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Science,

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science has allowed us to know so much

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about the far reaches of the universe,

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which is at the same time tremendously important

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and extremely remote,

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and yet much, much closer,

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much more directly related to us,

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there are many things we don't really understand.

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And one of them is the extraordinary

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social complexity of the animals around us,

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and today I want to tell you a few stories

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of animal complexity.

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But first, what do we call complexity?

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What is complex?

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Well, complex is not complicated.

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Something complicated comprises many small parts,

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all different, and each of them

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has its own precise role in the machinery.

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On the opposite, a complex system

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is made of many, many similar parts,

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and it is their interaction

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that produces a globally coherent behavior.

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Complex systems have many interacting parts

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which behave according to simple, individual rules,

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and this results in emergent properties.

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The behavior of the system as a whole

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cannot be predicted

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from the individual rules only.

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As Aristotle wrote,

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the whole is greater than the sum of its parts.

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But from Aristotle, let's move onto

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a more concrete example of complex systems.

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These are Scottish terriers.

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In the beginning, the system is disorganized.

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Then comes a perturbation: milk.

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Every individual starts pushing in one direction

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and this is what happens.

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The pinwheel is an emergent property

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of the interactions between puppies

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whose only rule is to try to keep access to the milk

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and therefore to push in a random direction.

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So it's all about finding the simple rules

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from which complexity emerges.

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I call this simplifying complexity,

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and it's what we do at the chair of systems design

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at ETH Zurich.

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We collect data on animal populations,

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analyze complex patterns, try to explain them.

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It requires physicists who work with biologists,

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with mathematicians and computer scientists,

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and it is their interaction that produces

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cross-boundary competence

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to solve these problems.

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So again, the whole is greater

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than the sum of the parts.

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In a way, collaboration

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is another example of a complex system.

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And you may be asking yourself

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which side I'm on, biology or physics?

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In fact, it's a little different,

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and to explain, I need to tell you

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a short story about myself.

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When I was a child,

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I loved to build stuff, to
create complicated machines.

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So I set out to study electrical engineering

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and robotics,

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and my end-of-studies project

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was about building a robot called ER-1 --

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it looked like this—

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that would collect information from its environment

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and proceed to follow a white line on the ground.

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It was very, very complicated,

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but it worked beautifully in our test room,

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and on demo day, professors had 
assembled to grade the project.

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So we took ER-1 to the evaluation room.

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It turned out, the light in that room

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was slightly different.

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The robot's vision system got confused.

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At the first bend in the line,

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it left its course, and crashed into a wall.

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We had spent weeks building it,

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and all it took to destroy it

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was a subtle change in the color of the light

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in the room.

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That's when I realized that

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the more complicated you make a machine,

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the more likely that it will fail

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due to something absolutely unexpected.

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And I decided that, in fact,

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I didn't really want to create complicated stuff.

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I wanted to understand complexity,

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the complexity of the world around us

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and especially in the animal kingdom.

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Which brings us to bats.

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Bechstein's bats are a common
species of European bats.

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They are very social animals.

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Mostly they roost, or sleep, together.

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And they live in maternity colonies,

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which means that every spring,

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the females meet after the winter hibernation,

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and they stay together for about six months

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to rear their young,

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and they all carry a very small chip,

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which means that every time one of them

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enters one of these specially equipped bat boxes,

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we know where she is,

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and more importantly,

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we know with whom she is.

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So I study roosting associations in bats,

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and this is what it looks like.

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During the day, the bats roost

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in a number of sub-groups in different boxes.

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It could be that on one day,

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the colony is split between two boxes,

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but on another day,

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it could be together in a single box,

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or split between three or more boxes,

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and that all seems rather erratic, really.

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It's called fission-fusion dynamics,

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the property for an animal group

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of regularly splitting and merging

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into different subgroups.

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So what we do is take all these data

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from all these different days

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and pool them together

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to extract a long-term association pattern

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by applying techniques with network analysis

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to get a complete picture

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of the social structure of the colony.

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Okay? So that's what this picture looks like.

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In this network, all the circles

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are nodes, individual bats,

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and the lines between them

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are social bonds, associations between individuals.

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It turns out this is a very interesting picture.

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This bat colony is organized

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in two different communities

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which cannot be predicted

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from the daily fission-fusion dynamics.

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We call them cryptic social units.

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Even more interesting, in fact:

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Every year, around October,

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the colony splits up,

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and all bats hibernate separately,

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but year after year,

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when the bats come together again in the spring,

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the communities stay the same.

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So these bats remember their friends

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for a really long time.

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With a brain the size of a peanut,

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they maintain individualized,

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long-term social bonds,

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We didn't know that was possible.

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We knew that primates

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and elephants and dolphins could do that,

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but compared to bats, they have huge brains.

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So how could it be

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that the bats maintain this complex,

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stable social structure

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with such limited cognitive abilities?

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And this is where complexity brings an answer.

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To understand this system,

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we built a computer model of roosting,

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based on simple, individual rules,

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and simulated thousands and thousands of days

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in the virtual bat colony.

