1 00:00:00,160 --> 00:00:02,320 Speaker 1: But knowledge to work and grow your business with c 2 00:00:02,520 --> 00:00:06,680 Speaker 1: i T from transportation to healthcare to manufacturing. C i 3 00:00:06,760 --> 00:00:10,520 Speaker 1: T offers commercial lending, leasing, and treasury management services for 4 00:00:10,600 --> 00:00:13,520 Speaker 1: small and middle market businesses. Learn more at c I 5 00:00:13,560 --> 00:00:26,160 Speaker 1: T dot com put Knowledge to Work. Hello, and welcome 6 00:00:26,200 --> 00:00:29,120 Speaker 1: to another episode of the Odd Lots podcast. I am 7 00:00:29,160 --> 00:00:34,639 Speaker 1: Tracy Alloway, executive editor of Bloomberg Markets, and uh, let 8 00:00:34,680 --> 00:00:39,120 Speaker 1: me just say that I think what you are about 9 00:00:39,159 --> 00:00:43,520 Speaker 1: to hear is probably the most random edition of Odd 10 00:00:43,520 --> 00:00:47,080 Speaker 1: Lots that we've done so far. And just to give 11 00:00:47,120 --> 00:00:50,239 Speaker 1: you an idea of how random it is, I have 12 00:00:50,479 --> 00:00:54,280 Speaker 1: with me a special co host. It is lauracan Roche Kelly. 13 00:00:54,640 --> 00:00:59,080 Speaker 1: Here's our resident cow expert. That's how random this is. 14 00:00:59,360 --> 00:01:02,120 Speaker 1: Say hello, lorcan h Tracy, how is it going? I 15 00:01:02,120 --> 00:01:04,160 Speaker 1: think I'm a cow expert? I think well, I'll definitely 16 00:01:04,200 --> 00:01:07,000 Speaker 1: that one is a compliment, co owner, if nothing else, 17 00:01:08,680 --> 00:01:11,560 Speaker 1: it is meant to be a compliment. Mark, we don't 18 00:01:11,600 --> 00:01:14,600 Speaker 1: have a lot of cow experts, so that that's pretty special. 19 00:01:14,800 --> 00:01:19,119 Speaker 1: You're reducing the compliment straight away, Tracy. I should say 20 00:01:19,160 --> 00:01:22,680 Speaker 1: that my normal co host, Joe Wisenhal is still on 21 00:01:22,920 --> 00:01:26,200 Speaker 1: his epic business trip and he's left me to do 22 00:01:26,280 --> 00:01:30,840 Speaker 1: this podcast? Up? How do I intro this podcast? Um? 23 00:01:30,920 --> 00:01:33,600 Speaker 1: All right, So a couple of weeks ago I noticed 24 00:01:34,120 --> 00:01:39,080 Speaker 1: mathematical paper. It was called quote, A Mathematical Model for 25 00:01:39,120 --> 00:01:45,440 Speaker 1: the Dynamics and Synchronization of Cows. Lorcan. I think I 26 00:01:45,480 --> 00:01:48,120 Speaker 1: sent it to you at the time. Yeah, I think 27 00:01:48,120 --> 00:01:50,560 Speaker 1: you said to me was this email subject was? Can 28 00:01:50,600 --> 00:01:53,160 Speaker 1: you believe this exists? I think may have been something 29 00:01:53,240 --> 00:01:56,200 Speaker 1: online what the email subject was. I was very excited 30 00:01:56,200 --> 00:01:58,200 Speaker 1: to see it, I must say, because anything to do 31 00:01:58,240 --> 00:02:03,000 Speaker 1: with cows excites me. Right, how many cows do you have? Now, Lorcan? 32 00:02:03,720 --> 00:02:05,760 Speaker 1: I think at first, I think you ever asked a 33 00:02:05,760 --> 00:02:07,400 Speaker 1: farm or how many cows? Yes, because it's a way 34 00:02:07,400 --> 00:02:08,960 Speaker 1: of working out for his income is but I think 35 00:02:09,480 --> 00:02:11,640 Speaker 1: enough to keep me busy is the standard answer. I 36 00:02:11,680 --> 00:02:15,320 Speaker 1: think that I'm sorry, I just made a bovine faux paw. 37 00:02:15,520 --> 00:02:19,560 Speaker 1: I guess, all right, enough to keep you busy? That's 38 00:02:19,560 --> 00:02:22,520 Speaker 1: good enough for this podcast. Okay, So we got this paper, 39 00:02:22,560 --> 00:02:25,920 Speaker 1: A mathematical Model for the Dynamics and Synchronization of cows. 40 00:02:26,000 --> 00:02:30,200 Speaker 1: It's written by let's see one, two, three, four mathematicians 41 00:02:30,200 --> 00:02:35,560 Speaker 1: at various universities, and it talks about hurting behavior in 42 00:02:35,760 --> 00:02:40,280 Speaker 1: cows and sort of mathematical models used to analyze that behavior. 43 00:02:40,400 --> 00:02:42,799 Speaker 1: And I know you're all thinking, what in the world 44 00:02:42,880 --> 00:02:47,320 Speaker 1: does this have to do with markets and investment and finance. 45 00:02:47,720 --> 00:02:52,240 Speaker 1: But if you'll remember, we often talk about investors acting 46 00:02:52,360 --> 00:02:55,720 Speaker 1: in markets like a herd. We often talk about hurting behavior, 47 00:02:55,840 --> 00:02:59,600 Speaker 1: people crowding into the same types of investments, the same 48 00:02:59,639 --> 00:03:05,320 Speaker 1: posit editions, basically following each other seeking the safety of numbers. 49 00:03:05,320 --> 00:03:09,480 Speaker 1: So it's not totally off the wall, lorcan am I 50 00:03:09,520 --> 00:03:11,600 Speaker 1: stretching that a bit? I think that's fair. I don't 51 00:03:11,639 --> 00:03:13,640 Speaker 1: think it's stretching it at all. I think I'm like 52 00:03:14,080 --> 00:03:16,200 Speaker 1: I suppose I spend a lot of my time looking 53 00:03:16,200 --> 00:03:18,520 Speaker 1: at the markets and some of my time looking at cows. 54 00:03:18,560 --> 00:03:21,760 Speaker 1: And while the similarities don't immediately jump out to me, 55 00:03:21,840 --> 00:03:24,200 Speaker 1: I must say when I'm working in either world both, 56 00:03:24,600 --> 00:03:28,080 Speaker 1: there is generally a feeling that markets gain momentum, and 57 00:03:28,280 --> 00:03:31,239 Speaker 1: the more I suppose more people talk about the trades, 58 00:03:31,320 --> 00:03:33,560 Speaker 1: the more likely people are to get on the trade 59 00:03:33,639 --> 00:03:35,680 Speaker 1: or just have an opinion on the trade anyway, And 60 00:03:35,800 --> 00:03:39,200 Speaker 1: much like with cows, if one cow finds good grass, 61 00:03:39,600 --> 00:03:41,240 Speaker 1: the rest of the cows will see the good grass 62 00:03:41,240 --> 00:03:43,720 Speaker 1: and run over and get some for themselves, so that 63 00:03:44,160 --> 00:03:47,120 Speaker 1: on the high level, the herding idea, I think it's 64 00:03:47,120 --> 00:03:51,280 Speaker 1: been well established within markets, and it's it comes from animals, 65 00:03:51,320 --> 00:03:53,080 Speaker 1: comes from herds of anis, That's where the world comes from. 66 00:03:53,120 --> 00:03:56,440 Speaker 1: So I'd imagine to examine what cows do should tell 67 00:03:56,480 --> 00:03:59,040 Speaker 1: us something about hurting in markets, or at least give 68 00:03:59,120 --> 00:04:01,320 Speaker 1: us away of modeling her new markets, which is why 69 00:04:01,360 --> 00:04:04,160 Speaker 1: I wanted to paper. Say, do you ever look out 70 00:04:04,200 --> 00:04:06,960 Speaker 1: your window on your farm in Ireland and watch your 71 00:04:07,000 --> 00:04:09,960 Speaker 1: cows and like ponder them as you think about markets? 