> 00:45:06,720
information out of the, you know, the social and physical milieu to try to figure

724
00:45:06,720 --> 00:45:07,280
that stuff out?

725
00:45:07,440 --> 00:45:08,640
Like, can I find examples?

726
00:45:08,800 --> 00:45:13,360
You know, so I think in the in the example you just described, David, um, it would be very

727
00:45:13,360 --> 00:45:19,130
powerful to have examples that you can look at of uh

728
00:45:19,450 --> 00:45:25,130
of interactions that then later led to a payoff that maybe wasn't obvious initially.

729
00:45:25,450 --> 00:45:30,730
Um, you know, that that would be information that you might imagine an organism would would

730
00:45:30,730 --> 00:45:34,490
add to their own understanding to basically learn the game they're in, right?

731
00:45:34,650 --> 00:45:35,770
As as they're playing it.

732
00:45:35,930 --> 00:45:39,770
I think that's maybe one way to phrase a lot of these problems is that you you don't know

733
00:45:39,770 --> 00:45:43,370
the rules, uh, you don't really know what the payoffs are going to be.

734
00:45:44,300 --> 00:45:46,860
Um, you might not even have any preferences.

735
00:45:47,020 --> 00:45:51,100
Like this is the other thing I think that's really neat about um looking at the variation

736
00:45:51,100 --> 00:45:56,780
between different subfields uh is that um it's not

737
00:45:56,860 --> 00:46:00,300
obvious in some circumstances where the preferences come from.

738
00:46:00,460 --> 00:46:01,660
Like cause they're on top.

739
00:46:01,740 --> 00:46:04,060
I mean, like would you prefer an Android or an Apple?

740
00:46:04,220 --> 00:46:05,090
Like, I don't know.

741
00:46:05,250 --> 00:46:09,730
Like, I mean, that's not something that you could easily map into the same kind of

742
00:46:09,730 --> 00:46:12,530
preference psychology that you would something else.

743
00:46:13,730 --> 00:46:19,410
And so how the preference itself develops, like getting like an endogenous theory of

744
00:46:19,410 --> 00:46:25,010
preferences, as they call it in economics, is also neat because it's sort of layers on top,

745
00:46:25,090 --> 00:46:26,530
it's sort of layers upon layers.

746
00:46:26,610 --> 00:46:32,280
Like you might imagine modeling the dynamics of the preference acquisition or the dynamics

747
00:46:32,280 --> 00:46:35,560
of learning about how risky your environment actually is.

748
00:46:35,800 --> 00:46:41,400
And then you model the behavior of the organism conditional on their understanding based on

749
00:46:41,400 --> 00:46:42,440
what they've learned.

750
00:46:43,400 --> 00:46:46,200
And it may very well play out exactly as you described.

751
00:46:46,280 --> 00:46:50,840
Like if they're if they're starting to pick up, you know, that uh I mean, there's this

752
00:46:50,840 --> 00:46:56,280
famous quote now I'm thinking of from uh uh Paul Samuelson, who's uh uh old school

753
00:46:56,280 --> 00:46:59,080
economist from the mid mid-20th century.

754
00:46:59,320 --> 00:47:03,870
Um And he wrote a very famous series of textbooks on economics.

755
00:47:03,950 --> 00:47:08,670
And there was this quote I remember from him that was something like I don't care which

756
00:47:08,670 --> 00:47:12,430
politicians are in office so long as I get to write the textbooks.

757
00:47:14,030 --> 00:47:19,550
And I mean, the basic premise is that, you know, thinking about market

758
00:47:19,550 --> 00:47:21,870
interactions is often quite counterintuitive.

759
00:47:21,950 --> 00:47:27,340
And you kind of have to learn about market interactions to participate in a market

760
00:47:27,340 --> 00:47:27,980
economy.

761
00:47:29,180 --> 00:47:32,860
And so we see things like, oh, you know, the possibility for mutually beneficial

762
00:47:32,860 --> 00:47:37,660
interactions, but you know, that might not be obvious to somebody without a little bit of

763
00:47:37,660 --> 00:47:39,740
cultural exposure to that.

764
00:47:41,420 --> 00:47:45,740
And I think that part gets really interesting, like how people come to understand the

765
00:47:45,740 --> 00:47:47,660
possibilities in a particular interaction.

766
00:47:47,820 --> 00:47:52,770
Like, is you know, am I am I approaching this particular interaction thinking about it

767
00:47:52,770 --> 00:47:54,770
like, you know, I might a prison dilemma.

768
00:47:54,930 --> 00:47:58,050
Or, you know, my I I'm I'm totally showing my cards here.

769
00:47:58,290 --> 00:48:03,330
When I was in college, I I remember reading Non Zero by Robert Wright and just being like,

770
00:48:03,490 --> 00:48:04,370
oh this is amazing.

771
00:48:04,530 --> 00:48:08,530
You know, like just uh the way that you understand a particular interaction having a big

772
00:48:08,530 --> 00:48:14,530
impact on uh potentially the way that you that you carry yourself or conduct yourself.

773
00:48:14,860 --> 00:48:14,940
Yeah.

774
00:48:15,100 --> 00:48:20,860
Can can and just to make it concrete, like one example might be like the level of

775
00:48:20,860 --> 00:48:22,140
trust in a culture.

776
00:48:22,300 --> 00:48:27,980
So for example, like let's say that there's two people in a culture and they detect some

777
00:48:27,980 --> 00:48:31,340
cultural distance from each other and therefore don't cooperate.

778
00:48:31,660 --> 00:48:36,540
And maybe uh objectively they would be better off if they did cooperate.

779
00:48:36,620 --> 00:48:42,410
And so, for example, imagine another culture where you have that same distance, but

780
00:48:42,410 --> 00:48:47,130
for whatever reason, and we'll just pause that for a second, there's like something like

781
00:48:47,130 --> 00:48:47,770
sports.

782
00:48:48,250 --> 00:48:53,930
And even though they have the same cultural distance, they both like the same sports team.

783
00:48:54,090 --> 00:49:00,010
And that causes them to override the evolved psychology preference they would have had

784
00:49:00,010 --> 00:49:02,650
in the other scenario, and they therefore cooperate.

785
00:49:02,860 --> 00:49:07,580
And so these are two hypothetical cultures where in it's the same distance the agents are

786
00:49:07,580 --> 00:49:11,660
from each other, but in one case um they cooperate and when they don't.

787
00:49:12,140 --> 00:49:14,060
Does one do better?

788
00:49:14,300 --> 00:49:20,300
And in some sense, then is it that the culture that has the sports team that

789
00:49:20,300 --> 00:49:25,990
sort of cultural uh trait or phenotype sort of would outcompete one where there

790
00:49:25,990 --> 00:49:31,030
aren't sports teams because it leads the evolved psychology to produce cooperation.

791
00:49:31,190 --> 00:49:36,470
Now that's a made-up scenario, but is that that would be like the kind of thing that

792
00:49:37,190 --> 00:49:42,550
this is sort of getting at is how do you take an evolved psychology that's sort of

793
00:49:42,710 --> 00:49:45,590
static in the sense of its underlying design?

794
00:49:45,830 --> 00:49:51,600
But then you get all these multiple either through drift or cultural inheritance or

795
00:49:51,600 --> 00:49:57,200
whatever, variable possible things that are thrown at the evolved psychology, like do you

796
00:49:57,200 --> 00:50:01,600
want to cooperate this person versus do you want to cooperate this person who like likes

797
00:50:01,680 --> 00:50:05,280
you know Hirtha Berlin Soccer Club, you know, like you do, right?

798
00:50:05,440 --> 00:50:07,520
Those those are two different worlds.

799
00:50:07,680 --> 00:50:09,520
And you can model those things.

800
00:50:09,600 --> 00:50:15,200
And then in some sense, it's asking the question of well, why do certain cultural traits

801
00:50:15,450 --> 00:50:19,770
persist and are some of them persisting because, for example, they affect cooperation.

802
00:50:19,930 --> 00:50:21,370
Is that sort of fair?

803
00:50:21,770 --> 00:50:26,730
Yeah, I uh one thing I would add to that, which I think is maybe more troubling about the

804
00:50:26,730 --> 00:50:32,570
topic of cultural similarity, is that um the calculation that you get

805
00:50:32,570 --> 00:50:36,970
depends heavily on the questions that you're using for for measuring cultural similarity.

806
00:50:37,050 --> 00:50:42,320
I mean, it's not like there's a fundamental unit that is called the cultural similarity

807
00:50:42,320 --> 00:50:46,800
between people from the UK and and Americans or people from Germany and France.

808
00:50:47,040 --> 00:50:50,640
I mean you're you're talking about a battery of questions that are asked in like a

809
00:50:50,640 --> 00:50:56,480
particular survey uh and then running calculations on that domain in the way that the

810
00:50:56,480 --> 00:50:57,680
questions are structured.

811
00:50:57,920 --> 00:51:03,120
Um is that consistent if you would ask a different set of questions or if the qu I mean

812
00:51:03,200 --> 00:51:08,650
like you know as you're framing it with this particular uh problem, Um depending on the

813
00:51:08,650 --> 00:51:12,810
domain, you might find a lot more affinity than in another domain.

814
00:51:13,210 --> 00:51:14,730
And it might matter a lot.

815
00:51:14,890 --> 00:51:19,530
Like so people I mean, basically if anything can come out of this conversation, I would say

816
00:51:19,530 --> 00:51:25,370
that this is a this is not uh something that has guarantees on the tin,

817
00:51:25,690 --> 00:51:27,050
you know, in as a topic.

818
00:51:27,130 --> 00:51:31,880
Like it's not something you can readily translate into any kind of policy uh, or you know,

819
00:51:32,040 --> 00:51:37,000
even apply it necessarily in in you know very casual circumstances, like obviously this way

820
00:51:37,000 --> 00:51:37,720
or that way.

821
00:51:37,880 --> 00:51:38,200
Yeah.

822
00:51:38,360 --> 00:51:43,480
Um so uh I mean it's it's useful to think about in specific circumstances.

823
00:51:43,640 --> 00:51:49,560
What like what you know, how might you organize uh, you know, things like a cooperative

824
00:51:49,560 --> 00:51:53,880
or uh coordination task given a particular institutional environment?

825
00:51:54,130 --> 00:51:58,930
and how might you know cultural distance help us understand how that's going to play out?

826
00:51:59,170 --> 00:52:04,530
Um, but uh you add in all these other factors, like you know, uh some

827
00:52:05,090 --> 00:52:11,090
shared affinity for a sports team, or you know, the the the the interaction itself

828
00:52:11,090 --> 00:52:16,050
is in a totally different context than the way that you know the quote unquote cultural

829
00:52:16,050 --> 00:52:18,370
similarity scores are being assessed.

830
00:52:18,610 --> 00:52:23,020
Um uh all of that I think adds way too many complications.

831
00:52:23,100 --> 00:52:27,740
And uh I don't think I don't think there's any obvious uh uh applications for it in that

832
00:52:27,740 --> 00:52:29,180
kind of uh in that kind of way.

833
00:52:29,500 --> 00:52:33,500
Yeah, I think like you have to have a dialogue with the psychologist, and just to do an

834
00:52:33,500 --> 00:52:36,860
analogy, you can say like was a whale more similar to a cow or a fish?

835
00:52:37,020 --> 00:52:40,620
And like it's a meaningless question because it means like similarity along which

836
00:52:40,620 --> 00:52:40,940
dimension.

837
00:52:41,020 --> 00:52:45,750
And if you take like a unidimensional similarity, you know, like well, maybe for swimming

838
00:52:45,830 --> 00:52:50,470
yeah like the whales more similar to the fish but you're not gonna get that same answer if

839
00:52:50,470 --> 00:52:54,710
you ask like you know does it produce milk or something like that I also just as an aside

840
00:52:54,950 --> 00:52:59,590
that also makes me a little I get uncomfortable when people talk about like a weird scale

841
00:52:59,590 --> 00:53:04,150
for countries like how weird are they for the same reason right like like the Western

842
00:53:04,230 --> 00:53:09,480
educated industrialized rich democratic um, there are these sort of measures of sort of

843
00:53:09,480 --> 00:53:11,240
that assign each country a single number.

844
00:53:11,400 --> 00:53:15,240
And I guess that's better than the alternative where that hasn't existed.

845
00:53:15,400 --> 00:53:19,640
But it's like with all of these things, with all these models, like Paul Smaldina says

846
00:53:19,720 --> 00:53:22,040
models are wrong and stupid, but we need more of them.

847
00:53:22,200 --> 00:53:27,690
You know, it's just that obviously assigning a single number to a country is is dumb, but

848
00:53:27,690 --> 00:53:30,090
not assigning any kind of numbers at all is dumb.

849
00:53:30,170 --> 00:53:33,050
But of course, we just want more numbers along different dimensions.

850
00:53:33,210 --> 00:53:38,250
And if doing one number to one country is a step towards multiple dimensions of numbers,

851
00:53:38,330 --> 00:53:39,850
that's the right direction.

852
00:53:40,810 --> 00:53:41,770
Yeah, I think that's right.

853
00:53:41,850 --> 00:53:47,690
And I mean there's, you know, uh that literature itself, I think, is quite interesting in

854
00:53:47,690 --> 00:53:53,260
the um the the specific things that are quite

855
00:53:53,260 --> 00:53:58,140
notable about humans themselves, like the uh I think the basis for the weird scale is

856
00:53:58,140 --> 00:53:59,420
actually cultural FST.

857
00:53:59,500 --> 00:54:02,220
I think that's Michael Muthakrishna and colleagues.

858
00:54:02,460 --> 00:54:08,220
Um and that calculation um specifically comes from a thing called

859
00:54:08,220 --> 00:54:12,060
genetic FST, uh, which is actually used to do as you just described.

860
00:54:12,140 --> 00:54:17,180
I mean, how similar are two different species from each other or within a population, how

861
00:54:17,180 --> 00:54:20,570
similar are uh two subgroups.

862
00:54:20,650 --> 00:54:25,370
Uh and the the basic idea with that calculation is using a variance ratio.

863
00:54:25,530 --> 00:54:31,210
So if you have uh a particular species that's divided up into uh populations or groups,

864
00:54:31,370 --> 00:54:36,970
you're asking how much of the total variation that exists genetically, um, looking at a

865
00:54:36,970 --> 00:54:42,540
number of different sites, how much of that variation is explained by

866
00:54:42,940 --> 00:54:44,620
the between group variation.

867
00:54:44,780 --> 00:54:47,820
So there's between group variation and there's within group variation.

868
00:54:48,700 --> 00:54:54,620
And if your cultural or genetic FST is very high, the implication is that

869
00:54:54,860 --> 00:55:00,380
within each group or within each population, they're relatively very low variance

870
00:55:00,860 --> 00:55:03,420
compared to the between group differences.

871
00:55:03,900 --> 00:55:08,990
Whereas if the uh variance ratio is quite low, uh, then

872
00:55:08,990 --> 00:55:12,670
there's almost no variance explained between groups.

873
00:55:12,750 --> 00:55:15,390
Like all the variation really exists within each group.

874
00:55:15,630 --> 00:55:21,150
Um, and so this is used as kind of a measure of how differentiated um uh populations are.

875
00:55:21,230 --> 00:55:25,390
And you can use this in a genetic context actually as the basis for identifying subspecies,

876
00:55:25,630 --> 00:55:30,190
um, if FST is very high, um, genetic FST, I should say.

877
00:55:30,460 --> 00:55:36,220
And uh one of the really remarkable things about humans is that we have extraordinarily

878
00:55:36,220 --> 00:55:38,380
low genetic FST measures.

879
00:55:38,460 --> 00:55:41,980
Um, like compared to other mammals, it's it's one of the lowest.

880
00:55:42,140 --> 00:55:43,660
Um and I mean this is completely.

881
00:55:44,140 --> 00:55:45,500
We talked about that in an earlier episode.

882
00:55:45,740 --> 00:55:46,300
Oh no, kidding.

883
00:55:46,620 --> 00:55:48,460
Yeah, just because of my my race work, right?

884
00:55:48,540 --> 00:55:52,140
Where it's just like I think this is might be outdated, but like the the thing of like

885
00:55:52,220 --> 00:55:55,740
chimps have like five times more genetic diversity, and even though you don't have

886
00:55:55,740 --> 00:56:01,290
subspecies, and we certainly're very all very similar to each other, consistent with like a

887
00:56:01,290 --> 00:56:03,770
pretty recent bottleneck pop in a population.

888
00:56:04,010 --> 00:56:07,930
I mean, the building you're in there doing incredible work figuring that out, right?

889
00:56:08,010 --> 00:56:10,570
The uh evolutionary history.

890
00:56:10,650 --> 00:56:10,810
Yeah.

