# Hannah Fry: Is Life Really That Complex? at TEDxUCL (Transcript)

Full transcript of British complex systems theorist Hannah Fry’s TEDx Talk: Is Life Really That Complex? at TEDxUCL conference.

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Hannah Fry – British complex systems theorist

Okay, thank you very much. I’m Hannah Fry, the badass, and today I’m asking the question: Is life really that complex?

Now, I’ve only got 9 minutes to provide you with an answer. So, what I’ve done is split this neatly into two parts: Part one – Yes, and later on, part two – No, or, to be more accurate: No?

Okay, so first of all let me try and define what I mean by complex. Now I could give you a host of formal definitions, but, in the simplest terms, any problem and complexity is something that Einstein and his peers can’t do.

So, let’s imagine — if the clicker works, there we go — Einstein is playing the game of snooker. He’s a clever chap, so he knows that when he hits the cue ball he could write you an equation and tell you exactly where the red ball is going to hit the sides, how fast it’s going and where it’s going to end up.

Now, if you scale these snooker balls up to the size of the solar system, Einstein can still help you. Sure, the physics changes, but, if you wanted to know about the path of the Earth around the Sun, Einstein could write you an equation telling you exactly where both objects are at any point in time.

Now, with a surprising increase in difficulty Einstein could include the Moon in his calculations, but, as you add more and more planets, Mars and Jupiter, say, the problem gets too tough for Einstein to solve with a pen and paper.

Now, strangely if instead of having a handful of planets you had millions of objects, or even billions, the problem actually becomes much simpler and Einstein is back in the game.

So let me explain what I mean by this, by scaling these objects back down to a molecular level. Now if you wanted to trace the erratic path of an individual air molecule you’d have absolutely no hope, but when you have millions of air molecules all together they start to act in a way which is quantifiable, predictable and well behaved, and, thank Goodness, air is well behaved because if it wasn’t, planes would fall out of the sky.

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Now, on an even bigger scale, across the whole of the world, the idea is exactly the same with all of these air molecules. It’s true that you can’t take an individual rain droplet and say where it’s come from, where it’s going to end up but you can say with pretty good certainty whether it’s going to be cloudy tomorrow. So, that’s it. In Einstein’s time this is how far science had got. We could do really small problems with a few objects, with simple interactions, or you could do huge problems with millions of objects and simple interactions.

But what about everything in the middle? Well, just seven years before Einstein’s death, an American scientist called Warren Weaver made exactly this point. He said that scientific methodology has gone from one extreme to another leaving out an untouched great middle region. Now, this middle region is where complexity science lies and this is what I mean by complex.

Now, unfortunately, almost every single problem you can think of to do with human behavior lies in this middle region. Einstein’s got absolutely no idea how to model the movement of a crowd, there are too many people to look at them all individually and too few to treat them as a gas.

Similarly, people are prone to annoying things like decisions of not wanting to walk into each other which makes the problem all the more complicated.

Einstein also couldn’t tell you when the next stock market crash is going to be, Einstein couldn’t tell you how to improve unemployment, Einstein can’t even tell you whether the next iPhone is going to be a hit or a flop.

So, to conclude part one, we’re completely screwed, we’ve got no tools to deal with this and life is way too complex.

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But, maybe there’s hope, because in the last few years we’ve begun to see the beginnings of a new area of science using mathematics to model our social systems. And I’m not just talking here about statistics and computer simulations, I’m talking about writing down equations about our society that will help us understand what is going on in the same ways with the snooker balls or the weather prediction. And this has come about because people have begun to realize that we can use and exploit analogies between our human systems and those of the physical world around us.

Now, to give you an example of the incredibly complex problem of migration across Europe. Actually, as it turns out, when you view all of the people together, collectively they behave as though they’re following the laws of gravity. But instead of planets being attracted to one another, it’s people who are attracted to areas with better job opportunities, higher pay, better quality of life and lower unemployment.

And in the same way as people are more likely to go for opportunities close to where they live already, London to Kent, for example, as opposed to London to Melbourne, the gravitational effect of planets faraway is felt much less.

So, to give you another example, in 2008 a group in UCLA were looking into the patterns of burglary hotspots in the city. Now, one thing about burglaries is this idea of repeat victimization. So, if you have a group of burglars who manage to successfully rob an area, what they’ll do is they’ll tend to return to that area and carry on burgling it, so they learn the layout of the houses, the escape routes and the local security measures that are in place, and this will continue to happen until local residents and police ramp up the security at which point the burglars will move off elsewhere. And it’s that balance between burglars and security which create these dynamic hotspots of the city.