0:00:03.393,0:00:04.621 Science, 0:00:04.621,0:00:07.958 science has allowed us to know so much 0:00:07.958,0:00:10.984 about the far reaches of the universe, 0:00:10.984,0:00:14.179 which is at the same time tremendously important 0:00:14.179,0:00:16.245 and extremely remote, 0:00:16.245,0:00:18.704 and yet much, much closer, 0:00:18.704,0:00:20.795 much more directly related to us, 0:00:20.795,0:00:23.263 there are many things we don't really understand. 0:00:23.263,0:00:25.392 And one of them is the extraordinary 0:00:25.392,0:00:28.718 social complexity of the animals around us, 0:00:28.718,0:00:30.734 and today I want to tell you a few stories 0:00:30.734,0:00:32.742 of animal complexity. 0:00:32.742,0:00:36.092 But first, what do we call complexity? 0:00:36.092,0:00:37.579 What is complex? 0:00:37.579,0:00:41.006 Well, complex is not complicated. 0:00:41.006,0:00:44.454 Something complicated comprises many small parts, 0:00:44.454,0:00:46.884 all different, and each of them 0:00:46.899,0:00:50.003 has its own precise role in the machinery. 0:00:50.003,0:00:52.814 On the opposite, a complex system 0:00:52.814,0:00:55.455 is made of many, many similar parts, 0:00:55.455,0:00:57.463 and it is their interaction 0:00:57.463,0:01:00.783 that produces a globally coherent behavior. 0:01:00.783,0:01:04.619 Complex systems have many interacting parts 0:01:04.619,0:01:08.045 which behave according to simple, individual rules, 0:01:08.045,0:01:11.394 and this results in emergent properties. 0:01:11.394,0:01:13.282 The behavior of the system as a whole 0:01:13.282,0:01:14.950 cannot be predicted 0:01:14.950,0:01:17.102 from the individual rules only. 0:01:17.102,0:01:18.912 As Aristotle wrote, 0:01:18.912,0:01:21.972 the whole is greater than the sum of its parts. 0:01:21.972,0:01:24.434 But from Aristotle, let's move onto 0:01:24.434,0:01:28.124 a more concrete example of complex systems. 0:01:28.124,0:01:30.080 These are Scottish terriers. 0:01:30.080,0:01:33.831 In the beginning, the system is disorganized. 0:01:33.831,0:01:37.632 Then comes a perturbation: milk. 0:01:37.632,0:01:41.482 Every individual starts pushing in one direction 0:01:41.482,0:01:44.791 and this is what happens. 0:01:44.791,0:01:47.617 The pinwheel is an emergent property 0:01:47.617,0:01:49.520 of the interactions between puppies 0:01:49.520,0:01:53.430 whose only rule is to try to keep access to the milk 0:01:53.430,0:01:57.037 and therefore to push in a random direction. 0:01:57.037,0:02:01.012 So it's all about finding the simple rules 0:02:01.012,0:02:03.770 from which complexity emerges. 0:02:03.770,0:02:06.710 I call this simplifying complexity, 0:02:06.710,0:02:08.845 and it's what we do at the chair of systems design 0:02:08.845,0:02:10.822 at ETH Zurich. 0:02:10.822,0:02:14.527 We collect data on animal populations, 0:02:14.527,0:02:18.338 analyze complex patterns, try to explain them. 0:02:18.338,0:02:20.957 It requires physicists who work with biologists, 0:02:20.957,0:02:23.680 with mathematicians and computer scientists, 0:02:23.680,0:02:26.500 and it is their interaction that produces 0:02:26.500,0:02:28.214 cross-boundary competence 0:02:28.214,0:02:29.792 to solve these problems. 0:02:29.792,0:02:32.064 So again, the whole is greater 0:02:32.064,0:02:33.464 than the sum of the parts. 0:02:33.464,0:02:35.614 In a way, collaboration 0:02:35.614,0:02:39.105 is another example of a complex system. 0:02:39.105,0:02:40.981 And you may be asking yourself 0:02:40.981,0:02:43.798 which side I'm on, biology or physics? 0:02:43.798,0:02:45.909 In fact, it's a little different, 0:02:45.909,0:02:47.498 and to explain, I need to tell you 0:02:47.498,0:02:49.840 a short story about myself. 0:02:49.840,0:02:51.567 When I was a child, 0:02:51.567,0:02:55.676 I loved to build stuff, to[br]create complicated machines. 