72 00:04:10,520 --> 00:04:13,280 Speaker 1: I have this image of you doing that. There there 73 00:04:13,360 --> 00:04:15,560 Speaker 1: is I can see my cows from my window. Depending 74 00:04:15,600 --> 00:04:17,800 Speaker 1: on what field they're in. Whether what I'm doing is 75 00:04:17,839 --> 00:04:20,360 Speaker 1: pondering them, are pining to be with them rather than 76 00:04:20,480 --> 00:04:24,440 Speaker 1: staring at some unfinished coffee and folking me, I'm not sure. 77 00:04:33,800 --> 00:04:36,320 Speaker 1: All right, Without further ado, we are going to bring 78 00:04:36,440 --> 00:04:39,440 Speaker 1: in the authors of this paper. We have three of 79 00:04:39,480 --> 00:04:42,920 Speaker 1: them with us, and because we have three guests, it's 80 00:04:43,000 --> 00:04:44,839 Speaker 1: the first time I think we've ever had a trio 81 00:04:44,880 --> 00:04:47,400 Speaker 1: of guests on this podcast. I'm going to ask them 82 00:04:47,440 --> 00:04:50,840 Speaker 1: to quickly introduce themselves so that you all know who 83 00:04:50,880 --> 00:04:54,360 Speaker 1: they are. UM, why don't we start with j j 84 00:04:54,560 --> 00:04:57,240 Speaker 1: Can you say hello and intro yourself? Hey, Hello, this 85 00:04:57,320 --> 00:05:00,680 Speaker 1: is j Um. Last name is Son actually come currently 86 00:05:00,720 --> 00:05:04,520 Speaker 1: assistant professor in the Mass Department at Clarkson University. It 87 00:05:04,680 --> 00:05:08,440 Speaker 1: is in Potsdam, New York, Upstate New York. UM. I 88 00:05:08,680 --> 00:05:12,360 Speaker 1: work with a lot of the complex systems, networks, no 89 00:05:12,440 --> 00:05:16,400 Speaker 1: linear dynamics and more recently UM studying the information flow 90 00:05:16,480 --> 00:05:19,719 Speaker 1: in those complex systems. By the way, the time the 91 00:05:19,720 --> 00:05:22,640 Speaker 1: paper was written, actually it was a graduate student visiting 92 00:05:22,880 --> 00:05:26,040 Speaker 1: UM with at the time my advisor or quote UM 93 00:05:26,400 --> 00:05:29,240 Speaker 1: who is here also today And it was a very 94 00:05:29,240 --> 00:05:33,400 Speaker 1: exciting UM journey for me to be on this project. 95 00:05:33,920 --> 00:05:38,080 Speaker 1: Fantastic Eric, why don't you say hello? Hello? I'm Eric Bolt, 96 00:05:38,160 --> 00:05:43,480 Speaker 1: also Clarkson University and John Harrington, Professor of Mathematics. So UM, 97 00:05:43,560 --> 00:05:45,680 Speaker 1: like real, we do a lot with nonleader dynamics, a 98 00:05:45,760 --> 00:05:48,880 Speaker 1: lots of that dad UM. In years past we'd called 99 00:05:49,000 --> 00:05:52,200 Speaker 1: chaos theory, but UM in recent years we do a 100 00:05:52,240 --> 00:05:55,880 Speaker 1: lot also with large scale complex systems, which is something 101 00:05:55,920 --> 00:05:58,680 Speaker 1: we're developing a center for a Clarkson so and this 102 00:05:58,760 --> 00:06:02,279 Speaker 1: is also a nonlo dynamics. And then finally we have 103 00:06:02,560 --> 00:06:06,599 Speaker 1: Mason Porter joining us, I think from l A. Yeah, 104 00:06:06,680 --> 00:06:10,920 Speaker 1: I'm Mason Porter. Um. I'm currently a professor of mathematics 105 00:06:10,920 --> 00:06:14,000 Speaker 1: at u C l A. UM I just moved over 106 00:06:14,040 --> 00:06:17,320 Speaker 1: from University of Oxford a few weeks ago. Um. I'm 107 00:06:17,360 --> 00:06:20,880 Speaker 1: also a specialist in complex systems and networks and non 108 00:06:20,920 --> 00:06:23,840 Speaker 1: their dynamics, And it's one of the things I wanted 109 00:06:23,880 --> 00:06:27,440 Speaker 1: to mention. You were talking about similarities between between hurting 110 00:06:27,480 --> 00:06:30,159 Speaker 1: and animals and hurting and markets. In fact, one of 111 00:06:30,160 --> 00:06:33,560 Speaker 1: those things that we specialize in is exactly collective behavior 112 00:06:33,600 --> 00:06:36,840 Speaker 1: and complex systems, which can be things like hurting in 113 00:06:36,880 --> 00:06:40,960 Speaker 1: all sorts of context, or ideas becoming viral and and 114 00:06:40,960 --> 00:06:43,120 Speaker 1: and so on. So we actually take an abstract point 115 00:06:43,120 --> 00:06:45,360 Speaker 1: of view and very specifically study these sorts of things 116 00:06:45,400 --> 00:06:48,320 Speaker 1: in many different types of systems. Exactly, we've all we've 117 00:06:48,320 --> 00:06:51,839 Speaker 1: all worked together in these different sorts of things, including swarming, 118 00:06:52,360 --> 00:06:55,360 Speaker 1: um schooling if it's fish, and then also human behaviors 119 00:06:55,400 --> 00:06:58,240 Speaker 1: when they work in groups. Well maybe that's a good 120 00:06:58,279 --> 00:07:01,600 Speaker 1: jumping off points. So we have collective behavior, and there's 121 00:07:01,640 --> 00:07:05,279 Speaker 1: been a lot of study of collective behavior, whether it's 122 00:07:05,320 --> 00:07:09,520 Speaker 1: in animals or humans or or systems and that sort 123 00:07:09,640 --> 00:07:14,440 Speaker 1: of thing. What made you decide to focus on cows 124 00:07:14,600 --> 00:07:18,720 Speaker 1: specifically for this paper? Okay, so maybe I should answer 125 00:07:18,760 --> 00:07:22,320 Speaker 1: that because the project actually UM started with me and 126 00:07:22,400 --> 00:07:25,600 Speaker 1: the fourth The fourth co author is Marian Dawkins. She's 127 00:07:25,640 --> 00:07:29,600 Speaker 1: actually she's a zoologist rather than a mathematician, and and 128 00:07:29,640 --> 00:07:32,320 Speaker 1: she and I know each other from being in the 129 00:07:32,400 --> 00:07:37,400 Speaker 1: same Oxford College UM, and we formulated a project actually 130 00:07:37,440 --> 00:07:41,400 Speaker 1: about a year or so before before Eric and Jay visited. 131 00:07:41,960 --> 00:07:45,840 Speaker 1: And one of the things that that had UM predated 132 00:07:46,280 --> 00:07:49,480 Speaker 1: the project was that Marian was sort of lamenting that 133 00:07:49,560 --> 00:07:52,400 Speaker 1: many UM people who are who are theorists and working 134 00:07:52,400 --> 00:07:55,960 Speaker 1: on problems that come from biology, were not sufficiently interfacing 135 00:07:55,960 --> 00:07:59,640 Speaker 1: with biology. And at some point the conversation turned to 136 00:07:59,680 --> 00:08:02,760 Speaker 1: her work on cows and other animals, which is something 137 00:08:02,760 --> 00:08:05,640 Speaker 1: that she's been doing for many years, and it seemed 138 00:08:05,680 --> 00:08:08,720 Speaker 1: interesting to me and I was interested in collective behavior 139 00:08:08,760 --> 00:08:12,080 Speaker 1: in general. So we formulated UM a project that we 140 00:08:12,120 --> 00:08:15,440 Speaker 1: did in a certain manner called an Agent based Model UM. 141 00:08:15,440 --> 00:08:18,640 Speaker 1: This was the year before Eric and Ja visited UM, 142 00:08:18,720 --> 00:08:21,720 Speaker 1: and then that one was attempting to be more realistic 143 00:08:21,720 --> 00:08:23,840 Speaker 1: but was a bit abstract, and so we wanted to 144 00:08:24,640 --> 00:08:28,080 Speaker 1: step back and have a bare bones project. So serendipity, 145 00:08:28,080 --> 00:08:30,200 Speaker 1: I suppose, is a short version of that answer. And 146 00:08:30,240 --> 00:08:32,560 Speaker 1: I tend to be interested in just about everything. And 147 00:08:32,559 --> 00:08:35,080 Speaker 1: I had a local expert, and so we worked on it, 148 00:08:35,120 --> 00:08:38,559 Speaker 1: and then Eric visited me along with Ja the next year, 149 00:08:38,600 --> 00:08:41,040 Speaker 1: and so we decided that we would pursue that further. 