WEBVTT 00:00:00.160 --> 00:00:01.920 Here we are again for another episode. 00:00:02.000 --> 00:00:03.040 I'm David Pinsov. 00:00:03.200 --> 00:00:03.520 Who are you? 00:00:03.680 --> 00:00:05.200 I'm Dave Petrazuki. 00:00:05.680 --> 00:00:11.200 And this is uh Evolutionary Psychology, the podcast, Actual Evolutionary Psychology by 00:00:11.520 --> 00:00:13.840 Actual Evolutionary Psychologist. 00:00:14.320 --> 00:00:14.960 That's right. 00:00:15.120 --> 00:00:16.080 Uh keep listening. 00:00:16.320 --> 00:00:17.120 Enjoy. 00:00:27.410 --> 00:00:32.290 David, today we have uh uh uh we're talking to somebody in Germany. 00:00:32.770 --> 00:00:33.250 That's right. 00:00:33.330 --> 00:00:35.170 His name is Brett Beheim. 00:00:35.250 --> 00:00:38.130 He is from the Max Planck Institute in Germany. 00:00:38.210 --> 00:00:40.530 Uh he is a brilliant thinker and scientist. 00:00:40.930 --> 00:00:41.570 Oh I have to interrupt you. 00:00:41.650 --> 00:00:45.250 It's the Max Planck Institute for Evolutionary Anthropology, because there's tons of Max 00:00:45.250 --> 00:00:45.970 Planck Institutes. 00:00:46.050 --> 00:00:47.330 Yes, the one in Leipzig. 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. 00:01:01.640 --> 00:01:06.280 Uh we talk about cultural distance, how we can actually quantify how different one cultural 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 00:01:10.840 --> 00:01:14.470 likely to help or less likely to help someone from a distant culture. 00:01:14.550 --> 00:01:15.910 Um, what else did we talk about, Dave? 00:01:16.310 --> 00:01:20.790 How genes are smooth and culture is crunchy, which is a really nice evocative image. 00:01:21.030 --> 00:01:24.150 Brett has an amazing whiteboard uh during this interview. 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. 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 00:01:34.470 --> 00:01:35.350 the game of go. 00:01:35.430 --> 00:01:38.630 Like lots of fun, wonderful things to talk about with Brett. 00:01:38.710 --> 00:01:40.950 The the two hours went by so quick. 00:01:41.240 --> 00:01:41.480 Yeah. 00:01:41.640 --> 00:01:43.160 Had an absolute blast talking to Brett. 00:01:43.240 --> 00:01:44.600 Uh it was a really fun conversation. 00:01:44.760 --> 00:01:46.120 I think you'll have a fun time listening to it. 00:01:46.440 --> 00:01:46.600 Okay. 00:01:46.760 --> 00:01:47.240 Enjoy. 00:01:48.120 --> 00:01:48.600 All right. 00:01:48.840 --> 00:01:54.840 Um today we have Brett Beheim, who is it's nighttime in Germany, but 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, 00:02:00.360 --> 00:02:00.920 nine? 00:02:01.160 --> 00:02:02.840 Uh what time is it there? 00:02:03.160 --> 00:02:04.120 It is six. 00:02:04.440 --> 00:02:04.840 Six. 00:02:05.490 --> 00:02:06.770 We'll edit that out. 00:02:06.850 --> 00:02:09.170 Uh I didn't look at the clock. 00:02:09.250 --> 00:02:10.850 Um yeah, it's six o'clock at night. 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 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 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 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 00:02:31.080 --> 00:02:34.280 never gets completely dark, which is kind of great in the summer. 00:02:34.360 --> 00:02:35.560 You know, like the days are really long. 00:02:35.640 --> 00:02:36.360 Do you like that, Brett? 00:02:36.440 --> 00:02:38.760 Or do you have to like, does it affect your sleeping? 00:02:39.160 --> 00:02:40.040 No, not at all. 00:02:40.520 --> 00:02:43.720 I mean, as you probably know, the shutters tend to be really good here. 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. 00:02:49.080 --> 00:02:52.670 Uh So yeah, I I like it quite a bit actually. 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 00:02:58.110 --> 00:02:58.750 scene for you here. 00:02:58.990 --> 00:03:04.830 Brett Brett is is sitting in front of a chalkboard full of mathematical 00:03:04.830 --> 00:03:06.510 equations and graphs. 00:03:06.750 --> 00:03:10.350 It looks like we're talking it looks like he's working on the Manhattan Project. 00:03:10.760 --> 00:03:14.120 Brett Brett, what what is on that trunk for boy. 00:03:14.280 --> 00:03:16.040 Uh that that is from a discussion earlier. 00:03:16.600 --> 00:03:17.400 Well how much time do we have? 00:03:17.480 --> 00:03:17.640 Yeah. 00:03:17.800 --> 00:03:19.240 So start from the top left. 00:03:19.320 --> 00:03:19.480 Okay. 00:03:20.440 --> 00:03:21.160 Yeah, hold on. 00:03:21.240 --> 00:03:24.440 Can I move the camera to you know pull it closer? 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 00:03:29.240 --> 00:03:34.490 through uh a particular technique uh that's used called network-based diffusion 00:03:34.490 --> 00:03:35.370 analysis. 00:03:35.690 --> 00:03:41.290 Um and it has a uh uh I think a very uh nice literature in 00:03:41.290 --> 00:03:47.210 animal behavior, uh looking at cultural diffusion processes inside uh uh many animal 00:03:47.210 --> 00:03:47.770 societies. 00:03:47.930 --> 00:03:49.370 And we're working on a project right now. 00:03:49.690 --> 00:03:49.850 Yeah. 00:03:50.090 --> 00:03:51.210 So what is cultural diffusion? 00:03:51.290 --> 00:03:52.410 What is network diffusion? 00:03:52.490 --> 00:03:53.850 What are what are we talking about? 00:03:54.460 --> 00:04:00.220 Uh so if you're looking at some kind of a measurable quantifiable 00:04:00.540 --> 00:04:06.220 cultural thing, uh give some concrete examples canoe design, right? 00:04:06.540 --> 00:04:11.900 Yeah, whether whether the well specifically whether the canoe has a certain motif or it's 00:04:11.980 --> 00:04:15.820 you know employs like a a a specific feature like an outrigger. 00:04:16.460 --> 00:04:22.010 Um if uh an animal is able to open a 00:04:22.010 --> 00:04:26.810 particular piece of food that's kind of hard to open using a specific technique. 00:04:27.130 --> 00:04:30.730 Um coconuts are really hard to open. 00:04:31.050 --> 00:04:31.690 Yeah, yeah. 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 00:04:36.810 --> 00:04:36.970 anyway. 00:04:37.130 --> 00:04:38.090 Christina can tell you about that. 00:04:38.250 --> 00:04:39.850 Yeah, so there's there's an art to it, right? 00:04:40.010 --> 00:04:43.850 And you know sometimes there's alternative solutions to the same problem. 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 00:04:50.580 --> 00:04:52.020 uh discretize this. 00:04:52.100 --> 00:04:57.380 You can say whether the technique was present or absent, or the technology, the item, uh 00:04:57.460 --> 00:05:03.060 the marker, the variance was present or absent in a particular uh observation. 00:05:03.780 --> 00:05:09.700 Um and then the uh the analysis for for looking at a diffusion 00:05:09.700 --> 00:05:15.270 process uh might concentrate on the timing of different individuals in a 00:05:15.270 --> 00:05:20.630 population showing that particular technique or incorporating that 00:05:20.630 --> 00:05:26.310 particular uh uh item or or motif uh into their 00:05:26.310 --> 00:05:27.270 repertoire. 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 00:05:32.230 --> 00:05:33.830 population somehow. 00:05:34.960 --> 00:05:39.680 And so, I mean, this works really well in uh experimental setups with animals. 00:05:39.760 --> 00:05:45.600 Like if you intentionally, you know, give them a problem that requires coming up with 00:05:45.600 --> 00:05:51.360 a very discrete and obvious solution, like you know, opening a puzzle box in a certain 00:05:51.360 --> 00:05:57.200 way, um, then mapping how that spreads among individuals in the 00:05:57.200 --> 00:06:01.050 population becomes the basis for the analysis. 00:06:01.210 --> 00:06:03.290 Like, how do we how do we describe that process? 00:06:03.450 --> 00:06:05.610 Like, what is the speed at which it goes? 00:06:05.770 --> 00:06:10.810 Um, can we figure out how specific individuals might be influencing other individuals in 00:06:10.810 --> 00:06:11.930 the acquisition? 00:06:12.330 --> 00:06:18.250 Um and that's where the the board That's where all the math comes from. 00:06:18.570 --> 00:06:19.130 So exactly. 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 00:06:22.890 --> 00:06:27.360 analogy, presumably, from the diffusion, say, of putting a bit of creamer in a and some 00:06:27.360 --> 00:06:30.720 coffee and the cream will slowly diffuse and blend with the coffee. 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 00:06:35.680 --> 00:06:37.040 the cold air will diffuse. 00:06:37.120 --> 00:06:42.720 Um if you cough during a pandemic, the pandemic will spread in the 00:06:44.320 --> 00:06:46.960 what you're interested in is the cultural version of that. 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? 00:06:52.510 --> 00:06:53.470 Not quickly at all, David. 00:06:53.790 --> 00:06:53.870 No. 00:06:54.110 --> 00:06:54.190 No. 00:06:54.430 --> 00:06:55.870 Uh you're insulting me too. 00:06:56.590 --> 00:06:57.150 Actually, I don't know. 00:06:57.230 --> 00:06:58.190 It's an empirical question. 00:06:58.350 --> 00:07:00.510 Brett can record and we can actually measure this. 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 00:07:05.150 --> 00:07:05.630 nineteen twenties. 00:07:07.070 --> 00:07:08.190 I'll edit that out. 00:07:08.270 --> 00:07:09.150 I'm sorry. 00:07:09.470 --> 00:07:13.710 Are are you studying how cultural diffusion can die out as well as spread? 00:07:13.870 --> 00:07:19.700 And what what are the factors that oh con that's the that's the whole 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 00:07:25.140 --> 00:07:31.140 rise and fall of particular trends um over uh long periods 00:07:31.140 --> 00:07:33.380 of time, multi-generational time spans? 00:07:33.620 --> 00:07:38.340 You know, do we see interesting changes in people's cultural repertoires or beliefs? 00:07:38.500 --> 00:07:41.450 Um but then also things that occur very quickly. 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 00:07:47.530 --> 00:07:52.410 sort of grappling with uh incorporating things like uh 00:07:52.650 --> 00:07:55.690 LLMs into our repertoires right now. 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 00:07:59.770 --> 00:08:01.850 the, you know, the workplace? 00:08:02.080 --> 00:08:07.120 Uh this is not a multi-generational process, like this is happening right now to everyone 00:08:07.120 --> 00:08:08.320 simultaneously. 00:08:08.480 --> 00:08:12.800 Uh just like, you know, I mean I remember when cell phones first became a thing. 00:08:12.960 --> 00:08:13.840 That was amazing. 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 00:08:18.720 --> 00:08:20.640 find like a payphone or something. 00:08:20.880 --> 00:08:22.960 Um smartphones too. 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 00:08:27.710 --> 00:08:32.270 great motivating example, you know, how rapidly things have changed since then. 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 00:08:38.270 --> 00:08:43.230 first um debut of the iPhone back in like 2006 or something. 00:08:43.390 --> 00:08:45.150 Um with Steve Jobs. 00:08:45.950 --> 00:08:46.510 Crazy recently. 00:08:46.910 --> 00:08:47.470 Exactly, right? 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 00:08:52.430 --> 00:08:55.140 edge was like Blackberries, right? 00:08:55.220 --> 00:09:00.740 These sort of very large uh brick like phones that had keypad keypads on them, like 00:09:00.740 --> 00:09:01.940 physical buttons. 00:09:02.260 --> 00:09:04.820 And it was like a miniature, miniature keyboard. 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 00:09:09.380 --> 00:09:10.900 just lunked down. 00:09:11.380 --> 00:09:11.620 Yeah. 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 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, 00:09:19.780 --> 00:09:22.740 or there's like all sorts of complicated sliding gimmicks. 00:09:22.990 --> 00:09:28.510 Um and then at like the Mac developer conference, uh Steve Jobs, you know, famously is 00:09:28.510 --> 00:09:30.990 pulling out this tiny little device. 00:09:31.470 --> 00:09:36.590 Um and we're looking at, you know, something now that is just ubiquitous worldwide. 00:09:36.670 --> 00:09:40.590 Like, you know, so many people engage with this technology, and you can see basically when 00:09:40.590 --> 00:09:42.670 it first really became visible. 00:09:42.830 --> 00:09:43.630 I mean, it's incredible. 00:09:43.710 --> 00:09:46.430 Like you watch this and he's, you know, he's got this little gizmo. 00:09:46.670 --> 00:09:47.950 Actually, can I show it to you? 00:09:48.110 --> 00:09:49.630 I have I have like please. 00:09:49.870 --> 00:09:51.150 I wonder if I have Russia. 00:09:51.550 --> 00:09:53.680 Um Maybe I don't. 00:09:53.840 --> 00:09:55.200 You're gonna have to edit this out too. 00:09:55.440 --> 00:09:56.000 That's okay. 00:09:56.640 --> 00:10:00.400 Yeah, I have it somewhere, but we're giving you a better view of the board. 00:10:00.560 --> 00:10:03.360 Um oh here it is. 00:10:03.440 --> 00:10:04.080 Yeah, okay. 00:10:04.320 --> 00:10:06.720 So uh check this out. 00:10:06.800 --> 00:10:08.640 I got this in my technology museum here. 00:10:08.720 --> 00:10:09.440 So there it is. 00:10:09.600 --> 00:10:10.960 Um wow. 00:10:11.520 --> 00:10:15.490 And uh he's up there holding this device. 00:10:16.850 --> 00:10:17.970 That's the first iPhone. 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 00:10:23.330 --> 00:10:24.290 like uh iPhones. 00:10:24.370 --> 00:10:25.650 It's the same form factor, I think. 00:10:25.890 --> 00:10:26.450 Okay, yeah. 00:10:26.690 --> 00:10:29.250 Um and a deck of cards. 00:10:29.570 --> 00:10:30.130 Yeah, right. 00:10:30.290 --> 00:10:34.610 And it's you know, compared to modern phones, it's this tiny little thing, too. 00:10:35.090 --> 00:10:39.100 Um but he's showing you know all these things you can do with it. 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 00:10:44.460 --> 00:10:47.020 these different apps, and people are like amazed. 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. 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. 00:10:55.500 --> 00:10:56.300 How do I do that? 00:10:56.460 --> 00:11:02.060 And he flicks his finger on the screen, and it has that sort of inertial scrolling 00:11:02.140 --> 00:11:08.050 mechanic that we use every day, and the audience gasps at watching this. 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 00:11:14.210 --> 00:11:20.050 of our daily lives now uh being shockingly new and amazing to 00:11:20.050 --> 00:11:22.850 people uh not even two decades ago. 00:11:23.010 --> 00:11:23.410 Yeah. 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 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 00:11:34.060 --> 00:11:38.540 over the course of many generations, but you also have very rapid diffusion type processes. 00:11:38.860 --> 00:11:44.620 Um and David, to your point, I think that the um the reason that we use 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, 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 00:11:53.710 --> 00:11:54.670 sciences. 00:11:54.910 --> 00:11:59.230 Um, like there's the famous Rogers book, The The Diffusion of Innovations. 00:12:00.190 --> 00:12:06.190 Um, but uh I think that the primary reason why the uh 00:12:06.190 --> 00:12:11.900 analogy is so firm is because there's a lot of similar models for describing these 00:12:11.900 --> 00:12:12.300 processes. 00:12:12.380 --> 00:12:16.300 There's sort of these uh symmetries in the way that we describe these different physical 00:12:16.300 --> 00:12:16.620 systems. 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 00:12:22.780 --> 00:12:26.860 in a physical uh medium of some kind, like a block of iron. 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 00:12:31.980 --> 00:12:37.260 immediate place where diffusion uh modeling comes in uh to cultural uh 00:12:37.500 --> 00:12:38.220 topics. 00:12:38.350 --> 00:12:43.630 And then you have epidemiological work, right, too, which is looking at the spread of 00:12:43.630 --> 00:12:46.670 particular contagions. 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 00:12:53.150 --> 00:12:58.990 that you can effectively model so many different processes, phenomena, using 00:12:59.470 --> 00:13:04.590 these kinds of toy models that capture a particular phenomenon. 00:13:05.620 --> 00:13:09.380 And so that I think is is really one of the cool sort of starting places. 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 00:13:14.100 --> 00:13:15.540 different study systems? 00:13:15.860 --> 00:13:19.460 And what does that help us maybe understand the topic a little bit more? 00:13:19.780 --> 00:13:20.020 Yeah. 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 00:13:24.500 --> 00:13:26.980 papers are, which means like the most recent ones. 00:13:27.140 --> 00:13:28.180 And there's two. 00:13:28.340 --> 00:13:32.340 One is called the development of risk attitudes in their cultural transmission. 00:13:32.570 --> 00:13:37.370 And the second one is called a model of coronary 00:13:37.370 --> 00:13:39.210 artherosclerosis. 00:13:39.450 --> 00:13:43.530 I can't say that can you say yeah artherosclerosis. 00:13:43.690 --> 00:13:44.410 Yes, thank you. 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 00:13:50.570 --> 00:13:55.290 it would be the equivalent of like studying I'm studying you know um um 00:13:56.660 --> 00:13:59.700 Like a galaxy and bicycle tires. 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 00:14:03.940 --> 00:14:05.140 bicycle tire have in common? 00:14:05.300 --> 00:14:05.620 Nothing. 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 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. 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, 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 00:14:27.610 --> 00:14:31.370 like a psychologist or like somebody who just sort of naively thinks about, like, well, 00:14:31.450 --> 00:14:33.610 yeah, galaxies and bike tires are different. 00:14:33.770 --> 00:14:38.970 Um, there are all these tools in the evolutionary behavioral sciences for 00:14:39.290 --> 00:14:41.290 adopting more stringent models. 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. 00:14:45.210 --> 00:14:48.730 Because so far we've talked a lot about psychology and individual behavior and 00:14:48.730 --> 00:14:52.200 methodological individualism, but we haven't really talked about why would you want to 00:14:52.200 --> 00:14:53.480 model these things precisely? 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 00:14:59.480 --> 00:15:00.120 cool. 00:15:00.280 --> 00:15:02.520 Um, you know, I think there's different kinds of sciences. 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 00:15:06.360 --> 00:15:08.280 snake, and that's cool because snakes are cool. 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 00:15:12.600 --> 00:15:13.160 galaxy. 