0:02:55.676,0:02:58.413 So I set out to study electrical engineering 0:02:58.413,0:02:59.965 and robotics, 0:02:59.965,0:03:02.058 and my end-of-studies project 0:03:02.058,0:03:04.984 was about building a robot called ER-1 -- 0:03:04.984,0:03:06.914 it looked like this— 0:03:06.914,0:03:09.285 that would collect information from its environment 0:03:09.285,0:03:12.783 and proceed to follow a white line on the ground. 0:03:12.783,0:03:15.162 It was very, very complicated, 0:03:15.162,0:03:18.146 but it worked beautifully in our test room, 0:03:18.146,0:03:21.599 and on demo day, professors had [br]assembled to grade the project. 0:03:21.607,0:03:24.509 So we took ER-1 to the evaluation room. 0:03:24.509,0:03:26.819 It turned out, the light in that room 0:03:26.819,0:03:28.638 was slightly different. 0:03:28.638,0:03:30.969 The robot's vision system got confused. 0:03:30.969,0:03:32.730 At the first bend in the line, 0:03:32.730,0:03:36.469 it left its course, and crashed into a wall. 0:03:36.469,0:03:38.556 We had spent weeks building it, 0:03:38.556,0:03:40.229 and all it took to destroy it 0:03:40.229,0:03:42.885 was a subtle change in the color of the light 0:03:42.885,0:03:44.481 in the room. 0:03:44.481,0:03:45.996 That's when I realized that 0:03:45.996,0:03:48.323 the more complicated you make a machine, 0:03:48.323,0:03:50.362 the more likely that it will fail 0:03:50.362,0:03:52.925 due to something absolutely unexpected. 0:03:52.925,0:03:54.755 And I decided that, in fact, 0:03:54.755,0:03:57.768 I didn't really want to create complicated stuff. 0:03:57.768,0:04:00.710 I wanted to understand complexity, 0:04:00.710,0:04:02.698 the complexity of the world around us 0:04:02.698,0:04:05.103 and especially in the animal kingdom. 0:04:05.103,0:04:08.423 Which brings us to bats. 0:04:08.423,0:04:11.474 Bechstein's bats are a common[br]species of European bats. 0:04:11.474,0:04:12.887 They are very social animals. 0:04:12.887,0:04:16.178 Mostly they roost, or sleep, together. 0:04:16.178,0:04:17.857 And they live in maternity colonies, 0:04:17.857,0:04:19.397 which means that every spring, 0:04:19.397,0:04:22.655 the females meet after the winter hibernation, 0:04:22.655,0:04:24.744 and they stay together for about six months 0:04:24.744,0:04:27.230 to rear their young, 0:04:27.230,0:04:30.035 and they all carry a very small chip, 0:04:30.035,0:04:31.906 which means that every time one of them 0:04:31.906,0:04:34.963 enters one of these specially equipped bat boxes, 0:04:34.963,0:04:36.606 we know where she is, 0:04:36.606,0:04:37.775 and more importantly, 0:04:37.775,0:04:40.338 we know with whom she is. 0:04:40.338,0:04:44.032 So I study roosting associations in bats, 0:04:44.032,0:04:46.477 and this is what it looks like. 0:04:46.477,0:04:48.919 During the day, the bats roost 0:04:48.919,0:04:51.223 in a number of sub-groups in different boxes. 0:04:51.223,0:04:53.152 It could be that on one day, 0:04:53.152,0:04:55.372 the colony is split between two boxes, 0:04:55.372,0:04:56.672 but on another day, 0:04:56.672,0:04:58.913 it could be together in a single box, 0:04:58.913,0:05:01.229 or split between three or more boxes, 0:05:01.229,0:05:04.156 and that all seems rather erratic, really. 0:05:04.156,0:05:07.359 It's called fission-fusion dynamics, 0:05:07.359,0:05:09.072 the property for an animal group 0:05:09.072,0:05:11.250 of regularly splitting and merging 0:05:11.250,0:05:12.911 into different subgroups. 0:05:12.911,0:05:15.473 So what we do is take all these data 0:05:15.473,0:05:17.135 from all these different days 0:05:17.135,0:05:18.639 and pool them together 0:05:18.639,0:05:21.256 to extract a long-term association pattern 0:05:21.256,0:05:23.761 by applying techniques with network analysis 0:05:23.761,0:05:25.382 to get a complete picture 0:05:25.382,0:05:27.919 of the social structure of the colony. 