WEBVTT 00:00:03.393 --> 00:00:04.698 Science, 00:00:04.698 --> 00:00:07.958 science has allowed us to know so much 00:00:07.958 --> 00:00:11.108 about the far reaches of the universe, 00:00:11.108 --> 00:00:14.303 which is at the same time tremendously important 00:00:14.303 --> 00:00:16.307 and extremely remote, 00:00:16.307 --> 00:00:18.828 and yet much, much closer, 00:00:18.828 --> 00:00:20.919 much more directly related to us. 00:00:20.919 --> 00:00:23.463 There are many things we don't really understand. 00:00:23.463 --> 00:00:25.531 And one of them is the extraordinary 00:00:25.531 --> 00:00:28.918 social complexity of the animals around us, 00:00:28.918 --> 00:00:30.842 and today I want to tell you a few stories 00:00:30.842 --> 00:00:32.866 of animal complexity. NOTE Paragraph 00:00:32.866 --> 00:00:36.354 But first, what do we call complexity? 00:00:36.354 --> 00:00:37.656 What is complex? 00:00:37.656 --> 00:00:41.222 Well, complex is not complicated. 00:00:41.222 --> 00:00:44.608 Something complicated comprises many small parts, 00:00:44.608 --> 00:00:47.005 all different, and each of them 00:00:47.005 --> 00:00:50.265 has its own precise role in the machinery. 00:00:50.265 --> 00:00:52.922 On the opposite, a complex system 00:00:52.922 --> 00:00:55.655 is made of many, many similar parts, 00:00:55.655 --> 00:00:57.525 and it is their interaction 00:00:57.525 --> 00:01:00.999 that produces a globally cohering behavior. 00:01:00.999 --> 00:01:04.758 Complex systems have many interacting parts 00:01:04.758 --> 00:01:08.245 which behave according to simple, individual rules, 00:01:08.245 --> 00:01:11.564 and this results in emergent properties. 00:01:11.564 --> 00:01:13.467 The behavior of the system as a whole 00:01:13.467 --> 00:01:15.052 cannot be predicted 00:01:15.052 --> 00:01:17.426 from the individual rules only. 00:01:17.426 --> 00:01:18.989 As Aristotle wrote, 00:01:18.989 --> 00:01:22.219 the whole is greater than the sum of its parts. 00:01:22.219 --> 00:01:24.896 But from Aristotle, let's move onto a more 00:01:24.896 --> 00:01:28.249 concrete example of complex systems. NOTE Paragraph 00:01:28.249 --> 00:01:30.219 These are Scottish terriers. 00:01:30.219 --> 00:01:34.054 In the beginning, the system is disorganized. 00:01:34.054 --> 00:01:37.863 Then comes a perturbation: milk. 00:01:37.863 --> 00:01:41.091 Every individual starts pushing in one direction 00:01:41.091 --> 00:01:42.036 — (Laughter) — 00:01:42.036 --> 00:01:45.073 and this is what happens. 00:01:45.073 --> 00:01:47.710 The pinwheel is an emergent property 00:01:47.710 --> 00:01:49.951 of the interactions between puppies 00:01:49.951 --> 00:01:53.815 whose only rule is to try to keep access to the milk 00:01:53.815 --> 00:01:57.314 and therefore to push in a random direction. NOTE Paragraph 00:01:57.314 --> 00:02:01.049 So it's all about finding the simple rules 00:02:01.049 --> 00:02:04.109 from which complexity emerges. 00:02:04.109 --> 00:02:06.895 I call this simplifying complexity, 00:02:06.895 --> 00:02:09.046 and it's what we do the Chair of Systems Design 00:02:09.046 --> 00:02:10.961 at ETH Zurich. 00:02:10.961 --> 00:02:14.527 We collect data on animal populations, 00:02:14.527 --> 00:02:18.370 analyze complex patterns, try to explain them. 00:02:18.370 --> 00:02:21.142 It requires physicists who work with biologists, 00:02:21.142 --> 00:02:23.957 with mathematicians and computer scientists, 00:02:23.957 --> 00:02:26.470 and it is their interaction that produces 00:02:26.470 --> 00:02:28.368 cross-boundary competence 00:02:28.368 --> 00:02:29.977 to solve these problems. 00:02:29.977 --> 00:02:32.204 So again, the whole is greater 00:02:32.204 --> 00:02:33.588 than the sum of its parts. 00:02:33.588 --> 00:02:35.815 In a way, collaboration 00:02:35.815 --> 00:02:39.522 is another example of complex systems. NOTE Paragraph 00:02:39.522 --> 00:02:41.151 And you may be asking yourself 00:02:41.151 --> 00:02:43.983 which side I'm on, biology or physics. 