150 00:08:41,679 --> 00:08:45,200 Speaker 1: So when we think about hurting behavior in cows, and 151 00:08:45,520 --> 00:08:50,120 Speaker 1: I guess other mammals like antelope, seabro whatever, um, we 152 00:08:50,200 --> 00:08:55,360 Speaker 1: usually think that they all move, I guess in tandem, 153 00:08:55,400 --> 00:08:58,160 Speaker 1: like like Lorcan was saying, if one cow sees fresh grass, 154 00:08:58,240 --> 00:09:01,600 Speaker 1: then all the cows migrate there. But also I guess 155 00:09:01,640 --> 00:09:06,080 Speaker 1: for protective reasons to protect themselves from predators. Is that 156 00:09:06,360 --> 00:09:10,440 Speaker 1: is that the accepted version of hurting behavior in cows? 157 00:09:11,520 --> 00:09:15,240 Speaker 1: I think that's at least part of it. There's also, um, 158 00:09:15,280 --> 00:09:18,360 Speaker 1: if they're in a pen, for instance, they actually may 159 00:09:18,400 --> 00:09:20,720 Speaker 1: also want to just all be able to lie down 160 00:09:20,920 --> 00:09:23,640 Speaker 1: at a similar time, especially if they're if they're under 161 00:09:23,679 --> 00:09:26,240 Speaker 1: similar um sort of forces from a from a day 162 00:09:26,320 --> 00:09:28,400 Speaker 1: night cycle. So so you know, some of this is 163 00:09:28,400 --> 00:09:32,559 Speaker 1: actually protection, but some of it is also similar needs. Yeah, 164 00:09:32,880 --> 00:09:35,880 Speaker 1: so um, all those elements are in our work Actually 165 00:09:35,920 --> 00:09:37,920 Speaker 1: we have a follow on work which actually includes things 166 00:09:37,920 --> 00:09:41,600 Speaker 1: like why would they do that, optimizing their um, their 167 00:09:41,640 --> 00:09:46,839 Speaker 1: resilience to petitors, and so forth. But the centerpiece of 168 00:09:46,880 --> 00:09:50,160 Speaker 1: the model is that the cow individually has these different 169 00:09:50,200 --> 00:09:52,160 Speaker 1: things that go on inside their bodies. You know, they 170 00:09:52,280 --> 00:09:54,640 Speaker 1: need to eat, their need to digest, which is kind 171 00:09:54,640 --> 00:09:57,679 Speaker 1: of complicated in the cow, and um, then they need 172 00:09:57,720 --> 00:10:00,000 Speaker 1: to sleep. So it's a little bit like a self 173 00:10:00,000 --> 00:10:03,160 Speaker 1: of parture kating rhythm in your own body. And for 174 00:10:03,240 --> 00:10:05,719 Speaker 1: the other reasons you's described. Then it actually turns out 175 00:10:05,720 --> 00:10:07,000 Speaker 1: to be a good thing if they do it together. 176 00:10:07,800 --> 00:10:10,160 Speaker 1: So that's the synchronization aspect, and whether they're in a 177 00:10:10,200 --> 00:10:12,960 Speaker 1: pen or or there in the wild, um, there's some 178 00:10:13,000 --> 00:10:14,599 Speaker 1: aspect of they want to do it together. Now in 179 00:10:14,600 --> 00:10:17,480 Speaker 1: the pen, they're not really predated anymore, but they carry 180 00:10:17,480 --> 00:10:20,920 Speaker 1: on that natural behavior. Yeah. So one of the things 181 00:10:21,000 --> 00:10:23,200 Speaker 1: was that so those interactions to not to be really 182 00:10:23,200 --> 00:10:27,400 Speaker 1: important as a determining factor of whether they could synchronize 183 00:10:27,440 --> 00:10:31,320 Speaker 1: and to what extent they do synchronize UM, which happened 184 00:10:31,360 --> 00:10:35,120 Speaker 1: to be also related to the production and even a 185 00:10:35,160 --> 00:10:37,560 Speaker 1: though we don't know how happy they are, people do 186 00:10:37,760 --> 00:10:40,360 Speaker 1: say that they seem to be happier when they actually 187 00:10:40,360 --> 00:10:43,760 Speaker 1: produce more and most synchronized. Just from the looking from 188 00:10:43,760 --> 00:10:46,719 Speaker 1: the paper, they are rich paper. Do you have the 189 00:10:46,960 --> 00:10:49,080 Speaker 1: look at a single cow model and then you looked 190 00:10:49,080 --> 00:10:52,160 Speaker 1: at what you call coupled cows. But just for your 191 00:10:52,200 --> 00:10:55,160 Speaker 1: information on the farming background, when you say cows are coupling, 192 00:10:55,240 --> 00:10:59,920 Speaker 1: you means something completely different. But they are. But they 193 00:11:00,080 --> 00:11:03,320 Speaker 1: said in a larger heard that the synchronicity seems to 194 00:11:03,320 --> 00:11:05,120 Speaker 1: break down. Is it's what you're seeing to be saying 195 00:11:05,120 --> 00:11:09,280 Speaker 1: the paper that well as the as the stand ups 196 00:11:09,360 --> 00:11:11,439 Speaker 1: sit down cyclist, the one that you're looking at, it 197 00:11:11,520 --> 00:11:13,360 Speaker 1: seems to break down. See if a mixture of cows 198 00:11:13,360 --> 00:11:16,200 Speaker 1: standing up and sitting down. And I'm wondering, is that 199 00:11:16,240 --> 00:11:19,320 Speaker 1: I think that you're soften observation or something that you 200 00:11:19,440 --> 00:11:23,839 Speaker 1: produce from your mathematical models yourself. Our second paper actually 201 00:11:23,840 --> 00:11:26,679 Speaker 1: has an aspect where um the groups can become too 202 00:11:26,760 --> 00:11:29,320 Speaker 1: large um two for their own good, and they break 203 00:11:29,320 --> 00:11:32,000 Speaker 1: apart and they may subsynchronize in the smaller groups. Now 204 00:11:32,000 --> 00:11:34,280 Speaker 1: do you see that in your farm? Yeah, well, the 205 00:11:34,280 --> 00:11:35,960 Speaker 1: way if I had that many cows, I'm sure i'd 206 00:11:35,960 --> 00:11:40,720 Speaker 1: say it. But it's I think I'm with with the 207 00:11:40,760 --> 00:11:43,120 Speaker 1: way I'm supposed to get back to nothing boast of farming. 