891
00:56:11,290 --> 00:56:17,210
So building on that, I mean, we have uh genetic quantifiable numbers, which suggests that

892
00:56:17,290 --> 00:56:22,890
um uh that we don't have the same kind of strong structural

893
00:56:22,890 --> 00:56:28,100
differentiation globally as a species that we see with other species.

894
00:56:29,460 --> 00:56:32,260
But behaviorally, we're extremely variable.

895
00:56:32,580 --> 00:56:38,260
And if you use the same calculation on culture, I mean this is the basis for the weird

896
00:56:38,260 --> 00:56:39,140
scale actually.

897
00:56:39,460 --> 00:56:45,140
If you take like world value survey data, you do measure really high FSTs,

898
00:56:45,370 --> 00:56:49,770
cultural FST specifically, um, that you know, the the the amount of variance.

899
00:56:49,850 --> 00:56:51,850
I mean, you can think about this in a regression context.

900
00:56:51,930 --> 00:56:53,450
You guys are psychologists, right?

901
00:56:53,610 --> 00:56:59,530
So um if you add it in, you know, group as a as a conditioning variable and trying

902
00:56:59,530 --> 00:57:04,010
to explain some measure that you're looking at, you usually get a really big effect from

903
00:57:04,010 --> 00:57:06,650
adding in group as like a predictive category.

904
00:57:06,970 --> 00:57:12,700
Um, Because the averages across groups tend to be quite

905
00:57:12,700 --> 00:57:13,100
different.

906
00:57:13,340 --> 00:57:17,260
Even if there's a lot of variable with variability within the groups, the averages

907
00:57:17,260 --> 00:57:22,300
themselves structure across groups in a usually in a very noticeable way.

908
00:57:23,020 --> 00:57:27,500
And actually, there is a way, you know, in the context of that specific scenario, there is

909
00:57:27,500 --> 00:57:31,950
a way to sort of interpret R squared as FST.

910
00:57:32,030 --> 00:57:34,670
Like they're they're conceptually very similar concepts.

911
00:57:34,750 --> 00:57:39,230
So if you have a high R squared from using group as a predicting variable, right, that's

912
00:57:39,230 --> 00:57:43,470
that's kind of like what uh what we mean by saying there's a high FST.

913
00:57:43,950 --> 00:57:45,150
If that if that makes sense.

914
00:57:53,310 --> 00:57:57,150
And in this case, the variable would be, you know, just just group identity or something

915
00:57:57,150 --> 00:57:57,710
like that.

916
00:57:58,180 --> 00:57:59,380
But this is just for one measure.

917
00:57:59,540 --> 00:58:03,940
I mean, there's different ways to calculate uh FST depending on uh how many variables

918
00:58:03,940 --> 00:58:04,740
you're using.

919
00:58:04,980 --> 00:58:09,860
Um and uh yeah, one of the really remarkable things that's come out of this literature is

920
00:58:09,860 --> 00:58:15,620
just measuring these absolutely massive cultural FST numbers, um, which which is

921
00:58:15,780 --> 00:58:20,510
which is quite a contrast with our genetic uh uh uh our genetic story.

922
00:58:20,750 --> 00:58:26,590
And um I mean one one way I've heard this described is that like uh uh genes are

923
00:58:26,590 --> 00:58:28,830
smooth and culture is crunchy.

924
00:58:29,070 --> 00:58:35,070
Um I like that you know uh it's it's it's quite you know quite remarkable

925
00:58:35,070 --> 00:58:40,430
how behaviorally variable uh people tend to be but then the the sort of genetic evidence

926
00:58:40,430 --> 00:58:45,920
suggests exactly what we see with like cross-cultural adoptions which is that um any

927
00:58:45,920 --> 00:58:51,360
individual who's adopted into another culture when they're young enough develop and

928
00:58:52,000 --> 00:58:56,560
internalize the norms and language of the place they're raised with no difficulty.

929
00:58:56,720 --> 00:59:02,560
Like this is not, you know, this is clearly not due to anything

930
00:59:02,560 --> 00:59:06,080
besides cultural differentiation in terms of socialization.

931
00:59:06,400 --> 00:59:12,110
And that also speaks to the recent genetic bottleneck and our absolute lack of subspecies

932
00:59:12,430 --> 00:59:15,470
from sort of the biological or genetic definition.

933
00:59:16,910 --> 00:59:21,950
And that's also really neat that we have really stark cultural differences

934
00:59:22,670 --> 00:59:28,670
globally, you know, where groups themselves or large cultural groups tend to be very

935
00:59:28,670 --> 00:59:31,630
distinct from each other in measurable ways.

936
00:59:32,350 --> 00:59:38,180
But then you also have the ability for individuals to acculturate

937
00:59:38,180 --> 00:59:43,220
into those groups if they're young enough with you know essentially no difficulty.

938
00:59:44,500 --> 00:59:49,220
And so those two things together, I think, make a very uh uh interesting research program

939
00:59:49,220 --> 00:59:52,820
in cultural evolution to sort of understand why that happens or what are the implications

940
00:59:52,820 --> 00:59:55,780
of that kind of uh uh that kind of process.

941
00:59:56,260 --> 00:59:56,500
Yeah.

942
00:59:56,660 --> 01:00:01,700
And I think historically, um, and Brett, you were a student of Pete Richardson, is that

943
01:00:01,700 --> 01:00:02,020
right?

944
01:00:02,870 --> 01:00:08,470
Uh well I actually don't know your is that or you worked at the so I

945
01:00:08,470 --> 01:00:10,070
started actually at Santa Barbara.

946
01:00:10,310 --> 01:00:10,710
Oh, you did?

947
01:00:10,950 --> 01:00:11,990
Oh, I knew this actually.

948
01:00:12,070 --> 01:00:13,990
Now that you say this out loud, we have this conversation.

949
01:00:14,070 --> 01:00:14,790
Yeah, please go through.

950
01:00:15,190 --> 01:00:21,030
I was I was an undergrad at UC Santa Barbara uh in the early 2000s, and

951
01:00:21,670 --> 01:00:24,950
I took a class from Don Simons actually.

952
01:00:25,030 --> 01:00:28,950
It was uh at the time it was Anth5, the intro to physical anthropology.

953
01:00:29,720 --> 01:00:32,200
I mean I'm from originally from uh San Diego.

954
01:00:32,280 --> 01:00:36,520
So I mean this this, you know, I here I am in Germany, but you know, I think that actually

955
01:00:36,520 --> 01:00:39,240
we we we probably have a lot of shared experiences here.

956
01:00:39,400 --> 01:00:39,560
Yeah.

957
01:00:39,640 --> 01:00:41,000
Um surf Leipzig.

958
01:00:41,320 --> 01:00:41,560
Yeah.

959
01:00:41,800 --> 01:00:42,920
Yeah, exactly.

960
01:00:43,080 --> 01:00:45,160
Um but you can surf in Munich actually.

961
01:00:45,240 --> 01:00:49,320
There's like a little uh river and you can you can watch it go look up YouTube, surf

962
01:00:49,320 --> 01:00:50,040
Munich, yeah.

963
01:00:50,450 --> 01:00:50,850
Uh-huh.

964
01:00:51,010 --> 01:00:56,050
There's a there's a rafting uh uh thing that's down by one of the lakes here.

965
01:00:56,450 --> 01:00:59,730
Uh they've got like a fake course you can go down in in river rafting.

966
01:01:00,050 --> 01:01:00,610
Fantastic.

967
01:01:01,490 --> 01:01:07,330
But uh yeah, so I started at um UC Santa Barbara and uh I took classes

968
01:01:07,330 --> 01:01:09,810
from Simons uh and also John Tubi.

969
01:01:10,210 --> 01:01:14,850
So uh I I got to interact with I remember Dad Lieberman was a grad student at the time, uh,

970
01:01:14,930 --> 01:01:18,080
I think I might have participated in her dissertation survey.

971
01:01:18,640 --> 01:01:18,720
Okay.

972
01:01:18,960 --> 01:01:24,400
One of the grossest questions, uh, like there's like disgust things about like, yeah, I I

973
01:01:24,560 --> 01:01:29,600
we were clearing out the Center for Evolutionary Psychology physically um from the Anthropo

974
01:01:29,600 --> 01:01:33,200
building, and like we came across some of Deb's old surveys, and there's like stuff on that

975
01:01:33,200 --> 01:01:37,360
where I'm like, oh my god, I can't believe like it's great science, but yeah, it's very

976
01:01:37,440 --> 01:01:42,320
it's designed to be disgusting stuff about like yeah, sexual things and yeah, and relatives

977
01:01:42,400 --> 01:01:44,310
and all, yeah, it's it's it's interesting.

978
01:01:44,390 --> 01:01:46,150
But anyway, yeah, less we digress.

979
01:01:46,390 --> 01:01:46,630
Yes.

980
01:01:46,710 --> 01:01:47,910
You uh yeah.

981
01:01:47,990 --> 01:01:51,990
So I I remember today I was I was very influenced and I read The Adapted Mind as an

982
01:01:51,990 --> 01:01:52,870
undergraduate.

983
01:01:52,950 --> 01:01:58,550
Um and I took uh both the undergrad courses that uh uh that John was offering at the time,

984
01:01:58,630 --> 01:02:01,350
which was Anth7 and Anth 107.

985
01:02:01,750 --> 01:02:05,720
Uh and I think I might have sat in on the lab group too when I was there.

986
01:02:05,800 --> 01:02:10,280
Um, so I got a lot of exposure to the to ideas in evolutionary psychology.

987
01:02:10,520 --> 01:02:16,200
Um the thing that I was primarily interested in, I was actually a history major at the time

988
01:02:16,440 --> 01:02:18,840
and doing a double double major with anthro.

989
01:02:19,320 --> 01:02:24,360
Uh, I mean, the thing that I'd always found myself most uh interested in still today is

990
01:02:24,360 --> 01:02:29,240
cultural change and understanding why culture uh you see large-scale cultural changes over

991
01:02:29,240 --> 01:02:29,560
time.

992
01:02:30,850 --> 01:02:36,610
And so finding a way to connect evolutionary theory, which I thought was a very compelling

993
01:02:36,610 --> 01:02:41,890
research program, with that topic, you know, is some kind of cultural evolution

994
01:02:43,330 --> 01:02:44,050
subfield.

995
01:02:44,130 --> 01:02:48,370
And it turned out that, you know, there is in fact there is, in fact, a group of

996
01:02:48,610 --> 01:02:50,050
researchers who do that.

997
01:02:50,930 --> 01:02:54,690
And the thing that really appealed to me the most when I started getting into this

998
01:02:54,690 --> 01:03:00,240
literature and why I applied to work with Pete in graduate school was that it was very

999
01:03:00,240 --> 01:03:01,680
heavily focused on models.

1000
01:03:01,840 --> 01:03:06,960
Like the the goal isn't necessarily to um try to

1001
01:03:07,840 --> 01:03:13,440
um you know make forays into social engineering or to

1002
01:03:14,000 --> 01:03:19,970
uh uh do a lot of applied work with you know saying oh we can use you know evolution to

1003
01:03:19,970 --> 01:03:22,850
evolve society or something kind of horrendous like that.

1004
01:03:23,010 --> 01:03:23,890
It was very humble.

1005
01:03:23,970 --> 01:03:26,450
And it was like, look, we just want to understand these processes.

1006
01:03:26,850 --> 01:03:32,130
Can we develop simple models to sort of capture some phenomena that we think might be going

1007
01:03:32,130 --> 01:03:32,370
on?

1008
01:03:33,250 --> 01:03:38,530
And I'm glad you mentioned Paul's quote, you know, from his from his famous paper, you

1009
01:03:38,530 --> 01:03:40,690
know, models are stupid and we need more of them.

1010
01:03:41,180 --> 01:03:42,460
Because that's that's exactly it.

1011
01:03:42,540 --> 01:03:45,900
I mean, we know, you know, from the beginning that these are just little toy models, you

1012
01:03:45,900 --> 01:03:48,940
know, like no population resembles Hardy Weinberg.

1013
01:03:49,180 --> 01:03:54,780
Um, but it gives us like a little toolbox or playground for thinking about some

1014
01:03:54,780 --> 01:03:55,340
topic.

1015
01:03:55,580 --> 01:04:00,620
And uh, you know, when we're looking at an empirical circumstance, that's the focus that I

1016
01:04:00,620 --> 01:04:01,340
was talking about, right?

1017
01:04:01,660 --> 01:04:06,060
Saying, okay, well, what kind of models do I know might be relevant in this circumstance

1018
01:04:06,140 --> 01:04:08,460
and what do they say is important to look at?

1019
01:04:09,670 --> 01:04:14,550
And uh so that's yeah, that's how I ended up uh working with with Pete and Davis.

1020
01:04:15,830 --> 01:04:21,670
And Pete and Rob uh Boyd were collaborators already at that time when

1021
01:04:21,670 --> 01:04:23,270
you were working with Pete?

1022
01:04:23,750 --> 01:04:24,230
Oh yeah.

1023
01:04:24,390 --> 01:04:28,790
I mean they they they've been collaborators since the late 70s, I think, is when they first

1024
01:04:28,790 --> 01:04:30,950
started co-authoring papers together.

1025
01:04:31,100 --> 01:04:32,620
Um, so I'm not not that old.

1026
01:04:33,020 --> 01:04:34,060
No, I know exactly.

1027
01:04:34,140 --> 01:04:34,300
Yeah.

1028
01:04:34,940 --> 01:04:35,180
Yeah.

1029
01:04:35,260 --> 01:04:38,780
We're we we need to have Pete and Rob on uh we're I'm working on that.

1030
01:04:38,940 --> 01:04:44,940
Um but uh can you talk a little bit about I mean my take on on that and we all we talked

1031
01:04:44,940 --> 01:04:50,300
about the Christina episode is that sort of the the older

1032
01:04:51,630 --> 01:04:56,110
I don't know, I don't know how to say this succinctly, but basically, one of the first

1033
01:04:56,270 --> 01:05:01,950
generation things was figuring out sort of is

1034
01:05:01,950 --> 01:05:06,670
cultural evolution and evolutionary psychology, and how do these two things sort of fit

1035
01:05:06,670 --> 01:05:06,910
together?

1036
01:05:07,070 --> 01:05:12,480
And I think there were sort of battles where there were concerns that the cultural

1037
01:05:12,480 --> 01:05:16,960
evolutionists were sort of very simplifying the psychology and the evolutionary

1038
01:05:16,960 --> 01:05:20,320
psychologists were like really oversimplifying the world.

1039
01:05:20,560 --> 01:05:25,680
And I think as like the division of labor has sort of grown up, um

1040
01:05:26,320 --> 01:05:31,840
I think the sophistication of what we're putting in the psychology and the

1041
01:05:31,840 --> 01:05:34,960
evolutionary models is increasing.

1042
01:05:35,310 --> 01:05:41,230
and maybe to some degree the sophistication of the evolutionary psychology models are

1043
01:05:41,230 --> 01:05:43,310
sort of getting better too.

1044
01:05:43,470 --> 01:05:47,150
Um, to the point where I'm not sure I think they're kind of fully blended.

1045
01:05:47,390 --> 01:05:52,510
But um, do you you know, what was your coming from UCSB and then going to Davis?

1046
01:05:52,750 --> 01:05:58,720
Did you get a sense of that sort of uh this within group conflict that maybe

1047
01:05:58,720 --> 01:06:01,360
has sort of waned since uh did you know.

1048
01:06:01,520 --> 01:06:07,280
Well, like like like David and I were just discussing, I I did have to learn these things

1049
01:06:07,360 --> 01:06:09,760
uh over the course of time as a student.

1050
01:06:10,160 --> 01:06:14,080
Um I mean it's like any I think anybody's experience when they get into science, you know,

1051
01:06:14,160 --> 01:06:17,520
you sort of have the view from the outside and then as you participate in it, you're like,

1052
01:06:17,680 --> 01:06:22,080
oh, like it's way more messy uh than you might have thought of in the past.

1053
01:06:22,720 --> 01:06:28,690
Um I mean uh the the

1054
01:06:28,690 --> 01:06:34,290
basic questions in cultural evolution often have to deal with you know what kind of what

1055
01:06:34,290 --> 01:06:39,810
kind of cognitive adaptations would be uh suited to a particular information

1056
01:06:39,810 --> 01:06:40,850
environment.

1057
01:06:41,250 --> 01:06:45,090
Um is that not evolutionary psychology?

1058
01:06:45,320 --> 01:06:48,040
Like you're you're thinking about how psychology might evolve.

1059
01:06:48,200 --> 01:06:51,800
I mean, like, just in the basic sense of, you know, this is a topic.