00:02:43.983 --> 00:02:46.294 In fact, it's a little different, 00:02:46.294 --> 00:02:47.561 and to explain, I need to tell you 00:02:47.561 --> 00:02:50.258 a short story about myself. 00:02:50.258 --> 00:02:51.829 When I was a child, 00:02:51.829 --> 00:02:55.815 I loved to build stuff, to create complicated machines. 00:02:55.815 --> 00:02:58.414 So I set out to study electrical engineering 00:02:58.414 --> 00:03:00.165 and robotics, 00:03:00.165 --> 00:03:02.032 and my end-of-studies project 00:03:02.032 --> 00:03:05.002 was about building a robot called ER-1 00:03:05.002 --> 00:03:06.899 —it looked like this— 00:03:06.899 --> 00:03:09.486 that would collect information from its environment 00:03:09.486 --> 00:03:12.968 and proceed to follow a white line on the ground. 00:03:12.968 --> 00:03:15.271 It was very, very complicated, 00:03:15.271 --> 00:03:18.331 but it worked beautifully in our test room, 00:03:18.331 --> 00:03:20.615 and on demo day, professors had assembled 00:03:20.615 --> 00:03:21.977 to grade to the project. 00:03:21.977 --> 00:03:24.756 So we took ER-1 to the evaluation room. 00:03:24.756 --> 00:03:27.079 It turned out, the light in that room 00:03:27.079 --> 00:03:28.824 was slightly different. 00:03:28.824 --> 00:03:31.123 The robot's vision system got confused. 00:03:31.123 --> 00:03:32.869 At the first bend in the line, 00:03:32.869 --> 00:03:36.532 it left its course, and crashed into a wall. 00:03:36.532 --> 00:03:38.695 We had spent weeks building it, 00:03:38.695 --> 00:03:40.337 and all it took to destroy it 00:03:40.337 --> 00:03:42.948 was a subtle change in the color of the light 00:03:42.948 --> 00:03:44.635 in the room. 00:03:44.635 --> 00:03:46.075 That's when I realized that 00:03:46.075 --> 00:03:48.201 the more complicated you make a machine, 00:03:48.201 --> 00:03:50.440 the more likely that it will fail 00:03:50.440 --> 00:03:53.088 due to something absolutely unexpected. 00:03:53.088 --> 00:03:54.757 And I decided that, in fact, 00:03:54.757 --> 00:03:57.768 I didn't really want to create complicated stuff. 00:03:57.768 --> 00:04:00.710 I wanted to understand complexity, 00:04:00.710 --> 00:04:02.698 complexity of the world around us 00:04:02.698 --> 00:04:05.396 and especially in the animal kingdom. NOTE Paragraph 00:04:05.396 --> 00:04:08.287 Which brings us to bats. 00:04:08.287 --> 00:04:11.628 Bechstein's bats are a common species of European bats. 00:04:11.628 --> 00:04:13.035 They are very social animals. 00:04:13.035 --> 00:04:15.427 Mostly they roost or sleep together. 00:04:15.427 --> 00:04:17.996 And they live in maternity colonies, 00:04:17.996 --> 00:04:19.582 which means that every spring, 00:04:19.582 --> 00:04:22.655 the females meet after the winter hybernation, 00:04:22.655 --> 00:04:24.914 and they stay together for about six months 00:04:24.914 --> 00:04:26.940 to rear their young, 00:04:26.940 --> 00:04:30.079 and they all carry a very small chip, 00:04:30.079 --> 00:04:31.845 which means that every time one of them 00:04:31.845 --> 00:04:35.164 enters one of these specially equipped bat boxes, 00:04:35.164 --> 00:04:36.807 we know where she is, 00:04:36.807 --> 00:04:38.090 and more importantly, 00:04:38.090 --> 00:04:40.508 we know with whom she is. 00:04:40.508 --> 00:04:44.187 So I study roosting associations in bats, 00:04:44.187 --> 00:04:46.832 and this is what it looks like. 00:04:46.832 --> 00:04:48.919 During the day, the bats roost 00:04:48.919 --> 00:04:51.517 in a number of sub-groups in different boxes. 00:04:51.517 --> 00:04:53.063 It could be that on one day, 00:04:53.063 --> 00:04:55.235 the colony is split between two boxes, 00:04:55.235 --> 00:04:56.934 but on another day, 00:04:56.934 --> 00:04:59.252 it could be together in a single box, 00:04:59.252 --> 00:05:01.399 or split between three or more boxes, 00:05:01.399 --> 00:05:04.449 and that all seems rather erratic, really. 