0:05:27.919,0:05:32.184 Okay? So that's what this picture looks like. 0:05:32.184,0:05:34.578 In this network, all the circles 0:05:34.578,0:05:37.355 are nodes, individual bats, 0:05:37.355,0:05:38.938 and the lines between them 0:05:38.938,0:05:42.602 are social bonds, associations between individuals. 0:05:42.602,0:05:45.280 It turns out this is a very interesting picture. 0:05:45.280,0:05:47.262 This bat colony is organized 0:05:47.262,0:05:49.130 in two different communities 0:05:49.130,0:05:50.969 which cannot be predicted 0:05:50.969,0:05:53.218 from the daily fission-fusion dynamics. 0:05:53.218,0:05:56.768 We call them cryptic social units. 0:05:56.768,0:05:58.384 Even more interesting, in fact: 0:05:58.384,0:06:00.748 Every year, around October, 0:06:00.748,0:06:02.309 the colony splits up, 0:06:02.309,0:06:05.007 and all bats hibernate separately, 0:06:05.007,0:06:06.468 but year after year, 0:06:06.468,0:06:09.541 when the bats come together again in the spring, 0:06:09.541,0:06:12.131 the communities stay the same. 0:06:12.131,0:06:14.851 So these bats remember their friends 0:06:14.851,0:06:16.681 for a really long time. 0:06:16.681,0:06:19.155 With a brain the size of a peanut, 0:06:19.155,0:06:21.280 they maintain individualized, 0:06:21.280,0:06:23.422 long-term social bonds, 0:06:23.422,0:06:25.146 We didn't know that was possible. 0:06:25.146,0:06:26.905 We knew that primates 0:06:26.905,0:06:29.473 and elephants and dolphins could do that, 0:06:29.473,0:06:32.101 but compared to bats, they have huge brains. 0:06:32.101,0:06:34.500 So how could it be 0:06:34.500,0:06:36.451 that the bats maintain this complex, 0:06:36.451,0:06:38.139 stable social structure 0:06:38.139,0:06:41.671 with such limited cognitive abilities? 0:06:41.671,0:06:44.560 And this is where complexity brings an answer. 0:06:44.560,0:06:46.701 To understand this system, 0:06:46.701,0:06:49.498 we built a computer model of roosting, 0:06:49.498,0:06:51.516 based on simple, individual rules, 0:06:51.516,0:06:53.951 and simulated thousands and thousands of days 0:06:53.951,0:06:55.970 in the virtual bat colony. 0:06:55.970,0:06:58.094 It's a mathematical model, 0:06:58.094,0:07:00.048 but it's not complicated. 0:07:00.048,0:07:03.146 What the model told us is that, in a nutshell, 0:07:03.146,0:07:06.332 each bat knows a few other colony members 0:07:06.332,0:07:08.820 as her friends, and is just slightly more likely 0:07:08.820,0:07:11.330 to roost in a box with them. 0:07:11.330,0:07:13.774 Simple, individual rules. 0:07:13.774,0:07:15.486 This is all it takes to explain 0:07:15.486,0:07:17.875 the social complexity of these bats. 0:07:17.875,0:07:19.593 But it gets better. 0:07:19.593,0:07:22.441 Between 2010 and 2011, 0:07:22.441,0:07:25.894 the colony lost more than two thirds of its members, 0:07:25.894,0:07:28.880 probably due to the very cold winter. 0:07:28.880,0:07:32.024 The next spring, it didn't form two communities 0:07:32.024,0:07:33.295 like every year, 0:07:33.295,0:07:35.498 which may have led the whole colony to die 0:07:35.498,0:07:37.593 because it had become too small. 0:07:37.593,0:07:42.966 Instead, it formed a single, cohesive social unit, 0:07:42.966,0:07:45.698 which allowed the colony to survive that season 0:07:45.698,0:07:48.802 and thrive again in the next two years. 0:07:48.802,0:07:50.580 What we know is that the bats 0:07:50.580,0:07:53.487 are not aware that their colony is doing this. 0:07:53.487,0:07:57.033 All they do is follow simple association rules, 0:07:57.033,0:07:58.382 and from this simplicity 0:07:58.382,0:08:00.823 emerges social complexity 0:08:00.823,0:08:03.663 which allows the colony to be resilient 0:08:03.663,0:08:06.644 against dramatic changes[br]in the population structure. 