00:15:13.240 --> 00:15:15.240 And I think what you're doing is sort of like that. 00:15:15.320 --> 00:15:16.600 It's sort of a superpower. 00:15:17.640 --> 00:15:18.360 Thank you. 00:15:18.600 --> 00:15:18.920 Yeah. 00:15:19.250 --> 00:15:25.170 Um so uh so can we get a little bit into then uh why 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? 00:15:31.250 --> 00:15:32.930 Oh, that's a great question. 00:15:33.250 --> 00:15:39.250 Um well uh as you may have talked about in the past, I don't 00:15:39.250 --> 00:15:39.490 know. 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, 00:15:45.170 --> 00:15:45.410 right? 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, 00:15:51.640 --> 00:15:53.320 at least not very formal math. 00:15:55.000 --> 00:16:00.360 Simultaneously, there was work being done, you know, by Gregor Mendel and his successors 00:16:00.440 --> 00:16:06.360 that was very quantitative, and it was trying to carefully measure uh inheritance um in 00:16:06.680 --> 00:16:11.240 in ways that you know lend themselves to uh statistical analysis. 00:16:12.370 --> 00:16:17.250 But it took a while for those two branches, I think, to intersect. 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 00:16:23.250 --> 00:16:24.690 uniting the two. 00:16:24.770 --> 00:16:27.570 I mean, that's what we refer to as the modern synthesis, right? 00:16:27.730 --> 00:16:33.010 Was the initial kind of wave of mathematical models that were taking 00:16:33.010 --> 00:16:38.660 Darwinian ideas and coupling them with what we knew about Mendelian inheritance 00:16:38.820 --> 00:16:43.220 and showing that they were completely compatible, which I think was actually a source of 00:16:43.220 --> 00:16:48.660 controversy in the late 1800s, was this view that Darwinian 00:16:48.980 --> 00:16:53.780 evolution uh requires things to be a little smoother than they are with Mendel's 00:16:54.260 --> 00:16:56.690 discrete inheritance mechanisms. 00:16:57.010 --> 00:16:58.850 And so these are incompatible frameworks. 00:16:59.010 --> 00:17:03.010 And people like Ronald Fisher and Sewell Wright show up and they're like, well, actually, 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 00:17:09.250 --> 00:17:14.370 distributions of diversity we see using discrete inheritance mechanisms. 00:17:14.930 --> 00:17:20.380 Um and I mean that it was so successful that we don't even really 00:17:20.380 --> 00:17:22.140 consciously think about that now. 00:17:22.300 --> 00:17:26.540 I think that these at one point were almost like rival paradigms for understanding the 00:17:26.540 --> 00:17:27.820 origin of life. 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. 00:17:32.860 --> 00:17:36.220 It wasn't necessarily, I mean, these were very forceful personalities as well. 00:17:36.300 --> 00:17:39.740 If you know the history of statistics, you know, Ronald Fisher and Sewell Wright. 00:17:39.980 --> 00:17:45.750 Um But uh I think that what really drove home the uh 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 00:17:52.310 --> 00:17:54.550 starting place to think about process. 00:17:55.350 --> 00:18:01.190 Um and uh that's I think very rhetorically useful, like to sort of say, 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 00:18:07.300 --> 00:18:08.980 the evolutionary process. 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 00:18:15.540 --> 00:18:17.380 idealized circumstances. 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 00:18:21.780 --> 00:18:22.980 turn one on, turn it off. 00:18:23.140 --> 00:18:27.460 If you turn them all off, you get like you know, Harvey Weinberg equilibrium, right? 00:18:27.940 --> 00:18:31.950 Um and that is what is Harvey Weinberg you could be able to do. 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. 00:18:35.710 --> 00:18:36.910 New board, new board, new porn. 00:18:37.150 --> 00:18:38.030 Yeah, exactly. 00:18:38.270 --> 00:18:41.790 But the uh book just swipe the board. 00:18:44.110 --> 00:18:49.950 Um so uh the the question here, I think, uh from what I 00:18:49.950 --> 00:18:55.700 remember from Population Genetics is um what happens if you turn off every force that we 00:18:55.700 --> 00:19:01.700 think of as kind of a directional force in acting on a 00:19:02.020 --> 00:19:04.180 population of genes. 00:19:04.740 --> 00:19:05.460 So there's no selection. 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 00:19:13.220 --> 00:19:13.620 traits. 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 00:19:17.140 --> 00:19:23.140 phenotypic gambit before, and some assumption about how they combine and sort of how 00:19:23.140 --> 00:19:24.180 they get inherited. 00:19:24.340 --> 00:19:28.310 And it can be simplified, but still simplified enough to be sort of accurate, right? 00:19:28.390 --> 00:19:31.270 Like eye color or some any trait, right? 00:19:31.670 --> 00:19:37.510 And then um and then the forces are if you just sort of let the 00:19:37.510 --> 00:19:43.190 model run, there's ideas about, well, some of the traits are favored more than 00:19:43.190 --> 00:19:46.950 others by the environment, for example, but the and that's an example of a force. 00:19:47.030 --> 00:19:51.940 So to what extent does the environment favor, for example, you know, like having 00:19:51.940 --> 00:19:55.140 photosynthesis if you're a plant versus not, you know. 00:19:55.300 --> 00:19:59.940 And so like you could just model that and say, does photosynthesis sort of diffuse 00:20:00.180 --> 00:20:01.940 throughout the population over time? 00:20:02.100 --> 00:20:03.700 So sorry, just to give that sort of background. 00:20:04.020 --> 00:20:05.220 Yeah, that's exactly it. 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 00:20:10.820 --> 00:20:15.830 organisms between you know, islands is the classic uh sort of dynamic here. 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 00:20:19.830 --> 00:20:22.390 that's occurring between each uh place. 00:20:23.270 --> 00:20:25.990 Um drift is also a force. 00:20:26.150 --> 00:20:31.430 This is something that uh is maybe not as talked about in 00:20:31.670 --> 00:20:36.390 evolutionary models, but some people have suggested that it's it's quite a powerful force 00:20:36.470 --> 00:20:41.180 actually for understanding how um distributions change over time, you know, if they're 00:20:41.180 --> 00:20:45.420 genetic distributions or behavioral distributions, cultural traits. 00:20:46.380 --> 00:20:51.420 And that's uh in the context of the equilibrium model, uh, 00:20:51.660 --> 00:20:55.900 it's uh a powerful force when you have small populations. 00:20:56.380 --> 00:21:01.580 Um so individual stochastic events can really shift things around. 00:21:03.130 --> 00:21:08.890 But if you have a very, very large population, then you know uh Laval's numbers 00:21:08.890 --> 00:21:11.930 tends to move things towards averages. 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 00:21:17.450 --> 00:21:19.610 not really modifying the distribution meaningfully. 00:21:19.770 --> 00:21:23.660 I mean, you know, an infinitely sized population has no drift, basically. 00:21:23.900 --> 00:21:27.020 Um, and so that's like all the idealized circumstances. 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 00:21:29.820 --> 00:21:35.020 there's, you know, there's no environmental favoring of specific uh variants in the 00:21:35.020 --> 00:21:35.660 population. 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 00:21:41.820 --> 00:21:45.130 there's um There's no mutation either. 00:21:45.290 --> 00:21:49.530 So there's no spontaneous change of one type into another type. 00:21:49.850 --> 00:21:51.050 I think that's all of them. 00:21:51.130 --> 00:21:52.010 I'm not sure. 00:21:52.810 --> 00:21:54.330 But that's a starting place. 00:21:54.410 --> 00:21:57.690 We're saying, okay, so we switched off all the different forces that could be acting. 00:21:58.010 --> 00:22:00.810 So then we would expect the population to be at equilibrium. 00:22:00.890 --> 00:22:03.850 Like it's not going to change over the course of time. 00:22:04.970 --> 00:22:07.210 But then, I mean, that's sort of a null condition, right? 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 00:22:13.140 --> 00:22:15.220 force or the other in the modeling? 00:22:15.300 --> 00:22:18.820 And does it look kind of like what we're seeing in real life? 00:22:18.980 --> 00:22:21.220 Like in terms of the rate at which it's happening? 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 00:22:27.860 --> 00:22:28.020 time? 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? 00:22:32.740 --> 00:22:38.180 Um R curves are more consistent with like mutation, uh, and S curves are um 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, 00:22:44.730 --> 00:22:45.930 a selective sweep. 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. 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 00:22:56.330 --> 00:23:00.010 coefficient of drag, and then you like measure leaf leaves falling. 00:23:00.090 --> 00:23:02.170 Like that was an example I used in an earlier episode. 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 00:23:06.570 --> 00:23:07.610 outside my window. 00:23:07.840 --> 00:23:11.200 What's the model look like when we only have gravity? 00:23:11.360 --> 00:23:14.240 What's the model look like when there's only coefficient of drag? 00:23:14.400 --> 00:23:16.720 What's the what does the model do when we combine both? 00:23:16.880 --> 00:23:19.680 And then how does that compare to the actually how the leaves fall? 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 00:23:25.360 --> 00:23:31.270 complicated thing, like how something behaves, and we're trying to capture um 00:23:31.750 --> 00:23:34.870 something more sophisticated than just down, right? 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 00:23:39.670 --> 00:23:44.550 recreate it or whatever, then you need it better than just like an arrow down, right? 00:23:44.630 --> 00:23:45.510 Um maybe. 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? 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 00:23:56.790 --> 00:23:59.190 treating behavior or a trait. 00:23:59.350 --> 00:24:04.020 Uh and obviously there's differences between like, you know, hair color versus like what 00:24:04.020 --> 00:24:07.380 kind of music I listen to, whether like or the design of a canoe. 00:24:07.540 --> 00:24:11.380 But as you said, it's this principle of like galaxies and bike tires. 00:24:11.460 --> 00:24:17.460 There's there's a level at which the modeling at least can capture the changes 00:24:17.460 --> 00:24:17.860 over time. 00:24:17.940 --> 00:24:20.740 And that's the thing that like unifies what you're looking at. 00:24:21.460 --> 00:24:23.060 Yeah, I think that's exactly it. 00:24:23.300 --> 00:24:23.860 Yeah. 00:24:24.180 --> 00:24:25.460 It's crazy. 00:24:27.590 --> 00:24:31.590 So uh what is cultural distance? 00:24:33.590 --> 00:24:36.870 Uh well yeah, how how long how much time do we have? 00:24:36.950 --> 00:24:41.430 Uh Brett lives in Germany, he's gonna tell you all kinds of anecdotes about cultural. 00:24:41.830 --> 00:24:42.550 Yeah, exactly. 00:24:42.790 --> 00:24:48.470 Um this is a uh this is a particular 00:24:48.470 --> 00:24:54.070 concept that has been interesting to a number of scholars in cultural 00:24:54.070 --> 00:24:55.270 evolution recently. 00:24:55.430 --> 00:24:59.800 Um and so there's been a lot of modeling effort directed at this. 00:25:00.040 --> 00:25:05.800 Um the basic idea is that we are 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 00:25:11.800 --> 00:25:17.640 you know two um two cultures or two uh sets 00:25:17.640 --> 00:25:23.430 of norms uh are more similar to each other than uh uh a 00:25:23.430 --> 00:25:28.630 third that you're comparing them against um similar to what that means the plant thing 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 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 00:25:37.350 --> 00:25:43.110 right that's right and just in concrete example you might intuitively think that America 00:25:43.110 --> 00:25:47.830 and the UK are closer to each other culturally than America is to China. 00:25:48.380 --> 00:25:49.980 Yeah, that's exactly the comparison. 00:25:50.300 --> 00:25:54.060 I used that that that those three actually in a recently. 00:25:55.500 --> 00:26:01.020 And then there's just, you know, uh various ways to try to quantify that somehow. 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 00:26:06.540 --> 00:26:12.530 been um a major focus for attention uh among uh sociologists uh 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 00:26:19.090 --> 00:26:24.370 world uh about uh you know large variety of different topics uh relating to people's 00:26:24.370 --> 00:26:25.010 values. 00:26:25.170 --> 00:26:30.690 And then uh there's a bunch of algorithms that people use. 00:26:30.850 --> 00:26:35.010 They they they calculate distance measures and they say these two places are more similar 00:26:35.010 --> 00:26:38.690 than these two places or somebody from this country is more similar to to somebody from 00:26:38.690 --> 00:26:39.730 this other country. 00:26:40.320 --> 00:26:42.560 I mean, this isn't just cultural evolution. 00:26:42.720 --> 00:26:47.600 Uh, sort of our uh uh contribution, the evolutionary sciences, I think, is to try to 00:26:47.600 --> 00:26:53.520 connect this um a little more formally to other kinds of distance measures in 00:26:53.520 --> 00:26:54.400 biology. 00:26:54.560 --> 00:27:00.560 Um, but outside of evolutionary theory, there's also a large number of different um 00:27:00.800 --> 00:27:04.080 distance measures that people have used or calculated. 00:27:04.320 --> 00:27:09.890 Um What what they mean exactly is kind of also a big issue. 00:27:09.970 --> 00:27:15.250 Like when you say, you know, I think, you know, intuitively that um, you know, two 00:27:15.490 --> 00:27:20.290 societies or two people might have similar cultural dispositions. 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 00:27:25.970 --> 00:27:26.850 actually do for us? 00:27:26.930 --> 00:27:29.010 Like, why why would you want to know that? 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. 00:27:35.360 --> 00:27:41.200 Like, you know, people have used cultural compatibility as a uh, you know, like 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 00:27:46.640 --> 00:27:47.520 opinions. 00:27:47.920 --> 00:27:53.680 Um I think you can flip it around and also think about this 00:27:53.680 --> 00:27:59.310 in terms of complementary skill sets or complementary uh 00:28:00.190 --> 00:28:01.390 interactive possibilities. 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 00:28:07.150 --> 00:28:10.670 exchange is having comparative advantage. 00:28:10.830 --> 00:28:16.670 And if you have two different, you know, quote unquote skill sets 00:28:16.830 --> 00:28:20.750 or norms or something like that, it could be that they actually are quite uh beneficial 00:28:20.750 --> 00:28:22.510 when they interact with each other. 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. 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 00:28:32.620 --> 00:28:35.260 come up with quantifications of cultural distance. 00:28:35.900 --> 00:28:41.740 Um and I think that you can map those on to lots of interesting 00:28:41.740 --> 00:28:47.570 um uh predictions about things like how people will behave in cooperative, uh 00:28:47.730 --> 00:28:50.850 uh cooperative experiments, for example. 00:28:51.090 --> 00:28:56.530 Um but uh what what would you what would you predict there? 00:28:57.250 --> 00:28:58.530 Well I wouldn't predict anything. 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 00:29:03.970 --> 00:29:09.580 work recently on using a measure called cultural FST to predict uh willingness to 00:29:09.580 --> 00:29:15.420 cooperate in um I believe they were in uh vignettes that resembled like 00:29:15.420 --> 00:29:19.900 a uh uh some kind of prisoner's dilemma type interaction, uh, where there was the 00:29:19.900 --> 00:29:23.020 possibility of giving an altruistic resource to somebody else. 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 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, 00:29:32.890 --> 00:29:36.650 they're interrogated by the police and they're interrogated separately. 00:29:36.890 --> 00:29:42.170 And uh if both of them stay quiet, they both are well off, right? 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 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. 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 00:29:57.450 --> 00:29:59.290 a temptation to defect on the other. 00:29:59.370 --> 00:30:01.050 But if you both defect, you're both screwed. 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 00:30:06.860 --> 00:30:12.220 things where it's sort of like, you know, um like parenting parenting off effort where 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, 00:30:17.180 --> 00:30:19.340 um, then uh your kid's okay. 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. 00:30:22.060 --> 00:30:24.300 So there's just lots of things in life that have that structure. 00:30:24.460 --> 00:30:25.420 Sorry to interrupt, but yeah. 00:30:25.750 --> 00:30:25.990 No worries. 00:30:26.070 --> 00:30:28.310 And and the and the key thing here is the ordering of the payoff. 00:30:28.470 --> 00:30:31.510 So for me, the best outcome is if I defect and you cooperate. 00:30:31.670 --> 00:30:33.350 Ha ha ha, I got one over on you. 00:30:33.750 --> 00:30:36.310 Uh the second best outcome is if we both cooperate. 00:30:36.550 --> 00:30:40.950 Uh the third best outcome is if we both defect. 00:30:41.190 --> 00:30:45.990 And then the worst outcome for me is if uh you defect while I cooperate. 00:30:46.150 --> 00:30:48.870 And so and and so that is the the the nature of the of the game. 00:30:51.020 --> 00:30:53.820 Yes, the suckers pay off is the would be the the worst outcome. 00:30:53.980 --> 00:30:57.180 So anyway, now that we know the prisoner's dilemma, continue with with what you were 00:30:57.180 --> 00:30:57.500 saying. 00:30:57.820 --> 00:30:58.060 Yeah. 00:30:58.140 --> 00:30:59.820 Well, I'm just thinking about this paper. 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. 00:31:04.940 --> 00:31:07.260 Um and they were doing in a little bit, yeah. 00:31:07.500 --> 00:31:08.780 Or scheduling Sarah. 00:31:08.860 --> 00:31:08.940 Yeah. 00:31:09.020 --> 00:31:09.100 Yeah. 00:31:09.420 --> 00:31:09.580 Yeah. 00:31:09.660 --> 00:31:11.180 Well, okay, then maybe I shouldn't talk about it. 00:31:11.340 --> 00:31:12.140 No, no, no, go for it. 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. 00:31:17.180 --> 00:31:17.420 So yeah. 00:31:17.770 --> 00:31:17.930 Right. 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 00:31:23.610 --> 00:31:24.730 worked with for some time now. 00:31:24.810 --> 00:31:26.010 Uh I think the Turkana. 