WEBVTT - 52: What Math Models of Herding Cows Can Teach Us About Markets 0:00:00.160 --> 0:00:02.320 But knowledge to work and grow your business with c 0:00:02.520 --> 0:00:06.680 i T from transportation to healthcare to manufacturing. C i 0:00:06.760 --> 0:00:10.520 T offers commercial lending, leasing, and treasury management services for 0:00:10.600 --> 0:00:13.520 small and middle market businesses. Learn more at c I 0:00:13.560 --> 0:00:26.160 T dot com put Knowledge to Work. Hello, and welcome 0:00:26.200 --> 0:00:29.120 to another episode of the Odd Lots podcast. I am 0:00:29.160 --> 0:00:34.639 Tracy Alloway, executive editor of Bloomberg Markets, and uh, let 0:00:34.680 --> 0:00:39.120 me just say that I think what you are about 0:00:39.159 --> 0:00:43.520 to hear is probably the most random edition of Odd 0:00:43.520 --> 0:00:47.080 Lots that we've done so far. And just to give 0:00:47.120 --> 0:00:50.239 you an idea of how random it is, I have 0:00:50.479 --> 0:00:54.280 with me a special co host. It is lauracan Roche Kelly. 0:00:54.640 --> 0:00:59.080 Here's our resident cow expert. That's how random this is. 0:00:59.360 --> 0:01:02.120 Say hello, lorcan h Tracy, how is it going? I 0:01:02.120 --> 0:01:04.160 think I'm a cow expert? I think well, I'll definitely 0:01:04.200 --> 0:01:07.000 that one is a compliment, co owner, if nothing else, 0:01:08.680 --> 0:01:11.560 it is meant to be a compliment. Mark, we don't 0:01:11.600 --> 0:01:14.600 have a lot of cow experts, so that that's pretty special. 0:01:14.800 --> 0:01:19.119 You're reducing the compliment straight away, Tracy. I should say 0:01:19.160 --> 0:01:22.680 that my normal co host, Joe Wisenhal is still on 0:01:22.920 --> 0:01:26.200 his epic business trip and he's left me to do 0:01:26.280 --> 0:01:30.840 this podcast? Up? How do I intro this podcast? Um? 0:01:30.920 --> 0:01:33.600 All right, So a couple of weeks ago I noticed 0:01:34.120 --> 0:01:39.080 mathematical paper. It was called quote, A Mathematical Model for 0:01:39.120 --> 0:01:45.440 the Dynamics and Synchronization of Cows. Lorcan. I think I 0:01:45.480 --> 0:01:48.120 sent it to you at the time. Yeah, I think 0:01:48.120 --> 0:01:50.560 you said to me was this email subject was? Can 0:01:50.600 --> 0:01:53.160 you believe this exists? I think may have been something 0:01:53.240 --> 0:01:56.200 online what the email subject was. I was very excited 0:01:56.200 --> 0:01:58.200 to see it, I must say, because anything to do 0:01:58.240 --> 0:02:03.000 with cows excites me. Right, how many cows do you have? Now, Lorcan? 0:02:03.720 --> 0:02:05.760 I think at first, I think you ever asked a 0:02:05.760 --> 0:02:07.400 farm or how many cows? Yes, because it's a way 0:02:07.400 --> 0:02:08.960 of working out for his income is but I think 0:02:09.480 --> 0:02:11.640 enough to keep me busy is the standard answer. I 0:02:11.680 --> 0:02:15.320 think that I'm sorry, I just made a bovine faux paw. 0:02:15.520 --> 0:02:19.560 I guess, all right, enough to keep you busy? That's 0:02:19.560 --> 0:02:22.520 good enough for this podcast. Okay, So we got this paper, 0:02:22.560 --> 0:02:25.920 A mathematical Model for the Dynamics and Synchronization of cows. 0:02:26.000 --> 0:02:30.200 It's written by let's see one, two, three, four mathematicians 0:02:30.200 --> 0:02:35.560 at various universities, and it talks about hurting behavior in 0:02:35.760 --> 0:02:40.280 cows and sort of mathematical models used to analyze that behavior. 0:02:40.400 --> 0:02:42.799 And I know you're all thinking, what in the world 0:02:42.880 --> 0:02:47.320 does this have to do with markets and investment and finance. 0:02:47.720 --> 0:02:52.240 But if you'll remember, we often talk about investors acting 0:02:52.360 --> 0:02:55.720 in markets like a herd. We often talk about hurting behavior, 0:02:55.840 --> 0:02:59.600 people crowding into the same types of investments, the same 0:02:59.639 --> 0:03:05.320 posit editions, basically following each other seeking the safety of numbers. 