0:08:06.644,0:08:09.338 And I find this incredible. 0:08:09.338,0:08:11.422 Now I want to tell you another story, 0:08:11.422,0:08:12.977 but for this we have to travel from Europe 0:08:12.977,0:08:16.025 to the Kalahari Desert in South Africa. 0:08:16.025,0:08:18.052 This is where meerkats live. 0:08:18.052,0:08:19.552 I'm sure you know meerkats. 0:08:19.552,0:08:21.658 They're fascinating creatures. 0:08:21.658,0:08:24.647 They live in groups with a[br]very strict social hierarchy. 0:08:24.647,0:08:26.106 There is one dominant pair, 0:08:26.106,0:08:27.488 and many subordinates, 0:08:27.488,0:08:29.202 some acting as sentinels, 0:08:29.202,0:08:30.539 some acting as babysitters, 0:08:30.539,0:08:32.436 some teaching pups, and so on. 0:08:32.436,0:08:35.757 What we do is put very small GPS collars 0:08:35.757,0:08:37.282 on these animals 0:08:37.282,0:08:39.157 to study how they move together, 0:08:39.157,0:08:42.874 and what this has to do with their social structure. 0:08:42.874,0:08:44.364 And there's a very interesting example 0:08:44.364,0:08:47.080 of collective movement in meerkats. 0:08:47.080,0:08:49.447 In the middle of the reserve which they live in 0:08:49.447,0:08:50.656 lies a road. 0:08:50.656,0:08:53.889 On this road there are cars, so it's dangerous. 0:08:53.889,0:08:56.173 But the meerkats have to cross it 0:08:56.173,0:08:58.747 to get from one feeding place to another. 0:08:58.747,0:09:03.498 So we asked, how exactly do they do this? 0:09:03.498,0:09:05.334 We found that the dominant female 0:09:05.334,0:09:07.955 is mostly the one who leads the group to the road, 0:09:07.955,0:09:11.227 but when it comes to crossing it, crossing the road, 0:09:11.227,0:09:13.578 she gives way to the subordinates, 0:09:13.578,0:09:15.355 a manner of saying, 0:09:15.355,0:09:18.037 "Go ahead, tell me if it's safe." 0:09:18.037,0:09:19.701 What I didn't know, in fact, 0:09:19.701,0:09:22.843 was what rules in their behavior the meerkats follow 0:09:22.843,0:09:25.768 for this change at the edge of the group to happen 0:09:25.768,0:09:29.618 and if simple rules were sufficient to explain it. 0:09:29.618,0:09:33.609 So I built a model, a model of simulated meerkats 0:09:33.609,0:09:35.522 crossing a simulated road. 0:09:35.522,0:09:37.394 It's a simplistic model. 0:09:37.394,0:09:40.234 Moving meerkats are like random particles 0:09:40.234,0:09:42.456 whose unique rule is one of alignment. 0:09:42.456,0:09:44.862 They simply move together. 0:09:44.862,0:09:48.046 When these particles get to the road, 0:09:48.046,0:09:49.988 they sense some kind of obstacle, 0:09:49.988,0:09:52.072 and they bounce against it. 0:09:52.072,0:09:53.228 The only difference 0:09:53.228,0:09:55.270 between the dominant female, here in red, 0:09:55.270,0:09:56.755 and the other individuals, 0:09:56.755,0:09:59.309 is that for her, the height of the obstacle, 0:09:59.309,0:10:01.814 which is in fact the risk perceived from the road, 0:10:01.814,0:10:03.763 is just slightly higher, 0:10:03.763,0:10:05.424 and this tiny difference 0:10:05.424,0:10:07.262 in the individual's rule of movement 0:10:07.262,0:10:09.708 is sufficient to explain what we observe, 0:10:09.708,0:10:12.268 that the dominant female 0:10:12.268,0:10:13.702 leads her group to the road 0:10:13.702,0:10:15.372 and then gives way to the others 0:10:15.372,0:10:18.235 for them to cross first. 0:10:18.235,0:10:21.886 George Box, who was an English statistician, 0:10:21.886,0:10:24.848 once wrote, "All models are false, 0:10:24.848,0:10:26.907 but some models are useful." 0:10:26.907,0:10:30.104 And in fact, this model is obviously false, 0:10:30.104,0:10:34.072 because in reality, meerkats are[br]anything but random particles. 