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 00:31:32.650 --> 00:31:38.490 in uh lots of cattle raiding taking place, um uh both within the Turkana 00:31:38.650 --> 00:31:42.010 and also between Turkana and uh neighboring ethnic groups. 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 00:31:47.080 --> 00:31:50.920 would say they would behave in these uh vignette circumstances where they're asked to 00:31:50.920 --> 00:31:56.920 provide aid to um uh to some hypothetical 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, 00:32:02.920 --> 00:32:07.080 as measured by the uh cultural distance measure. 00:32:08.230 --> 00:32:13.910 And their basic argument is that this measure itself is highly predictive 00:32:14.150 --> 00:32:19.110 of people's cooperativeness in these kinds of vignette circumstances, where they're being 00:32:19.110 --> 00:32:23.590 asked to provide a resource with no possibility really of return on that resource. 00:32:23.990 --> 00:32:25.910 So it's it's kind of like a prisoner's dilemma. 00:32:25.990 --> 00:32:28.390 It has that kind of altruistic vibe to it. 00:32:29.590 --> 00:32:31.910 And uh they found a really strong effect 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 00:32:47.440 --> 00:32:52.240 of questions asking people about um uh uh a different set of topics. 00:32:52.850 --> 00:32:57.410 So so the the the dependent measure here was not cooperation in a prisoner's dilemma, 00:32:57.490 --> 00:32:57.810 correct? 00:32:57.890 --> 00:33:03.730 It w it was the the willingness to altruistically benefit the the target uh without any 00:33:03.730 --> 00:33:05.090 obvious return benefit. 00:33:05.170 --> 00:33:06.610 Is that is that correct? 00:33:07.250 --> 00:33:07.810 Uh that's right. 00:33:07.890 --> 00:33:08.050 Yeah. 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 00:33:11.970 --> 00:33:17.740 then you benefit or or or or Aaron Powell So here the result is 00:33:17.820 --> 00:33:23.660 um if we're more culturally distant, I'm less likely to altruistically help you 00:33:23.740 --> 00:33:27.900 uh without any kind of expectation of of a return benefit. 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 00:33:32.940 --> 00:33:37.610 particular game where I help you uh just out of the goodness of my heart. 00:33:37.770 --> 00:33:42.490 Um, there could be other instances where, say, we're trading, and maybe um your cultural 00:33:42.490 --> 00:33:45.690 distance might increase the likelihood that I want to trade with you because you have 00:33:45.690 --> 00:33:49.930 different stuff and you do different things and you have a comparative advantage uh uh in 00:33:49.930 --> 00:33:51.130 some skills rather than others. 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 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 00:34:02.900 --> 00:34:08.100 interactions between individuals who aren't part of the same ethnic group potentially as a 00:34:08.100 --> 00:34:13.620 desired outcome, like you know, people consciously pursuing uh long-distance ties because 00:34:13.620 --> 00:34:19.300 they have exactly, as you say, uh the potential for providing unique resources or unique 00:34:19.300 --> 00:34:20.660 interactive uh abilities. 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 00:34:26.470 --> 00:34:31.750 altruism work, uh, where you know it really does depend on the interaction structures, uh, 00:34:31.830 --> 00:34:33.430 the kinds of games people are playing. 00:34:33.590 --> 00:34:38.150 In some circumstances, you can see cultural uh distance, you know, big cultural distances 00:34:38.150 --> 00:34:40.790 is like a big barrier for getting people organized. 00:34:41.190 --> 00:34:46.790 Uh but in a different circumstance, you know, cultural distance could also indicate a lot 00:34:46.790 --> 00:34:52.580 of potential for mutually beneficial exchange, because you have division of labor, you 00:34:52.580 --> 00:34:57.140 have uh comparative advantage, you have all these nice things that uh uh tend to that 00:34:57.140 --> 00:35:00.660 humans tend to uh figure out how to do pretty well, actually. 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 00:35:05.780 --> 00:35:09.780 structures affect our interpretations of things like cultural distance. 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 00:35:14.180 --> 00:35:19.620 circumstances uh it could potentially be uh highly advantageous to have a you know 00:35:20.010 --> 00:35:24.810 a lot of distance, cultural distance between people in the same uh same team. 00:35:25.850 --> 00:35:27.850 So w why do you think that is? 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 00:35:33.290 --> 00:35:34.970 you out of the goodness of my heart? 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 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. 00:35:43.930 --> 00:35:47.530 So wha what explains that difference in in in my in my behaviors there? 00:35:47.610 --> 00:35:49.930 What what do you think is going on psychologically? 00:35:51.440 --> 00:35:53.200 Psychologically, oh man. 00:35:56.880 --> 00:35:59.280 I I couldn't even hazard a guess actually. 00:35:59.760 --> 00:36:01.920 I mean that's sort of outside my wheelhouse. 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 00:36:08.080 --> 00:36:13.910 structuring uh I mean it's uh i i it it certainly seems like 00:36:13.910 --> 00:36:15.350 it's an important force, right? 00:36:15.430 --> 00:36:19.750 That's my my general understanding from reading this literature is that um humans are 00:36:19.750 --> 00:36:20.790 really groupy. 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 00:36:26.870 --> 00:36:32.150 how things like opinions form uh is heavily influenced by uh our group 00:36:32.150 --> 00:36:33.030 affiliations. 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 00:36:39.340 --> 00:36:40.540 speculation, to be honest. 00:36:40.940 --> 00:36:42.140 Me, me neither, actually. 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, 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 00:36:51.420 --> 00:36:52.460 different job. 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 00:36:58.410 --> 00:37:02.330 I always talk about like as an experimental psychologist, you know, I can control the 00:37:02.330 --> 00:37:04.570 variables that I show to participants. 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, 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 00:37:15.370 --> 00:37:17.370 people, what they remember, what they respond. 00:37:17.530 --> 00:37:19.050 I can even measure like the timing. 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? 00:37:23.620 --> 00:37:27.940 So I have to sort of like recapture real world dynamics in the lab. 00:37:28.100 --> 00:37:31.060 And it's sort of lame, like, you know, on the computer. 00:37:31.300 --> 00:37:35.380 And so I always want to talk to anthropologists to sort of keep me honest. 00:37:35.780 --> 00:37:41.460 And it sounds like, you know, in addition to being in part anthropologist, 00:37:41.540 --> 00:37:46.980 it's also there, it's a similar thing of, you know, psychology 00:37:47.650 --> 00:37:51.890 is interested in psychology, like the individual like wiring in the brain. 00:37:51.970 --> 00:37:57.650 And so, like, this question of like, why would we care about um uh helping people 00:37:57.650 --> 00:38:01.410 altruistically who are closer to us but trading more distantly? 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, 00:38:06.450 --> 00:38:06.690 right? 00:38:06.770 --> 00:38:07.890 Out in the real world. 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 00:38:13.840 --> 00:38:19.520 even what question to even ask is part of like this team effort, right? 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 00:38:25.600 --> 00:38:30.000 sort of describe behavior was sort of like just write down some stuff people do. 00:38:30.160 --> 00:38:33.520 And it wasn't very mathematical and it wasn't very precise. 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 00:38:38.830 --> 00:38:40.030 wasn't always clear it was true. 00:38:40.350 --> 00:38:44.830 Sort of a replication crisis of just documenting what are the things we even have to 00:38:44.830 --> 00:38:45.550 explain. 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 00:38:51.310 --> 00:38:57.150 like the people you work with are sort of in a wave of science 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 00:39:03.160 --> 00:39:08.760 ways to quantify and measure real-world like phenomena, right? 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 00:39:14.680 --> 00:39:19.000 more confident in like we can be sure that this thing is actually happening. 00:39:19.160 --> 00:39:20.920 Is that sort of fair? 00:39:22.850 --> 00:39:24.530 Uh yeah, I think so. 00:39:24.690 --> 00:39:27.570 Um I mean the the way I think about it is a little different. 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. 00:39:33.010 --> 00:39:37.170 Like there's just an infinite number of details that could be potentially recorded. 00:39:37.410 --> 00:39:43.090 Um and one of the values of having theory or sort of formal 00:39:43.090 --> 00:39:49.010 theory is that it focuses things a bit and says, well, some details might be irrelevant 00:39:49.010 --> 00:39:53.700 for a particular question, uh, even though they might seem like they might be important, 00:39:53.940 --> 00:39:56.980 uh, other details might be really important. 00:39:57.060 --> 00:39:58.980 And so you should focus in on those. 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, 00:40:04.580 --> 00:40:09.780 you know, theoretical, theoretical guidance for looking, you know, in the sea of infinite 00:40:09.780 --> 00:40:11.860 observation possibilities to get data. 00:40:12.070 --> 00:40:13.510 Uh, the data is interesting. 00:40:13.590 --> 00:40:16.630 It it builds on the theory and motivates new theory. 00:40:16.710 --> 00:40:21.430 And then the theory goes back and you know sharpens the focus a little bit empirically. 00:40:21.910 --> 00:40:22.070 Yeah. 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 00:40:25.350 --> 00:40:29.350 we talk to Chris von Ruden, we talked about like warfare and rates of conflict. 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 00:40:33.670 --> 00:40:35.190 like how belligerent people are. 00:40:35.350 --> 00:40:38.790 And there's just sort of like sometimes people are grumpy and other times not. 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 00:40:45.000 --> 00:40:50.920 with uh conflict is also the harshness, for example, of the circumstances for 00:40:50.920 --> 00:40:52.040 like having enough stuff. 00:40:52.200 --> 00:40:58.120 So if you're agrarian climate, and like if if climate becomes bad and inhospitable 00:40:58.120 --> 00:41:02.680 to having enough stuff, food, for example, then conflict goes up. 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 00:41:08.360 --> 00:41:13.610 talk about conflict, is con contrasted with a model that 00:41:13.610 --> 00:41:19.050 incorporates like, you know, resource scarcity, but then you need a model of resource 00:41:19.050 --> 00:41:20.650 scarcity and a way to quantify that. 00:41:20.730 --> 00:41:23.370 So you can actually test between those two models, right? 00:41:23.770 --> 00:41:24.250 Mm-hmm. 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 00:41:32.680 --> 00:41:38.040 uh again, just to bring it back to the um altruism toward culturally distant 00:41:38.040 --> 00:41:43.640 outgroups being low, but exchange with culturally distant outgroups in some cases being 00:41:43.640 --> 00:41:49.080 high, is that uh the selection pressures that gave rise to altruism in the first place 00:41:49.400 --> 00:41:55.160 um usually come from the expectation that the altruism will lead to a long-term 00:41:55.240 --> 00:41:58.050 mutually beneficial cooperative relationship. 00:41:58.210 --> 00:41:58.530 Right. 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 00:42:03.410 --> 00:42:08.210 myself, the odds that that benefit will return to me at some point, maybe they'll 00:42:08.210 --> 00:42:08.850 reciprocate. 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 00:42:13.090 --> 00:42:14.050 more likely to help me. 00:42:14.210 --> 00:42:20.180 These kinds of return benefits are likely going to be higher if I if I deliver 00:42:20.180 --> 00:42:23.060 them to someone who is culturally similar to me. 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 00:42:27.060 --> 00:42:29.780 likely that they're going to come back and reciprocate at some point. 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 00:42:34.180 --> 00:42:37.460 beneficial relationship with the person I'm helping is just so low. 00:42:37.620 --> 00:42:42.100 Just because it's just so hard for us to have a mutually beneficial relationship. 00:42:42.580 --> 00:42:45.300 If we speak a different language, that's going to be a huge barrier to that. 00:42:45.540 --> 00:42:51.350 So I think perhaps some part of the mind is estimating the likelihood that this 00:42:51.350 --> 00:42:55.830 act of helping will result in a long-term mutually beneficial relationship that will make 00:42:55.830 --> 00:42:59.750 the altruism worth it, that will make the altruism pay off. 00:43:00.470 --> 00:43:06.070 And so that might be shaping the low willingness to help as a result of 00:43:06.070 --> 00:43:07.030 cultural distance. 00:43:07.190 --> 00:43:12.260 But if the cooperation is already established and there already is an exchange relationship 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 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 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 00:43:26.980 --> 00:43:28.420 is my stuff to them, and so forth. 00:43:28.580 --> 00:43:31.780 And so that that I think might be explaining what's going on. 00:43:31.940 --> 00:43:33.620 Do you do you buy that story? 00:43:35.130 --> 00:43:37.770 Um yes and no. 00:43:38.010 --> 00:43:42.810 Um I think that the the the thing I would add to it is uh 00:43:43.770 --> 00:43:49.690 that it's often the case that we don't know this kind of 00:43:49.690 --> 00:43:49.930 thing. 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 00:43:56.010 --> 00:43:57.690 a particular interaction. 00:43:57.930 --> 00:44:03.640 Uh and that constraint is itself important, like how people get around that constraint or 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 00:44:09.480 --> 00:44:09.800 have. 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 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 00:44:20.360 --> 00:44:26.230 individuals uh or how do organisms figure 00:44:26.230 --> 00:44:29.990 out things that they would need to know that do those kinds of calculations. 00:44:32.070 --> 00:44:34.390 How do they know how variable the environment is? 00:44:34.550 --> 00:44:39.830 That itself is actually really hard because if it's a really variable environment, then 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. 00:44:46.710 --> 00:44:50.470 And so it's now it becomes like an information problem, right? 00:44:50.630 --> 00:44:55.280 Is you know, given the information that I have available, you know, what kind of cognition 00:44:55.280 --> 00:44:59.840 would you expect to develop that's trying to basically mine 00:45:00.800 --> 00:45:06.720 information out of the, you know, the social and physical milieu to try to figure 00:45:06.720 --> 00:45:07.280 that stuff out? 00:45:07.440 --> 00:45:08.640 Like, can I find examples? 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 00:45:13.360 --> 00:45:19.130 powerful to have examples that you can look at of uh 00:45:19.450 --> 00:45:25.130 of interactions that then later led to a payoff that maybe wasn't obvious initially. 00:45:25.450 --> 00:45:30.730 Um, you know, that that would be information that you might imagine an organism would would 00:45:30.730 --> 00:45:34.490 add to their own understanding to basically learn the game they're in, right? 00:45:34.650 --> 00:45:35.770 As as they're playing it. 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 00:45:39.770 --> 00:45:43.370 the rules, uh, you don't really know what the payoffs are going to be. 00:45:44.300 --> 00:45:46.860 Um, you might not even have any preferences. 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 00:45:51.100 --> 00:45:56.780 between different subfields uh is that um it's not 00:45:56.860 --> 00:46:00.300 obvious in some circumstances where the preferences come from. 00:46:00.460 --> 00:46:01.660 Like cause they're on top. 00:46:01.740 --> 00:46:04.060 I mean, like would you prefer an Android or an Apple? 00:46:04.220 --> 00:46:05.090 Like, I don't know. 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 00:46:09.730 --> 00:46:12.530 preference psychology that you would something else. 00:46:13.730 --> 00:46:19.410 And so how the preference itself develops, like getting like an endogenous theory of 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, 00:46:25.090 --> 00:46:26.530 it's sort of layers upon layers. 00:46:26.610 --> 00:46:32.280 Like you might imagine modeling the dynamics of the preference acquisition or the dynamics 00:46:32.280 --> 00:46:35.560 of learning about how risky your environment actually is. 00:46:35.800 --> 00:46:41.400 And then you model the behavior of the organism conditional on their understanding based on 00:46:41.400 --> 00:46:42.440 what they've learned. 00:46:43.400 --> 00:46:46.200 And it may very well play out exactly as you described. 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 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 00:46:56.280 --> 00:46:59.080 economist from the mid mid-20th century. 00:46:59.320 --> 00:47:03.870 Um And he wrote a very famous series of textbooks on economics. 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 00:47:08.670 --> 00:47:12.430 politicians are in office so long as I get to write the textbooks. 00:47:14.030 --> 00:47:19.550 And I mean, the basic premise is that, you know, thinking about market 00:47:19.550 --> 00:47:21.870 interactions is often quite counterintuitive. 00:47:21.950 --> 00:47:27.340 And you kind of have to learn about market interactions to participate in a market 00:47:27.340 --> 00:47:27.980 economy. 00:47:29.180 --> 00:47:32.860 And so we see things like, oh, you know, the possibility for mutually beneficial 00:47:32.860 --> 00:47:37.660 interactions, but you know, that might not be obvious to somebody without a little bit of 00:47:37.660 --> 00:47:39.740 cultural exposure to that. 00:47:41.420 --> 00:47:45.740 And I think that part gets really interesting, like how people come to understand the 00:47:45.740 --> 00:47:47.660 possibilities in a particular interaction. 00:47:47.820 --> 00:47:52.770 Like, is you know, am I am I approaching this particular interaction thinking about it 00:47:52.770 --> 00:47:54.770 like, you know, I might a prison dilemma. 