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00:00Oh
00:30A thousand years ago, this valley was the home of the Anasazi Indians.
00:45The Anasazi were fabulously wealthy.
00:48They imported food, they developed advanced technologies,
00:52they constructed these sophisticated and technologically complex
00:56four-storey high buildings.
01:00The Anasazi had it all.
01:07Yet, only a few hundred years after reaching the peak of their success,
01:11the society completely collapsed, leaving behind a ghost city,
01:15and the unanswerable question, what went wrong?
01:21This is a story that has been repeated since the birth of civilisation.
01:26Egypt.
01:29Rome.
01:31And more recently, the Soviet Union.
01:33Not just human societies follow this pattern of emerging from chaos,
01:40prospering and becoming stable, then suddenly disintegrating.
01:44Whole species, such as the dinosaurs, followed the same pattern.
01:48And seemingly the pattern is repeated everywhere.
01:52Planets and stars are formed from a mess of chaotically moving atoms.
01:57Order and stability seem to be imposed until finally, and usually catastrophically,
02:01chaos reigns once again.
02:03It's been the ambition of scientists for the last 5,000 years to try and understand this sort of basic pattern,
02:12which underlies nature and human life.
02:14They've been successful in explaining, predicting and even controlling relatively simple phenomena,
02:21but they cannot predict the weather,
02:24or the next earthquake,
02:29or the next extinction,
02:32which might be ours.
02:34Recently, scientists came up with a theory of chaos.
02:44It explains how unpredictable outcomes arise from seemingly simple rules.
02:50Now some researchers claim to have found a revolutionary new theory.
02:58It explains how order arises from chaos,
03:02and they've called this new science,
03:04anti-chaos.
03:06And it could overturn most of present scientific thinking.
03:14Fish, swimming around in a haphazard fashion,
03:17can, in an instant, change to a school which behaves as one entity.
03:26When hundreds of birds rise into the air,
03:28they do so as individuals flapping their wings out of rhythm with the bird next to them.
03:33Yet rapidly, this random motion of bodies and wings coalesces into a graceful formation.
03:39A flock of birds can swoop and wheel about as if directed by a single mind.
03:47High-speed film reveals that the turning motion travels through the flock as a wave in about 1 70th of a second,
03:57far less than a bird's reaction time.
03:59How do the messages, turn left, turn right, pass through the group?
04:03Telepathy has been suggested, but a supernatural explanation is unnecessary.
04:08Mathematicians have discovered that order can arise spontaneously from the interaction of many simple individuals.
04:19Each of these computer simulations, called boids, follows its own simple rules of behaviour.
04:25Yet surprisingly, the group flocks as in real life.
04:28A pattern has arisen from disorder.
04:31Patterns and structures are everywhere, in nature and in society.
04:37They appear in the arrangement of atoms to form crystals,
04:40or the collective behaviour of ants.
04:42In the way species evolve and become extinct,
04:45and even the rises and falls of financial markets.
04:48But it's beyond the power of traditional science to explain or predict these phenomena.
04:58When structure emerges from chaos, that breaks one of the cornerstones of modern science,
05:03the second law of thermodynamics.
05:06There's considerable evidence to support the second law.
05:09It explains how things such as plants decay with time.
05:14From the highly ordered structure of a plant's cells,
05:17we pass to stages of increasing disorder, or what's called increasing entropy.
05:22The greater the disorder, the more the original form disappears.
05:32In the extreme, this means that the order of the universe as we know it,
05:36with steadily burning stars and organised lumps of matter called planets,
05:40will eventually disintegrate.
05:42When all the energy is dissipated, everything will cease,
05:45and all structure will vanish in the heat death of the universe.
05:49That is, according to the second law of thermodynamics.
05:54Yet, this is not what we find.
05:56Somehow, as the universe moves towards its inevitable end,
05:59it continually creates interesting structures along the way.
06:03Scientists who rely on the second law find it impossible to explain the process.