00:05:04.449 --> 00:05:07.529 It's called fission-fusion dynamics, 00:05:07.529 --> 00:05:09.242 the property for an animal group 00:05:09.242 --> 00:05:11.481 of regularly splitting and merging 00:05:11.481 --> 00:05:13.204 into different subgroups. NOTE Paragraph 00:05:13.204 --> 00:05:15.689 So what we do is take all these data 00:05:15.689 --> 00:05:17.320 from all these different days 00:05:17.320 --> 00:05:18.839 and pool them together 00:05:18.839 --> 00:05:21.442 to extract a long-term association pattern 00:05:21.442 --> 00:05:23.823 by playing techniques with network analysis 00:05:23.823 --> 00:05:25.521 to get a complete picture 00:05:25.521 --> 00:05:28.072 of the social structure of the colony. 00:05:28.072 --> 00:05:32.263 Okay? So that's what this picture looks like. 00:05:32.263 --> 00:05:34.826 In this network, all the circles 00:05:34.826 --> 00:05:37.202 are nodes, individual bats, 00:05:37.202 --> 00:05:38.938 and the lines between them 00:05:38.938 --> 00:05:42.695 are social bonds, associations between individuals. 00:05:42.695 --> 00:05:45.373 It turns out this is a very interesting picture. 00:05:45.373 --> 00:05:47.479 This bat colony is organized 00:05:47.479 --> 00:05:49.254 in two different communities 00:05:49.254 --> 00:05:51.054 which cannot be predicted 00:05:51.054 --> 00:05:53.372 from the daily fission-future dynamics. 00:05:53.372 --> 00:05:57.000 We call them cryptic social units. 00:05:57.000 --> 00:05:58.615 Even more interesting, in fact: 00:05:58.615 --> 00:06:00.842 every year, around October, 00:06:00.842 --> 00:06:02.494 the colony splits up, 00:06:02.494 --> 00:06:05.050 and all bats hibernate separately, 00:06:05.050 --> 00:06:06.684 but year after year, 00:06:06.684 --> 00:06:09.573 when the bats come together again in the spring, 00:06:09.573 --> 00:06:12.394 the communities stay the same. NOTE Paragraph 00:06:12.394 --> 00:06:14.851 So these bats remember their friends 00:06:14.851 --> 00:06:16.928 for a really long time. 00:06:16.928 --> 00:06:19.034 With a brain the size of a peanut, 00:06:19.034 --> 00:06:21.220 they maintain individualized, 00:06:21.220 --> 00:06:23.546 long-term social bonds, 00:06:23.546 --> 00:06:25.502 We didn't know that was possible. 00:06:25.502 --> 00:06:26.814 We knew that primates 00:06:26.814 --> 00:06:29.382 and elephants and dolphins could do that, 00:06:29.382 --> 00:06:32.195 but compared to bats, they have huge brains. 00:06:32.195 --> 00:06:34.471 So how could it be 00:06:34.471 --> 00:06:36.760 that the bats maintain this complex, 00:06:36.760 --> 00:06:38.339 stable social structure 00:06:38.339 --> 00:06:41.841 with such limited cognitive abilities? NOTE Paragraph 00:06:41.841 --> 00:06:44.593 And this is where complexity brings an answer. 00:06:44.593 --> 00:06:46.379 To understand this system, 00:06:46.379 --> 00:06:49.498 we built a computer model of roosting, 00:06:49.498 --> 00:06:51.516 based on simple, individual rules, 00:06:51.516 --> 00:06:54.049 and simulated thousands and thousands of days 00:06:54.049 --> 00:06:55.956 in the virtual bat colony. 00:06:55.956 --> 00:06:57.712 It's a mathematical model, 00:06:57.712 --> 00:07:00.279 but it's not complicated. 00:07:00.279 --> 00:07:02.903 What the model told us is that, in a nutshell, 00:07:02.903 --> 00:07:06.409 each bat knows a few other colony members 00:07:06.409 --> 00:07:09.206 as her friends, and is just slightly more likely 00:07:09.206 --> 00:07:11.530 to roost in a box with them. 00:07:11.530 --> 00:07:13.928 Simple, individual rules. 00:07:13.928 --> 00:07:15.610 This is all it takes to explain 00:07:15.610 --> 00:07:18.215 the social complexity of these bats. NOTE Paragraph 00:07:18.215 --> 00:07:19.947 But it gets better. 00:07:19.947 --> 00:07:22.350 Between 2010 and 2011, 00:07:22.350 --> 00:07:26.011 the colony lost more than two thirds of its members, 00:07:26.011 --> 00:07:28.880 probably due to the very cold winter. 