00:47:54.930 --> 00:47:58.050 Or, you know, my I I'm I'm totally showing my cards here. 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, 00:48:03.490 --> 00:48:04.370 oh this is amazing. 00:48:04.530 --> 00:48:08.530 You know, like just uh the way that you understand a particular interaction having a big 00:48:08.530 --> 00:48:14.530 impact on uh potentially the way that you that you carry yourself or conduct yourself. 00:48:14.860 --> 00:48:14.940 Yeah. 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 00:48:20.860 --> 00:48:22.140 trust in a culture. 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 00:48:27.980 --> 00:48:31.340 cultural distance from each other and therefore don't cooperate. 00:48:31.660 --> 00:48:36.540 And maybe uh objectively they would be better off if they did cooperate. 00:48:36.620 --> 00:48:42.410 And so, for example, imagine another culture where you have that same distance, but 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 00:48:47.130 --> 00:48:47.770 sports. 00:48:48.250 --> 00:48:53.930 And even though they have the same cultural distance, they both like the same sports team. 00:48:54.090 --> 00:49:00.010 And that causes them to override the evolved psychology preference they would have had 00:49:00.010 --> 00:49:02.650 in the other scenario, and they therefore cooperate. 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 00:49:07.580 --> 00:49:11.660 from each other, but in one case um they cooperate and when they don't. 00:49:12.140 --> 00:49:14.060 Does one do better? 00:49:14.300 --> 00:49:20.300 And in some sense, then is it that the culture that has the sports team that 00:49:20.300 --> 00:49:25.990 sort of cultural uh trait or phenotype sort of would outcompete one where there 00:49:25.990 --> 00:49:31.030 aren't sports teams because it leads the evolved psychology to produce cooperation. 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 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 00:49:42.710 --> 00:49:45.590 static in the sense of its underlying design? 00:49:45.830 --> 00:49:51.600 But then you get all these multiple either through drift or cultural inheritance or 00:49:51.600 --> 00:49:57.200 whatever, variable possible things that are thrown at the evolved psychology, like do you 00:49:57.200 --> 00:50:01.600 want to cooperate this person versus do you want to cooperate this person who like likes 00:50:01.680 --> 00:50:05.280 you know Hirtha Berlin Soccer Club, you know, like you do, right? 00:50:05.440 --> 00:50:07.520 Those those are two different worlds. 00:50:07.680 --> 00:50:09.520 And you can model those things. 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 00:50:15.450 --> 00:50:19.770 persist and are some of them persisting because, for example, they affect cooperation. 00:50:19.930 --> 00:50:21.370 Is that sort of fair? 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 00:50:26.730 --> 00:50:32.570 topic of cultural similarity, is that um the calculation that you get 00:50:32.570 --> 00:50:36.970 depends heavily on the questions that you're using for for measuring cultural similarity. 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 00:50:42.320 --> 00:50:46.800 between people from the UK and and Americans or people from Germany and France. 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 00:50:50.640 --> 00:50:56.480 particular survey uh and then running calculations on that domain in the way that the 00:50:56.480 --> 00:50:57.680 questions are structured. 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 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 00:51:08.650 --> 00:51:12.810 domain, you might find a lot more affinity than in another domain. 00:51:13.210 --> 00:51:14.730 And it might matter a lot. 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 00:51:19.530 --> 00:51:25.370 that this is a this is not uh something that has guarantees on the tin, 00:51:25.690 --> 00:51:27.050 you know, in as a topic. 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, 00:51:32.040 --> 00:51:37.000 even apply it necessarily in in you know very casual circumstances, like obviously this way 00:51:37.000 --> 00:51:37.720 or that way. 00:51:37.880 --> 00:51:38.200 Yeah. 00:51:38.360 --> 00:51:43.480 Um so uh I mean it's it's useful to think about in specific circumstances. 00:51:43.640 --> 00:51:49.560 What like what you know, how might you organize uh, you know, things like a cooperative 00:51:49.560 --> 00:51:53.880 or uh coordination task given a particular institutional environment? 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? 00:51:59.170 --> 00:52:04.530 Um, but uh you add in all these other factors, like you know, uh some 00:52:05.090 --> 00:52:11.090 shared affinity for a sports team, or you know, the the the the interaction itself 00:52:11.090 --> 00:52:16.050 is in a totally different context than the way that you know the quote unquote cultural 00:52:16.050 --> 00:52:18.370 similarity scores are being assessed. 00:52:18.610 --> 00:52:23.020 Um uh all of that I think adds way too many complications. 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 00:52:27.740 --> 00:52:29.180 kind of uh in that kind of way. 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 00:52:33.500 --> 00:52:36.860 analogy, you can say like was a whale more similar to a cow or a fish? 00:52:37.020 --> 00:52:40.620 And like it's a meaningless question because it means like similarity along which 00:52:40.620 --> 00:52:40.940 dimension. 00:52:41.020 --> 00:52:45.750 And if you take like a unidimensional similarity, you know, like well, maybe for swimming 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 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 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 00:52:59.590 --> 00:53:04.150 for countries like how weird are they for the same reason right like like the Western 00:53:04.230 --> 00:53:09.480 educated industrialized rich democratic um, there are these sort of measures of sort of 00:53:09.480 --> 00:53:11.240 that assign each country a single number. 00:53:11.400 --> 00:53:15.240 And I guess that's better than the alternative where that hasn't existed. 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 00:53:19.720 --> 00:53:22.040 models are wrong and stupid, but we need more of them. 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 00:53:27.690 --> 00:53:30.090 not assigning any kind of numbers at all is dumb. 00:53:30.170 --> 00:53:33.050 But of course, we just want more numbers along different dimensions. 00:53:33.210 --> 00:53:38.250 And if doing one number to one country is a step towards multiple dimensions of numbers, 00:53:38.330 --> 00:53:39.850 that's the right direction. 00:53:40.810 --> 00:53:41.770 Yeah, I think that's right. 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 00:53:47.690 --> 00:53:53.260 the um the the specific things that are quite 00:53:53.260 --> 00:53:58.140 notable about humans themselves, like the uh I think the basis for the weird scale is 00:53:58.140 --> 00:53:59.420 actually cultural FST. 00:53:59.500 --> 00:54:02.220 I think that's Michael Muthakrishna and colleagues. 00:54:02.460 --> 00:54:08.220 Um and that calculation um specifically comes from a thing called 00:54:08.220 --> 00:54:12.060 genetic FST, uh, which is actually used to do as you just described. 00:54:12.140 --> 00:54:17.180 I mean, how similar are two different species from each other or within a population, how 00:54:17.180 --> 00:54:20.570 similar are uh two subgroups. 00:54:20.650 --> 00:54:25.370 Uh and the the basic idea with that calculation is using a variance ratio. 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, 00:54:31.370 --> 00:54:36.970 you're asking how much of the total variation that exists genetically, um, looking at a 00:54:36.970 --> 00:54:42.540 number of different sites, how much of that variation is explained by 00:54:42.940 --> 00:54:44.620 the between group variation. 00:54:44.780 --> 00:54:47.820 So there's between group variation and there's within group variation. 00:54:48.700 --> 00:54:54.620 And if your cultural or genetic FST is very high, the implication is that 00:54:54.860 --> 00:55:00.380 within each group or within each population, they're relatively very low variance 00:55:00.860 --> 00:55:03.420 compared to the between group differences. 00:55:03.900 --> 00:55:08.990 Whereas if the uh variance ratio is quite low, uh, then 00:55:08.990 --> 00:55:12.670 there's almost no variance explained between groups. 00:55:12.750 --> 00:55:15.390 Like all the variation really exists within each group. 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. 00:55:21.230 --> 00:55:25.390 And you can use this in a genetic context actually as the basis for identifying subspecies, 00:55:25.630 --> 00:55:30.190 um, if FST is very high, um, genetic FST, I should say. 00:55:30.460 --> 00:55:36.220 And uh one of the really remarkable things about humans is that we have extraordinarily 00:55:36.220 --> 00:55:38.380 low genetic FST measures. 00:55:38.460 --> 00:55:41.980 Um, like compared to other mammals, it's it's one of the lowest. 00:55:42.140 --> 00:55:43.660 Um and I mean this is completely. 00:55:44.140 --> 00:55:45.500 We talked about that in an earlier episode. 00:55:45.740 --> 00:55:46.300 Oh no, kidding. 00:55:46.620 --> 00:55:48.460 Yeah, just because of my my race work, right? 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 00:55:52.220 --> 00:55:55.740 chimps have like five times more genetic diversity, and even though you don't have 00:55:55.740 --> 00:56:01.290 subspecies, and we certainly're very all very similar to each other, consistent with like a 00:56:01.290 --> 00:56:03.770 pretty recent bottleneck pop in a population. 00:56:04.010 --> 00:56:07.930 I mean, the building you're in there doing incredible work figuring that out, right? 00:56:08.010 --> 00:56:10.570 The uh evolutionary history. 00:56:10.650 --> 00:56:10.810 Yeah. 00:56:11.290 --> 00:56:17.210 So building on that, I mean, we have uh genetic quantifiable numbers, which suggests that 00:56:17.290 --> 00:56:22.890 um uh that we don't have the same kind of strong structural 00:56:22.890 --> 00:56:28.100 differentiation globally as a species that we see with other species. 00:56:29.460 --> 00:56:32.260 But behaviorally, we're extremely variable. 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 00:56:38.260 --> 00:56:39.140 scale actually. 00:56:39.460 --> 00:56:45.140 If you take like world value survey data, you do measure really high FSTs, 00:56:45.370 --> 00:56:49.770 cultural FST specifically, um, that you know, the the the amount of variance. 00:56:49.850 --> 00:56:51.850 I mean, you can think about this in a regression context. 00:56:51.930 --> 00:56:53.450 You guys are psychologists, right? 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 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 00:57:04.010 --> 00:57:06.650 adding in group as like a predictive category. 00:57:06.970 --> 00:57:12.700 Um, Because the averages across groups tend to be quite 00:57:12.700 --> 00:57:13.100 different. 00:57:13.340 --> 00:57:17.260 Even if there's a lot of variable with variability within the groups, the averages 00:57:17.260 --> 00:57:22.300 themselves structure across groups in a usually in a very noticeable way. 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 00:57:27.500 --> 00:57:31.950 a way to sort of interpret R squared as FST. 00:57:32.030 --> 00:57:34.670 Like they're they're conceptually very similar concepts. 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 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. 00:57:43.950 --> 00:57:45.150 If that if that makes sense. 00:57:53.310 --> 00:57:57.150 And in this case, the variable would be, you know, just just group identity or something 00:57:57.150 --> 00:57:57.710 like that. 00:57:58.180 --> 00:57:59.380 But this is just for one measure. 00:57:59.540 --> 00:58:03.940 I mean, there's different ways to calculate uh FST depending on uh how many variables 00:58:03.940 --> 00:58:04.740 you're using. 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 00:58:09.860 --> 00:58:15.620 just measuring these absolutely massive cultural FST numbers, um, which which is 00:58:15.780 --> 00:58:20.510 which is quite a contrast with our genetic uh uh uh our genetic story. 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 00:58:26.590 --> 00:58:28.830 smooth and culture is crunchy. 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 00:58:35.070 --> 00:58:40.430 how behaviorally variable uh people tend to be but then the the sort of genetic evidence 00:58:40.430 --> 00:58:45.920 suggests exactly what we see with like cross-cultural adoptions which is that um any 00:58:45.920 --> 00:58:51.360 individual who's adopted into another culture when they're young enough develop and 00:58:52.000 --> 00:58:56.560 internalize the norms and language of the place they're raised with no difficulty. 00:58:56.720 --> 00:59:02.560 Like this is not, you know, this is clearly not due to anything 00:59:02.560 --> 00:59:06.080 besides cultural differentiation in terms of socialization. 00:59:06.400 --> 00:59:12.110 And that also speaks to the recent genetic bottleneck and our absolute lack of subspecies 00:59:12.430 --> 00:59:15.470 from sort of the biological or genetic definition. 00:59:16.910 --> 00:59:21.950 And that's also really neat that we have really stark cultural differences 00:59:22.670 --> 00:59:28.670 globally, you know, where groups themselves or large cultural groups tend to be very 00:59:28.670 --> 00:59:31.630 distinct from each other in measurable ways. 00:59:32.350 --> 00:59:38.180 But then you also have the ability for individuals to acculturate 00:59:38.180 --> 00:59:43.220 into those groups if they're young enough with you know essentially no difficulty. 00:59:44.500 --> 00:59:49.220 And so those two things together, I think, make a very uh uh interesting research program 00:59:49.220 --> 00:59:52.820 in cultural evolution to sort of understand why that happens or what are the implications 00:59:52.820 --> 00:59:55.780 of that kind of uh uh that kind of process. 00:59:56.260 --> 00:59:56.500 Yeah. 00:59:56.660 --> 01:00:01.700 And I think historically, um, and Brett, you were a student of Pete Richardson, is that 01:00:01.700 --> 01:00:02.020 right? 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 01:00:08.470 --> 01:00:10.070 started actually at Santa Barbara. 01:00:10.310 --> 01:00:10.710 Oh, you did? 01:00:10.950 --> 01:00:11.990 Oh, I knew this actually. 01:00:12.070 --> 01:00:13.990 Now that you say this out loud, we have this conversation. 01:00:14.070 --> 01:00:14.790 Yeah, please go through. 01:00:15.190 --> 01:00:21.030 I was I was an undergrad at UC Santa Barbara uh in the early 2000s, and 01:00:21.670 --> 01:00:24.950 I took a class from Don Simons actually. 01:00:25.030 --> 01:00:28.950 It was uh at the time it was Anth5, the intro to physical anthropology. 01:00:29.720 --> 01:00:32.200 I mean I'm from originally from uh San Diego. 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 01:00:36.520 --> 01:00:39.240 we we we probably have a lot of shared experiences here. 01:00:39.400 --> 01:00:39.560 Yeah. 01:00:39.640 --> 01:00:41.000 Um surf Leipzig. 01:00:41.320 --> 01:00:41.560 Yeah. 01:00:41.800 --> 01:00:42.920 Yeah, exactly. 01:00:43.080 --> 01:00:45.160 Um but you can surf in Munich actually. 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 01:00:49.320 --> 01:00:50.040 Munich, yeah. 01:00:50.450 --> 01:00:50.850 Uh-huh. 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. 01:00:56.450 --> 01:00:59.730 Uh they've got like a fake course you can go down in in river rafting. 01:01:00.050 --> 01:01:00.610 Fantastic. 01:01:01.490 --> 01:01:07.330 But uh yeah, so I started at um UC Santa Barbara and uh I took classes 01:01:07.330 --> 01:01:09.810 from Simons uh and also John Tubi. 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, 01:01:14.930 --> 01:01:18.080 I think I might have participated in her dissertation survey. 01:01:18.640 --> 01:01:18.720 Okay. 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 01:01:24.560 --> 01:01:29.600 we were clearing out the Center for Evolutionary Psychology physically um from the Anthropo 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 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 01:01:37.440 --> 01:01:42.320 it's designed to be disgusting stuff about like yeah, sexual things and yeah, and relatives 01:01:42.400 --> 01:01:44.310 and all, yeah, it's it's it's interesting. 01:01:44.390 --> 01:01:46.150 But anyway, yeah, less we digress. 01:01:46.390 --> 01:01:46.630 Yes. 01:01:46.710 --> 01:01:47.910 You uh yeah. 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 01:01:51.990 --> 01:01:52.870 undergraduate. 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, 01:01:58.630 --> 01:02:01.350 which was Anth7 and Anth 107. 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. 01:02:05.800 --> 01:02:10.280 Um, so I got a lot of exposure to the to ideas in evolutionary psychology. 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 01:02:16.440 --> 01:02:18.840 and doing a double double major with anthro. 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 01:02:24.360 --> 01:02:29.240 cultural change and understanding why culture uh you see large-scale cultural changes over 01:02:29.240 --> 01:02:29.560 time. 01:02:30.850 --> 01:02:36.610 And so finding a way to connect evolutionary theory, which I thought was a very compelling 01:02:36.610 --> 01:02:41.890 research program, with that topic, you know, is some kind of cultural evolution 01:02:43.330 --> 01:02:44.050 subfield. 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 01:02:48.610 --> 01:02:50.050 researchers who do that. 01:02:50.930 --> 01:02:54.690 And the thing that really appealed to me the most when I started getting into this 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 01:03:00.240 --> 01:03:01.680 heavily focused on models. 01:03:01.840 --> 01:03:06.960 Like the the goal isn't necessarily to um try to 01:03:07.840 --> 01:03:13.440 um you know make forays into social engineering or to 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 01:03:19.970 --> 01:03:22.850 evolve society or something kind of horrendous like that. 01:03:23.010 --> 01:03:23.890 It was very humble. 01:03:23.970 --> 01:03:26.450 And it was like, look, we just want to understand these processes. 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 01:03:32.130 --> 01:03:32.370 on? 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 01:03:38.530 --> 01:03:40.690 know, models are stupid and we need more of them. 01:03:41.180 --> 01:03:42.460 Because that's that's exactly it. 