0:03:05.320 --> 0:03:09.480 So it's not totally off the wall, lorcan am I 0:03:09.520 --> 0:03:11.600 stretching that a bit? I think that's fair. I don't 0:03:11.639 --> 0:03:13.640 think it's stretching it at all. I think I'm like 0:03:14.080 --> 0:03:16.200 I suppose I spend a lot of my time looking 0:03:16.200 --> 0:03:18.520 at the markets and some of my time looking at cows. 0:03:18.560 --> 0:03:21.760 And while the similarities don't immediately jump out to me, 0:03:21.840 --> 0:03:24.200 I must say when I'm working in either world both, 0:03:24.600 --> 0:03:28.080 there is generally a feeling that markets gain momentum, and 0:03:28.280 --> 0:03:31.239 the more I suppose more people talk about the trades, 0:03:31.320 --> 0:03:33.560 the more likely people are to get on the trade 0:03:33.639 --> 0:03:35.680 or just have an opinion on the trade anyway, And 0:03:35.800 --> 0:03:39.200 much like with cows, if one cow finds good grass, 0:03:39.600 --> 0:03:41.240 the rest of the cows will see the good grass 0:03:41.240 --> 0:03:43.720 and run over and get some for themselves, so that 0:03:44.160 --> 0:03:47.120 on the high level, the herding idea, I think it's 0:03:47.120 --> 0:03:51.280 been well established within markets, and it's it comes from animals, 0:03:51.320 --> 0:03:53.080 comes from herds of anis, That's where the world comes from. 0:03:53.120 --> 0:03:56.440 So I'd imagine to examine what cows do should tell 0:03:56.480 --> 0:03:59.040 us something about hurting in markets, or at least give 0:03:59.120 --> 0:04:01.320 us away of modeling her new markets, which is why 0:04:01.360 --> 0:04:04.160 I wanted to paper. Say, do you ever look out 0:04:04.200 --> 0:04:06.960 your window on your farm in Ireland and watch your 0:04:07.000 --> 0:04:09.960 cows and like ponder them as you think about markets? 0:04:10.520 --> 0:04:13.280 I have this image of you doing that. There there 0:04:13.360 --> 0:04:15.560 is I can see my cows from my window. Depending 0:04:15.600 --> 0:04:17.800 on what field they're in. Whether what I'm doing is 0:04:17.839 --> 0:04:20.360 pondering them, are pining to be with them rather than 0:04:20.480 --> 0:04:24.440 staring at some unfinished coffee and folking me, I'm not sure. 0:04:33.800 --> 0:04:36.320 All right, Without further ado, we are going to bring 0:04:36.440 --> 0:04:39.440 in the authors of this paper. We have three of 0:04:39.480 --> 0:04:42.920 them with us, and because we have three guests, it's 0:04:43.000 --> 0:04:44.839 the first time I think we've ever had a trio 0:04:44.880 --> 0:04:47.400 of guests on this podcast. I'm going to ask them 0:04:47.440 --> 0:04:50.840 to quickly introduce themselves so that you all know who 0:04:50.880 --> 0:04:54.360 they are. UM, why don't we start with j j 0:04:54.560 --> 0:04:57.240 Can you say hello and intro yourself? Hey, Hello, this 0:04:57.320 --> 0:05:00.680 is j Um. Last name is Son actually come currently 0:05:00.720 --> 0:05:04.520 assistant professor in the Mass Department at Clarkson University. It 0:05:04.680 --> 0:05:08.440 is in Potsdam, New York, Upstate New York. UM. I 0:05:08.680 --> 0:05:12.360 work with a lot of the complex systems, networks, no 0:05:12.440 --> 0:05:16.400 linear dynamics and more recently UM studying the information flow 0:05:16.480 --> 0:05:19.719 in those complex systems. By the way, the time the 0:05:19.720 --> 0:05:22.640 paper was written, actually it was a graduate student visiting 0:05:22.880 --> 0:05:26.040 UM with at the time my advisor or quote UM 0:05:26.400 --> 0:05:29.240 who is here also today And it was a very 0:05:29.240 --> 0:05:33.400 exciting UM journey for me to be on this project. 