06:10Those who study anti-chaos and are formulating the new science of complexity
06:17hope to explain how order arises from chaos.
06:21If there's any one big question that complexity is trying to address,
06:25it's why is there something rather than nothing?
06:27In some ways, that's the ultimate question in science.
06:29Physicists for well over a hundred years have been very good at understanding the universal tendency towards disorder and decay.
06:37It's often called the second law of thermodynamics, the increase in entropy.
06:43We look around and we can see very clearly that things decay, that iron rusts, that fallen logs rot,
06:52that temperature goes from hot to cold, things cool off.
06:56But at the same time, not only do fallen logs rot, but trees grow.
07:00If we look around and we see stars, galaxies, you know, people, plants, animals,
07:06there's enormous amounts of structure in the world.
07:08It's almost as if there were a universal yearning towards order, almost a force towards order.
07:15This could mean the second law of thermodynamics is wrong.
07:22It could mean that some people claim that physicists have proved that life can't exist.
07:27It could mean that, since life does exist, that something supernatural must be involved.
07:32My feeling is it's the interpretation of the second law of thermodynamics that's wrong.
07:36And that life is actually consistent with the second law, but that the kind of things it's doing are just not the sort of things the second law addresses.
07:48Another fundamental law is Isaac Newton's law of gravity, discovered in 1684.
07:54At first, scientists thought it would reveal many secrets of the universe.
07:58It accurately predicted the motions of all heavenly bodies, and was followed only three years later by his famous laws of motion.
08:05It was thought then that using these tools, it would only be a matter of time before the whole universe could be explained.
08:13These ideals have driven scientists to try and find simple laws underlying even the most complicated phenomena.
08:20Such laws became the basis of all scientific method, and they have been very productive.
08:27Biologists dug deep into cells at a molecular level, and found the double helix, the code of life itself.
08:36Physicists smashed atoms apart to discover the particles produced by the Big Bang, the building blocks of the entire universe.
08:47But in the process, they discovered something strange. Their laws weren't good enough.
08:56Newtonian laws can explain what we call linear systems. For a given cause, there is always a predictable outcome.
09:05That is not true, however, for complex systems.
09:09The behaviour, for instance, of water molecules, which when gathered in huge numbers and subjected to heat and pressure, create a very unpredictable system.
09:18We call it the weather. This type of behaviour has been dubbed emergent.
09:23You have an emergent phenomenon when the components out of which a system is made have one kind of behaviour, but the system as a whole shows a different kind of behaviour.
09:38So that you cannot reduce the behaviour of the whole system to the behaviour of the individual components.
09:43In other words, you have a kind of irreducibility. And emergence refers to that kind of irreducible order that comes out of a complex system.
09:55Look at an individual water molecule. Its behaviour in liquid water gives us few clues as to how the same molecule will behave when heated to form vapour.
10:08Or when it's cooled to form ice crystals.
10:13The idea of emergence, of course, has been around for a long time. We've known about it since we've known about hurricanes, I guess, perhaps before that.
10:23What is new in the science of complexity, what caused it to come out in the last ten years or so, is several things.
10:32One is that, starting in the 1970s, scientists had much more access to powerful computers and could begin to model complicated systems and see what kinds of behaviours they engaged in.
10:47Often not at all obvious from the equations that they fed into the computers.
10:51Another thing that happened was the spectacular success of molecular biology, where a biologist went in and dissected out and looked at the individual molecules that were the basis of life,
11:05and began to appreciate that the essence of life was the organization of those molecules, how they interacted with each other in a very dynamic, collective way.
11:17And then, of course, the understanding of the mathematics of non-linear equations and chaos had a profound impact also.
11:30Chaos is the name given to systems which appear to behave randomly, yet can be analyzed mathematically.
11:36The intricately beautiful shape of a fractal is the image of chaos at work.