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 01:03:45.900 --> 01:03:48.940 know, like no population resembles Hardy Weinberg. 01:03:49.180 --> 01:03:54.780 Um, but it gives us like a little toolbox or playground for thinking about some 01:03:54.780 --> 01:03:55.340 topic. 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 01:04:00.620 --> 01:04:01.340 was talking about, right? 01:04:01.660 --> 01:04:06.060 Saying, okay, well, what kind of models do I know might be relevant in this circumstance 01:04:06.140 --> 01:04:08.460 and what do they say is important to look at? 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. 01:04:15.830 --> 01:04:21.670 And Pete and Rob uh Boyd were collaborators already at that time when 01:04:21.670 --> 01:04:23.270 you were working with Pete? 01:04:23.750 --> 01:04:24.230 Oh yeah. 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 01:04:28.790 --> 01:04:30.950 started co-authoring papers together. 01:04:31.100 --> 01:04:32.620 Um, so I'm not not that old. 01:04:33.020 --> 01:04:34.060 No, I know exactly. 01:04:34.140 --> 01:04:34.300 Yeah. 01:04:34.940 --> 01:04:35.180 Yeah. 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. 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 01:04:44.940 --> 01:04:50.300 about the Christina episode is that sort of the the older 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 01:04:56.270 --> 01:05:01.950 generation things was figuring out sort of is 01:05:01.950 --> 01:05:06.670 cultural evolution and evolutionary psychology, and how do these two things sort of fit 01:05:06.670 --> 01:05:06.910 together? 01:05:07.070 --> 01:05:12.480 And I think there were sort of battles where there were concerns that the cultural 01:05:12.480 --> 01:05:16.960 evolutionists were sort of very simplifying the psychology and the evolutionary 01:05:16.960 --> 01:05:20.320 psychologists were like really oversimplifying the world. 01:05:20.560 --> 01:05:25.680 And I think as like the division of labor has sort of grown up, um 01:05:26.320 --> 01:05:31.840 I think the sophistication of what we're putting in the psychology and the 01:05:31.840 --> 01:05:34.960 evolutionary models is increasing. 01:05:35.310 --> 01:05:41.230 and maybe to some degree the sophistication of the evolutionary psychology models are 01:05:41.230 --> 01:05:43.310 sort of getting better too. 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. 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? 01:05:52.750 --> 01:05:58.720 Did you get a sense of that sort of uh this within group conflict that maybe 01:05:58.720 --> 01:06:01.360 has sort of waned since uh did you know. 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 01:06:07.360 --> 01:06:09.760 uh over the course of time as a student. 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, 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, 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. 01:06:22.720 --> 01:06:28.690 Um I mean uh the the 01:06:28.690 --> 01:06:34.290 basic questions in cultural evolution often have to deal with you know what kind of what 01:06:34.290 --> 01:06:39.810 kind of cognitive adaptations would be uh suited to a particular information 01:06:39.810 --> 01:06:40.850 environment. 01:06:41.250 --> 01:06:45.090 Um is that not evolutionary psychology? 01:06:45.320 --> 01:06:48.040 Like you're you're thinking about how psychology might evolve. 01:06:48.200 --> 01:06:51.800 I mean, like, just in the basic sense of, you know, this is a topic. 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. 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 01:07:03.480 --> 01:07:04.600 our just discussion earlier. 01:07:04.680 --> 01:07:09.960 I mean, I think that the cultural distance is quite small uh between these subfields, but 01:07:10.040 --> 01:07:14.610 you know, you wouldn't necessarily know it if you see people arguing with each other. 01:07:15.170 --> 01:07:17.810 Uh, because it's, you know, it's it's all relative, right? 01:07:17.890 --> 01:07:22.210 I mean, this is relative distance in a particular context, you know, matters quite a bit. 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, 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 01:07:31.810 --> 01:07:35.090 and trying to understand the world uh being criticized. 01:07:35.330 --> 01:07:41.080 Um so uh yeah, I mean I I I can definitely say having moved between these 01:07:41.160 --> 01:07:45.880 these circles that uh uh that there's different emphases for sure. 01:07:46.120 --> 01:07:48.360 Um and uh yeah, I think you're right. 01:07:48.520 --> 01:07:52.440 Like as as time has progressed, there's been a lot of blending taking place. 01:07:52.680 --> 01:07:58.600 Um uh I mean there's still some interesting differences which 01:07:58.840 --> 01:08:00.600 uh which come up. 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, 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 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 01:08:16.690 --> 01:08:20.770 spectrum of uh uh of evolutionary approaches to humans. 01:08:20.930 --> 01:08:25.330 And that's more of a critique towards academic psychology as a field. 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, 01:08:31.560 --> 01:08:34.360 on the sort of the long-term effect of weird. 01:08:34.440 --> 01:08:39.080 Uh do you think that's that's changed psychology itself or evolutionary psychology in 01:08:39.080 --> 01:08:40.040 particular? 01:08:40.280 --> 01:08:45.970 Um I mean I think that uh it's I think one, 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 01:08:52.050 --> 01:08:58.050 embarrassment to psychologists who have sort of not considered cultural variation 01:08:58.050 --> 01:09:00.850 or cross-cultural work pragmatically. 01:09:01.170 --> 01:09:07.090 Um I don't I think the deeper thing, which has sort of always been baked 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 01:09:12.130 --> 01:09:17.820 Richardson um disagreed uh with Lita Cosmoduse and John 01:09:17.900 --> 01:09:18.380 Tubi. 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 01:09:23.420 --> 01:09:27.100 Rob have only ever been gracious and kind and wonderful to us. 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 01:09:31.260 --> 01:09:35.180 conversations with them where we've talked about sort of the distances decreasing 01:09:35.180 --> 01:09:36.140 considerably. 01:09:36.430 --> 01:09:41.550 But all of the evolutionary behavioral sciences baked into it has been cross-cultural work, 01:09:41.710 --> 01:09:41.950 right? 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? 01:09:47.230 --> 01:09:49.710 This is part of the design of the evolved phenotype. 01:09:49.950 --> 01:09:55.950 It makes predictions about we might see cultural variation in this attribute, but not 01:09:55.950 --> 01:09:56.430 this other one. 01:09:56.510 --> 01:10:01.870 So, like, you know, if there's a cheater detection system, then certain parts should be 01:10:01.870 --> 01:10:05.150 culturally universal, certain parts should might vary, right? 01:10:05.420 --> 01:10:07.420 depending on different things, right? 01:10:07.500 --> 01:10:13.100 And it gives you that evolutionary approach, it gives you an invariance to look for, 01:10:13.260 --> 01:10:16.780 but then that invariance also includes a contingent design. 01:10:16.860 --> 01:10:22.460 So I think baked into the theory of adaptationism in design-mindedness 01:10:22.700 --> 01:10:26.940 gives you theory to predict cultural variability and uniformity. 01:10:27.100 --> 01:10:30.860 And because of that, that's been baked into the theory. 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 01:10:36.830 --> 01:10:42.750 true for every aspect, but the idea of weird has sort of been treated 01:10:42.750 --> 01:10:46.350 as like, oh no, we do need to like worry about cross-cultural things. 01:10:46.590 --> 01:10:51.950 But the deep insight of we need design-mindedness or theoretical 01:10:52.340 --> 01:10:57.540 predictions of variability or invariability, I think that still has not really 01:10:57.540 --> 01:10:58.420 happened. 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 01:11:03.220 --> 01:11:04.420 different places. 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 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 01:11:13.220 --> 01:11:15.220 feels different culturally. 01:11:15.410 --> 01:11:17.730 And I think that happens a lot. 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 01:11:23.810 --> 01:11:26.770 design mindedness is still happening. 01:11:27.250 --> 01:11:27.730 Mm-hmm. 01:11:29.170 --> 01:11:30.050 Yeah, I would agree. 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 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 01:11:40.300 --> 01:11:43.020 educated, industrialized, rich, democratic nations. 01:11:43.180 --> 01:11:44.060 That's a problem. 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, 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 01:11:53.660 --> 01:11:54.140 really need to. 01:11:54.220 --> 01:11:57.340 We don't have a principled theoretical reason to consider these places. 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 01:12:01.820 --> 01:12:03.330 not vary between these places. 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 01:12:07.730 --> 01:12:09.570 we're we're being good scientists. 01:12:10.530 --> 01:12:13.810 I think that is not very helpful. 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 01:12:18.290 --> 01:12:22.530 principled reason to consider the places you're considering, and you want to have actual uh 01:12:22.530 --> 01:12:23.890 predictions to make. 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 01:12:31.000 --> 01:12:33.880 weird or too non weird or whatever, it's it's sort of missing the point. 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 01:12:38.360 --> 01:12:43.080 understand uh uh where cultural variation comes from, where cultural uniformity comes from, 01:12:43.240 --> 01:12:47.480 and and we need to to dig deeper uh into the underlying mechanisms. 01:12:47.890 --> 01:12:48.130 Yeah. 01:12:48.530 --> 01:12:48.930 Well said. 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 01:12:54.050 --> 01:12:56.690 about the jukebox model if you guys remember that. 01:12:57.090 --> 01:12:59.730 Oh, there's lots lots of back and forth on the jukebox model. 01:13:00.050 --> 01:13:02.370 Enlightener was news on what what the jukebox model is. 01:13:02.690 --> 01:13:03.330 Jukebox model. 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 01:13:08.130 --> 01:13:10.130 Barco, Cosmones, and Tubi. 01:13:10.610 --> 01:13:16.050 Uh, and they were trying to imagine a particular mechanism that might 01:13:16.050 --> 01:13:21.560 produce geographic variation in behavior without necessarily 01:13:21.560 --> 01:13:23.640 involving what we might call cultural transmission. 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. 01:13:28.920 --> 01:13:29.720 Look it up, look it up. 01:13:30.040 --> 01:13:30.440 Exactly. 01:13:30.760 --> 01:13:35.080 It's sort of like a large phone that played music, you know. 01:13:35.160 --> 01:13:36.920 Um you could select from refrigerator. 01:13:37.800 --> 01:13:37.960 Exactly. 01:13:38.520 --> 01:13:42.790 Refrigerator size, you know, and so uh they they made the argument, well, imagine that 01:13:42.790 --> 01:13:47.910 these things are programmed so that they play a specific song or type of music, um, 01:13:48.870 --> 01:13:52.150 you know, given a particular set of coordinates that they're located at. 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. 01:13:58.150 --> 01:14:02.950 Uh if they are near the equator, uh they play, you know, punk rock. 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 01:14:07.420 --> 01:14:07.980 GPS, right? 01:14:08.060 --> 01:14:12.380 It's sort of K-pop, but yeah, exactly. 01:14:12.700 --> 01:14:13.980 And we need more of them, right? 01:14:14.220 --> 01:14:14.620 So yeah. 01:14:14.940 --> 01:14:17.180 Sorry, I just love the but I mean the physicality. 01:14:18.780 --> 01:14:20.220 Like they're really going on in on this. 01:14:20.300 --> 01:14:22.460 They're saying it's not even like the local ecology. 01:14:22.620 --> 01:14:26.860 It's not even like it's literally just the physical coordinates on the globe. 01:14:27.020 --> 01:14:27.420 Yeah. 01:14:27.660 --> 01:14:31.090 Um and uh this was like a verbal argument, right? 01:14:31.250 --> 01:14:34.770 So they said, okay, so imagine this scenario, and then imagine that you were, you know, 01:14:35.010 --> 01:14:38.610 looking at the diversity of music that was being produced by all these machines in 01:14:38.610 --> 01:14:39.330 different parts of the world. 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, 01:14:43.410 --> 01:14:44.530 you know, playing punk rock. 01:14:44.610 --> 01:14:46.690 You know, this must be a punk rock place. 01:14:46.770 --> 01:14:50.850 You know, like this is the cultural transmission mechanism is sustaining this tradition. 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? 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? 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. 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 01:15:10.020 --> 01:15:13.700 switches in in a cognition to produce a specific behavior. 01:15:14.590 --> 01:15:18.830 But there's nothing you would describe as kind of like cultural transmission or cultural 01:15:18.830 --> 01:15:19.630 influence. 01:15:20.110 --> 01:15:21.470 Uh right. 01:15:21.710 --> 01:15:26.270 I mean, even culture itself in that circumstance, like as a concept, potentially could be 01:15:26.350 --> 01:15:27.870 uh maybe not that usable. 01:15:28.110 --> 01:15:30.910 I mean, the term they can use is ev evoked culture, I believe. 01:15:31.310 --> 01:15:32.110 Evoked culture, exactly. 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 01:15:36.110 --> 01:15:38.110 this place and the culture is not evoked. 01:15:38.300 --> 01:15:38.460 Right. 01:15:38.700 --> 01:15:38.940 Right. 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 01:15:44.140 --> 01:15:49.980 where uh the I mean it's it's neat as a as an 01:15:49.980 --> 01:15:53.580 example to think, okay, this is a way that variation could be produced. 01:15:53.740 --> 01:15:57.740 Um I mean, empirically though, we we see a lot of transmission. 01:15:57.820 --> 01:15:59.180 Like we can measure it, right? 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 01:16:03.660 --> 01:16:07.410 necessarily need, you know, you can't just assume, oh, there's cultural variation, 01:16:07.490 --> 01:16:09.730 therefore it's being transmitted. 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 01:16:17.090 --> 01:16:18.210 transmitted culture? 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 01:16:22.370 --> 01:16:26.530 sort of a jukebox-evoked uh type scenario? 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 01:16:31.010 --> 01:16:33.650 mean, obviously there's transmission taking place. 01:16:33.810 --> 01:16:39.660 Um That kind of distinction is 01:16:39.660 --> 01:16:45.420 very useful for for uh as a starting place for thinking about uh cultural traditions um 01:16:45.900 --> 01:16:48.860 or what what could potentially be cultural traditions. 01:16:53.260 --> 01:16:57.730 So um okay so we we just went into the jukebox metaphor. 01:16:57.810 --> 01:17:01.970 I believe they brought it up to make this distinction between uh uh evoked culture and 01:17:01.970 --> 01:17:02.850 transmitted culture. 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 01:17:06.370 --> 01:17:09.010 we should assume automatically that it's transmitted. 01:17:09.250 --> 01:17:13.650 It could be a contingent response to an ecological difference, right? 01:17:13.810 --> 01:17:17.570 So if if there's an ecological difference there and we have adaptations that are sensitive 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 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 01:17:27.460 --> 01:17:28.500 shortages and windfalls. 01:17:28.580 --> 01:17:32.180 That could vary by ecology, and you could have an adaptation that is sensitive to that 01:17:32.180 --> 01:17:32.660 variable. 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 01:17:36.660 --> 01:17:41.700 necessarily need to assume that that is because um of you know transmitted culture or or 01:17:41.700 --> 01:17:43.540 norms shaping one place and not the other. 01:17:43.620 --> 01:17:48.790 It could just be a um an emergent output of the underlying sharing psychology. 01:17:48.950 --> 01:17:51.030 And so that's the argument that Tubian Cosmodies make. 01:17:51.830 --> 01:17:56.470 Incidentally, a lot of people misinterpret Tubian Cosmodies as saying they think all 01:17:56.470 --> 01:18:00.710 culture is evoked, or that, you know, and they and this is often caricatured. 01:18:01.030 --> 01:18:02.870 So that's absolutely not what they think. 01:18:03.270 --> 01:18:07.030 They brought this up as as to make a point that we just don't know. 01:18:07.110 --> 01:18:11.600 We cannot assume that that culture is transmitted or evoked uh without actually doing the 01:18:11.600 --> 01:18:13.440 hard work of of figuring out what's going on. 01:18:13.600 --> 01:18:16.560 And so that example uh was made for for that purpose. 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 01:18:21.600 --> 01:18:27.520 thinking in these terms uh I mean generative processes are the name of the 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 01:18:31.710 --> 01:18:32.430 generative model. 01:18:32.510 --> 01:18:36.750 Like, and uh of course it's ridiculous, you know, humans are not jukeboxes. 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 01:18:42.750 --> 01:18:44.030 leads to an outcome, right? 01:18:44.110 --> 01:18:49.230 And sort of inventorying the different kinds of mechanisms or imagining new ones, uh, or 01:18:49.230 --> 01:18:52.670 sort of, you know, thinking, okay, well, if this specific mechanism is happening, you know, 01:18:52.750 --> 01:18:55.390 how would we potentially interpret it or misinterpret it? 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 01:19:00.160 --> 01:19:02.000 coordination equilibria. 01:19:03.520 --> 01:19:06.800 So, like, let's I'd be curious to know what you both think about this. 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 01:19:10.240 --> 01:19:15.920 side of the road, um, you know, it's not like the jukebox 01:19:16.160 --> 01:19:19.360 model because uh people aren't just responding ASO. 01:19:19.680 --> 01:19:23.840 I mean, in the jukebox model, it literally doesn't matter what other jukeboxes are doing 01:19:23.840 --> 01:19:24.320 around you. 01:19:24.480 --> 01:19:30.110 Like, there's no influence from one jukebox to another in the original model, 01:19:30.190 --> 01:19:30.510 right? 01:19:30.670 --> 01:19:36.590 But in something like, you know, uh coordination equilibrium, uh, when I pull onto 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 01:19:41.710 --> 01:19:42.590 that I experience, right? 