0:05:33.920 --> 0:05:38.080 Fantastic Eric, why don't you say hello? Hello? I'm Eric Bolt, 0:05:38.160 --> 0:05:43.480 also Clarkson University and John Harrington, Professor of Mathematics. So UM, 0:05:43.560 --> 0:05:45.680 like real, we do a lot with nonleader dynamics, a 0:05:45.760 --> 0:05:48.880 lots of that dad UM. In years past we'd called 0:05:49.000 --> 0:05:52.200 chaos theory, but UM in recent years we do a 0:05:52.240 --> 0:05:55.880 lot also with large scale complex systems, which is something 0:05:55.920 --> 0:05:58.680 we're developing a center for a Clarkson so and this 0:05:58.760 --> 0:06:02.279 is also a nonlo dynamics. And then finally we have 0:06:02.560 --> 0:06:06.599 Mason Porter joining us, I think from l A. Yeah, 0:06:06.680 --> 0:06:10.920 I'm Mason Porter. Um. I'm currently a professor of mathematics 0:06:10.920 --> 0:06:14.000 at u C l A. UM I just moved over 0:06:14.040 --> 0:06:17.320 from University of Oxford a few weeks ago. Um. I'm 0:06:17.360 --> 0:06:20.880 also a specialist in complex systems and networks and non 0:06:20.920 --> 0:06:23.840 their dynamics, And it's one of the things I wanted 0:06:23.880 --> 0:06:27.440 to mention. You were talking about similarities between between hurting 0:06:27.480 --> 0:06:30.159 and animals and hurting and markets. In fact, one of 0:06:30.160 --> 0:06:33.560 those things that we specialize in is exactly collective behavior 0:06:33.600 --> 0:06:36.840 and complex systems, which can be things like hurting in 0:06:36.880 --> 0:06:40.960 all sorts of context, or ideas becoming viral and and 0:06:40.960 --> 0:06:43.120 and so on. So we actually take an abstract point 0:06:43.120 --> 0:06:45.360 of view and very specifically study these sorts of things 0:06:45.400 --> 0:06:48.320 in many different types of systems. Exactly, we've all we've 0:06:48.320 --> 0:06:51.839 all worked together in these different sorts of things, including swarming, 0:06:52.360 --> 0:06:55.360 um schooling if it's fish, and then also human behaviors 0:06:55.400 --> 0:06:58.240 when they work in groups. Well maybe that's a good 0:06:58.279 --> 0:07:01.600 jumping off points. So we have collective behavior, and there's 0:07:01.640 --> 0:07:05.279 been a lot of study of collective behavior, whether it's 0:07:05.320 --> 0:07:09.520 in animals or humans or or systems and that sort 0:07:09.640 --> 0:07:14.440 of thing. What made you decide to focus on cows 0:07:14.600 --> 0:07:18.720 specifically for this paper? Okay, so maybe I should answer 0:07:18.760 --> 0:07:22.320 that because the project actually UM started with me and 0:07:22.400 --> 0:07:25.600 the fourth The fourth co author is Marian Dawkins. She's 0:07:25.640 --> 0:07:29.600 actually she's a zoologist rather than a mathematician, and and 0:07:29.640 --> 0:07:32.320 she and I know each other from being in the 0:07:32.400 --> 0:07:37.400 same Oxford College UM, and we formulated a project actually 0:07:37.440 --> 0:07:41.400 about a year or so before before Eric and Jay visited. 0:07:41.960 --> 0:07:45.840 And one of the things that that had UM predated 0:07:46.280 --> 0:07:49.480 the project was that Marian was sort of lamenting that 0:07:49.560 --> 0:07:52.400 many UM people who are who are theorists and working 0:07:52.400 --> 0:07:55.960 on problems that come from biology, were not sufficiently interfacing 0:07:55.960 --> 0:07:59.640 with biology. And at some point the conversation turned to 0:07:59.680 --> 0:08:02.760 her work on cows and other animals, which is something 0:08:02.760 --> 0:08:05.640 that she's been doing for many years, and it seemed 0:08:05.680 --> 0:08:08.720 interesting to me and I was interested in collective behavior 0:08:08.760 --> 0:08:12.080 in general. So we formulated UM a project that we 0:08:12.120 --> 0:08:15.440 did in a certain manner called an Agent based Model UM. 0:08:15.440 --> 0:08:18.640 This was the year before Eric and Ja visited UM, 0:08:18.720 --> 0:08:21.720 and then that one was attempting to be more realistic 0:08:21.720 --> 0:08:23.840 but was a bit abstract, and so we wanted to 0:08:24.640 --> 0:08:28.080 step back and have a bare bones project. So serendipity, 0:08:28.080 --> 0:08:30.200 I suppose, is a short version of that answer. And 0:08:30.240 --> 0:08:32.560 I tend to be interested in just about everything. And 0:08:32.559 --> 0:08:35.080 I had a local expert, and so we worked on it, 0:08:35.120 --> 0:08:38.559 and then Eric visited me along with Ja the next year, 0:08:38.600 --> 0:08:41.040 and so we decided that we would pursue that further. 