11:41Its complex structure, repeating itself on infinitely diminishing scales, is created on the computer from the simplest of rules.
11:51The discovery that complex behaviour can come from simple rules is at the heart of chaos.
11:58The kind of thing where you can see chaos in action is a food mixer in the kitchen.
12:03The actual rules of a food mixer are very simple.
12:05You put the food in, you push the button, and little paddle wheels go round and round and round in circles.
12:10And you say, well, there's nothing very complicated about a food mixer.
12:13But actually there is.
12:15If somebody says, there is a particular grain of sugar in that food mixer,
12:19I'm going to press the button for 30 seconds and I want you to tell me where that grain is going to end up.
12:24It's very hard to answer that question.
12:26And this is the kind of prediction that science would expect to make.
12:37Chaotic systems are everywhere, from the fracturing of water drops falling to the ground,
12:42to the behaviour of larger bodies of water in the turbulent motion of a stream.
12:47But chaos is not randomness, it has form.
12:52Understanding chaos is to begin to understand the underlying nature of complex systems.
12:57Chaos is one piece of the theory of complexity.
13:02In a sense, it's probably the first piece people really understood fairly well.
13:06And it's because, in fact, it comes from simple causes.
13:10If the causes are simple, you can put them on a computer, you can follow through what happens,
13:14and you can see even if the results look complicated, you know where they came from.
13:18It's the other way around that's much more difficult.
13:21If the causes are very complicated, then you can't even get started.
13:26While chaotic systems create ever-increasing complexity and unpredictability,
13:36others behave in exactly the opposite way.
13:39Some quite disordered systems spontaneously crystallise into a high degree of order.
13:45This is the influence of anti-chaos.
13:48The Belisov-Zabatinsky experiment shows how order spontaneously arises in a chemical process.
13:58Four chemicals are mixed together.
14:01Gradually, concentric waves of red and blue separate out,
14:04in complete contrast with the expected random mixing of molecules.
14:09Such a degree of order stemming from the activity of billions of molecules seems incredible.
14:16But the process is not only observed in the laboratory.
14:19A similar pattern occurs in the reproductive process of one simple organism,
14:25when individual cells swarm together almost as if acting under orders
14:29to form a combined entity called slime mould.
14:33Striking patterns appear in the arrangement of molecules on a platinum catalyst.
14:45That the patterns can be found in so many different systems implies that,
14:49at the most fundamental level, universal principles of organisation are at work.
14:54From computer simulations of forest fires to the behaviour of ants.
14:59Now, for some time, it has occurred to a lot of scientists that ant colonies,
15:11and the social insects generally, have fascinating dynamic patterns of behaviour
15:18that are somewhat reminiscent of the way minds behave.
15:22In other words, you have all these simple elements,
15:26the ants, running about, doing their own thing,
15:29and out of that comes a particular kind of order.
15:32It's like neurons interacting, doing very simple things in the brain,
15:36but if you put enough of them together and have them interact in a particular way,
15:40you get the most extraordinary type of behaviour coming out.
15:45The behaviour of individual ants is much more random than that of the colony as a whole.
15:51The community follows a regular pattern of activity and inactivity.
15:56It appears to have a periodic rhythm of about 28 minutes.
16:00But why do all the ants stop and start at the same time?
16:04Nothing tells them to.
16:06Brian Goodwin turned to a computer model to try and find out.
16:10We tried to produce the simplest possible model that could account for this type of behaviour.
16:18And so we developed a model in which ants were represented as very simple automata.
16:24So they were just interacting, they could move, and what emerged very quickly from this model
16:33was the fact that periodicities at the level of the colony suddenly appeared.
16:39The model also showed that without the simple form of interaction between ants, no order emerged.
16:56The computer ants are not programmed to act as a unified group.
17:00Yet, if they're allowed to wander at will, bump into one another and react,
17:05the temporal pattern of activity returns.
17:17At first sight, the idea of order emerging from randomness is inherently unlikely.