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 01:19:47.070 --> 01:19:47.390 time. 01:19:47.790 --> 01:19:52.660 Um And so there's a component of social influence there, right? 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, 01:19:57.540 --> 01:19:59.140 and they're going to impact my choice. 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 01:20:02.900 --> 01:20:06.020 going to drive on the same side of the road as everybody else. 01:20:07.300 --> 01:20:08.980 So is that culture? 01:20:09.270 --> 01:20:11.430 That I think is a is a really interesting question. 01:20:11.510 --> 01:20:15.670 Like it's not quite the same thing as you know learning from somebody. 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 01:20:19.030 --> 01:20:20.630 should drive on the right side of the road. 01:20:20.790 --> 01:20:22.870 Um, although of course that does happen. 01:20:23.110 --> 01:20:28.310 But in many of these equilibrious circumstances, it's something that's reinforced by 01:20:28.950 --> 01:20:31.190 you know, just basically a rational calculation. 01:20:31.350 --> 01:20:35.180 Like given the circumstance, you know, the best thing for me to do is this. 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 01:20:38.220 --> 01:20:38.540 choices. 01:20:38.620 --> 01:20:40.860 That's just the circumstance in front of me, right? 01:20:41.340 --> 01:20:46.540 Um, so in a sense, it is also uh kind of a cultural, right? 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 01:20:51.740 --> 01:20:55.900 information about the choice is directly moving from person to person. 01:20:56.060 --> 01:20:57.820 Um or maybe it is though. 01:20:57.900 --> 01:20:58.780 I mean, it's debatable. 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 01:21:02.220 --> 01:21:03.750 jukebox model, right? 01:21:04.230 --> 01:21:04.470 Right. 01:21:04.870 --> 01:21:08.070 I mean, maybe a better analogy would be something like prices. 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 01:21:14.390 --> 01:21:17.110 everyone acting self-interestedly in a marketplace. 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. 01:21:21.510 --> 01:21:23.590 If I set the price too high, no one's gonna buy my stuff. 01:21:23.750 --> 01:21:27.190 If you have a bunch of different actors in a marketplace trying to make those local 01:21:27.190 --> 01:21:30.950 decisions about how to set the right price for the particular goods or services they're 01:21:30.950 --> 01:21:36.800 selling, you get something like a map of the value of different things or of the 01:21:36.800 --> 01:21:40.640 supply and demand of different goods and services in the local economy. 01:21:40.720 --> 01:21:44.480 And nobody actually sat down and planned out what the prices were going to be. 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 01:21:48.160 --> 01:21:49.280 chairs is going to be this. 01:21:49.600 --> 01:21:55.040 It's just an emergent product of everyone acting uh self-interestedly in a marketplace, 01:21:55.200 --> 01:21:55.520 right? 01:21:55.680 --> 01:21:59.230 Um and so you could then pose the question: are prices cultural? 01:21:59.390 --> 01:22:04.590 Nobody sat down in a centralized place and decided what the prices were going to be. 01:22:05.150 --> 01:22:11.070 And yet there's this strong correlation between prices and and supply and demand levels. 01:22:11.230 --> 01:22:12.590 And so how can we explain prices? 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 01:22:16.350 --> 01:22:17.790 road in similar terms. 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 01:22:22.080 --> 01:22:24.800 or the right side, but in some cases just kind of happened spontaneously. 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 01:22:28.880 --> 01:22:33.920 pursuing their self interest and trying to not crash, this emergent outcome uh naturally 01:22:33.920 --> 01:22:34.640 emerged. 01:22:34.800 --> 01:22:40.480 Um do you view that kind of thing as quote 01:22:40.560 --> 01:22:41.280 unquote culture? 01:22:41.360 --> 01:22:42.400 Do you view it as something else? 01:22:42.480 --> 01:22:46.000 How should we understand those kinds of emergent uh products? 01:22:46.450 --> 01:22:49.250 Yeah, well, I mean it's definitely social influence. 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 01:22:55.570 --> 01:23:01.330 who are essentially incapable of uh what we would describe as like social 01:23:01.330 --> 01:23:01.810 interaction. 01:23:01.890 --> 01:23:04.290 Like they just literally see a price in front of. 01:23:04.530 --> 01:23:08.800 I think the the classic economic description is imagining like an auctioneer, right, is 01:23:08.800 --> 01:23:11.120 sort of telling you know what the going price is for something. 01:23:11.200 --> 01:23:14.240 And that's that's a simplification for what the market is. 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 01:23:18.640 --> 01:23:18.800 that. 01:23:18.960 --> 01:23:20.640 They're just they're just getting a number, right? 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. 01:23:25.040 --> 01:23:30.960 Um so the agent itself isn't internalizing anything, it's not taking in what we 01:23:30.960 --> 01:23:35.910 might call like cultural information or cultural norms and passing those along, but they 01:23:35.910 --> 01:23:37.350 are being influenced by others. 01:23:37.510 --> 01:23:43.510 And then their choice is adding to the influence of the, you know, the next 01:23:43.510 --> 01:23:45.510 individual in the decision-making process, right? 01:23:45.590 --> 01:23:48.950 So there is clearly some kind of a transmission of influence. 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 01:23:54.710 --> 01:24:00.630 that clearly is cultural, that has sort of a similar shape to it, you 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 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. 01:24:09.340 --> 01:24:10.220 Very German, exactly. 01:24:10.300 --> 01:24:11.580 And as I go home here, right? 01:24:11.660 --> 01:24:13.020 So, I mean, people will be standing there. 01:24:13.100 --> 01:24:15.820 There's no the street is completely deserted, right? 01:24:16.220 --> 01:24:19.740 Um, but that light is red, and you're not supposed to cross on red. 01:24:20.060 --> 01:24:23.580 And so people are standing there and they wait for the green light to go. 01:24:24.350 --> 01:24:27.870 But this is highly variable across societies, right? 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. 01:24:31.550 --> 01:24:33.950 It is not stereotypical for other populations. 01:24:34.110 --> 01:24:35.950 I mean, they'll be like, what are you stupid? 01:24:36.110 --> 01:24:37.790 Like, why would you wait if nobody's around? 01:24:37.950 --> 01:24:39.150 You could just go, right? 01:24:39.390 --> 01:24:43.790 Um, and I mean the the answer is clearly there was a socialization process. 01:24:43.870 --> 01:24:47.470 Like people learned that you know, the appropriate thing to do in this circumstance is to 01:24:47.470 --> 01:24:50.520 do this through some process. 01:24:50.680 --> 01:24:55.720 Like they're they're their their brains have information in them, which they've learned 01:24:55.720 --> 01:25:00.680 over the course of a socialization process that structures their behavior in ways that you 01:25:00.680 --> 01:25:03.960 can predict uh maybe beyond rational calculation. 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. 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 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 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 01:25:21.230 --> 01:25:22.750 coming, like that could ruin my life. 01:25:22.830 --> 01:25:26.030 I I you know get you know a ticket, you know, whatever. 01:25:26.190 --> 01:25:27.950 Um that would be a terrible thing to happen. 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 01:25:31.470 --> 01:25:31.790 take the chance. 01:25:31.870 --> 01:25:32.830 I'll just I might as well wait. 01:25:32.910 --> 01:25:33.550 It's not a big deal. 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 01:25:37.550 --> 01:25:38.760 for for the light to change. 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 01:25:44.040 --> 01:25:47.960 product of whether they've quote unquote internalized this quote unquote norm, but just 01:25:47.960 --> 01:25:49.880 like how many police are there in the environment? 01:25:50.040 --> 01:25:51.400 Like how effective is policing? 01:25:51.640 --> 01:25:53.880 How scared are people of the local police? 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 01:25:58.200 --> 01:26:01.480 someone has quote unquote internalized a quote unquote norm. 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 01:26:07.510 --> 01:26:13.030 to find circumstances where there isn't really a strong policing presence. 01:26:13.910 --> 01:26:18.630 Uh but people are still really adherent to a particular behavior pattern. 01:26:18.710 --> 01:26:18.870 Right. 01:26:18.950 --> 01:26:20.550 I think that would be kind of the the thing. 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 01:26:24.150 --> 01:26:24.710 now, right? 01:26:24.790 --> 01:26:26.550 Like the specific scenarios. 01:26:27.830 --> 01:26:29.430 So if there if there are literally no costs. 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. 01:26:35.360 --> 01:26:36.720 And there are no social costs either. 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 01:26:41.520 --> 01:26:43.280 or a reckless driver or whatever. 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 01:26:48.000 --> 01:26:48.160 norm. 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 01:26:53.200 --> 01:26:53.840 back for a little bit. 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. 01:26:59.520 --> 01:27:02.550 So we live in a sleepy par Dahlem in Berlin. 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. 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 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 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 01:27:23.590 --> 01:27:24.310 very quiet. 01:27:24.470 --> 01:27:28.230 Um, but I still do think it's rational in like the following two senses. 01:27:28.390 --> 01:27:32.440 Number one, um, there is sometimes opacity. 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. 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 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 01:27:49.320 --> 01:27:50.360 I am gonna be fine. 01:27:50.520 --> 01:27:52.360 And then like a car would come. 01:27:52.890 --> 01:27:58.730 And then the second thing was the lights really work in the sense 01:27:58.730 --> 01:28:01.530 of they're really well engineered to be timed well. 01:28:01.610 --> 01:28:04.170 So you're not waiting an exorbitant amount of time. 01:28:04.330 --> 01:28:07.850 So like in the US, you might be waiting for seven minutes to cross a road. 01:28:08.010 --> 01:28:09.690 That never happens in Germany. 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 01:28:15.860 --> 01:28:17.540 like coordination device. 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 01:28:23.460 --> 01:28:24.660 that really works well. 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 01:28:27.940 --> 01:28:30.340 still a rational calculation of costs and benefits. 01:28:30.500 --> 01:28:34.100 It's just a different set in my experience, like why I would wait. 01:28:34.180 --> 01:28:36.410 And then like it does get tweaked. 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 01:28:41.370 --> 01:28:43.370 particular light, like cross the red light. 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 01:28:47.770 --> 01:28:48.730 well as the other ones. 01:28:48.890 --> 01:28:51.050 And so I would start like running that one. 01:28:51.210 --> 01:28:54.250 And then um, and then there's like a really breaking the social contract. 01:28:54.490 --> 01:28:55.210 Yeah, exactly. 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 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 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 01:29:06.520 --> 01:29:06.760 that. 01:29:07.000 --> 01:29:08.280 So that's at least my experience. 01:29:08.360 --> 01:29:09.320 Brett, you're still there. 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. 01:29:13.000 --> 01:29:15.320 But that was my experience of like a decade in Germany. 01:29:15.480 --> 01:29:15.880 Yeah. 01:29:16.200 --> 01:29:16.440 Yeah. 01:29:16.600 --> 01:29:19.800 I think there's something to be said for rational laziness. 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 01:29:26.040 --> 01:29:29.270 the you know I haven't driven a car in like, you know, eight years. 01:29:29.430 --> 01:29:33.350 So I'm totally disconnected from the car culture I grew up in. 01:29:33.510 --> 01:29:38.070 Um, but in the context of running a red light, which you should not do, right? 01:29:38.150 --> 01:29:40.230 I think you should advise your listeners to, you know. 01:29:40.950 --> 01:29:44.390 I'm so sorry, but I lived in Connecticut after moving from California. 01:29:44.470 --> 01:29:49.430 And Connecticut is wonderful in many ways, but it was so ridiculous because people would 01:29:49.430 --> 01:29:55.160 wait, people would run red lights so much that people would run red lights for 01:29:55.160 --> 01:29:58.920 maybe a second or two, but everybody knew that people were running red lights. 01:29:59.000 --> 01:30:03.720 So people would wait at green lights for like five to seven seconds. 01:30:04.040 --> 01:30:06.280 So everybody was way worse off. 01:30:06.600 --> 01:30:07.480 Coordination failure. 01:30:07.720 --> 01:30:11.800 Total coordination failure where like people were gaining a second or two, but everybody 01:30:11.800 --> 01:30:14.440 was losing five or seven seconds on the other end. 01:30:14.680 --> 01:30:16.520 Clear example of coordination failure. 01:30:16.600 --> 01:30:16.840 Sorry. 01:30:16.920 --> 01:30:17.080 Yeah. 01:30:17.670 --> 01:30:17.830 Yeah. 01:30:17.910 --> 01:30:17.990 Yeah. 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 01:30:23.510 --> 01:30:24.230 cultures. 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 01:30:29.510 --> 01:30:31.350 drivers and West Coast drivers. 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. 01:30:35.990 --> 01:30:36.150 Yeah. 01:30:36.310 --> 01:30:36.550 Yeah. 01:30:36.630 --> 01:30:36.870 Yeah. 01:30:37.590 --> 01:30:43.560 I mean, I I I notice intuitive differences between um uh Midwest drivers and and 01:30:43.560 --> 01:30:46.360 now I live in the West, I live in LA, and and and LA drivers. 01:30:46.600 --> 01:30:50.280 LA drivers are way bigger assholes than the Midwest Midwestern driver. 01:30:50.520 --> 01:30:53.000 In LA, nobody lets you in if you're trying to change lane. 01:30:53.080 --> 01:30:55.080 They just they they don't don't give a shit. 01:30:55.240 --> 01:30:57.720 And in the Midwest, people slow down and let you in. 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 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 01:31:06.850 --> 01:31:09.010 all the time in cars, then you don't want to let someone in. 01:31:09.090 --> 01:31:11.170 But if in the Midwest, if there's less traffic, it's not a big deal. 01:31:11.410 --> 01:31:12.290 So I would I don't know. 01:31:12.370 --> 01:31:13.010 I do LA. 01:31:13.250 --> 01:31:13.810 I'm under the impression. 01:31:15.810 --> 01:31:16.050 Yeah. 01:31:16.530 --> 01:31:19.490 I I'm under the impression that it's totally different, like in New York. 01:31:20.450 --> 01:31:21.810 The driving dynamics there. 01:31:22.210 --> 01:31:23.250 Connecticut is terrible. 01:31:23.410 --> 01:31:25.650 I mean, David, you lived in New York, but you didn't drive then, right? 01:31:25.810 --> 01:31:26.930 I didn't drive though, no. 01:31:27.090 --> 01:31:27.490 Yeah, yeah. 01:31:27.650 --> 01:31:30.850 I mean, I Connecticut was way worse than LA. 01:31:31.140 --> 01:31:35.300 Like I was I was almost like in tears compared to LA driving. 01:31:35.460 --> 01:31:38.500 Like LA drivers are Midwestern compared to Connecticut. 01:31:38.580 --> 01:31:41.620 I hope we get a lot of angry Connecticut people. 01:31:41.780 --> 01:31:42.820 Well that's the point, right? 01:31:43.140 --> 01:31:48.980 Like, but yeah, like that that like kind of uh megalopolis corridor um where it was 01:31:48.980 --> 01:31:53.300 sort of combining traffic with like the infrastructure doesn't work and people were just 01:31:53.300 --> 01:31:58.100 sort of free riding um all the coordination devices was sort of what it felt like. 01:31:58.470 --> 01:31:58.790 Yeah. 01:31:59.030 --> 01:32:03.190 So it seems I want to I want to make a perhaps more controversial point. 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 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 01:32:14.390 --> 01:32:17.830 norms and values of my culture, and you internalize them. 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 01:32:21.910 --> 01:32:23.110 supposed to follow those norms. 01:32:23.270 --> 01:32:28.600 Like that canonical stereotyped example of culture, we're having a hard time finding any 01:32:28.600 --> 01:32:30.040 clear examples of it. 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 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 01:32:37.960 --> 01:32:38.360 doesn't work. 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 01:32:42.920 --> 01:32:43.960 can, and it doesn't work. 01:32:44.120 --> 01:32:44.920 She doesn't listen. 01:32:45.080 --> 01:32:46.760 She does what she wants to do anyway. 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 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 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. 01:33:02.030 --> 01:33:06.270 It's more like this emergent product of everyone just following their self-interest, doing 01:33:06.270 --> 01:33:08.830 what is rational for them to do in their particular circumstances. 01:33:08.990 --> 01:33:11.550 And as a result, we get these emergent outcomes. 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. 01:33:15.740 --> 01:33:19.020 We get the outcome of people using this kind of slang or this kind of slang. 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 01:33:24.780 --> 01:33:28.540 rules or um aesthetic tastes and preferences. 01:33:28.780 --> 01:33:33.420 Everyone's just pursuing their local goals, trying to gain status, trying to outcompete 01:33:33.420 --> 01:33:34.620 other people in their environment. 01:33:34.700 --> 01:33:39.830 And and when everyone's doing that, you get this emergent outcome that results from it. 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 01:33:44.950 --> 01:33:46.790 the culture that was passed on to my ancestors. 01:33:46.870 --> 01:33:48.230 It's more just I'm just doing what I want. 