00:07:28.880 --> 00:07:32.098 The next spring, it didn't form two communities 00:07:32.098 --> 00:07:33.819 like every year, 00:07:33.819 --> 00:07:35.653 which may have led the whole colony to die 00:07:35.653 --> 00:07:37.824 because it had become too small. 00:07:37.824 --> 00:07:42.952 Instead, it formed a single, cohesive social unit, 00:07:42.952 --> 00:07:45.868 which allowed the colony to survive that season 00:07:45.868 --> 00:07:49.187 and thrive again in the next two years. 00:07:49.187 --> 00:07:50.796 What we know is that the bats 00:07:50.796 --> 00:07:53.657 are not aware that their colony is doing this. 00:07:53.657 --> 00:07:57.051 All they do is follow simple association rules, 00:07:57.051 --> 00:07:58.659 and from this simplicity 00:07:58.659 --> 00:08:00.962 emerges social complexity 00:08:00.962 --> 00:08:03.756 which allows the colony to be resilient 00:08:03.756 --> 00:08:07.043 against dramatic changes in the population structure. 00:08:07.043 --> 00:08:09.708 And I find this incredible. NOTE Paragraph 00:08:09.708 --> 00:08:11.530 Now I want to tell you another story, 00:08:11.530 --> 00:08:13.208 but from this we have to travel from Europe 00:08:13.208 --> 00:08:16.303 to the Kalahari Desert in South Africa. 00:08:16.303 --> 00:08:18.222 This is where meerkats live. 00:08:18.222 --> 00:08:19.799 I'm sure you know meerkats. 00:08:19.799 --> 00:08:21.397 They're fascinating creatures. 00:08:21.397 --> 00:08:24.817 They live in groups with a very strict social hierarchy. 00:08:24.817 --> 00:08:26.306 There is one dominant pair, 00:08:26.306 --> 00:08:27.596 and many subordinates, 00:08:27.596 --> 00:08:29.418 some acting as sentinels, 00:08:29.418 --> 00:08:30.724 some acting as babysitters, 00:08:30.724 --> 00:08:32.560 some teaching pups, and so on. 00:08:32.560 --> 00:08:35.850 What we do is put very small GPS collars 00:08:35.850 --> 00:08:37.575 on these animals 00:08:37.575 --> 00:08:39.450 to study how they move together, 00:08:39.450 --> 00:08:42.998 and what this has to do with their social structure. 00:08:42.998 --> 00:08:44.565 And there's a very interesting example 00:08:44.565 --> 00:08:47.127 of collective movement in meerkats. 00:08:47.127 --> 00:08:49.571 In the middle of the reserve which they live in 00:08:49.571 --> 00:08:50.795 lies a road. 00:08:50.795 --> 00:08:54.136 On this road there are cars, so it's dangerous. 00:08:54.136 --> 00:08:56.062 But the meerkats have to cross it 00:08:56.062 --> 00:08:58.794 to get from one feeding place to another. 00:08:58.794 --> 00:09:03.530 So we asked, how exactly do they do this? 00:09:03.530 --> 00:09:05.488 We found that the dominant female 00:09:05.488 --> 00:09:08.110 is mostly the one who leads the group to the road, 00:09:08.110 --> 00:09:11.166 but when it comes to crossing it, crossing the road, 00:09:11.166 --> 00:09:13.610 she gives way to the subordinates, 00:09:13.610 --> 00:09:15.074 a manner of saying, you know, 00:09:15.074 --> 00:09:18.639 "Go ahead, tell me if it's safe." 00:09:18.639 --> 00:09:19.963 But what I didn't know, in fact, 00:09:19.963 --> 00:09:22.690 was what rules in their behavior the meerkats follow 00:09:22.690 --> 00:09:25.615 for this change at the edge of the group to happen 00:09:25.615 --> 00:09:29.865 and if simple rules were sufficient to explain it. NOTE Paragraph 00:09:29.865 --> 00:09:33.503 So I built a model, a model of simulating meerkats 00:09:33.503 --> 00:09:35.876 crossing a simulated road. 00:09:35.876 --> 00:09:37.687 It's a simplistic model. 00:09:37.687 --> 00:09:40.388 Moving meerkats are like random particles 00:09:40.388 --> 00:09:42.672 whose unique rule is one of alignment. 00:09:42.672 --> 00:09:45.170 They simply move together. 