1060
01:06:51,960 --> 01:06:57,480
Um, I mean, clearly there's a lot of overlap here in in terms of personnel and interests.

1061
01:06:57,640 --> 01:07:03,480
Um, and actually, this is another thing which uh might be, you know, you connect it back to

1062
01:07:03,480 --> 01:07:04,600
our just discussion earlier.

1063
01:07:04,680 --> 01:07:09,960
I mean, I think that the cultural distance is quite small uh between these subfields, but

1064
01:07:10,040 --> 01:07:14,610
you know, you wouldn't necessarily know it if you see people arguing with each other.

1065
01:07:15,170 --> 01:07:17,810
Uh, because it's, you know, it's it's all relative, right?

1066
01:07:17,890 --> 01:07:22,210
I mean, this is relative distance in a particular context, you know, matters quite a bit.

1067
01:07:22,370 --> 01:07:26,290
Uh, and I think that, you know, we're just as capable of circling the wagons, you know,

1068
01:07:26,370 --> 01:07:31,810
when we see uh science itself being, you know, or just the the ability of doing empiricism

1069
01:07:31,810 --> 01:07:35,090
and trying to understand the world uh being criticized.

1070
01:07:35,330 --> 01:07:41,080
Um so uh yeah, I mean I I I can definitely say having moved between these

1071
01:07:41,160 --> 01:07:45,880
these circles that uh uh that there's different emphases for sure.

1072
01:07:46,120 --> 01:07:48,360
Um and uh yeah, I think you're right.

1073
01:07:48,520 --> 01:07:52,440
Like as as time has progressed, there's been a lot of blending taking place.

1074
01:07:52,680 --> 01:07:58,600
Um uh I mean there's still some interesting differences which

1075
01:07:58,840 --> 01:08:00,600
uh which come up.

1076
01:08:00,680 --> 01:08:06,210
I mean, there's not just different emphases, but I think that you know, places people work,

1077
01:08:06,290 --> 01:08:10,930
you know, I think it's actually not a coincidence that, you know, weird as a concept uh

1078
01:08:10,930 --> 01:08:16,690
comes from Joe Henrick, uh, which I would say is more on the cultural evolution side of the

1079
01:08:16,690 --> 01:08:20,770
spectrum of uh uh of evolutionary approaches to humans.

1080
01:08:20,930 --> 01:08:25,330
And that's more of a critique towards academic psychology as a field.

1081
01:08:25,560 --> 01:08:31,480
Um so uh yeah I mean the extent I actually I'd be curious to know about your take, Dave,

1082
01:08:31,560 --> 01:08:34,360
on the sort of the long-term effect of weird.

1083
01:08:34,440 --> 01:08:39,080
Uh do you think that's that's changed psychology itself or evolutionary psychology in

1084
01:08:39,080 --> 01:08:40,040
particular?

1085
01:08:40,280 --> 01:08:45,970
Um I mean I think that uh it's I think one,

1086
01:08:46,130 --> 01:08:51,890
it's had a very good effect in the sense of um it's been a moral um

1087
01:08:52,050 --> 01:08:58,050
embarrassment to psychologists who have sort of not considered cultural variation

1088
01:08:58,050 --> 01:09:00,850
or cross-cultural work pragmatically.

1089
01:09:01,170 --> 01:09:07,090
Um I don't I think the deeper thing, which has sort of always been baked

1090
01:09:07,090 --> 01:09:12,130
in, I think, to the evolutionary sciences, like, and even as much as Rob Boyd and Pete

1091
01:09:12,130 --> 01:09:17,820
Richardson um disagreed uh with Lita Cosmoduse and John

1092
01:09:17,900 --> 01:09:18,380
Tubi.

1093
01:09:18,460 --> 01:09:23,420
You know, and I and I will say, like, you know, as a student of Lita and John's, Pete and

1094
01:09:23,420 --> 01:09:27,100
Rob have only ever been gracious and kind and wonderful to us.

1095
01:09:27,260 --> 01:09:31,260
And I think we've we've all had conversations where I think with them, I've had

1096
01:09:31,260 --> 01:09:35,180
conversations with them where we've talked about sort of the distances decreasing

1097
01:09:35,180 --> 01:09:36,140
considerably.

1098
01:09:36,430 --> 01:09:41,550
But all of the evolutionary behavioral sciences baked into it has been cross-cultural work,

1099
01:09:41,710 --> 01:09:41,950
right?

1100
01:09:42,190 --> 01:09:47,150
Where, like, you know, if we think this is a human universal, this is human nature, right?

1101
01:09:47,230 --> 01:09:49,710
This is part of the design of the evolved phenotype.

1102
01:09:49,950 --> 01:09:55,950
It makes predictions about we might see cultural variation in this attribute, but not

1103
01:09:55,950 --> 01:09:56,430
this other one.

1104
01:09:56,510 --> 01:10:01,870
So, like, you know, if there's a cheater detection system, then certain parts should be

1105
01:10:01,870 --> 01:10:05,150
culturally universal, certain parts should might vary, right?

1106
01:10:05,420 --> 01:10:07,420
depending on different things, right?

1107
01:10:07,500 --> 01:10:13,100
And it gives you that evolutionary approach, it gives you an invariance to look for,

1108
01:10:13,260 --> 01:10:16,780
but then that invariance also includes a contingent design.

1109
01:10:16,860 --> 01:10:22,460
So I think baked into the theory of adaptationism in design-mindedness

1110
01:10:22,700 --> 01:10:26,940
gives you theory to predict cultural variability and uniformity.

1111
01:10:27,100 --> 01:10:30,860
And because of that, that's been baked into the theory.

1112
01:10:31,180 --> 01:10:36,830
I think in psychology, broadly, and I'm making a very broad generalization, and this is not

1113
01:10:36,830 --> 01:10:42,750
true for every aspect, but the idea of weird has sort of been treated

1114
01:10:42,750 --> 01:10:46,350
as like, oh no, we do need to like worry about cross-cultural things.

1115
01:10:46,590 --> 01:10:51,950
But the deep insight of we need design-mindedness or theoretical

1116
01:10:52,340 --> 01:10:57,540
predictions of variability or invariability, I think that still has not really

1117
01:10:57,540 --> 01:10:58,420
happened.

1118
01:10:58,660 --> 01:11:03,220
Um, so in other words, there's sort of a no notion that we should run things very in

1119
01:11:03,220 --> 01:11:04,420
different places.

1120
01:11:04,660 --> 01:11:09,540
But if you don't have a theory of that, it just sort of becomes this thing of, well, I have

1121
01:11:09,540 --> 01:11:13,220
this idea that people tend to do this, and I probably should also run it somewhere that

1122
01:11:13,220 --> 01:11:15,220
feels different culturally.

1123
01:11:15,410 --> 01:11:17,730
And I think that happens a lot.

1124
01:11:17,970 --> 01:11:23,810
So I think it's it's good in that sense, but I think the deeper transition to

1125
01:11:23,810 --> 01:11:26,770
design mindedness is still happening.

1126
01:11:27,250 --> 01:11:27,730
Mm-hmm.

1127
01:11:29,170 --> 01:11:30,050
Yeah, I would agree.

1128
01:11:30,130 --> 01:11:35,890
There that um the you know the weirdest people in the world that paper, you know, made a

1129
01:11:35,890 --> 01:11:40,220
big splash um saying that, you know, we're we're we have all of this research on Western

1130
01:11:40,300 --> 01:11:43,020
educated, industrialized, rich, democratic nations.

1131
01:11:43,180 --> 01:11:44,060
That's a problem.

1132
01:11:44,220 --> 01:11:48,540
But I think one of the things that has led to is a kind of performative diversity where,

1133
01:11:48,620 --> 01:11:53,660
oh, let's just throw in some some other samples in some other places um when we don't

1134
01:11:53,660 --> 01:11:54,140
really need to.

1135
01:11:54,220 --> 01:11:57,340
We don't have a principled theoretical reason to consider these places.

1136
01:11:57,420 --> 01:12:01,820
We don't have a principled prediction to make about how we expect culture to either vary or

1137
01:12:01,820 --> 01:12:03,330
not vary between these places.

1138
01:12:03,490 --> 01:12:07,730
We're just going to include these places just as a kind of performative thing to show that

1139
01:12:07,730 --> 01:12:09,570
we're we're being good scientists.

1140
01:12:10,530 --> 01:12:13,810
I think that is not very helpful.

1141
01:12:14,450 --> 01:12:18,290
Although, you know, it's always nice to broaden the data set, but still, you want to have a

1142
01:12:18,290 --> 01:12:22,530
principled reason to consider the places you're considering, and you want to have actual uh

1143
01:12:22,530 --> 01:12:23,890
predictions to make.

1144
01:12:25,170 --> 01:12:31,000
And without that, um the this uh uh a critique of, you know, we don't our samples are too

1145
01:12:31,000 --> 01:12:33,880
weird or too non weird or whatever, it's it's sort of missing the point.

1146
01:12:34,040 --> 01:12:38,360
I think the the deeper point is that we need we need better theories and we need um to

1147
01:12:38,360 --> 01:12:43,080
understand uh uh where cultural variation comes from, where cultural uniformity comes from,

1148
01:12:43,240 --> 01:12:47,480
and and we need to to dig deeper uh into the underlying mechanisms.

1149
01:12:47,890 --> 01:12:48,130
Yeah.

1150
01:12:48,530 --> 01:12:48,930
Well said.

1151
01:12:49,730 --> 01:12:54,050
I think there's this really interesting debate too about um, I mean, maybe we could talk

1152
01:12:54,050 --> 01:12:56,690
about the jukebox model if you guys remember that.

1153
01:12:57,090 --> 01:12:59,730
Oh, there's lots lots of back and forth on the jukebox model.

1154
01:13:00,050 --> 01:13:02,370
Enlightener was news on what what the jukebox model is.

1155
01:13:02,690 --> 01:13:03,330
Jukebox model.

1156
01:13:03,410 --> 01:13:07,970
Well, so the so the the scenario, I remember reading this in uh in the adapted mind, uh

1157
01:13:08,130 --> 01:13:10,130
Barco, Cosmones, and Tubi.

1158
01:13:10,610 --> 01:13:16,050
Uh, and they were trying to imagine a particular mechanism that might

1159
01:13:16,050 --> 01:13:21,560
produce geographic variation in behavior without necessarily

1160
01:13:21,560 --> 01:13:23,640
involving what we might call cultural transmission.

1161
01:13:23,720 --> 01:13:28,840
And so they had this, they were using the metaphor of a jukebox, which uh look it up, kids.

1162
01:13:28,920 --> 01:13:29,720
Look it up, look it up.

1163
01:13:30,040 --> 01:13:30,440
Exactly.

1164
01:13:30,760 --> 01:13:35,080
It's sort of like a large phone that played music, you know.

1165
01:13:35,160 --> 01:13:36,920
Um you could select from refrigerator.

1166
01:13:37,800 --> 01:13:37,960
Exactly.

1167
01:13:38,520 --> 01:13:42,790
Refrigerator size, you know, and so uh they they made the argument, well, imagine that

1168
01:13:42,790 --> 01:13:47,910
these things are programmed so that they play a specific song or type of music, um,

1169
01:13:48,870 --> 01:13:52,150
you know, given a particular set of coordinates that they're located at.

1170
01:13:52,230 --> 01:13:57,830
So if they're in a high latitude environment, uh they tend to play, I don't know, jazz.

1171
01:13:58,150 --> 01:14:02,950
Uh if they are near the equator, uh they play, you know, punk rock.

1172
01:14:03,740 --> 01:14:07,420
Can I just say I love this metaphor because it's like jukebox technology combined with like

1173
01:14:07,420 --> 01:14:07,980
GPS, right?

1174
01:14:08,060 --> 01:14:12,380
It's sort of K-pop, but yeah, exactly.

1175
01:14:12,700 --> 01:14:13,980
And we need more of them, right?

1176
01:14:14,220 --> 01:14:14,620
So yeah.

1177
01:14:14,940 --> 01:14:17,180
Sorry, I just love the but I mean the physicality.

1178
01:14:18,780 --> 01:14:20,220
Like they're really going on in on this.

1179
01:14:20,300 --> 01:14:22,460
They're saying it's not even like the local ecology.

1180
01:14:22,620 --> 01:14:26,860
It's not even like it's literally just the physical coordinates on the globe.

1181
01:14:27,020 --> 01:14:27,420
Yeah.

1182
01:14:27,660 --> 01:14:31,090
Um and uh this was like a verbal argument, right?

1183
01:14:31,250 --> 01:14:34,770
So they said, okay, so imagine this scenario, and then imagine that you were, you know,

1184
01:14:35,010 --> 01:14:38,610
looking at the diversity of music that was being produced by all these machines in

1185
01:14:38,610 --> 01:14:39,330
different parts of the world.

1186
01:14:39,410 --> 01:14:43,330
And you'd say, Oh, look, you know, there's this cluster in the tropics where they're all,

1187
01:14:43,410 --> 01:14:44,530
you know, playing punk rock.

1188
01:14:44,610 --> 01:14:46,690
You know, this must be a punk rock place.

1189
01:14:46,770 --> 01:14:50,850
You know, like this is the cultural transmission mechanism is sustaining this tradition.

1190
01:14:51,090 --> 01:14:56,420
Um and I mean that's, you know, as a cultural as you could get, basically, right?

1191
01:14:56,500 --> 01:15:01,300
So I mean, I I I would describe this, I think, as like a reaction norm type model, right?

1192
01:15:01,380 --> 01:15:04,900
That you you've got some instead of a cultural norm, you've got a reaction norm.

1193
01:15:05,220 --> 01:15:10,020
Um that uh there's some kind of trigger that's causing a particular set of conditional

1194
01:15:10,020 --> 01:15:13,700
switches in in a cognition to produce a specific behavior.

1195
01:15:14,590 --> 01:15:18,830
But there's nothing you would describe as kind of like cultural transmission or cultural

1196
01:15:18,830 --> 01:15:19,630
influence.

1197
01:15:20,110 --> 01:15:21,470
Uh right.

1198
01:15:21,710 --> 01:15:26,270
I mean, even culture itself in that circumstance, like as a concept, potentially could be

1199
01:15:26,350 --> 01:15:27,870
uh maybe not that usable.

1200
01:15:28,110 --> 01:15:30,910
I mean, the term they can use is ev evoked culture, I believe.

1201
01:15:31,310 --> 01:15:32,110
Evoked culture, exactly.

1202
01:15:32,350 --> 01:15:36,110
So like you push the button and the culture is evoked, and you don't push the button in

1203
01:15:36,110 --> 01:15:38,110
this place and the culture is not evoked.

1204
01:15:38,300 --> 01:15:38,460
Right.

1205
01:15:38,700 --> 01:15:38,940
Right.

1206
01:15:39,100 --> 01:15:43,740
So I mean, I would kind of put this in the same category as the Harvey Weinberg thing, like

1207
01:15:44,140 --> 01:15:49,980
where uh the I mean it's it's neat as a as an

1208
01:15:49,980 --> 01:15:53,580
example to think, okay, this is a way that variation could be produced.

1209
01:15:53,740 --> 01:15:57,740
Um I mean, empirically though, we we see a lot of transmission.

1210
01:15:57,820 --> 01:15:59,180
Like we can measure it, right?

1211
01:15:59,340 --> 01:16:03,660
And you know, so as as as a null model, I think it's very compelling to say like you don't

1212
01:16:03,660 --> 01:16:07,410
necessarily need, you know, you can't just assume, oh, there's cultural variation,

1213
01:16:07,490 --> 01:16:09,730
therefore it's being transmitted.

1214
01:16:11,090 --> 01:16:17,090
But uh then the work becomes, you know, okay, well, how how do you detect evidence for

1215
01:16:17,090 --> 01:16:18,210
transmitted culture?

1216
01:16:18,370 --> 01:16:22,370
Or, you know, what what what are the kinds of things that might you know lend themselves to

1217
01:16:22,370 --> 01:16:26,530
sort of a jukebox-evoked uh type scenario?

1218
01:16:26,690 --> 01:16:31,010
And then what other things, you know, like language, I think is a good example, where uh I

1219
01:16:31,010 --> 01:16:33,650
mean, obviously there's transmission taking place.

1220
01:16:33,810 --> 01:16:39,660
Um That kind of distinction is

1221
01:16:39,660 --> 01:16:45,420
very useful for for uh as a starting place for thinking about uh cultural traditions um

1222
01:16:45,900 --> 01:16:48,860
or what what could potentially be cultural traditions.

1223
01:16:53,260 --> 01:16:57,730
So um okay so we we just went into the jukebox metaphor.

1224
01:16:57,810 --> 01:17:01,970
I believe they brought it up to make this distinction between uh uh evoked culture and

1225
01:17:01,970 --> 01:17:02,850
transmitted culture.