0:08:41.679 --> 0:08:45.200 So when we think about hurting behavior in cows, and 0:08:45.520 --> 0:08:50.120 I guess other mammals like antelope, seabro whatever, um, we 0:08:50.200 --> 0:08:55.360 usually think that they all move, I guess in tandem, 0:08:55.400 --> 0:08:58.160 like like Lorcan was saying, if one cow sees fresh grass, 0:08:58.240 --> 0:09:01.600 then all the cows migrate there. But also I guess 0:09:01.640 --> 0:09:06.080 for protective reasons to protect themselves from predators. Is that 0:09:06.360 --> 0:09:10.440 is that the accepted version of hurting behavior in cows? 0:09:11.520 --> 0:09:15.240 I think that's at least part of it. There's also, um, 0:09:15.280 --> 0:09:18.360 if they're in a pen, for instance, they actually may 0:09:18.400 --> 0:09:20.720 also want to just all be able to lie down 0:09:20.920 --> 0:09:23.640 at a similar time, especially if they're if they're under 0:09:23.679 --> 0:09:26.240 similar um sort of forces from a from a day 0:09:26.320 --> 0:09:28.400 night cycle. So so you know, some of this is 0:09:28.400 --> 0:09:32.559 actually protection, but some of it is also similar needs. Yeah, 0:09:32.880 --> 0:09:35.880 so um, all those elements are in our work Actually 0:09:35.920 --> 0:09:37.920 we have a follow on work which actually includes things 0:09:37.920 --> 0:09:41.600 like why would they do that, optimizing their um, their 0:09:41.640 --> 0:09:46.839 resilience to petitors, and so forth. But the centerpiece of 0:09:46.880 --> 0:09:50.160 the model is that the cow individually has these different 0:09:50.200 --> 0:09:52.160 things that go on inside their bodies. You know, they 0:09:52.280 --> 0:09:54.640 need to eat, their need to digest, which is kind 0:09:54.640 --> 0:09:57.679 of complicated in the cow, and um, then they need 0:09:57.720 --> 0:10:00.000 to sleep. So it's a little bit like a self 0:10:00.000 --> 0:10:03.160 of parture kating rhythm in your own body. And for 0:10:03.240 --> 0:10:05.719 the other reasons you's described. Then it actually turns out 0:10:05.720 --> 0:10:07.000 to be a good thing if they do it together. 0:10:07.800 --> 0:10:10.160 So that's the synchronization aspect, and whether they're in a 0:10:10.200 --> 0:10:12.960 pen or or there in the wild, um, there's some 0:10:13.000 --> 0:10:14.599 aspect of they want to do it together. Now in 0:10:14.600 --> 0:10:17.480 the pen, they're not really predated anymore, but they carry 0:10:17.480 --> 0:10:20.920 on that natural behavior. Yeah. So one of the things 0:10:21.000 --> 0:10:23.200 was that so those interactions to not to be really 0:10:23.200 --> 0:10:27.400 important as a determining factor of whether they could synchronize 0:10:27.440 --> 0:10:31.320 and to what extent they do synchronize UM, which happened 0:10:31.360 --> 0:10:35.120 to be also related to the production and even a 0:10:35.160 --> 0:10:37.560 though we don't know how happy they are, people do 0:10:37.760 --> 0:10:40.360 say that they seem to be happier when they actually 0:10:40.360 --> 0:10:43.760 produce more and most synchronized. Just from the looking from 0:10:43.760 --> 0:10:46.719 the paper, they are rich paper. Do you have the 0:10:46.960 --> 0:10:49.080 look at a single cow model and then you looked 0:10:49.080 --> 0:10:52.160 at what you call coupled cows. But just for your 0:10:52.200 --> 0:10:55.160 information on the farming background, when you say cows are coupling, 0:10:55.240 --> 0:10:59.920 you means something completely different. But they are. But they 0:11:00.080 --> 0:11:03.320 said in a larger heard that the synchronicity seems to 0:11:03.320 --> 0:11:05.120 break down. Is it's what you're seeing to be saying 0:11:05.120 --> 0:11:09.280 the paper that well as the as the stand ups 0:11:09.360 --> 0:11:11.439 sit down cyclist, the one that you're looking at, it 0:11:11.520 --> 0:11:13.360 seems to break down. See if a mixture of cows 0:11:13.360 --> 0:11:16.200 standing up and sitting down. And I'm wondering, is that 0:11:16.240 --> 0:11:19.320 I think that you're soften observation or something that you 0:11:19.440 --> 0:11:23.839 produce from your mathematical models yourself. Our second paper actually 0:11:23.840 --> 0:11:26.679 has an aspect where um the groups can become too 0:11:26.760 --> 0:11:29.320 large um two for their own good, and they break 0:11:29.320 --> 0:11:32.000 apart and they may subsynchronize in the smaller groups. Now 0:11:32.000 --> 0:11:34.280 do you see that in your farm? Yeah, well, the 0:11:34.280 --> 0:11:35.960 way if I had that many cows, I'm sure i'd 0:11:35.960 --> 0:11:40.720 say it. But it's I think I'm with with the 0:11:40.760 --> 0:11:43.120 way I'm supposed to get back to nothing boast of farming. 