00:09:45.170 --> 00:09:47.970 When these particles get to the road, 00:09:47.970 --> 00:09:50.022 they sense some kind of obstacle, 00:09:50.022 --> 00:09:52.303 and they bounce against it. 00:09:52.303 --> 00:09:53.383 The only difference 00:09:53.383 --> 00:09:55.440 between the dominant female, here in red, 00:09:55.440 --> 00:09:56.925 and the other individuals, 00:09:56.925 --> 00:09:59.556 is that for her, the height of the obstacle, 00:09:59.556 --> 00:10:01.999 which is in fact the risk perceived from the road, 00:10:01.999 --> 00:10:03.979 is just slightly higher, 00:10:03.979 --> 00:10:05.717 and this tiny difference 00:10:05.717 --> 00:10:07.401 in the individual's rule of movement 00:10:07.401 --> 00:10:09.770 is sufficient to explain what we observe, 00:10:09.770 --> 00:10:12.499 that the dominant female 00:10:12.499 --> 00:10:13.995 leads her group to the road 00:10:13.995 --> 00:10:15.558 and then gives way to the others 00:10:15.558 --> 00:10:18.235 for them to cross first. 00:10:18.235 --> 00:10:21.886 George Box, who was an English statistician, 00:10:21.886 --> 00:10:24.848 once wrote, "All models are false, 00:10:24.848 --> 00:10:27.054 but some models are useful." 00:10:27.054 --> 00:10:30.166 And in fact, this model is obviously false, 00:10:30.166 --> 00:10:34.319 because in reality, meerkats are anything but random particles. 00:10:34.319 --> 00:10:35.863 But it's also useful, 00:10:35.863 --> 00:10:38.690 because it tells us that extreme simplicity 00:10:38.690 --> 00:10:41.679 in movement rules at the individual level 00:10:41.679 --> 00:10:44.167 can result in a great deal of complexity 00:10:44.167 --> 00:10:46.367 at the level of the group. 00:10:46.367 --> 00:10:50.486 So again, that's simplifying complexity. NOTE Paragraph 00:10:50.486 --> 00:10:51.671 Now, I would like to conclude 00:10:51.671 --> 00:10:54.212 on what this means for the whole species. 00:10:54.212 --> 00:10:55.967 When the dominant female 00:10:55.967 --> 00:10:57.749 gives way to a subordinate, 00:10:57.749 --> 00:11:00.082 it's not out of courtesy. 00:11:00.082 --> 00:11:01.361 In fact, the dominant female 00:11:01.361 --> 00:11:04.292 is extremely important for the cohesion of the group. 00:11:04.292 --> 00:11:07.496 If she dies on the road, the whole group is at risk. 00:11:07.496 --> 00:11:09.778 So this behavior of risk avoidance 00:11:09.778 --> 00:11:12.411 is a very old evolutionary response. 00:11:12.411 --> 00:11:16.371 These meerkats are replicating an evolved tactic 00:11:16.371 --> 00:11:18.558 that is thousands of generations old, 00:11:18.558 --> 00:11:20.849 and they're adapting it to a modern risk, 00:11:20.849 --> 00:11:24.236 in this case a road built by humans. 00:11:24.236 --> 00:11:26.723 They adapt very simple rules, 00:11:26.723 --> 00:11:28.966 and the resulting complex behavior 00:11:28.966 --> 00:11:31.876 allows them to resist human encroachment 00:11:31.876 --> 00:11:34.648 into their natural habitat. NOTE Paragraph 00:11:34.648 --> 00:11:36.265 In the end, 00:11:36.265 --> 00:11:38.887 it may be bats which change their social structure 00:11:38.887 --> 00:11:41.044 in response to a population crush, 00:11:41.044 --> 00:11:42.778 or it may be meerkats 00:11:42.778 --> 00:11:45.673 who show a novel adaptation to a human road, 00:11:45.673 --> 00:11:48.220 or it may be another species. 00:11:48.220 --> 00:11:51.304 My message here, and it's not a complicated one 00:11:51.304 --> 00:11:54.206 but a simple one of wonder and hope, 00:11:54.206 --> 00:11:57.050 my message here is that animals 00:11:57.050 --> 00:11:59.754 show extraordinary social complexity, 00:11:59.754 --> 00:12:02.149 and this allows them to adapt 00:12:02.149 --> 00:12:05.615 and respond to changes in their environment. 00:12:05.615 --> 00:12:08.337 In three words, in the animal kingdom, 00:12:08.337 --> 00:12:11.234 simplicity leads to complexity 00:12:11.234 --> 00:12:12.902 which leads to resilience. NOTE Paragraph 00:12:12.902 --> 00:12:14.754 Thank you. NOTE Paragraph 00:12:14.754 --> 00:12:21.434 (Applause)