208 00:11:43,200 --> 00:11:45,000 Speaker 1: The way the farming works here is that it is 209 00:11:45,120 --> 00:11:48,720 Speaker 1: very The synchronicity I see tends to be much more. 210 00:11:48,920 --> 00:11:50,800 Speaker 1: If it's going to rain in the next twenty minutes, 211 00:11:50,880 --> 00:11:53,160 Speaker 1: most of the cows are sitting down. If it's very hot, 212 00:11:53,200 --> 00:11:55,959 Speaker 1: most are standing up. But beyond that they will generally 213 00:11:56,920 --> 00:11:58,600 Speaker 1: I suppose that the heart is big enough that someone 214 00:11:58,600 --> 00:12:00,880 Speaker 1: will be sitting down, someone be standing up at any time. 215 00:12:01,240 --> 00:12:03,720 Speaker 1: Whereas if you put a small number of cows in 216 00:12:03,720 --> 00:12:05,760 Speaker 1: the shed, that occasion will do with some are lane. 217 00:12:05,840 --> 00:12:07,360 Speaker 1: If we take if we have cows learn name, we 218 00:12:07,400 --> 00:12:09,280 Speaker 1: take them out of the herd because they can keep 219 00:12:09,360 --> 00:12:10,640 Speaker 1: up to your house, and we have three or four 220 00:12:10,640 --> 00:12:13,880 Speaker 1: cows together, and they will synchronize very strongly, like so 221 00:12:13,960 --> 00:12:16,080 Speaker 1: the four will decision down, order four will be standing up. 222 00:12:16,400 --> 00:12:18,200 Speaker 1: But whether that's because they're in the shed and not 223 00:12:18,240 --> 00:12:20,520 Speaker 1: out in the field, it's you know, that's what the 224 00:12:20,559 --> 00:12:23,680 Speaker 1: externalities would be very hard to calculate within a model 225 00:12:23,679 --> 00:12:28,320 Speaker 1: like this. The externalities are a very big deal. And 226 00:12:28,480 --> 00:12:32,520 Speaker 1: um one of the things that the people argue about is, 227 00:12:32,559 --> 00:12:34,800 Speaker 1: you know how much of this is from circadian rhythms, 228 00:12:35,040 --> 00:12:37,760 Speaker 1: and how much of it is from from say, signals 229 00:12:37,760 --> 00:12:40,320 Speaker 1: from other animals that are that are nearby. Um. It's 230 00:12:40,360 --> 00:12:44,480 Speaker 1: a very difficult thing to um um disentangle from each other. UM. 231 00:12:44,600 --> 00:12:46,120 Speaker 1: One thing I want to mention it is kind of 232 00:12:46,160 --> 00:12:48,160 Speaker 1: going back on your on your earlier comment in terms 233 00:12:48,200 --> 00:12:52,040 Speaker 1: of having a larger herd having kind of not complete synchrony. 234 00:12:52,320 --> 00:12:56,800 Speaker 1: In the paper, we're not actually demanding um complete synchrony. 235 00:12:56,800 --> 00:12:59,240 Speaker 1: We're just measuring how synchronized they are and trying to 236 00:12:59,280 --> 00:13:01,760 Speaker 1: do it in an a sort of a quantitative way 237 00:13:01,800 --> 00:13:04,120 Speaker 1: so you can measure. And this is something that comes 238 00:13:04,200 --> 00:13:07,000 Speaker 1: originally from the theory of coupled oscillators. The term the 239 00:13:07,080 --> 00:13:09,600 Speaker 1: term coupled has a very specific meaning in mathematics and 240 00:13:09,600 --> 00:13:12,040 Speaker 1: physics that's not quite the same as the English meaning. 241 00:13:12,200 --> 00:13:14,840 Speaker 1: That just means that they're interacting. UM. So if you 242 00:13:14,880 --> 00:13:17,680 Speaker 1: write down equations and you have some some term that 243 00:13:17,720 --> 00:13:20,160 Speaker 1: has has parts of two different equations, this is a 244 00:13:20,200 --> 00:13:22,280 Speaker 1: way for for this is a way for them to 245 00:13:22,280 --> 00:13:26,160 Speaker 1: be coupled together. UM. But um, there there are there 246 00:13:26,160 --> 00:13:29,520 Speaker 1: are some measures that that are from um from long 247 00:13:29,559 --> 00:13:33,080 Speaker 1: studies of oscillations, from biological rhythms, for example, that tries 248 00:13:33,120 --> 00:13:36,480 Speaker 1: to measure how synchronized things are. And so it's not 249 00:13:36,520 --> 00:13:38,880 Speaker 1: that you have a yes or no that everybody is synchronized. 250 00:13:38,920 --> 00:13:40,920 Speaker 1: You have sort of how much they are. And you 251 00:13:40,960 --> 00:13:44,800 Speaker 1: can imagine doing this um with with animal behavior as well, 252 00:13:45,280 --> 00:13:48,680 Speaker 1: um by just saying, okay, well do they do different 253 00:13:48,720 --> 00:13:51,640 Speaker 1: cows stand up at a similar time? You know, maybe 254 00:13:51,679 --> 00:13:54,280 Speaker 1: there's a delay of one second versus ten seconds, and 255 00:13:54,320 --> 00:13:56,240 Speaker 1: so you would say that the delay of one second, 256 00:13:56,280 --> 00:13:57,880 Speaker 1: if you n able, if you're able to measure that 257 00:13:57,880 --> 00:14:00,560 Speaker 1: would be more synchronized than if then if it's ten seconds. 258 00:14:00,559 --> 00:14:04,280 Speaker 1: Apart whether any extent to which that comes from cows 259 00:14:04,280 --> 00:14:06,560 Speaker 1: getting signals from others by watching what others are doing, 260 00:14:06,760 --> 00:14:09,559 Speaker 1: and how much comes from having similar desires, that's very 261 00:14:09,600 --> 00:14:12,559 Speaker 1: difficult to dista. I think that suppost initiate aside is 262 00:14:12,640 --> 00:14:16,440 Speaker 1: um um. The one thing that work cows completely appos 263 00:14:16,520 --> 00:14:18,520 Speaker 1: de synchronized themselves from the rest of her is when 264 00:14:18,520 --> 00:14:21,760 Speaker 1: they're about to give birth to a calf. And I 265 00:14:21,760 --> 00:14:24,240 Speaker 1: think there's a product available. I don't know if I 266 00:14:24,320 --> 00:14:27,280 Speaker 1: have it from white House my farm. It's called moo 267 00:14:27,400 --> 00:14:32,600 Speaker 1: called mlo cl What is it is? A small Are 268 00:14:32,600 --> 00:14:34,360 Speaker 1: you making this up. No, I'm not making that. You 269 00:14:34,360 --> 00:14:37,240 Speaker 1: can google it does exist. It's a it's a lectronic 270 00:14:37,320 --> 00:14:39,600 Speaker 1: part that I attached to the cow's tail, and what 271 00:14:39,680 --> 00:14:42,720 Speaker 1: it measures is how much the cow switches her tail. 272 00:14:43,280 --> 00:14:45,960 Speaker 1: And before cow gets birth, she becomes agitated, she has 273 00:14:46,000 --> 00:14:49,440 Speaker 1: more tail swishing, and this product notices the extra tail 274 00:14:49,440 --> 00:14:51,960 Speaker 1: swishing and it sends me a text message to say 275 00:14:52,000 --> 00:14:53,480 Speaker 1: that this cow is going to calve in the next 276 00:14:53,480 --> 00:14:57,200 Speaker 1: two hours. And what what that How that that thing 277 00:14:57,240 --> 00:14:58,920 Speaker 1: works is that you've put it on the cow and 278 00:14:58,960 --> 00:15:00,960 Speaker 1: it stays and going to It's an idea of what 279 00:15:01,000 --> 00:15:04,760 Speaker 1: the cow's rhythm is, and then it noticed the changing 280 00:15:04,840 --> 00:15:07,760 Speaker 1: rhythm and acknowledge that that there's something big is happen there. 