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 01:33:51.990 --> 01:33:52.710 to gain status. 01:33:52.790 --> 01:33:55.670 And when everyone is doing that, you get culture. 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. 01:33:59.270 --> 01:34:02.470 What what do you think about that that most culture is just is like that? 01:34:05.320 --> 01:34:09.160 Uh I mean I don't know what most means in this context. 01:34:09.240 --> 01:34:14.280 You know, like if we do like a list, you know, like listens to K pop, 01:34:15.160 --> 01:34:19.240 uh plays Monopoly, you know, like there. 01:34:19.400 --> 01:34:21.640 That was just where does the where does the list go, right? 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 01:34:27.640 --> 01:34:33.160 that behavioral feedbacks set conditions for decision making 01:34:33.850 --> 01:34:39.370 in ways that create behavioral variation, like persistent behavioral variation between 01:34:39.370 --> 01:34:44.810 populations can be produced by those kinds of coordination equilibria in different places. 01:34:45.290 --> 01:34:51.290 Um I think that the fact that it's transmitted in some way is 01:34:51.290 --> 01:34:51.770 interesting. 01:34:51.930 --> 01:34:54.810 Like it's there's clearly a social influence here, right? 01:34:54.970 --> 01:34:57.290 That you know, individuals might attend to. 01:34:57.370 --> 01:35:00.890 And then the question becomes I mean, this is where the cultural evolution modeling 01:35:00.890 --> 01:35:06.760 literature kind of starts is saying, okay, imagine that you've got a selective environment 01:35:06.760 --> 01:35:11.320 now where your cognition is evolving in this kind of a you know 01:35:11.880 --> 01:35:15.320 coordination type problem, and it's costly. 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 01:35:20.680 --> 01:35:23.400 this one time from running the red light or there's a cop around. 01:35:23.560 --> 01:35:29.550 Like it's costly to um Try to calculate how much 01:35:29.550 --> 01:35:32.990 social sanctioning I might get in this particular because you have to pay attention, right? 01:35:33.070 --> 01:35:36.670 I mean, like it requires attention is a resource, right? 01:35:37.150 --> 01:35:42.910 Um and I think that one of the things you get from studying this in like 01:35:42.910 --> 01:35:48.740 an informational sense is that norm psychology is actually a nice 01:35:48.740 --> 01:35:49.540 solution to this. 01:35:49.700 --> 01:35:55.700 Like the idea that I can internalize a rule and it actually helps everybody coordinate, and 01:35:55.700 --> 01:35:58.820 I don't have to pay any costs to paying attention now. 01:35:58.980 --> 01:35:59.300 Right? 01:35:59.460 --> 01:36:03.940 This is this is basically, you know, potentially a model for explaining why you might get 01:36:03.940 --> 01:36:08.180 the emergence of a norm psychology, which is quite stark, starkly different, I think, from 01:36:08.180 --> 01:36:09.860 the mechanism you're talking about. 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 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 01:36:22.030 --> 01:36:25.230 part of our story when we're understanding why people behave the way they do. 01:36:25.470 --> 01:36:30.990 I mean, uh I was thinking about this paper by um Bailey House and colleagues uh 01:36:31.230 --> 01:36:36.600 looking at the way people behave cross culturally in the economic game experiments, right? 01:36:36.680 --> 01:36:38.920 Like the dictator game and the ultimatum game. 01:36:39.160 --> 01:36:44.280 Um, like you have persistent variation between populations, but you also can track in in 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 01:36:49.480 --> 01:36:55.160 children like moving towards the adult values that that are that are 01:36:55.160 --> 01:36:57.640 consistently displayed across populations. 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 01:37:03.990 --> 01:37:07.430 what's appropriate or what's inappropriate in a particular circumstance. 01:37:07.670 --> 01:37:12.630 Um but I mean, I think more functionally you could just appeal to the idea that 01:37:14.070 --> 01:37:19.670 if it's cheaper to learn a particular condition that helps you maintain a coordination 01:37:19.670 --> 01:37:25.270 equilibrium, and then you don't have to calculate, uh, that you might 01:37:25.270 --> 01:37:30.940 expect uh a psychology to develop an adaptation to just learn the learn what the 01:37:30.940 --> 01:37:33.260 equilibrium is and then internalize that. 01:37:34.300 --> 01:37:38.780 And I think, I mean, to just sort of add to both things. 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 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 01:37:51.020 --> 01:37:56.470 this sort of weird species that really the cultural phenotype is really different And it's 01:37:56.470 --> 01:37:59.670 independent of our smooth genes, our crunchy cultural differences. 01:38:01.670 --> 01:38:07.670 We that has itself been sort of an enduring part of our evolved psychology, you know, 01:38:07.750 --> 01:38:11.350 that we have this idea that there will be transmitted information. 01:38:11.510 --> 01:38:14.630 And then we have this evolved psychology, this norm psychology. 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 01:38:20.470 --> 01:38:25.620 complex adaptation that's sort of calculating, is what I'm doing 01:38:25.860 --> 01:38:29.780 based on this sequential social transmission process? 01:38:29.860 --> 01:38:32.500 And then is it rationally for me to follow it or not? 01:38:32.660 --> 01:38:35.860 And is what I'm going to do affect how other people act? 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, 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 01:38:46.260 --> 01:38:52.070 learning that isn't some result of sort of like a cost-benefit individual 01:38:52.070 --> 01:38:57.510 analysis, whether the evolved psychology figuring that cost-benefit utility over 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 01:39:01.990 --> 01:39:03.190 figure out what's costly or not. 01:39:03.270 --> 01:39:06.950 You're just sort of doing this quote-unquote innate, you know, contingency. 01:39:07.350 --> 01:39:12.230 But then, even um, but still, even for something like this red light thing, you know, 01:39:12.310 --> 01:39:17.760 there's still some grammar of cost-benefit analysis, even though there's 01:39:17.760 --> 01:39:19.280 represented as a norm. 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 01:39:25.840 --> 01:39:30.800 it's suffused with a cost-benefit analysis too, because that would be selected for, right? 01:39:30.880 --> 01:39:36.800 Like to have a really naive norm psychology that is completely blind 01:39:36.800 --> 01:39:39.930 to cost-benefits, and is it beneficial for me or not? 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 01:39:43.370 --> 01:39:49.130 theoretical uh disagreement is like the the 1984 thing of like there's a norm, 01:39:49.290 --> 01:39:51.930 it's really strong, and it's fucking stupid. 01:39:52.170 --> 01:39:55.770 It's like it's it's horrible, it's bad, we shouldn't obey it. 01:39:55.930 --> 01:40:01.050 And I think that's a result of this psychology that's simultaneously like representing what 01:40:01.050 --> 01:40:06.400 has been the transmitted information, and then also like what's the landscape, how it 01:40:06.400 --> 01:40:07.600 benefits me and others? 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 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 01:40:19.120 --> 01:40:20.640 at school who are like this is dumb. 01:40:20.880 --> 01:40:22.800 So I I kind of agree with both of you. 01:40:22.960 --> 01:40:25.680 I don't know if that's uh helpful, but yeah. 01:40:26.350 --> 01:40:26.750 Yeah. 01:40:27.470 --> 01:40:29.710 There's a study I was thinking of just now. 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 01:40:36.190 --> 01:40:37.070 pick up their kids. 01:40:37.230 --> 01:40:38.750 Do you know this study? 01:40:39.070 --> 01:40:40.590 Uh it's amazing. 01:40:40.750 --> 01:40:43.950 I think it was done in Haifa in Israel uh some time ago. 01:40:44.030 --> 01:40:45.310 And it was an economics study. 01:40:45.390 --> 01:40:47.070 It was published in economics. 01:40:47.390 --> 01:40:53.230 And they were studying the effect of changing the policy in the daycare from 01:40:53.660 --> 01:40:56.460 please don't come late because we're waiting. 01:40:56.540 --> 01:40:59.340 You know, your your kids are here and we can't leave. 01:40:59.580 --> 01:41:01.740 So it's very inconvenient. 01:41:01.980 --> 01:41:05.420 To uh, you're gonna pay a price if you come too late. 01:41:05.500 --> 01:41:06.460 You're gonna get fined. 01:41:06.620 --> 01:41:07.740 I mean, this was my experience. 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 01:41:12.460 --> 01:41:15.580 fine that was experienced if you came too late to pick up your kids. 01:41:15.900 --> 01:41:17.740 Is this in the US or Germany? 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. 01:41:22.570 --> 01:41:24.330 It was just like, please don't come late. 01:41:24.490 --> 01:41:26.010 We we have to we have to go home. 01:41:26.090 --> 01:41:26.330 Yeah. 01:41:26.570 --> 01:41:29.930 Um but uh yeah in the US it was totally commercialized, right? 01:41:30.010 --> 01:41:33.610 It was like you will get sixty dollars for every half hour, right? 01:41:33.690 --> 01:41:35.370 Or something astronomically high. 01:41:35.610 --> 01:41:39.440 And so in the uh Israeli daycare, they made this switch. 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 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 01:41:47.680 --> 01:41:49.200 much if you come late. 01:41:49.440 --> 01:41:55.040 Uh and paradoxically, perhaps, it increased the rate of tardiness because now there was a 01:41:55.040 --> 01:41:56.480 price associated with the lateness. 01:41:56.560 --> 01:41:59.600 And so people are like, is 30 minutes worth this much to me? 01:41:59.680 --> 01:42:01.680 And the answer is sometimes yes, right. 01:42:02.000 --> 01:42:06.480 And so the title, the title of the paper is A Fine is a Price. 01:42:06.590 --> 01:42:08.990 It's this extremely famous paper in economics. 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, 01:42:15.070 --> 01:42:19.870 uh the differences between looking at it in terms of rational cost benefit analysis and 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 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. 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. 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 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 01:42:42.160 --> 01:42:42.960 more minutes, right? 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 01:42:47.040 --> 01:42:47.680 this is where we're gonna do it. 01:42:47.840 --> 01:42:50.640 This is the market this is clearly the market rate here, right? 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 01:42:56.240 --> 01:42:58.160 way, then it really becomes kind of ghoulish, right? 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 01:43:02.490 --> 01:43:04.090 you're paying to break the law, right? 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? 01:43:08.410 --> 01:43:14.170 Um, but uh, you know, is that is that the way we think about norms? 01:43:14.330 --> 01:43:18.890 I mean, it feels to me like in normal circumstances, uh, it's not like that. 01:43:19.050 --> 01:43:24.860 That people do actually behave prosocially without the explicit threat of fining, because 01:43:25.020 --> 01:43:31.020 they don't think about it as like an economic exchange with an explicit 01:43:31.020 --> 01:43:31.740 cost-benefit. 01:43:31.900 --> 01:43:34.060 And as soon as you do do that, it it shifts modes. 01:43:34.140 --> 01:43:37.740 And then it does actually become much more like a market uh interaction. 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 01:43:43.420 --> 01:43:47.820 rational calculation is the right way to think about this because you you have a price 01:43:47.820 --> 01:43:48.380 schedule, right? 01:43:48.540 --> 01:43:49.660 You know exactly what it's gonna be. 01:43:49.740 --> 01:43:54.130 Like you can map out, you know, one hour of their time is this much money. 01:43:54.290 --> 01:43:59.490 Um so anyway, I just think you know, there's different ways of thinking about um 01:43:59.810 --> 01:44:01.330 institutional structures. 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 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 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? 01:44:17.260 --> 01:44:20.460 And I'm trying to put all these variables together in this complex calculus. 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. 01:44:25.980 --> 01:44:31.900 And it's you know, vaguely creating a coordination equilibrium and it's 01:44:31.900 --> 01:44:35.900 solving some kind of you know social, you know, collective action problem. 01:44:36.380 --> 01:44:39.260 Uh, but I don't know if people think about it more than that. 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 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 01:44:47.090 --> 01:44:47.330 do. 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 01:44:52.690 --> 01:44:53.490 controversy, right? 01:44:53.570 --> 01:44:54.210 It comes down to this. 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 01:44:59.170 --> 01:45:03.620 way we do it around here, is that they're implicitly simulating someone who might judge 01:45:03.620 --> 01:45:04.580 them for not doing that. 01:45:04.740 --> 01:45:07.140 And that they're interpreting that as a social cost. 01:45:07.220 --> 01:45:09.940 Like I'm going to pay reputational costs for not doing this. 01:45:10.100 --> 01:45:12.580 Uh, people are going to make the following inferences about me. 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 01:45:16.660 --> 01:45:17.940 unreliable, whatever. 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. 01:45:21.700 --> 01:45:23.940 It's not just it's a norm, therefore I follow it. 01:45:24.100 --> 01:45:26.410 It's oh, what what how what are people gonna think of me? 01:45:26.490 --> 01:45:27.610 What's gonna happen if I don't do this? 01:45:27.770 --> 01:45:31.450 And they're simulating those outcomes, they're simulating the audience and and and what 01:45:31.450 --> 01:45:32.410 people are gonna think of them. 01:45:32.490 --> 01:45:34.490 And I think we're constantly doing this all the time. 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 01:45:38.730 --> 01:45:41.210 thing we do and plan on doing and anticipate doing. 01:45:41.290 --> 01:45:44.490 And we're factoring those anticipated costs into everything we do. 01:45:44.570 --> 01:45:49.660 Um, and so I think just positing that a social 01:45:49.740 --> 01:45:55.500 psychology, that uh impression management, for lack of a better term, gets you explains a 01:45:55.500 --> 01:45:55.660 lot. 01:45:55.820 --> 01:45:59.500 It explains a lot of what we intuitively think of as norms. 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 01:46:04.380 --> 01:46:05.340 late fee, right? 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, 01:46:09.100 --> 01:46:13.090 Oh, uh, if they're charging me money uh for for showing up late. 01:46:13.250 --> 01:46:14.770 That means that they don't think I'm a bad person. 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, 01:46:17.970 --> 01:46:18.210 right? 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. 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 01:46:25.810 --> 01:46:27.330 you as a bad parent, right? 01:46:27.650 --> 01:46:29.810 And that's gonna uh hurt your reputation. 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 01:46:34.940 --> 01:46:39.740 is that we have a a sophisticated social psychology that is anticipating the inferences 01:46:39.740 --> 01:46:41.820 that are made about us for every little thing we do. 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 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 01:46:49.900 --> 01:46:53.900 social world, we get these emergent things that we call norms. 01:46:54.460 --> 01:46:57.990 Um Anyway, that that's my spiel. 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 01:47:01.670 --> 01:47:03.190 psychology and cultural evolution. 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 01:47:06.550 --> 01:47:09.590 think about culture, what what is the role of evolved psychology? 01:47:09.750 --> 01:47:11.590 What is the evolved psychology like? 01:47:12.310 --> 01:47:17.190 Uh Brett, we are we are running up on what two hours now? 01:47:17.510 --> 01:47:17.910 Almost. 01:47:18.470 --> 01:47:19.350 Yeah, this one quick. 01:47:19.430 --> 01:47:19.990 This is wonderful. 01:47:20.150 --> 01:47:22.070 And I think just a put a lot of fun. 01:47:22.470 --> 01:47:23.190 Put a pin on that. 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. 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 01:47:31.640 --> 01:47:34.120 that people are worrying about is part of like the norm. 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? 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 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, 01:47:48.120 --> 01:47:49.560 $10 for the bag of flour. 01:47:49.720 --> 01:47:53.080 I'm like astounded at the price of groceries in the US compared to Germany. 01:47:53.160 --> 01:47:54.090 It's like crazy. 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 01:47:58.650 --> 01:47:59.050 Germany. 01:47:59.210 --> 01:47:59.690 It's crazy. 01:47:59.850 --> 01:48:01.530 But anyway, yeah, my sympathies. 01:48:01.850 --> 01:48:02.490 Yeah. 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 01:48:08.730 --> 01:48:09.370 see that, right? 01:48:09.450 --> 01:48:13.690 And that becomes part of then his cost benefit analysis, right? 01:48:13.850 --> 01:48:19.210 So like I think the the transmission chain of 01:48:19.450 --> 01:48:23.460 behaviors were psychologically aware of that. 01:48:23.620 --> 01:48:27.540 And then also the David's doing a cost benefit analysis. 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 01:48:31.860 --> 01:48:32.180 doing. 01:48:32.340 --> 01:48:36.180 And that factors into my cost benefit analysis of like, should I do it? 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 01:48:41.700 --> 01:48:43.140 these views as very compatible. 01:48:43.300 --> 01:48:48.670 It's just that there's no pure cultural transmission without a cost benefit analysis. 01:48:48.750 --> 01:48:53.630 It's like just like fractal, where there's always like a uh inside that. 01:48:53.710 --> 01:48:55.230 And it might in it, but it's different. 01:48:55.310 --> 01:49:00.190 It's different than like a purely fungible market economy thing. 01:49:00.350 --> 01:49:04.510 It's like this uncertainty about social costs, which have a very different dynamic, for 01:49:04.510 --> 01:49:06.350 example, than like the market. 