1226
01:17:03,010 --> 01:17:06,370
And the argument is there is just because we see cultural variation, it doesn't mean that

1227
01:17:06,370 --> 01:17:09,010
we should assume automatically that it's transmitted.

1228
01:17:09,250 --> 01:17:13,650
It could be a contingent response to an ecological difference, right?

1229
01:17:13,810 --> 01:17:17,570
So if if there's an ecological difference there and we have adaptations that are sensitive

1230
01:17:17,570 --> 01:17:23,220
to that ecological difference, say we share when there's a lot of variance in uh resource

1231
01:17:23,220 --> 01:17:27,460
shortages and windfalls, and we don't share when there's not a lot of variance in resource

1232
01:17:27,460 --> 01:17:28,500
shortages and windfalls.

1233
01:17:28,580 --> 01:17:32,180
That could vary by ecology, and you could have an adaptation that is sensitive to that

1234
01:17:32,180 --> 01:17:32,660
variable.

1235
01:17:32,820 --> 01:17:36,660
And if one culture shares and another culture doesn't share so much, uh, you don't

1236
01:17:36,660 --> 01:17:41,700
necessarily need to assume that that is because um of you know transmitted culture or or

1237
01:17:41,700 --> 01:17:43,540
norms shaping one place and not the other.

1238
01:17:43,620 --> 01:17:48,790
It could just be a um an emergent output of the underlying sharing psychology.

1239
01:17:48,950 --> 01:17:51,030
And so that's the argument that Tubian Cosmodies make.

1240
01:17:51,830 --> 01:17:56,470
Incidentally, a lot of people misinterpret Tubian Cosmodies as saying they think all

1241
01:17:56,470 --> 01:18:00,710
culture is evoked, or that, you know, and they and this is often caricatured.

1242
01:18:01,030 --> 01:18:02,870
So that's absolutely not what they think.

1243
01:18:03,270 --> 01:18:07,030
They brought this up as as to make a point that we just don't know.

1244
01:18:07,110 --> 01:18:11,600
We cannot assume that that culture is transmitted or evoked uh without actually doing the

1245
01:18:11,600 --> 01:18:13,440
hard work of of figuring out what's going on.

1246
01:18:13,600 --> 01:18:16,560
And so that example uh was made for for that purpose.

1247
01:18:17,200 --> 01:18:21,440
So what what what what do you think is is the the broader lesson or takeaway from from uh

1248
01:18:21,600 --> 01:18:27,520
thinking in these terms uh I mean generative processes are the name of the

1249
01:18:27,520 --> 01:18:31,710
game here right I mean you're you're trying to think of a specific like a literally a

1250
01:18:31,710 --> 01:18:32,430
generative model.

1251
01:18:32,510 --> 01:18:36,750
Like, and uh of course it's ridiculous, you know, humans are not jukeboxes.

1252
01:18:36,910 --> 01:18:42,750
Um, but it's a starting place for thinking about uh a specific, you know, mechanism that

1253
01:18:42,750 --> 01:18:44,030
leads to an outcome, right?

1254
01:18:44,110 --> 01:18:49,230
And sort of inventorying the different kinds of mechanisms or imagining new ones, uh, or

1255
01:18:49,230 --> 01:18:52,670
sort of, you know, thinking, okay, well, if this specific mechanism is happening, you know,

1256
01:18:52,750 --> 01:18:55,390
how would we potentially interpret it or misinterpret it?

1257
01:18:55,550 --> 01:18:59,710
Um so one of the things I've been spending a lot of time on recently is thinking about um

1258
01:19:00,160 --> 01:19:02,000
coordination equilibria.

1259
01:19:03,520 --> 01:19:06,800
So, like, let's I'd be curious to know what you both think about this.

1260
01:19:06,880 --> 01:19:10,240
So, like, if you take the example of like driving on the left side of the road or the right

1261
01:19:10,240 --> 01:19:15,920
side of the road, um, you know, it's not like the jukebox

1262
01:19:16,160 --> 01:19:19,360
model because uh people aren't just responding ASO.

1263
01:19:19,680 --> 01:19:23,840
I mean, in the jukebox model, it literally doesn't matter what other jukeboxes are doing

1264
01:19:23,840 --> 01:19:24,320
around you.

1265
01:19:24,480 --> 01:19:30,110
Like, there's no influence from one jukebox to another in the original model,

1266
01:19:30,190 --> 01:19:30,510
right?

1267
01:19:30,670 --> 01:19:36,590
But in something like, you know, uh coordination equilibrium, uh, when I pull onto

1268
01:19:36,590 --> 01:19:41,710
the road, what everybody else has already decided to do has a big impact on the payoffs

1269
01:19:41,710 --> 01:19:42,590
that I experience, right?

1270
01:19:42,750 --> 01:19:47,070
Like if I choose to drive on the left side of the road in California, I'm gonna have a bad

1271
01:19:47,070 --> 01:19:47,390
time.

1272
01:19:47,790 --> 01:19:52,660
Um And so there's a component of social influence there, right?

1273
01:19:52,740 --> 01:19:57,380
In the sense that people have made choices, and then those choices are going to impact me,

1274
01:19:57,540 --> 01:19:59,140
and they're going to impact my choice.

1275
01:19:59,220 --> 01:20:02,900
And so, okay, I would, you know, as a rational agent, maybe I would say, okay, well, I'm

1276
01:20:02,900 --> 01:20:06,020
going to drive on the same side of the road as everybody else.

1277
01:20:07,300 --> 01:20:08,980
So is that culture?

1278
01:20:09,270 --> 01:20:11,430
That I think is a is a really interesting question.

1279
01:20:11,510 --> 01:20:15,670
Like it's not quite the same thing as you know learning from somebody.

1280
01:20:15,750 --> 01:20:19,030
Like, you know, you're being taught, oh, you should drive on the left side of the road, you

1281
01:20:19,030 --> 01:20:20,630
should drive on the right side of the road.

1282
01:20:20,790 --> 01:20:22,870
Um, although of course that does happen.

1283
01:20:23,110 --> 01:20:28,310
But in many of these equilibrious circumstances, it's something that's reinforced by

1284
01:20:28,950 --> 01:20:31,190
you know, just basically a rational calculation.

1285
01:20:31,350 --> 01:20:35,180
Like given the circumstance, you know, the best thing for me to do is this.

1286
01:20:35,340 --> 01:20:38,220
And I mean, it doesn't really matter that that's being produced by everybody else's

1287
01:20:38,220 --> 01:20:38,540
choices.

1288
01:20:38,620 --> 01:20:40,860
That's just the circumstance in front of me, right?

1289
01:20:41,340 --> 01:20:46,540
Um, so in a sense, it is also uh kind of a cultural, right?

1290
01:20:46,780 --> 01:20:51,580
Because uh uh because there really isn't any, you know, there isn't any way that like

1291
01:20:51,740 --> 01:20:55,900
information about the choice is directly moving from person to person.

1292
01:20:56,060 --> 01:20:57,820
Um or maybe it is though.

1293
01:20:57,900 --> 01:20:58,780
I mean, it's debatable.

1294
01:20:58,860 --> 01:21:02,220
Like if you think of that, you know, it's definitely there's more social influence than the

1295
01:21:02,220 --> 01:21:03,750
jukebox model, right?

1296
01:21:04,230 --> 01:21:04,470
Right.

1297
01:21:04,870 --> 01:21:08,070
I mean, maybe a better analogy would be something like prices.

1298
01:21:08,630 --> 01:21:14,390
So if you think in you know Hayekian terms, you know, prices are an emergent product of

1299
01:21:14,390 --> 01:21:17,110
everyone acting self-interestedly in a marketplace.

1300
01:21:17,350 --> 01:21:21,350
So I, if if I make the price too low, then I'm gonna run out of my stuff.

1301
01:21:21,510 --> 01:21:23,590
If I set the price too high, no one's gonna buy my stuff.

1302
01:21:23,750 --> 01:21:27,190
If you have a bunch of different actors in a marketplace trying to make those local

1303
01:21:27,190 --> 01:21:30,950
decisions about how to set the right price for the particular goods or services they're

1304
01:21:30,950 --> 01:21:36,800
selling, you get something like a map of the value of different things or of the

1305
01:21:36,800 --> 01:21:40,640
supply and demand of different goods and services in the local economy.

1306
01:21:40,720 --> 01:21:44,480
And nobody actually sat down and planned out what the prices were going to be.

1307
01:21:44,560 --> 01:21:48,160
There's no central authority that said the price of bread is going to be this, the price of

1308
01:21:48,160 --> 01:21:49,280
chairs is going to be this.

1309
01:21:49,600 --> 01:21:55,040
It's just an emergent product of everyone acting uh self-interestedly in a marketplace,

1310
01:21:55,200 --> 01:21:55,520
right?

1311
01:21:55,680 --> 01:21:59,230
Um and so you could then pose the question: are prices cultural?

1312
01:21:59,390 --> 01:22:04,590
Nobody sat down in a centralized place and decided what the prices were going to be.

1313
01:22:05,150 --> 01:22:11,070
And yet there's this strong correlation between prices and and supply and demand levels.

1314
01:22:11,230 --> 01:22:12,590
And so how can we explain prices?

1315
01:22:12,670 --> 01:22:16,350
And it's you could think of um driving on one side of the road or the other side of the

1316
01:22:16,350 --> 01:22:17,790
road in similar terms.

1317
01:22:17,950 --> 01:22:22,080
Um, you know, in some cases we all sat down and decided we're gonna drive on the left side

1318
01:22:22,080 --> 01:22:24,800
or the right side, but in some cases just kind of happened spontaneously.

1319
01:22:24,880 --> 01:22:28,880
Like everyone's trying to, you know, drive and not crash, and as a result of everyone

1320
01:22:28,880 --> 01:22:33,920
pursuing their self interest and trying to not crash, this emergent outcome uh naturally

1321
01:22:33,920 --> 01:22:34,640
emerged.

1322
01:22:34,800 --> 01:22:40,480
Um do you view that kind of thing as quote

1323
01:22:40,560 --> 01:22:41,280
unquote culture?

1324
01:22:41,360 --> 01:22:42,400
Do you view it as something else?

1325
01:22:42,480 --> 01:22:46,000
How should we understand those kinds of emergent uh products?

1326
01:22:46,450 --> 01:22:49,250
Yeah, well, I mean it's definitely social influence.

1327
01:22:49,410 --> 01:22:55,250
I think that's one of the neat things is that um what you're basically describing is agents

1328
01:22:55,570 --> 01:23:01,330
who are essentially incapable of uh what we would describe as like social

1329
01:23:01,330 --> 01:23:01,810
interaction.

1330
01:23:01,890 --> 01:23:04,290
Like they just literally see a price in front of.

1331
01:23:04,530 --> 01:23:08,800
I think the the classic economic description is imagining like an auctioneer, right, is

1332
01:23:08,800 --> 01:23:11,120
sort of telling you know what the going price is for something.

1333
01:23:11,200 --> 01:23:14,240
And that's that's a simplification for what the market is.

1334
01:23:14,400 --> 01:23:18,640
It is a single auctioneer who's you know listing off prices, and the agent is responding to

1335
01:23:18,640 --> 01:23:18,800
that.

1336
01:23:18,960 --> 01:23:20,640
They're just they're just getting a number, right?

1337
01:23:20,720 --> 01:23:24,560
It's not like they are in a social context and they're looking around at other people.

1338
01:23:25,040 --> 01:23:30,960
Um so the agent itself isn't internalizing anything, it's not taking in what we

1339
01:23:30,960 --> 01:23:35,910
might call like cultural information or cultural norms and passing those along, but they

1340
01:23:35,910 --> 01:23:37,350
are being influenced by others.

1341
01:23:37,510 --> 01:23:43,510
And then their choice is adding to the influence of the, you know, the next

1342
01:23:43,510 --> 01:23:45,510
individual in the decision-making process, right?

1343
01:23:45,590 --> 01:23:48,950
So there is clearly some kind of a transmission of influence.

1344
01:23:50,390 --> 01:23:54,710
But I mean, if we really go to the opposite extreme here and think about like something

1345
01:23:54,710 --> 01:24:00,630
that clearly is cultural, that has sort of a similar shape to it, you

1346
01:24:00,700 --> 01:24:05,980
know, it might be something like um, I don't know, standing at a red light, you know, at

1347
01:24:05,980 --> 01:24:09,020
midnight when nobody's gonna get a little bit of a little bit of very German, very German.

1348
01:24:09,340 --> 01:24:10,220
Very German, exactly.

1349
01:24:10,300 --> 01:24:11,580
And as I go home here, right?

1350
01:24:11,660 --> 01:24:13,020
So, I mean, people will be standing there.

1351
01:24:13,100 --> 01:24:15,820
There's no the street is completely deserted, right?

1352
01:24:16,220 --> 01:24:19,740
Um, but that light is red, and you're not supposed to cross on red.

1353
01:24:20,060 --> 01:24:23,580
And so people are standing there and they wait for the green light to go.

1354
01:24:24,350 --> 01:24:27,870
But this is highly variable across societies, right?

1355
01:24:27,950 --> 01:24:31,390
This, I mean, you mentioned, oh, it's you know it's stereotypically German to do this.

1356
01:24:31,550 --> 01:24:33,950
It is not stereotypical for other populations.

1357
01:24:34,110 --> 01:24:35,950
I mean, they'll be like, what are you stupid?

1358
01:24:36,110 --> 01:24:37,790
Like, why would you wait if nobody's around?

1359
01:24:37,950 --> 01:24:39,150
You could just go, right?

1360
01:24:39,390 --> 01:24:43,790
Um, and I mean the the answer is clearly there was a socialization process.

1361
01:24:43,870 --> 01:24:47,470
Like people learned that you know, the appropriate thing to do in this circumstance is to

1362
01:24:47,470 --> 01:24:50,520
do this through some process.

1363
01:24:50,680 --> 01:24:55,720
Like they're they're their their brains have information in them, which they've learned

1364
01:24:55,720 --> 01:25:00,680
over the course of a socialization process that structures their behavior in ways that you

1365
01:25:00,680 --> 01:25:03,960
can predict uh maybe beyond rational calculation.

1366
01:25:04,040 --> 01:25:07,480
Like if if you had a rational agent, they're looking at the street, they're like, I can go.

1367
01:25:07,640 --> 01:25:12,680
You know, like I I might quibble with, I mean, I uh intuitively, like my the reason why I

1368
01:25:12,680 --> 01:25:17,310
would not run a red light at midnight when no one is around is just on the off chance that

1369
01:25:17,390 --> 01:25:21,230
like there happens to be a cop driving by at that exact moment and I didn't see them

1370
01:25:21,230 --> 01:25:22,750
coming, like that could ruin my life.

1371
01:25:22,830 --> 01:25:26,030
I I you know get you know a ticket, you know, whatever.

1372
01:25:26,190 --> 01:25:27,950
Um that would be a terrible thing to happen.

1373
01:25:28,030 --> 01:25:31,470
And I just it's such a it's a small probability that'll happen, but I just don't want to

1374
01:25:31,470 --> 01:25:31,790
take the chance.

1375
01:25:31,870 --> 01:25:32,830
I'll just I might as well wait.

1376
01:25:32,910 --> 01:25:33,550
It's not a big deal.

1377
01:25:33,630 --> 01:25:37,470
It's not a big cost on me to wait a few minutes, you know, a few seconds or a few minutes

1378
01:25:37,550 --> 01:25:38,760
for for the light to change.

1379
01:25:38,920 --> 01:25:44,040
And so I would expect um variation in whether s people blow red lights to not so much be a

1380
01:25:44,040 --> 01:25:47,960
product of whether they've quote unquote internalized this quote unquote norm, but just

1381
01:25:47,960 --> 01:25:49,880
like how many police are there in the environment?

1382
01:25:50,040 --> 01:25:51,400
Like how effective is policing?

1383
01:25:51,640 --> 01:25:53,880
How scared are people of the local police?

1384
01:25:54,040 --> 01:25:58,200
Like that would be, I think, the the the key variable to look at rather than whether

1385
01:25:58,200 --> 01:26:01,480
someone has quote unquote internalized a quote unquote norm.

1386
01:26:01,640 --> 01:26:07,510
Uh do you would you would you buy that Uh well, I guess the way to to look for it would be

1387
01:26:07,510 --> 01:26:13,030
to find circumstances where there isn't really a strong policing presence.

1388
01:26:13,910 --> 01:26:18,630
Uh but people are still really adherent to a particular behavior pattern.

1389
01:26:18,710 --> 01:26:18,870
Right.

1390
01:26:18,950 --> 01:26:20,550
I think that would be kind of the the thing.