281 00:15:07,760 --> 00:15:11,520 Speaker 1: That's a change room. So cows generally have a strong 282 00:15:11,600 --> 00:15:14,240 Speaker 1: rhythm within themselves. Suppose it's what this company is taken 283 00:15:14,240 --> 00:15:17,080 Speaker 1: advantage of that they're just stand up, sit down switched 284 00:15:17,080 --> 00:15:20,560 Speaker 1: their tail. Is can be very easily predicted within a 285 00:15:20,600 --> 00:15:23,600 Speaker 1: certain sucset of circumstances. So when the circumstances changed, as 286 00:15:23,600 --> 00:15:25,240 Speaker 1: in the cow is about to have a calf, it 287 00:15:25,360 --> 00:15:28,520 Speaker 1: connect the exect use that information to send me a 288 00:15:28,520 --> 00:15:31,160 Speaker 1: text message to say, this is about the calf. Now, 289 00:15:31,240 --> 00:15:33,120 Speaker 1: is there an analog of moo cow for the market. 290 00:15:33,840 --> 00:15:36,000 Speaker 1: It's on the market. Yes, it's something that I mean 291 00:15:36,000 --> 00:15:38,600 Speaker 1: from market market prediction. You can put it on the traders. 292 00:15:40,480 --> 00:15:41,920 Speaker 1: Although if you could figure out what it is, I 293 00:15:41,920 --> 00:15:43,800 Speaker 1: imagine you get very rich. You won't tell me about this. 294 00:15:44,760 --> 00:15:47,160 Speaker 1: I'm actually surprised that we've gotten this far and you 295 00:15:47,240 --> 00:15:52,080 Speaker 1: haven't remarked that, actually, what does a bullmarket mean? We're 296 00:15:52,120 --> 00:15:56,240 Speaker 1: saving that first. We are going to take a short 297 00:15:56,320 --> 00:16:03,320 Speaker 1: break for a word from our sponsors. But knowledge to 298 00:16:03,360 --> 00:16:06,000 Speaker 1: work and grow your business with c i T. From 299 00:16:06,000 --> 00:16:11,120 Speaker 1: transportation to healthcare to manufacturing. C i T offers commercial lending, leasing, 300 00:16:11,160 --> 00:16:14,840 Speaker 1: and treasury management services for small and middle market businesses. 301 00:16:15,040 --> 00:16:17,720 Speaker 1: Learn more at c i T dot com put Knowledge 302 00:16:17,760 --> 00:16:26,160 Speaker 1: to Work. Okay, and we are back. We are talking 303 00:16:26,280 --> 00:16:31,840 Speaker 1: cows hurting behavior and mathematics. UM. Just to kick off 304 00:16:31,840 --> 00:16:33,960 Speaker 1: the second half of the conversation, maybe could you just 305 00:16:34,040 --> 00:16:37,600 Speaker 1: walk us through in very simple terms what you found 306 00:16:37,760 --> 00:16:40,560 Speaker 1: in your paper from the mathematical model you use, and 307 00:16:40,640 --> 00:16:44,720 Speaker 1: what it says about cows hurting behavior. UM. I think 308 00:16:44,720 --> 00:16:46,960 Speaker 1: from the math medical point of view, UM, there are 309 00:16:47,040 --> 00:16:51,320 Speaker 1: something that's very unique about this particular model because one 310 00:16:51,360 --> 00:16:55,000 Speaker 1: thing about calls and some other animals are that, um, 311 00:16:55,000 --> 00:16:58,200 Speaker 1: they actually have different modes, right, It's not um that 312 00:16:58,320 --> 00:17:01,320 Speaker 1: they follow one type of motion or dynamics and then 313 00:17:01,360 --> 00:17:05,080 Speaker 1: they just continue. For cars, there are three distinct modes. 314 00:17:05,400 --> 00:17:09,840 Speaker 1: They can walk, stand, they eat, or they lie down. 315 00:17:10,440 --> 00:17:12,560 Speaker 1: And the turns are that there are you know, very 316 00:17:12,600 --> 00:17:16,280 Speaker 1: traditional and machinery in mathematics that that we could use 317 00:17:16,480 --> 00:17:20,080 Speaker 1: for for particularly to model this behavior um as well 318 00:17:20,080 --> 00:17:22,439 Speaker 1: as their interactions. So so one thing we found that 319 00:17:22,520 --> 00:17:26,720 Speaker 1: sort of contraintuitive is you would imagine that maybe by 320 00:17:26,800 --> 00:17:31,320 Speaker 1: interacting more or more intensively, they would necessarily synchronize more, 321 00:17:31,400 --> 00:17:34,960 Speaker 1: and that wasn't um the case. So what that means 322 00:17:34,960 --> 00:17:37,000 Speaker 1: in reality is if you start to you know, put 323 00:17:37,040 --> 00:17:41,480 Speaker 1: them in fans and with higher density, it's not necessarily 324 00:17:41,480 --> 00:17:44,480 Speaker 1: true that you make them synchronize more. They actually could 325 00:17:44,760 --> 00:17:48,600 Speaker 1: break the synchrony by increasing those coupling. So when you 326 00:17:48,640 --> 00:17:52,919 Speaker 1: have more cows together and they're in a crowded, confined area, 327 00:17:53,000 --> 00:17:57,359 Speaker 1: they don't actually exhibit hurting behavior. Is that right? I 328 00:17:57,359 --> 00:18:00,320 Speaker 1: mean again it's not not yes, no question, UM. It's 329 00:18:00,400 --> 00:18:04,160 Speaker 1: the extent to which they synchronize could actually decrease. Where 330 00:18:04,200 --> 00:18:08,000 Speaker 1: you put more in the finite vertical space, and is 331 00:18:08,040 --> 00:18:12,760 Speaker 1: that competition for resources or they just start to feel 332 00:18:12,760 --> 00:18:16,119 Speaker 1: pressure because there's too many other cows. It's more of 333 00:18:16,119 --> 00:18:20,280 Speaker 1: a pressure scenario. Yeah, well, why don't we widen out 334 00:18:20,280 --> 00:18:23,520 Speaker 1: the discussion because I know that you all, um also 335 00:18:23,600 --> 00:18:27,800 Speaker 1: study network effects and chaos theory and things like that. 336 00:18:28,119 --> 00:18:34,000 Speaker 1: So how much can we extrapolate from cow behavior into 337 00:18:34,400 --> 00:18:42,280 Speaker 1: other types of behavior and specifically humans and or human investors. So, um, 338 00:18:42,400 --> 00:18:45,200 Speaker 1: one of the things one of the advantages of mathematics 339 00:18:45,320 --> 00:18:48,440 Speaker 1: is that it's automatically massively parallel. You know, people talk 340 00:18:48,480 --> 00:18:52,760 Speaker 1: about mathsily parallel UM computation. With mathematics, you can get 341 00:18:52,800 --> 00:18:56,840 Speaker 1: insights on a specific system and then other other systems 342 00:18:56,880 --> 00:19:00,520 Speaker 1: that might have similar model equations possible. It will teach 343 00:19:00,560 --> 00:19:04,600 Speaker 1: you something about that. So UM Jay was talking about 344 00:19:04,600 --> 00:19:07,359 Speaker 1: the fact that you could have stronger coupling in this 345 00:19:07,440 --> 00:19:10,960 Speaker 1: situation leading to less synchronized behavior. So that can also 346 00:19:11,040 --> 00:19:13,960 Speaker 1: potentially occur elsewhere. So if people are interacting with each 347 00:19:14,000 --> 00:19:16,639 Speaker 1: other more strongly, UM, at least this is known in 348 00:19:16,720 --> 00:19:21,160 Speaker 1: mathematical models, you can have situations where they're not necessarily 349 00:19:21,200 --> 00:19:24,919 Speaker 1: more synchronized as a result, UM, I don't know how 350 00:19:24,960 --> 00:19:27,600 Speaker 1: to experimentally verify that. I mean, that's it's much more 351 00:19:27,640 --> 00:19:30,360 Speaker 1: reality is much more complicated than mathematical model. But it's 352 00:19:30,400 --> 00:19:34,160 Speaker 1: a very general um situation that one sees mathematically, not 353 00:19:34,359 --> 00:19:37,480 Speaker 1: just in the specific model that we did, And others 354 00:19:37,600 --> 00:19:41,960 Speaker 1: have um reported similar results using other models of synchronization 355 00:19:42,000 --> 00:19:44,200 Speaker 1: in the last few years. So that's that's one example. 