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 01:49:10.460 --> 01:49:12.620 from Buffalo and that's the way people are. 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 01:49:18.220 --> 01:49:19.580 a it's a real thing, right? 01:49:19.820 --> 01:49:20.140 Exactly. 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 01:49:24.860 --> 01:49:25.580 this circumstance. 01:49:25.660 --> 01:49:31.020 I mean, if if you really wanted to communicate to your kid, for example, that certain 01:49:31.020 --> 01:49:36.790 behavior will result in social sanctioning, Like presumably that would 01:49:36.790 --> 01:49:38.870 be really important to impart upon them. 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. 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. 01:49:46.470 --> 01:49:48.310 Uh, but you will die, right? 01:49:48.470 --> 01:49:49.750 And I know this isn't obvious. 01:49:49.830 --> 01:49:52.790 Like you're counterintuitively, it doesn't seem like there's a threat here. 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 01:49:59.030 --> 01:50:04.840 me is what kind of cognitive heuristics or psychology would develop to impart that kind 01:50:04.840 --> 01:50:10.120 of information if it maps onto these sort of status game equilibria that people are in? 01:50:10.280 --> 01:50:10.520 Right. 01:50:10.600 --> 01:50:12.360 Like, because you you might expect that, right? 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 01:50:16.680 --> 01:50:22.600 coordination uh equilibria are, uh, then learning it in a 01:50:22.600 --> 01:50:27.830 very fast way from social communication, someone telling you who wants to give you 01:50:27.830 --> 01:50:33.510 adaptive information, uh, sort of opens up the door, right, for that kind of a cognitive 01:50:33.510 --> 01:50:34.390 learning mechanism. 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, 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 01:50:43.190 --> 01:50:46.070 you're gonna get taken away if you don't, right? 01:50:46.920 --> 01:50:47.720 So I don't know. 01:50:47.800 --> 01:50:53.720 That maybe we can uh agree on that point is that obviously there's an underlying 01:50:53.720 --> 01:50:58.520 physical reality here that is what's mapping behavior onto payoffs. 01:50:58.760 --> 01:51:04.520 Um and the basic task is to figure out what kind of evolutionary dynamics 01:51:05.000 --> 01:51:09.720 uh might lead to certain cognitive heuristics in that kind of circumstance. 01:51:10.040 --> 01:51:10.360 Yeah. 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 01:51:16.330 --> 01:51:18.250 like, my God, how long is this gonna go? 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 01:51:23.770 --> 01:51:26.970 mandatory question of if we should have asked you anything. 01:51:27.130 --> 01:51:31.690 Um, and then I also wanted to ask for you if and you can do both. 01:51:31.770 --> 01:51:34.970 Uh you we still have plenty of time because we started a little later. 01:51:35.210 --> 01:51:40.340 Um uh is can you talk a little bit about the Leipzig Institute. 01:51:40.740 --> 01:51:41.940 So you're at a Mox Planck. 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 01:51:47.620 --> 01:51:48.260 research groups. 01:51:48.340 --> 01:51:52.020 You're you're in one of the permanent research groups in Leipzig. 01:51:52.260 --> 01:51:57.700 Can you talk a little bit either about sort of um what the group more 01:51:57.700 --> 01:52:02.340 broadly does or just also sort of what the structure is at that Mox Planck? 01:52:02.970 --> 01:52:07.370 Yeah, so I'm I'm in the Department of Human Behavior, Ecology and Culture. 01:52:08.890 --> 01:52:14.010 And our institute is Mox Planck for Evolutionary 01:52:14.010 --> 01:52:17.210 Anthropology is the is the main focus. 01:52:17.850 --> 01:52:21.770 So in our institute itself, we have a number of different departments. 01:52:22.490 --> 01:52:28.260 There's one on cross-cultural psychology, there's genetics, archaeology, 01:52:28.900 --> 01:52:29.940 linguistics. 01:52:30.260 --> 01:52:34.900 So there's a number of different kinds of affiliated teams that are working in the 01:52:34.900 --> 01:52:35.540 building. 01:52:36.420 --> 01:52:41.940 And our group specifically is focusing on topics related to 01:52:41.940 --> 01:52:46.020 behavioral ecology, mostly for humans. 01:52:46.100 --> 01:52:50.650 You know, the title has human in it, human behavior, ecology, and culture, but we also 01:52:50.650 --> 01:52:56.250 include non-human uh uh uh organisms like birds, uh and uh 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 01:53:02.250 --> 01:53:03.930 out, uh Richard McElreath. 01:53:04.090 --> 01:53:08.980 That um basically all of these questions that we're talking about have 01:53:10.100 --> 01:53:11.780 a need for lots of data. 01:53:11.940 --> 01:53:17.540 Like we need to have more data, potentially sharper theory based on new kinds of 01:53:17.540 --> 01:53:18.100 data. 01:53:18.900 --> 01:53:23.140 And so our mandate is to try to develop both of those simultaneously. 01:53:23.220 --> 01:53:29.220 So we're building up new theoretical sort of stages for looking at particular kinds 01:53:29.220 --> 01:53:30.020 of data sets. 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 01:53:36.250 --> 01:53:38.330 collecting longitudinal field data. 01:53:38.810 --> 01:53:42.570 So my my particular side of things has been working with the Chimani Project. 01:53:42.650 --> 01:53:45.210 I know you had Chris on earlier, so you might have talked about. 01:53:46.090 --> 01:53:47.130 Yeah, we're gonna have the Chimani. 01:53:48.890 --> 01:53:49.450 Oh there we go. 01:53:49.610 --> 01:53:49.850 Yeah. 01:53:50.010 --> 01:53:55.850 So yeah, I've been collaborating with Mike Gervin for um God, like 13 years 01:53:55.850 --> 01:53:56.170 now. 01:53:56.640 --> 01:53:58.720 uh, and working in Bolivia. 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 01:54:04.080 --> 01:54:08.800 we want to have lots of data to specifically target these kinds of questions. 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 01:54:14.560 --> 01:54:18.240 making decisions in like a naturalistic setting? 01:54:18.480 --> 01:54:24.110 Um And uh more specifically to my interest too is what are the kind of 01:54:24.110 --> 01:54:26.430 mechanisms that are causing cultural change to happen? 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 01:54:29.710 --> 01:54:32.030 of cultural variation spatially? 01:54:32.190 --> 01:54:34.910 Um, but we also see changes in those patterns too, right? 01:54:34.990 --> 01:54:39.870 As some get bigger and some shrink, um, new ideas emerge and spread, some die 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 01:54:48.080 --> 01:54:53.840 is figuring out ways to combine sort of evolutionary theory with uh empirical 01:54:53.840 --> 01:54:59.840 data collection uh in sort of a high-resolution field context that helps us 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 01:55:05.040 --> 01:55:08.720 or the standard questions, I think, in evolutionary anthropology or evolutionary science. 01:55:09.090 --> 01:55:10.210 Yeah, it's a beautiful building. 01:55:10.370 --> 01:55:11.970 Leipzig is very important historically. 01:55:12.130 --> 01:55:16.610 Wilhelm Wont was uh, you know, lived knocking around in Leipzig in the 01:55:16.610 --> 01:55:21.650 1854. 01:55:21.970 --> 01:55:23.010 I don't remember anymore. 01:55:23.250 --> 01:55:24.370 59, something like that. 01:55:24.450 --> 01:55:24.770 Yeah. 01:55:25.010 --> 01:55:27.810 Um, and uh there's a climbing wall in the institute, too. 01:55:27.890 --> 01:55:28.610 It's a beautiful building. 01:55:28.930 --> 01:55:29.490 That is true. 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. 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 01:55:37.460 --> 01:55:38.260 to talk about that. 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, 01:55:44.100 --> 01:55:48.020 David, you're a a game master yourself of sorts. 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? 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. 01:55:58.900 --> 01:56:04.270 Uh people have commentaries on you know games that are hundreds of years old. 01:56:04.510 --> 01:56:08.270 Uh, and then the way in which those strategies change over time. 01:56:08.430 --> 01:56:12.510 I mean, people are learning these strategies on the board, they're changing them, there's 01:56:12.510 --> 01:56:13.950 successes, there's failures. 01:56:14.190 --> 01:56:18.510 The most recent, of course, uh uh interesting thing has been the arrival of AlphaGo. 01:56:19.310 --> 01:56:24.350 Uh, and the sort of the effect of now having um these 01:56:24.670 --> 01:56:28.110 superhuman players that we can consult with all the time. 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 01:56:32.750 --> 01:56:37.380 transmission from non-human agents, essentially, right? 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 01:56:41.300 --> 01:56:42.420 learning algorithms. 01:56:42.580 --> 01:56:45.140 Um but I mean this is true for a lot of games, I think. 01:56:45.220 --> 01:56:50.660 In general we've got um this sort of revolutionary uh revolutionary effect of having like 01:56:50.820 --> 01:56:56.420 saber metrics uh for professional sports, uh doing very careful analyses of particular 01:56:56.420 --> 01:57:00.490 tactics in baseball and soccer, uh, football. 01:57:00.810 --> 01:57:05.770 Uh there's, you know, things like stockfish and chess and alpha go and go. 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 01:57:11.210 --> 01:57:11.450 game. 01:57:11.530 --> 01:57:17.050 Like actually, you know, quantifying the way that ideas move through these uh these games. 01:57:17.610 --> 01:57:19.450 Uh it's it's quite noticeable. 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 01:57:24.650 --> 01:57:25.690 evolutionary topic. 01:57:25.850 --> 01:57:27.520 Um Because these are quite complex. 01:57:27.680 --> 01:57:31.120 I mean, this is like, you know, am I using this particular strategy or that strategy? 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. 01:57:38.400 --> 01:57:42.000 So I think in that circumstance, it's like, okay, yeah, you know, cultural evolution, like 01:57:42.080 --> 01:57:44.800 what are the what are the evolutionary dynamics of the idea? 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? 01:57:50.000 --> 01:57:51.750 I mean, 3,000 years. 01:57:51.910 --> 01:57:52.790 It's ridiculous. 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 01:57:58.790 --> 01:58:04.230 topic about gaming, like um what is it about games that makes them 01:58:04.230 --> 01:58:05.190 successful? 01:58:06.470 --> 01:58:07.350 I don't know. 01:58:07.590 --> 01:58:09.110 Question for David, perhaps. 01:58:09.270 --> 01:58:15.190 Like Oh, I mean, yeah, uh I would I would need you know uh 01:58:15.350 --> 01:58:16.950 many books to answer that question. 01:58:17.520 --> 01:58:18.560 It's extremely complicated. 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 01:58:22.960 --> 01:58:26.720 like that that itself is an area ripe for investigation. 01:58:26.960 --> 01:58:32.640 I remember some philosopher described it as like a form of crystallized agency, like in the 01:58:32.640 --> 01:58:35.040 same way as like a painting would be crystallized seeing. 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 01:58:39.760 --> 01:58:40.720 form of hearing. 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 01:58:45.200 --> 01:58:45.760 decisions. 01:58:46.250 --> 01:58:48.010 And I think that's an insightful way of looking at it. 01:58:48.090 --> 01:58:52.090 But honestly, like our understanding of the evolutionary psychology of gaming and why we're 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 01:58:56.090 --> 01:59:01.450 very mysterious and ripe for examination. 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 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 01:59:15.400 --> 01:59:15.800 possible. 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 01:59:21.160 --> 01:59:21.640 differentials. 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 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. 01:59:29.880 --> 01:59:31.400 Look look at me go, you know. 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 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. 01:59:41.090 --> 01:59:41.490 Interesting. 01:59:41.570 --> 01:59:43.250 Um that's interesting, Brett. 01:59:43.410 --> 01:59:46.530 That's actually that that is actually a real important part of the psychology, David, for 01:59:46.530 --> 01:59:50.610 status games is is like having a folk theory of the variability, right? 01:59:50.770 --> 01:59:52.050 Exactly, exactly right. 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. 01:59:56.450 --> 01:59:58.610 You know, everybody's breathing pretty well. 01:59:59.500 --> 02:00:00.220 Yeah, totally. 02:00:00.460 --> 02:00:01.500 I love that way of thinking about it. 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 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 02:00:09.660 --> 02:00:13.260 follow the typical kinds of diffusion that are shaped by conformity. 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 02:00:17.580 --> 02:00:20.140 strategy that everyone else is performing, because then people are going to be able to 02:00:20.140 --> 02:00:22.300 anticipate you and uh out fox you. 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 02:00:25.740 --> 02:00:28.210 to do something like the opposite of imitation and conformity. 02:00:28.290 --> 02:00:30.850 And that leads to a whole different set of cultural dynamics. 02:00:31.010 --> 02:00:33.250 Um and so it's it's it's a really fascinating topic. 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 02:00:39.170 --> 02:00:41.490 chess that basically looks at exactly that. 02:00:41.650 --> 02:00:46.130 That um you can think about this like a negative frequency dependence, you know, moves that 02:00:46.130 --> 02:00:50.880 are popular potentially would be things that you would want to avoid because your opponent 02:00:50.880 --> 02:00:52.240 is prepared for it, right? 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 02:00:58.240 --> 02:01:00.640 I just play implicitly and I don't even know what I'm doing. 02:01:00.800 --> 02:01:04.800 And I actually will do quite well because I'm just completely unpredictable. 02:01:06.240 --> 02:01:06.480 Yeah. 02:01:06.640 --> 02:01:07.280 So yeah. 02:01:07.440 --> 02:01:11.680 Um yeah, actually I have a paper on that, the the conformity aspect in Go. 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 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 02:01:21.350 --> 02:01:22.390 figure out what to do next. 02:01:22.550 --> 02:01:23.350 No, no, don't know. 02:01:23.590 --> 02:01:27.030 Um and if you're I mean, it's it's a computational problem. 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 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 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 02:01:41.680 --> 02:01:45.040 truce, we're gonna continue going down this line that we both know very well. 02:01:45.280 --> 02:01:47.360 Um, at really high-level play, though. 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 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 02:01:55.920 --> 02:01:59.280 matches where people want to know what their opponent is training on, like which lines are 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 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 02:02:09.490 --> 02:02:10.930 ready to respond on. 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 02:02:16.930 --> 02:02:19.570 his opponent responded to him too quickly, according to him. 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 02:02:24.210 --> 02:02:25.330 about what I was planning to do. 02:02:25.650 --> 02:02:28.850 Yeah, you read my computer files and yeah, yeah, exactly. 02:02:28.930 --> 02:02:30.450 Yeah, that's interesting. 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 02:02:35.540 --> 02:02:38.260 frequency dependence here is a is a key factor. 02:02:38.420 --> 02:02:42.660 Um but uh yeah, how that how that shapes long-term evolutionary dynamics. 02:02:42.740 --> 02:02:44.420 I think that's a really cool question. 02:02:44.580 --> 02:02:47.940 Um and uh I've been spending a lot of time looking at that recently. 02:02:48.020 --> 02:02:49.700 So awesome. 02:02:49.860 --> 02:02:52.580 Well, this flew by, so we'll have to have you on again. 02:02:52.890 --> 02:02:53.690 Oh, that'd be great. 02:02:53.850 --> 02:02:54.730 I had a great time. 02:02:54.890 --> 02:02:57.290 Uh it's really interesting talking to you both. 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, 02:03:03.770 --> 02:03:09.690 and uh all the cool stuff from uh galaxies to bike tires, although um not, but 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, 02:03:14.490 --> 02:03:15.770 which is super cool. 02:03:15.960 --> 02:03:18.360 So um yeah, thank you so much. 02:03:18.440 --> 02:03:19.720 Uh have a good evening. 02:03:19.800 --> 02:03:22.760 Thanks so much for uh for being with us in the evening here. 02:03:22.840 --> 02:03:26.280 Um Shinaband and uh yeah yeah. 02:03:26.760 --> 02:03:28.680 Yeah, thanks a lot, guys. 02:03:31.800 --> 02:03:37.480 Uh David, um when you're uh all done after a long 02:03:37.480 --> 02:03:40.920 day, um do you sometimes drink beer? 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 02:03:47.230 --> 02:03:49.310 the podcasts that I enjoy. 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 02:03:53.870 --> 02:03:54.990 my favorite podcasts. 02:03:55.310 --> 02:04:00.590 Sometimes I just, you know, post messages on social media saying how awesome those podcasts 02:04:00.590 --> 02:04:00.830 are. 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 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. 02:04:09.630 --> 02:04:10.830 A propos of nothing. 02:04:11.140 --> 02:04:12.580 Yeah, it's just something I like to do. 02:04:13.060 --> 02:04:14.500 Something just kind of like that kind of person. 02:04:14.580 --> 02:04:14.740 Yeah. 02:04:15.220 --> 02:04:16.420 Yeah, that's just the kind of guy I am. 02:04:16.500 --> 02:04:17.060 Uh uh. 02:04:18.820 --> 02:04:19.060 Yeah. 02:04:19.300 --> 02:04:19.540 Yeah. 02:04:19.780 --> 02:04:19.940 Right. 02:04:20.020 --> 02:04:22.580 Well, we're not gonna interrogate that or question that at all. 02:04:22.660 --> 02:04:23.060 So yeah. 02:04:23.540 --> 02:04:23.700 No. 02:04:23.860 --> 02:04:24.020 Okay. 02:04:24.100 --> 02:04:25.300 No, I think we'll just uh yeah. 02:04:25.860 --> 02:04:26.580 We'll leave it at that. 02:04:26.820 --> 02:04:27.140 Okay. 02:04:27.380 --> 02:04:27.780 All right. 02:04:27.940 --> 02:04:28.180 Bye. 02:04:28.420 --> 02:04:29.220 See you next week.