1391
01:26:20,790 --> 01:26:24,150
I mean, we're just basically saying, Okay, well the theory might suggest to look for this

1392
01:26:24,150 --> 01:26:24,710
now, right?

1393
01:26:24,790 --> 01:26:26,550
Like the specific scenarios.

1394
01:26:27,830 --> 01:26:29,430
So if there if there are literally no costs.

1395
01:26:29,510 --> 01:26:35,280
So if if if if police never uh pull you over or stop you from breaking this rule.

1396
01:26:35,360 --> 01:26:36,720
And there are no social costs either.

1397
01:26:36,800 --> 01:26:41,440
Like no one frowns on upon you, no one looks down on you or thinks you're a bad person or

1398
01:26:41,520 --> 01:26:43,280
or a reckless driver or whatever.

1399
01:26:43,520 --> 01:26:48,000
If there are no costs at all, then I would expect uh people to do to to not follow the

1400
01:26:48,000 --> 01:26:48,160
norm.

1401
01:26:48,560 --> 01:26:53,200
Can I just say as a I so like we lived in a very and we were just visiting, we just went

1402
01:26:53,200 --> 01:26:53,840
back for a little bit.

1403
01:26:54,000 --> 01:26:57,840
Brett, I should have we have next time we visit we'll come down and say hi to Leipzig.

1404
01:26:59,520 --> 01:27:02,550
So we live in a sleepy par Dahlem in Berlin.

1405
01:27:02,710 --> 01:27:07,350
And there's like even the pedestrian like stop, you know, like red and green um goes.

1406
01:27:07,430 --> 01:27:13,350
The um and it would not be uncommon for everybody like getting off the U-ba on the subway

1407
01:27:13,350 --> 01:27:19,190
stop when it's red to just stop and wait, even if it's dark, you know, and like there's no

1408
01:27:19,190 --> 01:27:23,590
cops around, nobody like, you know, there's foxes like running around, you know, like it's

1409
01:27:23,590 --> 01:27:24,310
very quiet.

1410
01:27:24,470 --> 01:27:28,230
Um, but I still do think it's rational in like the following two senses.

1411
01:27:28,390 --> 01:27:32,440
Number one, um, there is sometimes opacity.

1412
01:27:32,600 --> 01:27:37,720
So sometimes there will be a green light and a red light, and you think it's clear.

1413
01:27:37,880 --> 01:27:43,640
And I had the distinct experience in Germany of like as an American when I first got

1414
01:27:43,640 --> 01:27:49,320
there, of uh being like, Well, it's it's red, but clearly there's nothing gonna happen and

1415
01:27:49,320 --> 01:27:50,360
I am gonna be fine.

1416
01:27:50,520 --> 01:27:52,360
And then like a car would come.

1417
01:27:52,890 --> 01:27:58,730
And then the second thing was the lights really work in the sense

1418
01:27:58,730 --> 01:28:01,530
of they're really well engineered to be timed well.

1419
01:28:01,610 --> 01:28:04,170
So you're not waiting an exorbitant amount of time.

1420
01:28:04,330 --> 01:28:07,850
So like in the US, you might be waiting for seven minutes to cross a road.

1421
01:28:08,010 --> 01:28:09,690
That never happens in Germany.

1422
01:28:09,850 --> 01:28:15,780
So I experienced not worrying about the cops, but just sort of like this is a sensible

1423
01:28:15,860 --> 01:28:17,540
like coordination device.

1424
01:28:17,700 --> 01:28:23,460
And it's actually just sort of like easier to like offload my processing to this thing

1425
01:28:23,460 --> 01:28:24,660
that really works well.

1426
01:28:24,740 --> 01:28:27,940
And then I've actually it's so it's I think it's David, it's your point that like it's

1427
01:28:27,940 --> 01:28:30,340
still a rational calculation of costs and benefits.

1428
01:28:30,500 --> 01:28:34,100
It's just a different set in my experience, like why I would wait.

1429
01:28:34,180 --> 01:28:36,410
And then like it does get tweaked.

1430
01:28:36,490 --> 01:28:41,370
I mean, I can nerd out about like why people cross, but like I'd seen Germans like at a

1431
01:28:41,370 --> 01:28:43,370
particular light, like cross the red light.

1432
01:28:43,450 --> 01:28:47,770
And I'm like, oh, okay, that one, like it's that coordination device doesn't quite work as

1433
01:28:47,770 --> 01:28:48,730
well as the other ones.

1434
01:28:48,890 --> 01:28:51,050
And so I would start like running that one.

1435
01:28:51,210 --> 01:28:54,250
And then um, and then there's like a really breaking the social contract.

1436
01:28:54,490 --> 01:28:55,210
Yeah, exactly.

1437
01:28:55,370 --> 01:28:59,770
And then if there's exactly and then if there's kids around, you really don't go because

1438
01:28:59,880 --> 01:29:03,400
that's like and that's like a high social cost and there's like recursive common knowledge

1439
01:29:03,400 --> 01:29:06,520
that like you don't do that when kids are around and like people will look at you if you do

1440
01:29:06,520 --> 01:29:06,760
that.

1441
01:29:07,000 --> 01:29:08,280
So that's at least my experience.

1442
01:29:08,360 --> 01:29:09,320
Brett, you're still there.

1443
01:29:09,400 --> 01:29:12,920
So like you know you can you can flesh that out or if your experience is different.

1444
01:29:13,000 --> 01:29:15,320
But that was my experience of like a decade in Germany.

1445
01:29:15,480 --> 01:29:15,880
Yeah.

1446
01:29:16,200 --> 01:29:16,440
Yeah.

1447
01:29:16,600 --> 01:29:19,800
I think there's something to be said for rational laziness.

1448
01:29:20,120 --> 01:29:26,040
Uh in the sense rational enough I mean so David, like to to your scenario here of running

1449
01:29:26,040 --> 01:29:29,270
the you know I haven't driven a car in like, you know, eight years.

1450
01:29:29,430 --> 01:29:33,350
So I'm totally disconnected from the car culture I grew up in.

1451
01:29:33,510 --> 01:29:38,070
Um, but in the context of running a red light, which you should not do, right?

1452
01:29:38,150 --> 01:29:40,230
I think you should advise your listeners to, you know.

1453
01:29:40,950 --> 01:29:44,390
I'm so sorry, but I lived in Connecticut after moving from California.

1454
01:29:44,470 --> 01:29:49,430
And Connecticut is wonderful in many ways, but it was so ridiculous because people would

1455
01:29:49,430 --> 01:29:55,160
wait, people would run red lights so much that people would run red lights for

1456
01:29:55,160 --> 01:29:58,920
maybe a second or two, but everybody knew that people were running red lights.

1457
01:29:59,000 --> 01:30:03,720
So people would wait at green lights for like five to seven seconds.

1458
01:30:04,040 --> 01:30:06,280
So everybody was way worse off.

1459
01:30:06,600 --> 01:30:07,480
Coordination failure.

1460
01:30:07,720 --> 01:30:11,800
Total coordination failure where like people were gaining a second or two, but everybody

1461
01:30:11,800 --> 01:30:14,440
was losing five or seven seconds on the other end.

1462
01:30:14,680 --> 01:30:16,520
Clear example of coordination failure.

1463
01:30:16,600 --> 01:30:16,840
Sorry.

1464
01:30:16,920 --> 01:30:17,080
Yeah.

1465
01:30:17,670 --> 01:30:17,830
Yeah.

1466
01:30:17,910 --> 01:30:17,990
Yeah.

1467
01:30:18,070 --> 01:30:23,510
Well, I mean, there's the I think there's actually a totally undeveloped topic of traffic

1468
01:30:23,510 --> 01:30:24,230
cultures.

1469
01:30:24,870 --> 01:30:29,510
Since we've gotten on this, I mean, everybody seems to have opinions about like East Coast

1470
01:30:29,510 --> 01:30:31,350
drivers and West Coast drivers.

1471
01:30:31,510 --> 01:30:34,310
I don't know where you guys are from, but you know, if you're in California.

1472
01:30:35,990 --> 01:30:36,150
Yeah.

1473
01:30:36,310 --> 01:30:36,550
Yeah.

1474
01:30:36,630 --> 01:30:36,870
Yeah.

1475
01:30:37,590 --> 01:30:43,560
I mean, I I I notice intuitive differences between um uh Midwest drivers and and

1476
01:30:43,560 --> 01:30:46,360
now I live in the West, I live in LA, and and and LA drivers.

1477
01:30:46,600 --> 01:30:50,280
LA drivers are way bigger assholes than the Midwest Midwestern driver.

1478
01:30:50,520 --> 01:30:53,000
In LA, nobody lets you in if you're trying to change lane.

1479
01:30:53,080 --> 01:30:55,080
They just they they don't don't give a shit.

1480
01:30:55,240 --> 01:30:57,720
And in the Midwest, people slow down and let you in.

1481
01:30:57,960 --> 01:31:03,480
Uh uh I I would imagine that has something to do with the level of traffic uh in LA, where

1482
01:31:03,480 --> 01:31:06,850
letting someone in is sort of letting them cut in line, and if everyone's waiting in line

1483
01:31:06,850 --> 01:31:09,010
all the time in cars, then you don't want to let someone in.

1484
01:31:09,090 --> 01:31:11,170
But if in the Midwest, if there's less traffic, it's not a big deal.

1485
01:31:11,410 --> 01:31:12,290
So I would I don't know.

1486
01:31:12,370 --> 01:31:13,010
I do LA.

1487
01:31:13,250 --> 01:31:13,810
I'm under the impression.

1488
01:31:15,810 --> 01:31:16,050
Yeah.

1489
01:31:16,530 --> 01:31:19,490
I I'm under the impression that it's totally different, like in New York.

1490
01:31:20,450 --> 01:31:21,810
The driving dynamics there.

1491
01:31:22,210 --> 01:31:23,250
Connecticut is terrible.

1492
01:31:23,410 --> 01:31:25,650
I mean, David, you lived in New York, but you didn't drive then, right?

1493
01:31:25,810 --> 01:31:26,930
I didn't drive though, no.

1494
01:31:27,090 --> 01:31:27,490
Yeah, yeah.

1495
01:31:27,650 --> 01:31:30,850
I mean, I Connecticut was way worse than LA.

1496
01:31:31,140 --> 01:31:35,300
Like I was I was almost like in tears compared to LA driving.

1497
01:31:35,460 --> 01:31:38,500
Like LA drivers are Midwestern compared to Connecticut.

1498
01:31:38,580 --> 01:31:41,620
I hope we get a lot of angry Connecticut people.

1499
01:31:41,780 --> 01:31:42,820
Well that's the point, right?

1500
01:31:43,140 --> 01:31:48,980
Like, but yeah, like that that like kind of uh megalopolis corridor um where it was

1501
01:31:48,980 --> 01:31:53,300
sort of combining traffic with like the infrastructure doesn't work and people were just

1502
01:31:53,300 --> 01:31:58,100
sort of free riding um all the coordination devices was sort of what it felt like.

1503
01:31:58,470 --> 01:31:58,790
Yeah.

1504
01:31:59,030 --> 01:32:03,190
So it seems I want to I want to make a perhaps more controversial point.

1505
01:32:03,510 --> 01:32:08,470
Because it seems like we're we're we're hard pressed to find a canonical example of quote

1506
01:32:08,550 --> 01:32:14,390
unquote culture, where like I sit you down on my lap, I in I you know bequeath to you the

1507
01:32:14,390 --> 01:32:17,830
norms and values of my culture, and you internalize them.

1508
01:32:17,910 --> 01:32:21,910
And then you you follow those norms just because they're norms, and you know that you're

1509
01:32:21,910 --> 01:32:23,110
supposed to follow those norms.

1510
01:32:23,270 --> 01:32:28,600
Like that canonical stereotyped example of culture, we're having a hard time finding any

1511
01:32:28,600 --> 01:32:30,040
clear examples of it.

1512
01:32:30,200 --> 01:32:34,360
In fact, a lot of the times when we try to impart our culture to people, they just kind of

1513
01:32:34,360 --> 01:32:37,960
ignore us, they roll their eyes, they do what they want to do anyway, and like it just

1514
01:32:37,960 --> 01:32:38,360
doesn't work.

1515
01:32:38,440 --> 01:32:42,920
Like we try I believe me, I've tried to impart culture onto my daughter uh as hard as I

1516
01:32:42,920 --> 01:32:43,960
can, and it doesn't work.

1517
01:32:44,120 --> 01:32:44,920
She doesn't listen.

1518
01:32:45,080 --> 01:32:46,760
She does what she wants to do anyway.

1519
01:32:46,920 --> 01:32:52,750
And so what I the controversial point I want to make to you is it seems like most culture

1520
01:32:52,750 --> 01:32:58,110
is actually not this, you know, uh vertical transmission of I give you the norms and values

1521
01:32:58,190 --> 01:33:01,870
and you accept them and you internalize them, but it's more just it's more Hayekian.

1522
01:33:02,030 --> 01:33:06,270
It's more like this emergent product of everyone just following their self-interest, doing

1523
01:33:06,270 --> 01:33:08,830
what is rational for them to do in their particular circumstances.

1524
01:33:08,990 --> 01:33:11,550
And as a result, we get these emergent outcomes.

1525
01:33:11,710 --> 01:33:15,580
We get the the outcome of people driving on the left side or the right side of the road.

1526
01:33:15,740 --> 01:33:19,020
We get the outcome of people using this kind of slang or this kind of slang.

1527
01:33:19,180 --> 01:33:24,780
We get the outcome of uh people having these different uh sharing norms or moral or moral

1528
01:33:24,780 --> 01:33:28,540
rules or um aesthetic tastes and preferences.

1529
01:33:28,780 --> 01:33:33,420
Everyone's just pursuing their local goals, trying to gain status, trying to outcompete

1530
01:33:33,420 --> 01:33:34,620
other people in their environment.

1531
01:33:34,700 --> 01:33:39,830
And and when everyone's doing that, you get this emergent outcome that results from it.

1532
01:33:39,990 --> 01:33:44,950
And so it's less this I am vertically transmitting culture to you and bequeathing to you

1533
01:33:44,950 --> 01:33:46,790
the culture that was passed on to my ancestors.

1534
01:33:46,870 --> 01:33:48,230
It's more just I'm just doing what I want.

1535
01:33:48,310 --> 01:33:51,990
I'm just pursuing my goals, just trying to make a living, trying to impress people, trying

1536
01:33:51,990 --> 01:33:52,710
to gain status.

1537
01:33:52,790 --> 01:33:55,670
And when everyone is doing that, you get culture.

1538
01:33:56,310 --> 01:33:59,190
And so that's that would be the the point I would I would make to you.

1539
01:33:59,270 --> 01:34:02,470
What what do you think about that that most culture is just is like that?

1540
01:34:05,320 --> 01:34:09,160
Uh I mean I don't know what most means in this context.

1541
01:34:09,240 --> 01:34:14,280
You know, like if we do like a list, you know, like listens to K pop,

1542
01:34:15,160 --> 01:34:19,240
uh plays Monopoly, you know, like there.

1543
01:34:19,400 --> 01:34:21,640
That was just where does the where does the list go, right?

1544
01:34:21,720 --> 01:34:27,640
I mean, uh, I think that what we're probably on the same page about is the idea

1545
01:34:27,640 --> 01:34:33,160
that behavioral feedbacks set conditions for decision making

1546
01:34:33,850 --> 01:34:39,370
in ways that create behavioral variation, like persistent behavioral variation between

1547
01:34:39,370 --> 01:34:44,810
populations can be produced by those kinds of coordination equilibria in different places.

1548
01:34:45,290 --> 01:34:51,290
Um I think that the fact that it's transmitted in some way is

1549
01:34:51,290 --> 01:34:51,770
interesting.

1550
01:34:51,930 --> 01:34:54,810
Like it's there's clearly a social influence here, right?

1551
01:34:54,970 --> 01:34:57,290
That you know, individuals might attend to.

1552
01:34:57,370 --> 01:35:00,890
And then the question becomes I mean, this is where the cultural evolution modeling

1553
01:35:00,890 --> 01:35:06,760
literature kind of starts is saying, okay, imagine that you've got a selective environment

1554
01:35:06,760 --> 01:35:11,320
now where your cognition is evolving in this kind of a you know

1555
01:35:11,880 --> 01:35:15,320
coordination type problem, and it's costly.

1556
01:35:15,480 --> 01:35:20,680
Like it's costly to pay attention all the time and try to figure out if I would benefit

1557
01:35:20,680 --> 01:35:23,400
this one time from running the red light or there's a cop around.

1558
01:35:23,560 --> 01:35:29,550
Like it's costly to um Try to calculate how much

1559
01:35:29,550 --> 01:35:32,990
social sanctioning I might get in this particular because you have to pay attention, right?