356 00:19:45,320 --> 00:19:47,200 Speaker 1: Another thing I say about this this work is it's 357 00:19:47,240 --> 00:19:50,520 Speaker 1: it's actually um it's a scientific study on two levels. 358 00:19:50,920 --> 00:19:53,120 Speaker 1: So it's about cows and we're studying the topical area 359 00:19:53,119 --> 00:19:56,400 Speaker 1: of cows, and we want to make conclusions about cows. 360 00:19:56,440 --> 00:19:58,000 Speaker 1: But the tools that we bring to it is actually 361 00:19:58,080 --> 00:20:00,080 Speaker 1: an unique kind of tool set in the area of 362 00:20:01,040 --> 00:20:04,960 Speaker 1: modeling a complex system like an animal, because it, as 363 00:20:05,600 --> 00:20:07,680 Speaker 1: Jay said, it's a uh, what's called a piece wise 364 00:20:07,720 --> 00:20:10,919 Speaker 1: impulsive system as we've modeled it, which means it's a 365 00:20:10,960 --> 00:20:14,600 Speaker 1: bit like a bouncing ball. Something continues continuously for a 366 00:20:14,640 --> 00:20:16,919 Speaker 1: while and then it reaches a threshold it switches. So 367 00:20:16,920 --> 00:20:20,560 Speaker 1: it might switch from um the lyne digesting state to say, okay, 368 00:20:20,560 --> 00:20:23,800 Speaker 1: now I'm done with that, onto onto sleeping. So those 369 00:20:23,840 --> 00:20:26,399 Speaker 1: states and switching between the states is actually a unique 370 00:20:26,760 --> 00:20:28,600 Speaker 1: a neat neat element in the in the area of 371 00:20:28,640 --> 00:20:33,320 Speaker 1: modeling um uh dynamical systems like a cow UM. Now, 372 00:20:33,320 --> 00:20:35,200 Speaker 1: if we want to bring that over to people, then 373 00:20:35,200 --> 00:20:37,000 Speaker 1: you might say, okay, great, the cow is a kind 374 00:20:37,000 --> 00:20:39,520 Speaker 1: of a simple system um compared to a person. And 375 00:20:39,760 --> 00:20:42,080 Speaker 1: if we said a person's like this, then they would 376 00:20:42,119 --> 00:20:44,680 Speaker 1: have many many states, perhaps because I think we would 377 00:20:44,720 --> 00:20:47,919 Speaker 1: think the cow is probably somewhat m a simpleton in 378 00:20:47,960 --> 00:20:49,880 Speaker 1: the sense of the different kind of scenarios they would 379 00:20:49,960 --> 00:20:53,040 Speaker 1: run through. So if I were to be courageous enough 380 00:20:53,040 --> 00:20:55,520 Speaker 1: to advance this into human behavior, I would want a 381 00:20:55,600 --> 00:20:59,120 Speaker 1: many part um scenario and switches between them, and then 382 00:20:59,160 --> 00:21:01,840 Speaker 1: we can ask do those synchronized. So we haven't done 383 00:21:01,840 --> 00:21:04,120 Speaker 1: that study, but I think that's how I would roll 384 00:21:04,200 --> 00:21:06,680 Speaker 1: this forward if I were to do so. We keep 385 00:21:06,680 --> 00:21:09,960 Speaker 1: finding that the interaction is that just as as important 386 00:21:10,040 --> 00:21:15,080 Speaker 1: as the individual. Well that that's actually that's a very 387 00:21:15,080 --> 00:21:17,520 Speaker 1: good point. I want to expand on that. In the 388 00:21:17,560 --> 00:21:20,520 Speaker 1: study of you know, in traditional studies where people are reductionists, 389 00:21:20,560 --> 00:21:24,440 Speaker 1: you often talk about how an individual um, an individual 390 00:21:24,520 --> 00:21:27,960 Speaker 1: entity behaves. And one of the things that that that 391 00:21:28,280 --> 00:21:31,000 Speaker 1: people try to convey in the study of networks more generally, 392 00:21:31,600 --> 00:21:34,840 Speaker 1: UM is that you know, the interactions really matter. And 393 00:21:34,840 --> 00:21:36,680 Speaker 1: this is something, of course now in the modern world 394 00:21:36,680 --> 00:21:39,320 Speaker 1: we see i would say, much more than we see before, 395 00:21:39,720 --> 00:21:42,800 Speaker 1: and the study of networks and complex systems really tries 396 00:21:42,920 --> 00:21:46,439 Speaker 1: to focus on what effects can emerge from interactions that 397 00:21:46,560 --> 00:21:49,760 Speaker 1: you don't just see from individual components, and so things 398 00:21:49,880 --> 00:21:52,920 Speaker 1: like you know, which memes go viral? You know it's 399 00:21:53,280 --> 00:21:55,760 Speaker 1: you know, there's a bunch of cat memes that go viral. 400 00:21:55,800 --> 00:21:58,919 Speaker 1: It's probably not because of the intrinsic quality, but probably 401 00:21:58,920 --> 00:22:02,600 Speaker 1: because of interaction. Uh. Now we're in in my area 402 00:22:02,640 --> 00:22:07,760 Speaker 1: of expertise, which is of course cat memes, cat videos. Yeah, exactly. 403 00:22:08,320 --> 00:22:12,280 Speaker 1: UM well, I mean this idea of how things impact 404 00:22:12,320 --> 00:22:15,160 Speaker 1: on each other is really interesting and it's really important 405 00:22:15,240 --> 00:22:19,160 Speaker 1: in markets and finance, and we've seen various attempts over 406 00:22:19,640 --> 00:22:24,080 Speaker 1: the past decades, I suppose, UM, with different degrees of 407 00:22:24,119 --> 00:22:29,080 Speaker 1: success to model that how exactly. This has always fascinated me. 408 00:22:29,520 --> 00:22:33,160 Speaker 1: Before the financial crisis, I looked at things like ghostsian 409 00:22:33,280 --> 00:22:36,040 Speaker 1: copulas on Wall Street, the things that we're used to 410 00:22:36,720 --> 00:22:40,000 Speaker 1: UM try to model how you know, one corporate or 411 00:22:40,040 --> 00:22:43,879 Speaker 1: one mortgage default would impact other defaults in the same space. 412 00:22:44,200 --> 00:22:48,720 Speaker 1: How difficult is it to mathematically model things that are 413 00:22:48,800 --> 00:22:53,360 Speaker 1: impacting on something else. This actually is something that Eric 414 00:22:53,400 --> 00:22:56,200 Speaker 1: and I have started to walk on starting a few 415 00:22:56,280 --> 00:22:59,680 Speaker 1: years ago. UM. We we think it's a very difficult 416 00:22:59,680 --> 00:23:03,639 Speaker 1: proper and scientists try to find this so called causality 417 00:23:03,680 --> 00:23:06,240 Speaker 1: of causation between different components in a very big system 418 00:23:06,280 --> 00:23:09,000 Speaker 1: in the financial sector will be like different corporations, as 419 00:23:09,040 --> 00:23:13,840 Speaker 1: you said, Um, So the challenge comes from two means. 