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It's a mathematical model,

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but it's not complicated.

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What the model told us is that, in a nutshell,

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each bat knows a few other colony members

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as her friends, and is just slightly more likely

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to roost in a box with them.

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Simple, individual rules.

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This is all it takes to explain

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the social complexity of these bats.

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But it gets better.

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Between 2010 and 2011,

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the colony lost more than two thirds of its members,

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probably due to the very cold winter.

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The next spring, it didn't form two communities

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like every year,

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which may have led the whole colony to die

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because it had become too small.

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Instead, it formed a single, cohesive social unit,

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which allowed the colony to survive that season

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and thrive again in the next two years.

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What we know is that the bats

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are not aware that their colony is doing this.

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All they do is follow simple association rules,

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and from this simplicity

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emerges social complexity

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which allows the colony to be resilient

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against dramatic changes
in the population structure.

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And I find this incredible.

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Now I want to tell you another story,

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but for this we have to travel from Europe

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to the Kalahari Desert in South Africa.

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This is where meerkats live.

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I'm sure you know meerkats.

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They're fascinating creatures.

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They live in groups with a
very strict social hierarchy.

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There is one dominant pair,

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and many subordinates,

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some acting as sentinels,

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some acting as babysitters,

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some teaching pups, and so on.

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What we do is put very small GPS collars

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on these animals

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to study how they move together,

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and what this has to do with their social structure.

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And there's a very interesting example

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of collective movement in meerkats.

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In the middle of the reserve which they live in

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lies a road.

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On this road there are cars, so it's dangerous.

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But the meerkats have to cross it

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to get from one feeding place to another.

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So we asked, how exactly do they do this?

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We found that the dominant female

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is mostly the one who leads the group to the road,

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but when it comes to crossing it, crossing the road,

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she gives way to the subordinates,

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a manner of saying,

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"Go ahead, tell me if it's safe."

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What I didn't know, in fact,

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was what rules in their behavior the meerkats follow

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for this change at the edge of the group to happen

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and if simple rules were sufficient to explain it.

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So I built a model, a model of simulated meerkats

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crossing a simulated road.

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It's a simplistic model.

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Moving meerkats are like random particles

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whose unique rule is one of alignment.

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They simply move together.

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When these particles get to the road,

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they sense some kind of obstacle,

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and they bounce against it.

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The only difference

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between the dominant female, here in red,

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and the other individuals,

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is that for her, the height of the obstacle,

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which is in fact the risk perceived from the road,

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is just slightly higher,

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and this tiny difference

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in the individual's rule of movement

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is sufficient to explain what we observe,

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that the dominant female

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leads her group to the road

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and then gives way to the others

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for them to cross first.

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George Box, who was an English statistician,

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once wrote, "All models are false,

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but some models are useful."

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And in fact, this model is obviously false,

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because in reality, meerkats are
anything but random particles.

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But it's also useful,

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because it tells us that extreme simplicity

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in movement rules at the individual level

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can result in a great deal of complexity

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at the level of the group.

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So again, that's simplifying complexity.

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I would like to conclude

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on what this means for the whole species.

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When the dominant female

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gives way to a subordinate,

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it's not out of courtesy.

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In fact, the dominant female

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is extremely important for the cohesion of the group.

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If she dies on the road, the whole group is at risk.

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So this behavior of risk avoidance

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is a very old evolutionary response.

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These meerkats are replicating an evolved tactic

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that is thousands of generations old,

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and they're adapting it to a modern risk,

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in this case a road built by humans.

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They adapt very simple rules,

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and the resulting complex behavior

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allows them to resist human encroachment

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into their natural habitat.

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In the end,

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it may be bats which change their social structure

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in response to a population crash,

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or it may be meerkats

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who show a novel adaptation to a human road,

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or it may be another species.

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My message here -- and it's not a complicated one,

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but a simple one of wonder and hope --

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my message here is that animals

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show extraordinary social complexity,

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and this allows them to adapt

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and respond to changes in their environment.

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In three words, in the animal kingdom,

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simplicity leads to complexity

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which leads to resilience.

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Thank you.

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(Applause)

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Dania Gerhardt: Thank you very much, Nicolas,

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for this great start. Little bit nervous?

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Nicolas Perony: I'm okay, thanks.

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DG: Okay, great. I'm sure a 
lot of people in the audience

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somehow tried to make associations

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between the animals you were talking about --

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the bats, meerkats -- and humans.

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You brought some examples:

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The females are the social ones,

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the females are the dominant ones,

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I'm not sure who thinks how.

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But is it okay to do these associations?

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Are there stereotypes you can confirm in this regard

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that can be valid across all species?

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NP: Well, I would say there are also

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counter-examples to these stereotypes.

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For examples, in sea horses or in koalas, in fact,

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it is the males who take care of the young always.

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And the lesson is that it's often difficult,

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and sometimes even a bit dangerous,

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to draw parallels between humans and animals.

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So that's it.

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DG: Okay. Thank you very much for this great start.

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Thank you, Nicolas Perony.