0:11:43.200 --> 0:11:45.000 The way the farming works here is that it is 0:11:45.120 --> 0:11:48.720 very The synchronicity I see tends to be much more. 0:11:48.920 --> 0:11:50.800 If it's going to rain in the next twenty minutes, 0:11:50.880 --> 0:11:53.160 most of the cows are sitting down. If it's very hot, 0:11:53.200 --> 0:11:55.959 most are standing up. But beyond that they will generally 0:11:56.920 --> 0:11:58.600 I suppose that the heart is big enough that someone 0:11:58.600 --> 0:12:00.880 will be sitting down, someone be standing up at any time. 0:12:01.240 --> 0:12:03.720 Whereas if you put a small number of cows in 0:12:03.720 --> 0:12:05.760 the shed, that occasion will do with some are lane. 0:12:05.840 --> 0:12:07.360 If we take if we have cows learn name, we 0:12:07.400 --> 0:12:09.280 take them out of the herd because they can keep 0:12:09.360 --> 0:12:10.640 up to your house, and we have three or four 0:12:10.640 --> 0:12:13.880 cows together, and they will synchronize very strongly, like so 0:12:13.960 --> 0:12:16.080 the four will decision down, order four will be standing up. 0:12:16.400 --> 0:12:18.200 But whether that's because they're in the shed and not 0:12:18.240 --> 0:12:20.520 out in the field, it's you know, that's what the 0:12:20.559 --> 0:12:23.680 externalities would be very hard to calculate within a model 0:12:23.679 --> 0:12:28.320 like this. The externalities are a very big deal. And 0:12:28.480 --> 0:12:32.520 um one of the things that the people argue about is, 0:12:32.559 --> 0:12:34.800 you know how much of this is from circadian rhythms, 0:12:35.040 --> 0:12:37.760 and how much of it is from from say, signals 0:12:37.760 --> 0:12:40.320 from other animals that are that are nearby. Um. It's 0:12:40.360 --> 0:12:44.480 a very difficult thing to um um disentangle from each other. UM. 0:12:44.600 --> 0:12:46.120 One thing I want to mention it is kind of 0:12:46.160 --> 0:12:48.160 going back on your on your earlier comment in terms 0:12:48.200 --> 0:12:52.040 of having a larger herd having kind of not complete synchrony. 0:12:52.320 --> 0:12:56.800 In the paper, we're not actually demanding um complete synchrony. 0:12:56.800 --> 0:12:59.240 We're just measuring how synchronized they are and trying to 0:12:59.280 --> 0:13:01.760 do it in an a sort of a quantitative way 0:13:01.800 --> 0:13:04.120 so you can measure. And this is something that comes 0:13:04.200 --> 0:13:07.000 originally from the theory of coupled oscillators. The term the 0:13:07.080 --> 0:13:09.600 term coupled has a very specific meaning in mathematics and 0:13:09.600 --> 0:13:12.040 physics that's not quite the same as the English meaning. 0:13:12.200 --> 0:13:14.840 That just means that they're interacting. UM. So if you 0:13:14.880 --> 0:13:17.680 write down equations and you have some some term that 0:13:17.720 --> 0:13:20.160 has has parts of two different equations, this is a 0:13:20.200 --> 0:13:22.280 way for for this is a way for them to 0:13:22.280 --> 0:13:26.160 be coupled together. UM. But um, there there are there 0:13:26.160 --> 0:13:29.520 are some measures that that are from um from long 0:13:29.559 --> 0:13:33.080 studies of oscillations, from biological rhythms, for example, that tries 0:13:33.120 --> 0:13:36.480 to measure how synchronized things are. And so it's not 0:13:36.520 --> 0:13:38.880 that you have a yes or no that everybody is synchronized. 