0:10:34.072,0:10:35.709 But it's also useful, 0:10:35.709,0:10:38.458 because it tells us that extreme simplicity 0:10:38.458,0:10:41.816 in movement rules at the individual level 0:10:41.816,0:10:44.167 can result in a great deal of complexity 0:10:44.167,0:10:46.105 at the level of the group. 0:10:46.105,0:10:50.161 So again, that's simplifying complexity. 0:10:50.161,0:10:51.609 I would like to conclude 0:10:51.609,0:10:54.426 on what this means for the whole species. 0:10:54.426,0:10:56.090 When the dominant female 0:10:56.090,0:10:57.656 gives way to a subordinate, 0:10:57.656,0:10:59.773 it's not out of courtesy. 0:10:59.773,0:11:01.280 In fact, the dominant female 0:11:01.280,0:11:03.799 is extremely important for the cohesion of the group. 0:11:03.799,0:11:07.311 If she dies on the road, the whole group is at risk. 0:11:07.311,0:11:09.547 So this behavior of risk avoidance 0:11:09.547,0:11:12.348 is a very old evolutionary response. 0:11:12.348,0:11:16.217 These meerkats are replicating an evolved tactic 0:11:16.217,0:11:18.450 that is thousands of generations old, 0:11:18.450,0:11:20.864 and they're adapting it to a modern risk, 0:11:20.864,0:11:24.189 in this case a road built by humans. 0:11:24.189,0:11:26.584 They adapt very simple rules, 0:11:26.584,0:11:28.873 and the resulting complex behavior 0:11:28.873,0:11:31.829 allows them to resist human encroachment 0:11:31.829,0:11:34.277 into their natural habitat. 0:11:34.277,0:11:36.079 In the end, 0:11:36.079,0:11:38.779 it may be bats which change their social structure 0:11:38.779,0:11:41.163 in response to a population crash, 0:11:41.163,0:11:42.562 or it may be meerkats 0:11:42.562,0:11:45.764 who show a novel adaptation to a human road, 0:11:45.764,0:11:48.449 or it may be another species. 0:11:48.449,0:11:51.242 My message here -- and it's not a complicated one, 0:11:51.242,0:11:54.006 but a simple one of wonder and hope -- 0:11:54.006,0:11:57.099 my message here is that animals 0:11:57.099,0:11:59.523 show extraordinary social complexity, 0:11:59.523,0:12:01.964 and this allows them to adapt 0:12:01.964,0:12:05.445 and respond to changes in their environment. 0:12:05.445,0:12:08.213 In three words, in the animal kingdom, 0:12:08.213,0:12:10.987 simplicity leads to complexity 0:12:10.987,0:12:12.470 which leads to resilience. 0:12:12.470,0:12:14.754 Thank you. 0:12:14.754,0:12:21.434 (Applause) 0:12:30.694,0:12:32.647 Dania Gerhardt: Thank you very much, Nicolas, 0:12:32.647,0:12:35.926 for this great start. Little bit nervous? 0:12:35.926,0:12:37.570 Nicolas Perony: I'm okay, thanks. 0:12:37.570,0:12:40.030 DG: Okay, great. I'm sure a [br]lot of people in the audience 0:12:40.030,0:12:41.894 somehow tried to make associations 0:12:41.894,0:12:43.718 between the animals you were talking about -- 0:12:43.718,0:12:45.774 the bats, meerkats -- and humans. 0:12:45.774,0:12:46.982 You brought some examples: 0:12:46.982,0:12:48.717 The females are the social ones, 0:12:48.717,0:12:50.430 the females are the dominant ones, 0:12:50.430,0:12:52.103 I'm not sure who thinks how. 0:12:52.103,0:12:54.998 But is it okay to do these associations? 0:12:54.998,0:12:57.798 Are there stereotypes you can confirm in this regard 0:12:57.798,0:13:01.071 that can be valid across all species? 0:13:01.071,0:13:02.674 NP: Well, I would say there are also 0:13:02.674,0:13:04.626 counter-examples to these stereotypes. 0:13:04.626,0:13:07.766 For examples, in sea horses or in koalas, in fact, 0:13:07.766,0:13:11.464 it is the males who take care of the young always. 0:13:11.464,0:13:16.505 And the lesson is that it's often difficult, 0:13:16.505,0:13:18.257 and sometimes even a bit dangerous, 0:13:18.257,0:13:20.929 to draw parallels between humans and animals. 0:13:20.929,0:13:23.035 So that's it. 0:13:23.035,0:13:25.881 DG: Okay. Thank you very much for this great start. 0:13:25.881,0:13:27.961 Thank you, Nicolas Perony.