17:23Imagine a system of 26 elements, the letters of the alphabet.
17:27Someone blindfolded and with no knowledge of the keyboard taps the keys of a typewriter at random.
17:33What are the chances of a whole word appearing from the resulting letters?
17:37And what are the chances of the letters organising themselves into, say, a Shakespeare play?
17:42Hamlet, for example.
17:45Then, if you ran the experiment again, what are the chances of the same play materialising again and again?
17:52In nature, this kind of organisation can and does occur.
17:56Does the theory of anti-chaos tell us why?
18:00I became interested in anti-chaos on an intuitive basis.
18:04I wanted to understand the order of development, the way a fertilised egg develops into an adult organism,
18:11which is a most amazing process that nobody understands yet.
18:15You have to understand that one begins with a fertilised egg, the zygote,
18:19and it then goes through 50 or so cell divisions.
18:23During the course of that, cells become different from one another.
18:26This is a process called cell differentiation.
18:28So that the beginning fertilised egg, the zygote, winds up making about 250 different cell types.
18:36Muscle cells, nerve cells, spleen cells, kidney cells, and so on.
18:40And it's quite clear that all of the cells in your body have the same set of genes, about 100,000 or so in us.
18:49Different cells differ because different genes are active in them.
18:52Being active means making an RNA molecule and making a protein.
18:56So liver cells make one set of proteins and kidney cells another.
18:59And one knew even then that genes turned one another on and off in some complex orchestrated dance.
19:07Anti-chaos from the outset was my intuition and hope.
19:11Namely, that one would find that the order that you see in development and cell differentiation
19:17is spontaneous and natural and rather inevitable,
19:20rather than being merely the improbable consequences of mutation and selection.
19:26If we want to think of the genome as 100,000 genes in some kind of network turning one another on and off,
19:34then there's no mathematical tools readily available to look at a system of that complexity.
19:39We're going to have to invent some new ones.
19:41The ones that I invented some time ago were based on thinking of a gene as just active or inactive,
19:47as if it were a light bulb, and its product molecule just present or absent, so also like a light bulb.
19:56To get an array of light bulbs to behave in a way reminiscent of that which controls our genes,
20:04they have to be wired up following simple rules.
20:07The resulting network is called a Boolean network.
20:11In the most complex version of a Boolean,
20:14each light bulb is wired to every other one via a switch which obeys a rule.
20:20The rule might be an individual bulb will light only if two others are lit.
20:32Incredibly, with even a smaller number of light bulbs as 200,
20:37the time it would take to cycle through every possible state of the network
20:41is billions of times longer than the age of the universe.
20:44So arriving at any given pattern of lighting would be extremely unlikely.
20:51Here bulbs coloured green are lighting up randomly,
20:56but the few islands of red show where some stability has been achieved.
21:01But a Boolean network model of the human genome would have not 200 elements but 100,000.
21:08If it followed these rules, life would probably never have happened.
21:13By making a simple change in the way elements are connected,
21:16when each light bulb is only wired to two others,
21:19things get considerably better.
21:21Order and stability emerge.
21:23The most stunning results happen if you build a network
21:27in which every light bulb has inputs from two other light bulbs.
21:30Even if you assign those connections at random,
21:34even if you assign the rule governing every light bulb at random,
21:38such a mad scramble of wiring and logic nevertheless exhibits anti-chaos.
21:45This is order for free.
21:47Even if the network has 100,000 light bulbs,
21:50then the total possible number of states of the system turns out to be a mere 320 or so,
21:56roughly the number of cell types in the human body.
21:59Simplifying the connections produces just a tiny number of states of the network or the genome.
22:05And to give you a sense of how tiny it is,
22:07we have to count to the number of possible states that your genome could be in.
22:11If each gene could be active or inactive, and there's 100,000 genes,
22:15the number of possible alternative patterns of any genome in your body,
22:20in any one of your cells, is one followed by 30,000 zeros.