420 00:23:13,880 --> 00:23:16,320 Speaker 1: One is you have to disentangle the effect from their 421 00:23:16,359 --> 00:23:20,400 Speaker 1: individual motion dynamics from the actual interactions. What you observe 422 00:23:20,560 --> 00:23:23,080 Speaker 1: is the aggregating effect. So you first have to find 423 00:23:23,080 --> 00:23:26,639 Speaker 1: a way to distangle that. And we've been using um 424 00:23:27,080 --> 00:23:30,159 Speaker 1: truls from information theory, which seems to be very natural 425 00:23:30,280 --> 00:23:33,359 Speaker 1: for for those types of analysis. The real challenge I 426 00:23:33,400 --> 00:23:37,040 Speaker 1: think applying this to any practical situation is that depending 427 00:23:37,080 --> 00:23:42,320 Speaker 1: on the environment, UM, you know, the actual interactions might 428 00:23:42,400 --> 00:23:45,520 Speaker 1: really change. And that's something that's very difficult to predict. 429 00:23:45,640 --> 00:23:49,679 Speaker 1: It's like an extreme event. UM. You sort of have 430 00:23:49,800 --> 00:23:55,440 Speaker 1: to believe that. Um, your process is sort of stationary. Um, 431 00:23:55,440 --> 00:23:59,560 Speaker 1: the underlying rules don't change in order to make those predictions, 432 00:23:59,600 --> 00:24:02,199 Speaker 1: but they do change, and then you can see you know, 433 00:24:02,240 --> 00:24:06,080 Speaker 1: your model may fail to predict those situations, but people 434 00:24:06,080 --> 00:24:08,920 Speaker 1: do look at it's also called early warning science, and 435 00:24:08,960 --> 00:24:12,320 Speaker 1: that's an encouraging interaction to go basically by looking at 436 00:24:13,240 --> 00:24:17,399 Speaker 1: um the science that since my statue change, and that 437 00:24:17,400 --> 00:24:21,479 Speaker 1: that's that's hope. There's hope there. Yeah. Just I suppose 438 00:24:21,480 --> 00:24:23,080 Speaker 1: that with that idea to go back and look, I 439 00:24:23,080 --> 00:24:25,240 Speaker 1: suppose that something we talked about earlier where you said 440 00:24:25,720 --> 00:24:28,160 Speaker 1: a larger herd will tend to break up, the synchronicity 441 00:24:28,200 --> 00:24:30,359 Speaker 1: will lose. Once I heard me, I don't know if 442 00:24:30,440 --> 00:24:32,439 Speaker 1: we gotta say the herd reach the critical science. But 443 00:24:32,480 --> 00:24:34,920 Speaker 1: in a larger heard you of less synchronicity, do we 444 00:24:35,320 --> 00:24:36,919 Speaker 1: Is there a chance that we can see some of that? 445 00:24:37,000 --> 00:24:39,680 Speaker 1: Like you look at say bubble behavior of bubbles and 446 00:24:39,760 --> 00:24:43,760 Speaker 1: markets for herding becomes particularly intense in an area like 447 00:24:43,760 --> 00:24:47,040 Speaker 1: Beau supposed two thousand and seven it was about properly 448 00:24:47,080 --> 00:24:50,280 Speaker 1: there was a large more bubble in property partickly in 449 00:24:50,359 --> 00:24:52,280 Speaker 1: some countries in Europe and the US with the mortgage 450 00:24:52,320 --> 00:24:56,400 Speaker 1: markets in US, and that I suppose the herds internet 451 00:24:56,480 --> 00:24:59,720 Speaker 1: became unsustainable and had to break up and within turnasial 452 00:24:59,760 --> 00:25:02,159 Speaker 1: market it's a breakup of heart like that. Almost all 453 00:25:02,200 --> 00:25:04,960 Speaker 1: those things to be catastrophic. Am I I suppose my 454 00:25:05,040 --> 00:25:06,800 Speaker 1: getting towards the right end of the stick or is 455 00:25:06,920 --> 00:25:08,840 Speaker 1: it something completely different? Well, for cows, I think there'll 456 00:25:08,880 --> 00:25:10,920 Speaker 1: be two reasons why they wouldn't want to UM, well, 457 00:25:11,080 --> 00:25:15,280 Speaker 1: they wouldn't synchronize in large groups. One is the difficulty 458 00:25:15,280 --> 00:25:18,280 Speaker 1: if I keep them all together right and all synchronizing um. 459 00:25:18,320 --> 00:25:21,280 Speaker 1: And the other is the communication from one one end 460 00:25:21,280 --> 00:25:22,760 Speaker 1: of the herd the other end of the herd at 461 00:25:22,800 --> 00:25:26,160 Speaker 1: some larger scale, UM, the information may not be going 462 00:25:26,160 --> 00:25:29,080 Speaker 1: back and forth between the large group such that they 463 00:25:29,080 --> 00:25:31,000 Speaker 1: can stay together. So you can maybe think more like 464 00:25:31,000 --> 00:25:34,560 Speaker 1: like a wave in a stadium. UM. And then the 465 00:25:34,600 --> 00:25:39,399 Speaker 1: other aspect is, uh what UM, there's some benefit to 466 00:25:39,640 --> 00:25:42,320 Speaker 1: synchronizing on a certain scale and maybe not in a 467 00:25:42,400 --> 00:25:44,720 Speaker 1: larger scale. And that second aspect I would guess has 468 00:25:44,720 --> 00:25:47,840 Speaker 1: more to do with the market, because in the market system, 469 00:25:47,880 --> 00:25:51,880 Speaker 1: I think the communication across large scales, you know, in distance, 470 00:25:52,119 --> 00:25:54,879 Speaker 1: isn't the problem. We all just check our iPhone. Do 471 00:25:54,920 --> 00:25:59,600 Speaker 1: you think, given our conversation that maybe we've entices to 472 00:25:59,800 --> 00:26:03,000 Speaker 1: do you some research on markets and hurting behavior and 473 00:26:03,080 --> 00:26:07,479 Speaker 1: markets specifically Yeah, I've been always very be interested in 474 00:26:08,000 --> 00:26:12,119 Speaker 1: things like bank run because that's essentially UM where you 475 00:26:12,200 --> 00:26:16,359 Speaker 1: study how the different banks with their customers say, how 476 00:26:16,400 --> 00:26:19,159 Speaker 1: they interact and what happens in a financial crisis. Was 477 00:26:19,240 --> 00:26:21,639 Speaker 1: there this extra layer of coupling from the media, right, 478 00:26:21,640 --> 00:26:24,399 Speaker 1: because when the media is reporting that we have a problem, 479 00:26:24,520 --> 00:26:27,080 Speaker 1: then you know, we think there's a problem. And because 480 00:26:27,119 --> 00:26:29,720 Speaker 1: we think there's a problem, that there's this coupling leads 481 00:26:29,720 --> 00:26:32,879 Speaker 1: me to say, with your m deposit and if I 482 00:26:32,960 --> 00:26:35,040 Speaker 1: do that, my friends is my doing that? They do 483 00:26:35,160 --> 00:26:37,879 Speaker 1: the same, And when everyone does that, you have a 484 00:26:37,880 --> 00:26:40,440 Speaker 1: bank run and bank banks start to you know, go 485 00:26:40,440 --> 00:26:45,919 Speaker 1: go bankruptcy. Um. Obviously the federal government has UM policies 486 00:26:45,960 --> 00:26:49,760 Speaker 1: now ensure a certain amount of deposit being safe, but 487 00:26:50,040 --> 00:26:53,320 Speaker 1: that that that in general can happen in different levels, 488 00:26:53,400 --> 00:26:55,800 Speaker 1: like the property market. If you see all your friends 489 00:26:55,800 --> 00:26:57,840 Speaker 1: set in their houses, you're more likely to do the same. 490 00:26:58,200 --> 00:27:00,440 Speaker 1: So this couple actually not is not account it my 491 00:27:00,480 --> 00:27:02,520 Speaker 1: actually change And I think the media is playing a 492 00:27:02,600 --> 00:27:06,840 Speaker 1: very big role there to influence sort of the behavior 493 00:27:06,880 --> 00:27:09,639 Speaker 1: of consumers in joining them. So so I'm very interested 494 00:27:09,640 --> 00:27:12,679 Speaker 1: in this topic. And as I said, the tools Eric 495 00:27:12,720 --> 00:27:16,359 Speaker 1: and I have been developing called causation entropy. We think 496 00:27:16,520 --> 00:27:19,439 Speaker 1: we we actually might want to utilize this to study 497 00:27:19,600 --> 00:27:23,760 Speaker 1: data collected from those past years. Interesting. Um, we'll have 498 00:27:23,840 --> 00:27:26,560 Speaker 1: to have you on again once you've completed that project. 