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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 cohering 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 a more
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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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— (Laughter) —
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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 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 its parts.
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In a way, collaboration
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is another example of complex systems.
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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
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to grade to 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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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 hybernation,
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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 playing 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-future 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 from 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, you know,
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"Go ahead, tell me if it's safe."
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But 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 simulating 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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Now, 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 crush,
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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)