1560
01:35:33,070 --> 01:35:36,670
I mean, like it requires attention is a resource, right?

1561
01:35:37,150 --> 01:35:42,910
Um and I think that one of the things you get from studying this in like

1562
01:35:42,910 --> 01:35:48,740
an informational sense is that norm psychology is actually a nice

1563
01:35:48,740 --> 01:35:49,540
solution to this.

1564
01:35:49,700 --> 01:35:55,700
Like the idea that I can internalize a rule and it actually helps everybody coordinate, and

1565
01:35:55,700 --> 01:35:58,820
I don't have to pay any costs to paying attention now.

1566
01:35:58,980 --> 01:35:59,300
Right?

1567
01:35:59,460 --> 01:36:03,940
This is this is basically, you know, potentially a model for explaining why you might get

1568
01:36:03,940 --> 01:36:08,180
the emergence of a norm psychology, which is quite stark, starkly different, I think, from

1569
01:36:08,180 --> 01:36:09,860
the mechanism you're talking about.

1570
01:36:10,100 --> 01:36:16,030
Um I mean empirically, this is the this is the other thing, is that um it really does

1571
01:36:16,030 --> 01:36:22,030
seem like there's uh uh the the the there's a norm psychology is a big

1572
01:36:22,030 --> 01:36:25,230
part of our story when we're understanding why people behave the way they do.

1573
01:36:25,470 --> 01:36:30,990
I mean, uh I was thinking about this paper by um Bailey House and colleagues uh

1574
01:36:31,230 --> 01:36:36,600
looking at the way people behave cross culturally in the economic game experiments, right?

1575
01:36:36,680 --> 01:36:38,920
Like the dictator game and the ultimatum game.

1576
01:36:39,160 --> 01:36:44,280
Um, like you have persistent variation between populations, but you also can track in in

1577
01:36:44,280 --> 01:36:49,480
their study, which came out a couple of years ago, um, you can track the trajectory of

1578
01:36:49,480 --> 01:36:55,160
children like moving towards the adult values that that are that are

1579
01:36:55,160 --> 01:36:57,640
consistently displayed across populations.

1580
01:36:58,280 --> 01:37:03,990
Um Which I mean, it seems like that's more consistent with the idea that they're learning

1581
01:37:03,990 --> 01:37:07,430
what's appropriate or what's inappropriate in a particular circumstance.

1582
01:37:07,670 --> 01:37:12,630
Um but I mean, I think more functionally you could just appeal to the idea that

1583
01:37:14,070 --> 01:37:19,670
if it's cheaper to learn a particular condition that helps you maintain a coordination

1584
01:37:19,670 --> 01:37:25,270
equilibrium, and then you don't have to calculate, uh, that you might

1585
01:37:25,270 --> 01:37:30,940
expect uh a psychology to develop an adaptation to just learn the learn what the

1586
01:37:30,940 --> 01:37:33,260
equilibrium is and then internalize that.

1587
01:37:34,300 --> 01:37:38,780
And I think, I mean, to just sort of add to both things.

1588
01:37:39,020 --> 01:37:44,940
Um also wonder if it isn't that it's a little bit of both in the sense of

1589
01:37:45,820 --> 01:37:51,020
you know, one of the arguments, and Brett, you talked about this earlier, is that we're

1590
01:37:51,020 --> 01:37:56,470
this sort of weird species that really the cultural phenotype is really different And it's

1591
01:37:56,470 --> 01:37:59,670
independent of our smooth genes, our crunchy cultural differences.

1592
01:38:01,670 --> 01:38:07,670
We that has itself been sort of an enduring part of our evolved psychology, you know,

1593
01:38:07,750 --> 01:38:11,350
that we have this idea that there will be transmitted information.

1594
01:38:11,510 --> 01:38:14,630
And then we have this evolved psychology, this norm psychology.

1595
01:38:14,710 --> 01:38:20,470
And I think the way I think about it is that the norm psychology is itself a

1596
01:38:20,470 --> 01:38:25,620
complex adaptation that's sort of calculating, is what I'm doing

1597
01:38:25,860 --> 01:38:29,780
based on this sequential social transmission process?

1598
01:38:29,860 --> 01:38:32,500
And then is it rationally for me to follow it or not?

1599
01:38:32,660 --> 01:38:35,860
And is what I'm going to do affect how other people act?

1600
01:38:35,940 --> 01:38:40,100
So I think it's all coordination and rational choice all the way down in this respect,

1601
01:38:40,260 --> 01:38:45,780
which is that I don't think, to David's point, I don't think there's any social

1602
01:38:46,260 --> 01:38:52,070
learning that isn't some result of sort of like a cost-benefit individual

1603
01:38:52,070 --> 01:38:57,510
analysis, whether the evolved psychology figuring that cost-benefit utility over

1604
01:38:57,510 --> 01:39:01,910
evolutionary time, like you know, when you're a baby, you're not going to trial and error

1605
01:39:01,990 --> 01:39:03,190
figure out what's costly or not.

1606
01:39:03,270 --> 01:39:06,950
You're just sort of doing this quote-unquote innate, you know, contingency.

1607
01:39:07,350 --> 01:39:12,230
But then, even um, but still, even for something like this red light thing, you know,

1608
01:39:12,310 --> 01:39:17,760
there's still some grammar of cost-benefit analysis, even though there's

1609
01:39:17,760 --> 01:39:19,280
represented as a norm.

1610
01:39:19,920 --> 01:39:25,840
So I think like the norms, I do think you know there is an evolved norm psychology, but

1611
01:39:25,840 --> 01:39:30,800
it's suffused with a cost-benefit analysis too, because that would be selected for, right?

1612
01:39:30,880 --> 01:39:36,800
Like to have a really naive norm psychology that is completely blind

1613
01:39:36,800 --> 01:39:39,930
to cost-benefits, and is it beneficial for me or not?

1614
01:39:40,170 --> 01:39:43,370
And I think we all have this experience, and I think this is part of why we have this

1615
01:39:43,370 --> 01:39:49,130
theoretical uh disagreement is like the the 1984 thing of like there's a norm,

1616
01:39:49,290 --> 01:39:51,930
it's really strong, and it's fucking stupid.

1617
01:39:52,170 --> 01:39:55,770
It's like it's it's horrible, it's bad, we shouldn't obey it.

1618
01:39:55,930 --> 01:40:01,050
And I think that's a result of this psychology that's simultaneously like representing what

1619
01:40:01,050 --> 01:40:06,400
has been the transmitted information, and then also like what's the landscape, how it

1620
01:40:06,400 --> 01:40:07,600
benefits me and others?

1621
01:40:07,680 --> 01:40:13,440
And I think the expression of that um is is sort of like the moral outrage in like

1622
01:40:13,440 --> 01:40:19,120
1984 and so much other sort of like fiction of like we shouldn't follow this, or like kids

1623
01:40:19,120 --> 01:40:20,640
at school who are like this is dumb.

1624
01:40:20,880 --> 01:40:22,800
So I I kind of agree with both of you.

1625
01:40:22,960 --> 01:40:25,680
I don't know if that's uh helpful, but yeah.

1626
01:40:26,350 --> 01:40:26,750
Yeah.

1627
01:40:27,470 --> 01:40:29,710
There's a study I was thinking of just now.

1628
01:40:30,190 --> 01:40:36,190
Um that was done in a daycare, uh, looking at the timing of people coming late to

1629
01:40:36,190 --> 01:40:37,070
pick up their kids.

1630
01:40:37,230 --> 01:40:38,750
Do you know this study?

1631
01:40:39,070 --> 01:40:40,590
Uh it's amazing.

1632
01:40:40,750 --> 01:40:43,950
I think it was done in Haifa in Israel uh some time ago.

1633
01:40:44,030 --> 01:40:45,310
And it was an economics study.

1634
01:40:45,390 --> 01:40:47,070
It was published in economics.

1635
01:40:47,390 --> 01:40:53,230
And they were studying the effect of changing the policy in the daycare from

1636
01:40:53,660 --> 01:40:56,460
please don't come late because we're waiting.

1637
01:40:56,540 --> 01:40:59,340
You know, your your kids are here and we can't leave.

1638
01:40:59,580 --> 01:41:01,740
So it's very inconvenient.

1639
01:41:01,980 --> 01:41:05,420
To uh, you're gonna pay a price if you come too late.

1640
01:41:05,500 --> 01:41:06,460
You're gonna get fined.

1641
01:41:06,620 --> 01:41:07,740
I mean, this was my experience.

1642
01:41:07,820 --> 01:41:12,220
I've got you know older kids now, but when they were young, uh, there was definitely like a

1643
01:41:12,460 --> 01:41:15,580
fine that was experienced if you came too late to pick up your kids.

1644
01:41:15,900 --> 01:41:17,740
Is this in the US or Germany?

1645
01:41:18,250 --> 01:41:22,490
Uh this was in the US when I was when I was in Germany there was never a fine.

1646
01:41:22,570 --> 01:41:24,330
It was just like, please don't come late.

1647
01:41:24,490 --> 01:41:26,010
We we have to we have to go home.

1648
01:41:26,090 --> 01:41:26,330
Yeah.

1649
01:41:26,570 --> 01:41:29,930
Um but uh yeah in the US it was totally commercialized, right?

1650
01:41:30,010 --> 01:41:33,610
It was like you will get sixty dollars for every half hour, right?

1651
01:41:33,690 --> 01:41:35,370
Or something astronomically high.

1652
01:41:35,610 --> 01:41:39,440
And so in the uh Israeli daycare, they made this switch.

1653
01:41:39,520 --> 01:41:43,840
They moved from like, you know, basically just like a pleading with people not to come late

1654
01:41:44,000 --> 01:41:47,680
to here's here's what we're gonna pay, you know, you're gonna you're gonna have to pay this

1655
01:41:47,680 --> 01:41:49,200
much if you come late.

1656
01:41:49,440 --> 01:41:55,040
Uh and paradoxically, perhaps, it increased the rate of tardiness because now there was a

1657
01:41:55,040 --> 01:41:56,480
price associated with the lateness.

1658
01:41:56,560 --> 01:41:59,600
And so people are like, is 30 minutes worth this much to me?

1659
01:41:59,680 --> 01:42:01,680
And the answer is sometimes yes, right.

1660
01:42:02,000 --> 01:42:06,480
And so the title, the title of the paper is A Fine is a Price.

1661
01:42:06,590 --> 01:42:08,990
It's this extremely famous paper in economics.

1662
01:42:09,150 --> 01:42:14,910
Uh and it's it's such a wonderful illustration of um, at least from my point of view,

1663
01:42:15,070 --> 01:42:19,870
uh the differences between looking at it in terms of rational cost benefit analysis and

1664
01:42:19,870 --> 01:42:24,030
sort of looking at it as this vague, you know, pro social behavior that we're just asking

1665
01:42:24,030 --> 01:42:28,350
you to do, like to not please don't come late because you're inconveniencing somebody else.

1666
01:42:28,510 --> 01:42:33,150
Um and I don't know, it almost seems to me like there's two modes of thinking about these.

1667
01:42:33,230 --> 01:42:37,440
Like as soon as you start adding money involved, it really does just seem like a commercial

1668
01:42:37,440 --> 01:42:42,160
exchange where it's like, okay, I'm I'm paying you sixty dollars to stay here for thirty

1669
01:42:42,160 --> 01:42:42,960
more minutes, right?

1670
01:42:43,200 --> 01:42:46,960
Yeah, you wouldn't have offered me sixty bucks or you wouldn't have exactly your price for

1671
01:42:47,040 --> 01:42:47,680
this is where we're gonna do it.

1672
01:42:47,840 --> 01:42:50,640
This is the market this is clearly the market rate here, right?

1673
01:42:50,720 --> 01:42:56,240
So uh but thinking about it that way, you know, I mean if you think about any fine that

1674
01:42:56,240 --> 01:42:58,160
way, then it really becomes kind of ghoulish, right?

1675
01:42:58,240 --> 01:43:02,490
Where it's just like, Oh well if you have enough money, you know, then it's just like

1676
01:43:02,490 --> 01:43:04,090
you're paying to break the law, right?

1677
01:43:04,170 --> 01:43:07,930
It's just like, okay, I will pay this much to to to not have to do this, right?

1678
01:43:08,410 --> 01:43:14,170
Um, but uh, you know, is that is that the way we think about norms?

1679
01:43:14,330 --> 01:43:18,890
I mean, it feels to me like in normal circumstances, uh, it's not like that.

1680
01:43:19,050 --> 01:43:24,860
That people do actually behave prosocially without the explicit threat of fining, because

1681
01:43:25,020 --> 01:43:31,020
they don't think about it as like an economic exchange with an explicit

1682
01:43:31,020 --> 01:43:31,740
cost-benefit.

1683
01:43:31,900 --> 01:43:34,060
And as soon as you do do that, it it shifts modes.

1684
01:43:34,140 --> 01:43:37,740
And then it does actually become much more like a market uh interaction.

1685
01:43:37,980 --> 01:43:43,420
Um and in that that circumstance, I think it's very clear that you know that that uh

1686
01:43:43,420 --> 01:43:47,820
rational calculation is the right way to think about this because you you have a price

1687
01:43:47,820 --> 01:43:48,380
schedule, right?

1688
01:43:48,540 --> 01:43:49,660
You know exactly what it's gonna be.

1689
01:43:49,740 --> 01:43:54,130
Like you can map out, you know, one hour of their time is this much money.

1690
01:43:54,290 --> 01:43:59,490
Um so anyway, I just think you know, there's different ways of thinking about um

1691
01:43:59,810 --> 01:44:01,330
institutional structures.

1692
01:44:01,410 --> 01:44:06,690
Uh sometimes it emphasizes very explicit costs on the part of the agent where they have to

1693
01:44:06,690 --> 01:44:12,210
make these calculations, you know, like is this um you know, is this the right thing for me

1694
01:44:12,210 --> 01:44:17,180
to do right now to give me some payoff that I want with some acceptable risk, right?

1695
01:44:17,260 --> 01:44:20,460
And I'm trying to put all these variables together in this complex calculus.

1696
01:44:20,620 --> 01:44:25,740
Um other times I think it's like, you know, I just do this because that's what we do here.

1697
01:44:25,980 --> 01:44:31,900
And it's you know, vaguely creating a coordination equilibrium and it's

1698
01:44:31,900 --> 01:44:35,900
solving some kind of you know social, you know, collective action problem.

1699
01:44:36,380 --> 01:44:39,260
Uh, but I don't know if people think about it more than that.

1700
01:44:39,570 --> 01:44:43,570
Like that's that's just my vague guess here is that if you actually see how people are

1701
01:44:43,570 --> 01:44:47,090
thinking about this, they just say, Well, I do that because that's what people told me to

1702
01:44:47,090 --> 01:44:47,330
do.

1703
01:44:47,570 --> 01:44:52,690
Um, which I think is quite, it's it's quite it's quite the you know the the point of

1704
01:44:52,690 --> 01:44:53,490
controversy, right?

1705
01:44:53,570 --> 01:44:54,210
It comes down to this.

1706
01:44:55,010 --> 01:44:59,170
I I would expect that even when people say, you know, I'm just doing it because that's the

1707
01:44:59,170 --> 01:45:03,620
way we do it around here, is that they're implicitly simulating someone who might judge

1708
01:45:03,620 --> 01:45:04,580
them for not doing that.

1709
01:45:04,740 --> 01:45:07,140
And that they're interpreting that as a social cost.

1710
01:45:07,220 --> 01:45:09,940
Like I'm going to pay reputational costs for not doing this.

1711
01:45:10,100 --> 01:45:12,580
Uh, people are going to make the following inferences about me.

1712
01:45:12,660 --> 01:45:16,500
If I violate this norm, they're gonna think I'm a bad person, I'm lazy, I'm you know, uh

1713
01:45:16,660 --> 01:45:17,940
unreliable, whatever.

1714
01:45:18,100 --> 01:45:21,620
And I do not want people to think that about me, so that therefore I'm not going to do it.

1715
01:45:21,700 --> 01:45:23,940
It's not just it's a norm, therefore I follow it.

1716
01:45:24,100 --> 01:45:26,410
It's oh, what what how what are people gonna think of me?

1717
01:45:26,490 --> 01:45:27,610
What's gonna happen if I don't do this?

1718
01:45:27,770 --> 01:45:31,450
And they're simulating those outcomes, they're simulating the audience and and and what

1719
01:45:31,450 --> 01:45:32,410
people are gonna think of them.

1720
01:45:32,490 --> 01:45:34,490
And I think we're constantly doing this all the time.

1721
01:45:34,650 --> 01:45:38,730
We have a little audience in our head that's constantly judging us uh for every little

1722
01:45:38,730 --> 01:45:41,210
thing we do and plan on doing and anticipate doing.