0:13:38.920 --> 0:13:40.920 You have sort of how much they are. And you 0:13:40.960 --> 0:13:44.800 can imagine doing this um with with animal behavior as well, 0:13:45.280 --> 0:13:48.680 um by just saying, okay, well do they do different 0:13:48.720 --> 0:13:51.640 cows stand up at a similar time? You know, maybe 0:13:51.679 --> 0:13:54.280 there's a delay of one second versus ten seconds, and 0:13:54.320 --> 0:13:56.240 so you would say that the delay of one second, 0:13:56.280 --> 0:13:57.880 if you n able, if you're able to measure that 0:13:57.880 --> 0:14:00.560 would be more synchronized than if then if it's ten seconds. 0:14:00.559 --> 0:14:04.280 Apart whether any extent to which that comes from cows 0:14:04.280 --> 0:14:06.560 getting signals from others by watching what others are doing, 0:14:06.760 --> 0:14:09.559 and how much comes from having similar desires, that's very 0:14:09.600 --> 0:14:12.559 difficult to dista. I think that suppost initiate aside is 0:14:12.640 --> 0:14:16.440 um um. The one thing that work cows completely appos 0:14:16.520 --> 0:14:18.520 de synchronized themselves from the rest of her is when 0:14:18.520 --> 0:14:21.760 they're about to give birth to a calf. And I 0:14:21.760 --> 0:14:24.240 think there's a product available. I don't know if I 0:14:24.320 --> 0:14:27.280 have it from white House my farm. It's called moo 0:14:27.400 --> 0:14:32.600 called mlo cl What is it is? A small Are 0:14:32.600 --> 0:14:34.360 you making this up. No, I'm not making that. You 0:14:34.360 --> 0:14:37.240 can google it does exist. It's a it's a lectronic 0:14:37.320 --> 0:14:39.600 part that I attached to the cow's tail, and what 0:14:39.680 --> 0:14:42.720 it measures is how much the cow switches her tail. 0:14:43.280 --> 0:14:45.960 And before cow gets birth, she becomes agitated, she has 0:14:46.000 --> 0:14:49.440 more tail swishing, and this product notices the extra tail 0:14:49.440 --> 0:14:51.960 swishing and it sends me a text message to say 0:14:52.000 --> 0:14:53.480 that this cow is going to calve in the next 0:14:53.480 --> 0:14:57.200 two hours. And what what that How that that thing 0:14:57.240 --> 0:14:58.920 works is that you've put it on the cow and 0:14:58.960 --> 0:15:00.960 it stays and going to It's an idea of what 0:15:01.000 --> 0:15:04.760 the cow's rhythm is, and then it noticed the changing 0:15:04.840 --> 0:15:07.760 rhythm and acknowledge that that there's something big is happen there. 0:15:07.760 --> 0:15:11.520 That's a change room. So cows generally have a strong 0:15:11.600 --> 0:15:14.240 rhythm within themselves. Suppose it's what this company is taken 0:15:14.240 --> 0:15:17.080 advantage of that they're just stand up, sit down switched 0:15:17.080 --> 0:15:20.560 their tail. Is can be very easily predicted within a 0:15:20.600 --> 0:15:23.600 certain sucset of circumstances. So when the circumstances changed, as 0:15:23.600 --> 0:15:25.240 in the cow is about to have a calf, it 0:15:25.360 --> 0:15:28.520 connect the exect use that information to send me a 0:15:28.520 --> 0:15:31.160 text message to say, this is about the calf. Now, 0:15:31.240 --> 0:15:33.120 is there an analog of moo cow for the market. 0:15:33.840 --> 0:15:36.000 It's on the market. Yes, it's something that I mean 0:15:36.000 --> 0:15:38.600 from market market prediction. You can put it on the traders. 0:15:40.480 --> 0:15:41.920 Although if you could figure out what it is, I 0:15:41.920 --> 0:15:43.800 imagine you get very rich. You won't tell me about this. 0:15:44.760 --> 0:15:47.160 I'm actually surprised that we've gotten this far and you 0:15:47.240 --> 0:15:52.080 haven't remarked that, actually, what does a bullmarket mean? We're 0:15:52.120 --> 0:15:56.240 saving that first. We are going to take a short 0:15:56.320 --> 0:16:03.320 break for a word from our sponsors. But knowledge to 0:16:03.360 --> 0:16:06.000 work and grow your business with c i T. From 0:16:06.000 --> 0:16:11.120 transportation to healthcare to manufacturing. C i T offers commercial lending, leasing, 0:16:11.160 --> 0:16:14.840 and treasury management services for small and middle market businesses. 