499 00:27:27,119 --> 00:27:30,320 Speaker 1: We have to leave it for today, though, um, Jay, 500 00:27:30,800 --> 00:27:32,960 Speaker 1: Eric and Mason, I'd like to thank you so much 501 00:27:32,960 --> 00:27:37,400 Speaker 1: for coming on and talking to us about cows, mathematics, hurting, 502 00:27:37,560 --> 00:27:41,880 Speaker 1: and markets. Thank you, thank you for having us. Thank 503 00:27:41,920 --> 00:27:55,760 Speaker 1: you so much. I actually think, Um, I thought that 504 00:27:55,840 --> 00:27:59,080 Speaker 1: was really really interesting, and if I do say so myself, 505 00:27:59,119 --> 00:28:02,679 Speaker 1: I think we manage to connect it quite well to 506 00:28:03,040 --> 00:28:07,480 Speaker 1: markets and financial behavior. So I'm pretty happy. I always 507 00:28:07,520 --> 00:28:09,479 Speaker 1: knew it was a reason why it's attracted to markets. 508 00:28:09,520 --> 00:28:12,520 Speaker 1: I think, yes, that the herding thing, but I think 509 00:28:12,520 --> 00:28:16,320 Speaker 1: it's it's interesting that that the research that suppose is 510 00:28:16,520 --> 00:28:19,600 Speaker 1: continuing to go into to understand the behavior of people 511 00:28:19,880 --> 00:28:23,080 Speaker 1: and particularly people in markets, has been has been going 512 00:28:23,119 --> 00:28:25,119 Speaker 1: on for hundreds of years and will continue to go on. 513 00:28:25,440 --> 00:28:27,680 Speaker 1: So the more angles that has looked at from is interesting. 514 00:28:27,720 --> 00:28:31,720 Speaker 1: And if cows comprove the basis for investor behavior, I 515 00:28:31,720 --> 00:28:35,360 Speaker 1: think that would be an interesting breakthrough. Right. But I mean, 516 00:28:35,400 --> 00:28:38,600 Speaker 1: this is one of the most intractable problems of finance 517 00:28:38,720 --> 00:28:43,240 Speaker 1: and markets and mathematics is trying to calculate this sort 518 00:28:43,240 --> 00:28:46,800 Speaker 1: of network theory and connectivity and how one thing impacts 519 00:28:47,160 --> 00:28:50,760 Speaker 1: the other. Um. One thing I did think was interesting, 520 00:28:50,840 --> 00:28:54,400 Speaker 1: and you brought this up, Lorgan, in the context of bubbles, 521 00:28:54,960 --> 00:28:58,960 Speaker 1: was this idea that at some point the herd becomes 522 00:28:59,040 --> 00:29:03,280 Speaker 1: so big that the hurting instinct or the hurting behavior 523 00:29:03,440 --> 00:29:07,440 Speaker 1: starts to break down a bit and you see cows, 524 00:29:07,840 --> 00:29:10,360 Speaker 1: and I suppose you could extrapolate to investors, but you 525 00:29:10,400 --> 00:29:14,000 Speaker 1: see cows start to kind of group together and do 526 00:29:14,080 --> 00:29:17,080 Speaker 1: their own thing. Um. I thought that was interesting when 527 00:29:17,080 --> 00:29:20,560 Speaker 1: we think about bubbles and markets and how they seem 528 00:29:20,600 --> 00:29:23,920 Speaker 1: to go on and on and on until suddenly they don't, 529 00:29:24,080 --> 00:29:27,800 Speaker 1: and then they very quickly break down. As you mentioned, Yes, 530 00:29:27,920 --> 00:29:29,960 Speaker 1: and I think that it is that that kind of view, 531 00:29:30,000 --> 00:29:32,480 Speaker 1: like there's will always be in market throws would be 532 00:29:32,600 --> 00:29:35,840 Speaker 1: contrarians because I suppose in order to buy something, you 533 00:29:35,840 --> 00:29:37,640 Speaker 1: always hated someone to sell it to you, so they 534 00:29:37,680 --> 00:29:40,160 Speaker 1: always have to two You need to views in the market. 535 00:29:40,440 --> 00:29:42,840 Speaker 1: But if you get the market moving directionally in one way, 536 00:29:42,960 --> 00:29:46,200 Speaker 1: like house prices of the two houses and seven, there 537 00:29:46,200 --> 00:29:50,120 Speaker 1: comes a point where the suppose the herd stops wanting, 538 00:29:50,160 --> 00:29:53,640 Speaker 1: stops wanting to buy, or that you're getting imbalance and 539 00:29:53,680 --> 00:29:55,480 Speaker 1: her So I think it is interesting and I think 540 00:29:56,000 --> 00:29:58,440 Speaker 1: it is again the Holy grade. Like we said, it's 541 00:29:58,480 --> 00:30:00,200 Speaker 1: eased for me to get a piece of technology that 542 00:30:00,280 --> 00:30:02,560 Speaker 1: will predict when my cow is going to calve. It's 543 00:30:02,680 --> 00:30:04,600 Speaker 1: very hard to get a piece of technology that will 544 00:30:04,640 --> 00:30:06,280 Speaker 1: predict when the market is going to turn from a 545 00:30:06,320 --> 00:30:09,080 Speaker 1: bullet into a cow or a bulle into a bear, whichever. 546 00:30:10,560 --> 00:30:13,000 Speaker 1: I can't believe you're getting text messages about when your 547 00:30:13,040 --> 00:30:16,680 Speaker 1: cows are going to give birth to calves um modern technology. 548 00:30:17,520 --> 00:30:20,200 Speaker 1: So tell me, has this conversation changed your view of 549 00:30:20,240 --> 00:30:23,320 Speaker 1: your cows? No, I'm very solid in my view of 550 00:30:23,360 --> 00:30:27,240 Speaker 1: my cows. And we go back a long way and 551 00:30:27,280 --> 00:30:29,000 Speaker 1: then their view with my job is to feed them 552 00:30:29,000 --> 00:30:31,440 Speaker 1: and keep them happy. So happy cow is a productive 553 00:30:31,480 --> 00:30:35,040 Speaker 1: co Oh that's nice. We are going to leave it 554 00:30:35,040 --> 00:30:39,000 Speaker 1: there for now. You can follow me on Twitter. I'm 555 00:30:39,040 --> 00:30:43,320 Speaker 1: at Tracy Alloway and I'm at Lorcan r K. Thanks 556 00:30:43,360 --> 00:30:54,880 Speaker 1: for listening. But knowledge to work and grow your business 557 00:30:54,880 --> 00:30:58,959 Speaker 1: with c i T from transportation to healthcare to manufacturing. 558 00:30:59,160 --> 00:31:02,560 Speaker 1: C i T Opera commercial lending, leasing, and treasury management 559 00:31:02,600 --> 00:31:05,960 Speaker 1: services for small and middle market businesses. 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