1723
01:45:41,290 --> 01:45:44,490
And we're factoring those anticipated costs into everything we do.

1724
01:45:44,570 --> 01:45:49,660
Um, and so I think just positing that a social

1725
01:45:49,740 --> 01:45:55,500
psychology, that uh impression management, for lack of a better term, gets you explains a

1726
01:45:55,500 --> 01:45:55,660
lot.

1727
01:45:55,820 --> 01:45:59,500
It explains a lot of what we intuitively think of as norms.

1728
01:45:59,660 --> 01:46:04,380
And I think it even explains the example you just mentioned with um, you know, paying the

1729
01:46:04,380 --> 01:46:05,340
late fee, right?

1730
01:46:05,500 --> 01:46:08,940
I think what we're doing is we're running a social calculation where where we're saying,

1731
01:46:09,100 --> 01:46:13,090
Oh, uh, if they're charging me money uh for for showing up late.

1732
01:46:13,250 --> 01:46:14,770
That means that they don't think I'm a bad person.

1733
01:46:14,930 --> 01:46:17,890
Then if I pay the fine, then I've made amends and then they're then they're cool with it,

1734
01:46:17,970 --> 01:46:18,210
right?

1735
01:46:18,290 --> 01:46:20,930
So I might as well pay the pay the fine and they're and they'll be cool with it.

1736
01:46:21,170 --> 01:46:25,810
But um in the absence of that fine, you might make the inference that they're gonna judge

1737
01:46:25,810 --> 01:46:27,330
you as a bad parent, right?

1738
01:46:27,650 --> 01:46:29,810
And that's gonna uh hurt your reputation.

1739
01:46:30,050 --> 01:46:34,860
Um and so I think that is probably what's going on and explaining these different outcomes

1740
01:46:34,940 --> 01:46:39,740
is that we have a a sophisticated social psychology that is anticipating the inferences

1741
01:46:39,740 --> 01:46:41,820
that are made about us for every little thing we do.

1742
01:46:41,980 --> 01:46:46,380
And that as a result of that social psychology, as a result of us playing our status games

1743
01:46:46,460 --> 01:46:49,900
and and and thinking about how other people are gonna judge us and trying to navigate the

1744
01:46:49,900 --> 01:46:53,900
social world, we get these emergent things that we call norms.

1745
01:46:54,460 --> 01:46:57,990
Um Anyway, that that's my spiel.

1746
01:46:58,230 --> 01:47:01,670
Um, I I think it's an it continues to be an interesting debate uh in evolutionary

1747
01:47:01,670 --> 01:47:03,190
psychology and cultural evolution.

1748
01:47:03,350 --> 01:47:06,550
Um, how we should think about these things, how we should think about norms, how we should

1749
01:47:06,550 --> 01:47:09,590
think about culture, what what is the role of evolved psychology?

1750
01:47:09,750 --> 01:47:11,590
What is the evolved psychology like?

1751
01:47:12,310 --> 01:47:17,190
Uh Brett, we are we are running up on what two hours now?

1752
01:47:17,510 --> 01:47:17,910
Almost.

1753
01:47:18,470 --> 01:47:19,350
Yeah, this one quick.

1754
01:47:19,430 --> 01:47:19,990
This is wonderful.

1755
01:47:20,150 --> 01:47:22,070
And I think just a put a lot of fun.

1756
01:47:22,470 --> 01:47:23,190
Put a pin on that.

1757
01:47:23,270 --> 01:47:27,240
I I think like Brett, I don't think you would disagree with that thing that David said too.

1758
01:47:27,320 --> 01:47:31,640
I think like part of it is just that the content then of like what are the implicit things

1759
01:47:31,640 --> 01:47:34,120
that people are worrying about is part of like the norm.

1760
01:47:34,360 --> 01:47:39,160
So like in the same way, like the um you Brett talked about the um auction, right?

1761
01:47:39,240 --> 01:47:43,800
In a in a more sophisticated version, it's not just that you're willing to go with the

1762
01:47:43,800 --> 01:47:48,040
price in the auction, but like you're you're going and you're paying like, I don't know,

1763
01:47:48,120 --> 01:47:49,560
$10 for the bag of flour.

1764
01:47:49,720 --> 01:47:53,080
I'm like astounded at the price of groceries in the US compared to Germany.

1765
01:47:53,160 --> 01:47:54,090
It's like crazy.

1766
01:47:54,250 --> 01:47:58,650
We're paying like 10, three to 10 times more for like every item of food compared to

1767
01:47:58,650 --> 01:47:59,050
Germany.

1768
01:47:59,210 --> 01:47:59,690
It's crazy.

1769
01:47:59,850 --> 01:48:01,530
But anyway, yeah, my sympathies.

1770
01:48:01,850 --> 01:48:02,490
Yeah.

1771
01:48:02,730 --> 01:48:08,730
Um, so, but like, you know, me, me actually like paying a price for something, David will

1772
01:48:08,730 --> 01:48:09,370
see that, right?

1773
01:48:09,450 --> 01:48:13,690
And that becomes part of then his cost benefit analysis, right?

1774
01:48:13,850 --> 01:48:19,210
So like I think the the transmission chain of

1775
01:48:19,450 --> 01:48:23,460
behaviors were psychologically aware of that.

1776
01:48:23,620 --> 01:48:27,540
And then also the David's doing a cost benefit analysis.

1777
01:48:27,620 --> 01:48:31,860
And I'm sort of aware that David might do a cost benefit analysis of what I'm what I'm

1778
01:48:31,860 --> 01:48:32,180
doing.

1779
01:48:32,340 --> 01:48:36,180
And that factors into my cost benefit analysis of like, should I do it?

1780
01:48:36,420 --> 01:48:41,700
And so I think that yeah, I think this just comes back to like from my perspective, I see

1781
01:48:41,700 --> 01:48:43,140
these views as very compatible.

1782
01:48:43,300 --> 01:48:48,670
It's just that there's no pure cultural transmission without a cost benefit analysis.

1783
01:48:48,750 --> 01:48:53,630
It's like just like fractal, where there's always like a uh inside that.

1784
01:48:53,710 --> 01:48:55,230
And it might in it, but it's different.

1785
01:48:55,310 --> 01:49:00,190
It's different than like a purely fungible market economy thing.

1786
01:49:00,350 --> 01:49:04,510
It's like this uncertainty about social costs, which have a very different dynamic, for

1787
01:49:04,510 --> 01:49:06,350
example, than like the market.

1788
01:49:06,510 --> 01:49:10,460
And so I I I view these as like I think you're both right, but maybe that's because I'm

1789
01:49:10,460 --> 01:49:12,620
from Buffalo and that's the way people are.

1790
01:49:13,260 --> 01:49:18,220
Well, maybe I mean that that that variation I think is, I mean, would Midwest nice is also

1791
01:49:18,220 --> 01:49:19,580
a it's a real thing, right?

1792
01:49:19,820 --> 01:49:20,140
Exactly.

1793
01:49:20,380 --> 01:49:24,860
Um well I guess this would be the the the the question that I that I would think about in

1794
01:49:24,860 --> 01:49:25,580
this circumstance.

1795
01:49:25,660 --> 01:49:31,020
I mean, if if you really wanted to communicate to your kid, for example, that certain

1796
01:49:31,020 --> 01:49:36,790
behavior will result in social sanctioning, Like presumably that would

1797
01:49:36,790 --> 01:49:38,870
be really important to impart upon them.

1798
01:49:39,030 --> 01:49:42,550
I mean, they they might not appreciate it, but it's like, look, kid, you gotta believe me.

1799
01:49:42,630 --> 01:49:46,070
I know that it doesn't seem like it's a big deal to cross the red light.

1800
01:49:46,470 --> 01:49:48,310
Uh, but you will die, right?

1801
01:49:48,470 --> 01:49:49,750
And I know this isn't obvious.

1802
01:49:49,830 --> 01:49:52,790
Like you're counterintuitively, it doesn't seem like there's a threat here.

1803
01:49:53,030 --> 01:49:59,030
Um, so I think that's maybe uh uh one one of the topics that's most interesting to

1804
01:49:59,030 --> 01:50:04,840
me is what kind of cognitive heuristics or psychology would develop to impart that kind

1805
01:50:04,840 --> 01:50:10,120
of information if it maps onto these sort of status game equilibria that people are in?

1806
01:50:10,280 --> 01:50:10,520
Right.

1807
01:50:10,600 --> 01:50:12,360
Like, because you you might expect that, right?

1808
01:50:12,440 --> 01:50:16,680
You would expect that, like, if it's adaptive for you to figure out what local behavioral

1809
01:50:16,680 --> 01:50:22,600
coordination uh equilibria are, uh, then learning it in a

1810
01:50:22,600 --> 01:50:27,830
very fast way from social communication, someone telling you who wants to give you

1811
01:50:27,830 --> 01:50:33,510
adaptive information, uh, sort of opens up the door, right, for that kind of a cognitive

1812
01:50:33,510 --> 01:50:34,390
learning mechanism.

1813
01:50:34,550 --> 01:50:38,390
And it does that does seem like you know, sitting on the lap and hearing grandpa tell you,

1814
01:50:38,470 --> 01:50:43,190
you know, in our country, you know, we stand at the red light instead of crossing because

1815
01:50:43,190 --> 01:50:46,070
you're gonna get taken away if you don't, right?

1816
01:50:46,920 --> 01:50:47,720
So I don't know.

1817
01:50:47,800 --> 01:50:53,720
That maybe we can uh agree on that point is that obviously there's an underlying

1818
01:50:53,720 --> 01:50:58,520
physical reality here that is what's mapping behavior onto payoffs.

1819
01:50:58,760 --> 01:51:04,520
Um and the basic task is to figure out what kind of evolutionary dynamics

1820
01:51:05,000 --> 01:51:09,720
uh might lead to certain cognitive heuristics in that kind of circumstance.

1821
01:51:10,040 --> 01:51:10,360
Yeah.

1822
01:51:10,650 --> 01:51:16,330
Well, uh, we, as David mentioned, we uh the audience in our heads about the people who are

1823
01:51:16,330 --> 01:51:18,250
like, my God, how long is this gonna go?

1824
01:51:18,410 --> 01:51:23,370
We do need to wrap up, but I do want to ask, uh, we have to ask the uh the

1825
01:51:23,770 --> 01:51:26,970
mandatory question of if we should have asked you anything.

1826
01:51:27,130 --> 01:51:31,690
Um, and then I also wanted to ask for you if and you can do both.

1827
01:51:31,770 --> 01:51:34,970
Uh you we still have plenty of time because we started a little later.

1828
01:51:35,210 --> 01:51:40,340
Um uh is can you talk a little bit about the Leipzig Institute.

1829
01:51:40,740 --> 01:51:41,940
So you're at a Mox Planck.

1830
01:51:42,020 --> 01:51:47,620
We had Annie Woods on so far, who was a research group leader in one of the independent

1831
01:51:47,620 --> 01:51:48,260
research groups.

1832
01:51:48,340 --> 01:51:52,020
You're you're in one of the permanent research groups in Leipzig.

1833
01:51:52,260 --> 01:51:57,700
Can you talk a little bit either about sort of um what the group more

1834
01:51:57,700 --> 01:52:02,340
broadly does or just also sort of what the structure is at that Mox Planck?

1835
01:52:02,970 --> 01:52:07,370
Yeah, so I'm I'm in the Department of Human Behavior, Ecology and Culture.

1836
01:52:08,890 --> 01:52:14,010
And our institute is Mox Planck for Evolutionary

1837
01:52:14,010 --> 01:52:17,210
Anthropology is the is the main focus.

1838
01:52:17,850 --> 01:52:21,770
So in our institute itself, we have a number of different departments.

1839
01:52:22,490 --> 01:52:28,260
There's one on cross-cultural psychology, there's genetics, archaeology,

1840
01:52:28,900 --> 01:52:29,940
linguistics.

1841
01:52:30,260 --> 01:52:34,900
So there's a number of different kinds of affiliated teams that are working in the

1842
01:52:34,900 --> 01:52:35,540
building.

1843
01:52:36,420 --> 01:52:41,940
And our group specifically is focusing on topics related to

1844
01:52:41,940 --> 01:52:46,020
behavioral ecology, mostly for humans.

1845
01:52:46,100 --> 01:52:50,650
You know, the title has human in it, human behavior, ecology, and culture, but we also

1846
01:52:50,650 --> 01:52:56,250
include non-human uh uh uh organisms like birds, uh and uh

1847
01:52:56,570 --> 01:53:02,250
and the main focus is it's I mean it's a really compelling program that our director laid

1848
01:53:02,250 --> 01:53:03,930
out, uh Richard McElreath.

1849
01:53:04,090 --> 01:53:08,980
That um basically all of these questions that we're talking about have

1850
01:53:10,100 --> 01:53:11,780
a need for lots of data.

1851
01:53:11,940 --> 01:53:17,540
Like we need to have more data, potentially sharper theory based on new kinds of

1852
01:53:17,540 --> 01:53:18,100
data.

1853
01:53:18,900 --> 01:53:23,140
And so our mandate is to try to develop both of those simultaneously.

1854
01:53:23,220 --> 01:53:29,220
So we're building up new theoretical sort of stages for looking at particular kinds

1855
01:53:29,220 --> 01:53:30,020
of data sets.

1856
01:53:30,260 --> 01:53:36,250
And we're also looking for new kinds of data sets, or specifically in the context of

1857
01:53:36,250 --> 01:53:38,330
collecting longitudinal field data.

1858
01:53:38,810 --> 01:53:42,570
So my my particular side of things has been working with the Chimani Project.

1859
01:53:42,650 --> 01:53:45,210
I know you had Chris on earlier, so you might have talked about.

1860
01:53:46,090 --> 01:53:47,130
Yeah, we're gonna have the Chimani.

1861
01:53:48,890 --> 01:53:49,450
Oh there we go.

1862
01:53:49,610 --> 01:53:49,850
Yeah.

1863
01:53:50,010 --> 01:53:55,850
So yeah, I've been collaborating with Mike Gervin for um God, like 13 years

1864
01:53:55,850 --> 01:53:56,170
now.

1865
01:53:56,640 --> 01:53:58,720
uh, and working in Bolivia.

1866
01:53:58,800 --> 01:54:04,080
Uh, and I think that, you know, the the way forward, that's just my general sense, is that

1867
01:54:04,080 --> 01:54:08,800
we want to have lots of data to specifically target these kinds of questions.

1868
01:54:08,960 --> 01:54:14,560
Like what can we say about um the information environment that people are in when they're

1869
01:54:14,560 --> 01:54:18,240
making decisions in like a naturalistic setting?

1870
01:54:18,480 --> 01:54:24,110
Um And uh more specifically to my interest too is what are the kind of

1871
01:54:24,110 --> 01:54:26,430
mechanisms that are causing cultural change to happen?

1872
01:54:26,590 --> 01:54:29,710
So, like if you, you know, I mean, it's one thing to talk about, oh, why are there patterns

1873
01:54:29,710 --> 01:54:32,030
of cultural variation spatially?

1874
01:54:32,190 --> 01:54:34,910
Um, but we also see changes in those patterns too, right?

1875
01:54:34,990 --> 01:54:39,870
As some get bigger and some shrink, um, new ideas emerge and spread, some die

1876
01:54:41,760 --> 01:54:47,600
And so that's uh I think kind of the overall theme of the work that we do

1877
01:54:48,080 --> 01:54:53,840
is figuring out ways to combine sort of evolutionary theory with uh empirical

1878
01:54:53,840 --> 01:54:59,840
data collection uh in sort of a high-resolution field context that helps us

1879
01:54:59,840 --> 01:55:05,040
maybe make forward you know make forward progress on on uh sort of the the standard debates

1880
01:55:05,040 --> 01:55:08,720
or the standard questions, I think, in evolutionary anthropology or evolutionary science.

1881
01:55:09,090 --> 01:55:10,210
Yeah, it's a beautiful building.

1882
01:55:10,370 --> 01:55:11,970
Leipzig is very important historically.

1883
01:55:12,130 --> 01:55:16,610
Wilhelm Wont was uh, you know, lived knocking around in Leipzig in the

1884
01:55:16,610 --> 01:55:21,650
1854.

1885
01:55:21,970 --> 01:55:23,010
I don't remember anymore.

1886
01:55:23,250 --> 01:55:24,370
59, something like that.

1887
01:55:24,450 --> 01:55:24,770
Yeah.

1888
01:55:25,010 --> 01:55:27,810
Um, and uh there's a climbing wall in the institute, too.

1889
01:55:27,890 --> 01:55:28,610
It's a beautiful building.

1890
01:55:28,930 --> 01:55:29,490
That is true.