0:16:15.040 --> 0:16:17.720 Learn more at c i T dot com put Knowledge 0:16:17.760 --> 0:16:26.160 to Work. Okay, and we are back. We are talking 0:16:26.280 --> 0:16:31.840 cows hurting behavior and mathematics. UM. Just to kick off 0:16:31.840 --> 0:16:33.960 the second half of the conversation, maybe could you just 0:16:34.040 --> 0:16:37.600 walk us through in very simple terms what you found 0:16:37.760 --> 0:16:40.560 in your paper from the mathematical model you use, and 0:16:40.640 --> 0:16:44.720 what it says about cows hurting behavior. UM. I think 0:16:44.720 --> 0:16:46.960 from the math medical point of view, UM, there are 0:16:47.040 --> 0:16:51.320 something that's very unique about this particular model because one 0:16:51.360 --> 0:16:55.000 thing about calls and some other animals are that, um, 0:16:55.000 --> 0:16:58.200 they actually have different modes, right, It's not um that 0:16:58.320 --> 0:17:01.320 they follow one type of motion or dynamics and then 0:17:01.360 --> 0:17:05.080 they just continue. For cars, there are three distinct modes. 0:17:05.400 --> 0:17:09.840 They can walk, stand, they eat, or they lie down. 0:17:10.440 --> 0:17:12.560 And the turns are that there are you know, very 0:17:12.600 --> 0:17:16.280 traditional and machinery in mathematics that that we could use 0:17:16.480 --> 0:17:20.080 for for particularly to model this behavior um as well 0:17:20.080 --> 0:17:22.439 as their interactions. So so one thing we found that 0:17:22.520 --> 0:17:26.720 sort of contraintuitive is you would imagine that maybe by 0:17:26.800 --> 0:17:31.320 interacting more or more intensively, they would necessarily synchronize more, 0:17:31.400 --> 0:17:34.960 and that wasn't um the case. So what that means 0:17:34.960 --> 0:17:37.000 in reality is if you start to you know, put 0:17:37.040 --> 0:17:41.480 them in fans and with higher density, it's not necessarily 0:17:41.480 --> 0:17:44.480 true that you make them synchronize more. They actually could 0:17:44.760 --> 0:17:48.600 break the synchrony by increasing those coupling. So when you 0:17:48.640 --> 0:17:52.919 have more cows together and they're in a crowded, confined area, 0:17:53.000 --> 0:17:57.359 they don't actually exhibit hurting behavior. Is that right? I 0:17:57.359 --> 0:18:00.320 mean again it's not not yes, no question, UM. It's 0:18:00.400 --> 0:18:04.160 the extent to which they synchronize could actually decrease. Where 0:18:04.200 --> 0:18:08.000 you put more in the finite vertical space, and is 0:18:08.040 --> 0:18:12.760 that competition for resources or they just start to feel 0:18:12.760 --> 0:18:16.119 pressure because there's too many other cows. It's more of 0:18:16.119 --> 0:18:20.280 a pressure scenario. Yeah, well, why don't we widen out 0:18:20.280 --> 0:18:23.520 the discussion because I know that you all, um also 0:18:23.600 --> 0:18:27.800 study network effects and chaos theory and things like that. 0:18:28.119 --> 0:18:34.000 So how much can we extrapolate from cow behavior into 0:18:34.400 --> 0:18:42.280 other types of behavior and specifically humans and or human investors. So, um, 0:18:42.400 --> 0:18:45.200 one of the things one of the advantages of mathematics 0:18:45.320 --> 0:18:48.440 is that it's automatically massively parallel. You know, people talk 0:18:48.480 --> 0:18:52.760 about mathsily parallel UM computation. With mathematics, you can get 0:18:52.800 --> 0:18:56.840 insights on a specific system and then other other systems 0:18:56.880 --> 0:19:00.520 that might have similar model equations possible. It will teach 0:19:00.560 --> 0:19:04.600 you something about that. So UM Jay was talking about 0:19:04.600 --> 0:19:07.359 the fact that you could have stronger coupling in this 0:19:07.440 --> 0:19:10.960 situation leading to less synchronized behavior. So that can also 0:19:11.040 --> 0:19:13.960 potentially occur elsewhere. So if people are interacting with each 0:19:14.000 --> 0:19:16.639 other more strongly, UM, at least this is known in 0:19:16.720 --> 0:19:21.160 mathematical models, you can have situations where they're not necessarily 0:19:21.200 --> 0:19:24.919 more synchronized as a result, UM, I don't know how 0:19:24.960 --> 0:19:27.600 to experimentally verify that. I mean, that's it's much more 0:19:27.640 --> 0:19:30.360 reality is much more complicated than mathematical model. But it's 0:19:30.400 --> 0:19:34.160 a very general um situation that one sees mathematically, not 0:19:34.359 --> 0:19:37.480 just in the specific model that we did, And others 0:19:37.600 --> 0:19:41.960 have um reported similar results using other models of synchronization 0:19:42.000 --> 0:19:44.200 in the last few years. So that's that's one example. 