1891
01:55:29,650 --> 01:55:32,930
I've never actually used it though, but some of our some of our department members had.

1892
01:55:33,090 --> 01:55:37,460
Uh in terms of questions, uh, I was hoping you'd ask me about Go, but we never got a chance

1893
01:55:37,460 --> 01:55:38,260
to talk about that.

1894
01:55:38,740 --> 01:55:44,020
Um, I don't know if that's something that uh either of you are familiar with as a I mean,

1895
01:55:44,100 --> 01:55:48,020
David, you're a a game master yourself of sorts.

1896
01:55:48,180 --> 01:55:53,860
Um and uh there's there's there's there's a piece of cultural transmission for you, right?

1897
01:55:53,940 --> 01:55:58,500
I mean, we have a a board game which has been around for like 3,000 years.

1898
01:55:58,900 --> 01:56:04,270
Uh people have commentaries on you know games that are hundreds of years old.

1899
01:56:04,510 --> 01:56:08,270
Uh, and then the way in which those strategies change over time.

1900
01:56:08,430 --> 01:56:12,510
I mean, people are learning these strategies on the board, they're changing them, there's

1901
01:56:12,510 --> 01:56:13,950
successes, there's failures.

1902
01:56:14,190 --> 01:56:18,510
The most recent, of course, uh uh interesting thing has been the arrival of AlphaGo.

1903
01:56:19,310 --> 01:56:24,350
Uh, and the sort of the effect of now having um these

1904
01:56:24,670 --> 01:56:28,110
superhuman players that we can consult with all the time.

1905
01:56:28,270 --> 01:56:32,750
I mean, that's that's kind of neat because we're talking about like, you know, uh cultural

1906
01:56:32,750 --> 01:56:37,380
transmission from non-human agents, essentially, right?

1907
01:56:37,460 --> 01:56:41,300
That you're you're you're getting feedback from L well not LLMs, they're called deep

1908
01:56:41,300 --> 01:56:42,420
learning algorithms.

1909
01:56:42,580 --> 01:56:45,140
Um but I mean this is true for a lot of games, I think.

1910
01:56:45,220 --> 01:56:50,660
In general we've got um this sort of revolutionary uh revolutionary effect of having like

1911
01:56:50,820 --> 01:56:56,420
saber metrics uh for professional sports, uh doing very careful analyses of particular

1912
01:56:56,420 --> 01:57:00,490
tactics in baseball and soccer, uh, football.

1913
01:57:00,810 --> 01:57:05,770
Uh there's, you know, things like stockfish and chess and alpha go and go.

1914
01:57:06,090 --> 01:57:11,210
Um, and I mean, it's really having a huge impact on the actual strategic environment in the

1915
01:57:11,210 --> 01:57:11,450
game.

1916
01:57:11,530 --> 01:57:17,050
Like actually, you know, quantifying the way that ideas move through these uh these games.

1917
01:57:17,610 --> 01:57:19,450
Uh it's it's quite noticeable.

1918
01:57:19,530 --> 01:57:24,650
And that's that's something I think that's uh maybe ripe for looking at as a cultural

1919
01:57:24,650 --> 01:57:25,690
evolutionary topic.

1920
01:57:25,850 --> 01:57:27,520
Um Because these are quite complex.

1921
01:57:27,680 --> 01:57:31,120
I mean, this is like, you know, am I using this particular strategy or that strategy?

1922
01:57:32,400 --> 01:57:37,920
To some extent, you have to learn it, or it's very difficult to rediscover it on your own.

1923
01:57:38,400 --> 01:57:42,000
So I think in that circumstance, it's like, okay, yeah, you know, cultural evolution, like

1924
01:57:42,080 --> 01:57:44,800
what are the what are the evolutionary dynamics of the idea?

1925
01:57:45,040 --> 01:57:49,840
Um, but then also it's like, why, you know, why has this game been around for so long?

1926
01:57:50,000 --> 01:57:51,750
I mean, 3,000 years.

1927
01:57:51,910 --> 01:57:52,790
It's ridiculous.

1928
01:57:52,950 --> 01:57:58,790
Like uh and uh looking at the way in which I mean uh it's just sort of a general

1929
01:57:58,790 --> 01:58:04,230
topic about gaming, like um what is it about games that makes them

1930
01:58:04,230 --> 01:58:05,190
successful?

1931
01:58:06,470 --> 01:58:07,350
I don't know.

1932
01:58:07,590 --> 01:58:09,110
Question for David, perhaps.

1933
01:58:09,270 --> 01:58:15,190
Like Oh, I mean, yeah, uh I would I would need you know uh

1934
01:58:15,350 --> 01:58:16,950
many books to answer that question.

1935
01:58:17,520 --> 01:58:18,560
It's extremely complicated.

1936
01:58:18,720 --> 01:58:22,960
I mean, we don't really even have a good theory of what gaming is and what it's for, I feel

1937
01:58:22,960 --> 01:58:26,720
like that that itself is an area ripe for investigation.

1938
01:58:26,960 --> 01:58:32,640
I remember some philosopher described it as like a form of crystallized agency, like in the

1939
01:58:32,640 --> 01:58:35,040
same way as like a painting would be crystallized seeing.

1940
01:58:35,120 --> 01:58:39,760
It's like the you know the the essence of of what seeing is and music is like the purest

1941
01:58:39,760 --> 01:58:40,720
form of hearing.

1942
01:58:40,880 --> 01:58:45,200
Um he makes the the case that gaming is like the purest form of agency, of like making

1943
01:58:45,200 --> 01:58:45,760
decisions.

1944
01:58:46,250 --> 01:58:48,010
And I think that's an insightful way of looking at it.

1945
01:58:48,090 --> 01:58:52,090
But honestly, like our understanding of the evolutionary psychology of gaming and why we're

1946
01:58:52,170 --> 01:58:56,090
why we're attracted to it, what we get from it, what the benefits are, I think that's still

1947
01:58:56,090 --> 01:59:01,450
very mysterious and ripe for examination.

1948
01:59:04,970 --> 01:59:09,690
I think I remember hearing from uh John Tuby when I was an undergrad that I mean the the

1949
01:59:09,690 --> 01:59:15,400
the attractiveness of the game is that it uh it allows a certain degree of virtuosity to be

1950
01:59:15,400 --> 01:59:15,800
possible.

1951
01:59:15,880 --> 01:59:21,160
That you know, it's like we have to be kind of bad at the game to be able to get skill

1952
01:59:21,160 --> 01:59:21,640
differentials.

1953
01:59:21,720 --> 01:59:25,480
And if it's something that everybody's just amazing at, I don't know, like facial

1954
01:59:25,480 --> 01:59:29,720
recognition or something, you know, like it's like a talker in this in this exactly right.

1955
01:59:29,880 --> 01:59:31,400
Look look at me go, you know.

1956
01:59:31,960 --> 01:59:36,610
Um and so that that's something about like we want we're attracted to things that we're bad

1957
01:59:36,610 --> 01:59:40,850
at and then turn it into a game because then people can get good, as the kids say.

1958
01:59:41,090 --> 01:59:41,490
Interesting.

1959
01:59:41,570 --> 01:59:43,250
Um that's interesting, Brett.

1960
01:59:43,410 --> 01:59:46,530
That's actually that that is actually a real important part of the psychology, David, for

1961
01:59:46,530 --> 01:59:50,610
status games is is like having a folk theory of the variability, right?

1962
01:59:50,770 --> 01:59:52,050
Exactly, exactly right.

1963
01:59:52,210 --> 01:59:56,290
If everybody does it, it's like, man, I'm not gonna get any status being the best breather.

1964
01:59:56,450 --> 01:59:58,610
You know, everybody's breathing pretty well.

1965
01:59:59,500 --> 02:00:00,220
Yeah, totally.

1966
02:00:00,460 --> 02:00:01,500
I love that way of thinking about it.

1967
02:00:01,580 --> 02:00:05,500
Um, and it's and it's a particularly interesting topic, this idea of Go, because it's um

1968
02:00:05,660 --> 02:00:09,660
it's a it's a case of of cultural evolution where you wouldn't necessarily expect it to

1969
02:00:09,660 --> 02:00:13,260
follow the typical kinds of diffusion that are shaped by conformity.

1970
02:00:13,340 --> 02:00:17,580
Because in a strategic uh game like Go, you don't necessarily want to copy the same

1971
02:00:17,580 --> 02:00:20,140
strategy that everyone else is performing, because then people are going to be able to

1972
02:00:20,140 --> 02:00:22,300
anticipate you and uh out fox you.

1973
02:00:22,380 --> 02:00:25,740
You might want to be unpredictable and have the element of surprise, in which case you want

1974
02:00:25,740 --> 02:00:28,210
to do something like the opposite of imitation and conformity.

1975
02:00:28,290 --> 02:00:30,850
And that leads to a whole different set of cultural dynamics.

1976
02:00:31,010 --> 02:00:33,250
Um and so it's it's it's a really fascinating topic.

1977
02:00:33,330 --> 02:00:39,170
Um and uh yeah, there's there's great paper by Igor Lapo and Mark Feldman on

1978
02:00:39,170 --> 02:00:41,490
chess that basically looks at exactly that.

1979
02:00:41,650 --> 02:00:46,130
That um you can think about this like a negative frequency dependence, you know, moves that

1980
02:00:46,130 --> 02:00:50,880
are popular potentially would be things that you would want to avoid because your opponent

1981
02:00:50,880 --> 02:00:52,240
is prepared for it, right?

1982
02:00:52,400 --> 02:00:58,240
Um I think that's how I do okay in poker is like I get bored with playing it explicitly, so

1983
02:00:58,240 --> 02:01:00,640
I just play implicitly and I don't even know what I'm doing.

1984
02:01:00,800 --> 02:01:04,800
And I actually will do quite well because I'm just completely unpredictable.

1985
02:01:06,240 --> 02:01:06,480
Yeah.

1986
02:01:06,640 --> 02:01:07,280
So yeah.

1987
02:01:07,440 --> 02:01:11,680
Um yeah, actually I have a paper on that, the the conformity aspect in Go.

1988
02:01:11,760 --> 02:01:17,350
I mean, the the the trade off is that um if you start a variation that's totally

1989
02:01:17,350 --> 02:01:21,350
wild, uh, you could also get yourself into trouble very quickly too, because it's hard to

1990
02:01:21,350 --> 02:01:22,390
figure out what to do next.

1991
02:01:22,550 --> 02:01:23,350
No, no, don't know.

1992
02:01:23,590 --> 02:01:27,030
Um and if you're I mean, it's it's a computational problem.

1993
02:01:27,190 --> 02:01:31,830
If like people sort of know, you know, okay, the next 10 moves, if we just follow like a

1994
02:01:31,830 --> 02:01:37,510
normal uh opening sequence, it's kind of like a, you know, it's a gentleman's agreement in

1995
02:01:37,510 --> 02:01:41,680
some ways that like the the action's gonna start later, but we're gonna, you know, as as a

1996
02:01:41,680 --> 02:01:45,040
truce, we're gonna continue going down this line that we both know very well.

1997
02:01:45,280 --> 02:01:47,360
Um, at really high-level play, though.

1998
02:01:47,440 --> 02:01:51,360
I mean, uh this is what I'm hearing from like, you know, YouTube videos and podcasts and

1999
02:01:51,360 --> 02:01:55,920
stuff that like there's a lot of like espionage for really high you know high-stakes

2000
02:01:55,920 --> 02:01:59,280
matches where people want to know what their opponent is training on, like which lines are

2001
02:01:59,280 --> 02:02:03,810
they prepared for so that they can do the opposite and use something that they're not And

2002
02:02:03,810 --> 02:02:09,330
so it's like really high stakes to know like what people are, you know, uh ready to

2003
02:02:09,490 --> 02:02:10,930
ready to respond on.

2004
02:02:11,170 --> 02:02:16,770
Um I think that was the source of that controversy with Magnus Carlson uh last year, that

2005
02:02:16,930 --> 02:02:19,570
his opponent responded to him too quickly, according to him.

2006
02:02:19,650 --> 02:02:24,210
And he's like, Oh, you must have like cheated somehow and found out about uh found out

2007
02:02:24,210 --> 02:02:25,330
about what I was planning to do.

2008
02:02:25,650 --> 02:02:28,850
Yeah, you read my computer files and yeah, yeah, exactly.

2009
02:02:28,930 --> 02:02:30,450
Yeah, that's interesting.

2010
02:02:30,820 --> 02:02:35,540
Um so yeah, I mean that's I mean absolutely like in a strategic context, uh the the

2011
02:02:35,540 --> 02:02:38,260
frequency dependence here is a is a key factor.

2012
02:02:38,420 --> 02:02:42,660
Um but uh yeah, how that how that shapes long-term evolutionary dynamics.

2013
02:02:42,740 --> 02:02:44,420
I think that's a really cool question.

2014
02:02:44,580 --> 02:02:47,940
Um and uh I've been spending a lot of time looking at that recently.

2015
02:02:48,020 --> 02:02:49,700
So awesome.

2016
02:02:49,860 --> 02:02:52,580
Well, this flew by, so we'll have to have you on again.

2017
02:02:52,890 --> 02:02:53,690
Oh, that'd be great.

2018
02:02:53,850 --> 02:02:54,730
I had a great time.

2019
02:02:54,890 --> 02:02:57,290
Uh it's really interesting talking to you both.

2020
02:02:57,610 --> 02:03:03,610
Uh well, Brett, uh, we'll uh we'll post some uh link to your website, um, which is great,

2021
02:03:03,770 --> 02:03:09,690
and uh all the cool stuff from uh galaxies to bike tires, although um not, but

2022
02:03:09,770 --> 02:03:14,410
you know, the uh Shimani Health Project, you know, the risk to attitudes and game of go,

2023
02:03:14,490 --> 02:03:15,770
which is super cool.

2024
02:03:15,960 --> 02:03:18,360
So um yeah, thank you so much.

2025
02:03:18,440 --> 02:03:19,720
Uh have a good evening.

2026
02:03:19,800 --> 02:03:22,760
Thanks so much for uh for being with us in the evening here.

2027
02:03:22,840 --> 02:03:26,280
Um Shinaband and uh yeah yeah.

2028
02:03:26,760 --> 02:03:28,680
Yeah, thanks a lot, guys.

2029
02:03:31,800 --> 02:03:37,480
Uh David, um when you're uh all done after a long

2030
02:03:37,480 --> 02:03:40,920
day, um do you sometimes drink beer?

2031
02:03:41,390 --> 02:03:47,230
Sometimes I drink beer, but uh usually what I do is I write kind reviews uh about all

2032
02:03:47,230 --> 02:03:49,310
the podcasts that I enjoy.

2033
02:03:49,550 --> 02:03:53,870
That's one of my favorite activities to do late at night, is I I write kind reviews about

2034
02:03:53,870 --> 02:03:54,990
my favorite podcasts.

2035
02:03:55,310 --> 02:04:00,590
Sometimes I just, you know, post messages on social media saying how awesome those podcasts

2036
02:04:00,590 --> 02:04:00,830
are.

2037
02:04:00,910 --> 02:04:05,070
And occasionally, you know, if I'm if I've had you know a few beers, I'll even go onto the

2038
02:04:05,070 --> 02:04:09,310
Patreon pages of some of those podcasts and uh throw throw them a few a few bucks.

2039
02:04:09,630 --> 02:04:10,830
A propos of nothing.

2040
02:04:11,140 --> 02:04:12,580
Yeah, it's just something I like to do.

2041
02:04:13,060 --> 02:04:14,500
Something just kind of like that kind of person.

2042
02:04:14,580 --> 02:04:14,740
Yeah.

2043
02:04:15,220 --> 02:04:16,420
Yeah, that's just the kind of guy I am.

2044
02:04:16,500 --> 02:04:17,060
Uh uh.

2045
02:04:18,820 --> 02:04:19,060
Yeah.

2046
02:04:19,300 --> 02:04:19,540
Yeah.

2047
02:04:19,780 --> 02:04:19,940
Right.

2048
02:04:20,020 --> 02:04:22,580
Well, we're not gonna interrogate that or question that at all.

2049
02:04:22,660 --> 02:04:23,060
So yeah.

2050
02:04:23,540 --> 02:04:23,700
No.

2051
02:04:23,860 --> 02:04:24,020
Okay.

2052
02:04:24,100 --> 02:04:25,300
No, I think we'll just uh yeah.

2053
02:04:25,860 --> 02:04:26,580
We'll leave it at that.

2054
02:04:26,820 --> 02:04:27,140
Okay.

2055
02:04:27,380 --> 02:04:27,780
All right.

2056
02:04:27,940 --> 02:04:28,180
Bye.

2057
02:04:28,420 --> 02:04:29,220
See you next week.