0:19:45.320 --> 0:19:47.200 Another thing I say about this this work is it's 0:19:47.240 --> 0:19:50.520 it's actually um it's a scientific study on two levels. 0:19:50.920 --> 0:19:53.120 So it's about cows and we're studying the topical area 0:19:53.119 --> 0:19:56.400 of cows, and we want to make conclusions about cows. 0:19:56.440 --> 0:19:58.000 But the tools that we bring to it is actually 0:19:58.080 --> 0:20:00.080 an unique kind of tool set in the area of 0:20:01.040 --> 0:20:04.960 modeling a complex system like an animal, because it, as 0:20:05.600 --> 0:20:07.680 Jay said, it's a uh, what's called a piece wise 0:20:07.720 --> 0:20:10.919 impulsive system as we've modeled it, which means it's a 0:20:10.960 --> 0:20:14.600 bit like a bouncing ball. Something continues continuously for a 0:20:14.640 --> 0:20:16.919 while and then it reaches a threshold it switches. So 0:20:16.920 --> 0:20:20.560 it might switch from um the lyne digesting state to say, okay, 0:20:20.560 --> 0:20:23.800 now I'm done with that, onto onto sleeping. So those 0:20:23.840 --> 0:20:26.399 states and switching between the states is actually a unique 0:20:26.760 --> 0:20:28.600 a neat neat element in the in the area of 0:20:28.640 --> 0:20:33.320 modeling um uh dynamical systems like a cow UM. Now, 0:20:33.320 --> 0:20:35.200 if we want to bring that over to people, then 0:20:35.200 --> 0:20:37.000 you might say, okay, great, the cow is a kind 0:20:37.000 --> 0:20:39.520 of a simple system um compared to a person. And 0:20:39.760 --> 0:20:42.080 if we said a person's like this, then they would 0:20:42.119 --> 0:20:44.680 have many many states, perhaps because I think we would 0:20:44.720 --> 0:20:47.919 think the cow is probably somewhat m a simpleton in 0:20:47.960 --> 0:20:49.880 the sense of the different kind of scenarios they would 0:20:49.960 --> 0:20:53.040 run through. So if I were to be courageous enough 0:20:53.040 --> 0:20:55.520 to advance this into human behavior, I would want a 0:20:55.600 --> 0:20:59.120 many part um scenario and switches between them, and then 0:20:59.160 --> 0:21:01.840 we can ask do those synchronized. So we haven't done 0:21:01.840 --> 0:21:04.120 that study, but I think that's how I would roll 0:21:04.200 --> 0:21:06.680 this forward if I were to do so. We keep 0:21:06.680 --> 0:21:09.960 finding that the interaction is that just as as important 0:21:10.040 --> 0:21:15.080 as the individual. Well that that's actually that's a very 0:21:15.080 --> 0:21:17.520 good point. I want to expand on that. In the 0:21:17.560 --> 0:21:20.520 study of you know, in traditional studies where people are reductionists, 0:21:20.560 --> 0:21:24.440 you often talk about how an individual um, an individual 0:21:24.520 --> 0:21:27.960 entity behaves. And one of the things that that that 0:21:28.280 --> 0:21:31.000 people try to convey in the study of networks more generally, 0:21:31.600 --> 0:21:34.840 UM is that you know, the interactions really matter. And 0:21:34.840 --> 0:21:36.680 this is something, of course now in the modern world 0:21:36.680 --> 0:21:39.320 we see i would say, much more than we see before, 0:21:39.720 --> 0:21:42.800 and the study of networks and complex systems really tries 0:21:42.920 --> 0:21:46.439 to focus on what effects can emerge from interactions that 0:21:46.560 --> 0:21:49.760 you don't just see from individual components, and so things 0:21:49.880 --> 0:21:52.920 like you know, which memes go viral? You know it's 0:21:53.280 --> 0:21:55.760 you know, there's a bunch of cat memes that go viral. 0:21:55.800 --> 0:21:58.919 It's probably not because of the intrinsic quality, but probably 0:21:58.920 --> 0:22:02.600