22:25It's an enormous, hyper-astronomical, unthinkable number.
22:30Anti-chaos says that a system of that kind, despite that vast complexity,
22:34boxes its behavior into a tiny, tiny, tiny region of its space of possibilities and cycles through them.
22:43That's the order that's pointed to when one says anti-chaos.
22:48In the same way that a network of light bulbs can represent certain aspects of the behavior of the human genome,
22:55more dynamic complex systems can be modeled and studied by watching piles of sand form.
23:02The sand pile model is a model of a large dynamical system which consists of many parts.
23:08Suppose you build the sand pile by dropping grains of sand, one at a time, on your desk.
23:13In the beginning, the pile will be flat, and it will be near equilibrium.
23:18All the sand grains of sand will be lying very low, and they won't see each other, they won't interact with each other.
23:24As you keep adding particles to the system, the heap will become steeper and steeper,
23:30and you will start getting small avalanches in the system.
23:33That means that the individual parts of the system start interacting with each other.
23:38And eventually, the avalanches will become bigger and bigger and bigger until you reach a critical slope
23:45where the system will not grow any further.
23:48And at that state, you have avalanches of all sizes.
23:52That means that the system interacts globally.
23:56What happens at one part of the heap can eventually affect what happens at any other part of the heap.
24:04We don't have separate grains of sand anymore.
24:07We don't have many, many distinct systems.
24:10We have only one pile of sand that form one big dynamical system.
24:15Such systems are all around us.
24:19The extremes of the weather.
24:21The extinction of species.
24:25Wild fluctuations of currency markets and many other phenomena.
24:31Think of earthquakes as being generated by pushing tectonic plates into each other.
24:38In the beginning, the system is at equilibrium just as the sand pile is in equilibrium in the flat state.
24:45But eventually, as the tectonic plates keeps squeezing into each other, you get into a state that is further and further and further out of equilibrium.
24:57And again, the earthquakes then will become bigger and bigger and bigger because the forces will become bigger and bigger and bigger.
25:05And the law for the sand slides in the sand slide models is exactly equivalent to what is called the Gutenberg-Richter law for earthquakes.
25:16Namely, that every time you have, say, one earthquake of size 6, you have 10 earthquakes of size 5, and 100 earthquakes of size 4, and so on.
25:26And the sand pile model exhibits precisely this feature.
25:31So that gives an understanding of why we have this very peculiar law for earthquakes.
25:39Actually, what it means is that the crust of the earth on which we are walking is at what we call a critical state.
25:46It's precisely as this sand pile when it's at this state where it can grow no further.
25:51So this is completely different from the usual picture that people have of what's going on around us.
26:00Namely, that we think of things as being in some kind of equilibrium, but nothing could be further from the truth.
26:08If this view of nature is correct, then we're not living in a nice, stable, gently evolving world and universe as we once thought.
26:16Instead, we're part of a system which is just balanced between order and disorder.
26:22Anti-chaos, nature's yearning for order, is counteracted by chaos.
26:27Understanding the rules of chaos and anti-chaos may finally be leading us to a clear view of the complex environment we live in and create.
26:37Manipulating the rules of complexity might offer a glimpse of the future and may one day permit us to control our own destiny.
26:46From the equations of anti-chaos, new life forms are stirring.
26:58Working from just a few simple principles, a rich variety of extraordinary forms spring spontaneously from the ground.
27:17They compete for space and evolve.
27:27A stable environment is created.
27:30As the rules of anti-chaos are observed, order arrives.
27:36Perhaps the most profound problem in biology is how life itself started.
27:40Of course nobody knows.
27:42There really are two broad views.
27:44One is that life is based on the special properties of the magical double helix that everyone knows about, either DNA molecules or RNA molecules.
27:53What I've wanted to find myself for years is a deeper grounds to think that life is, in a sense, an absolutely expected property of complex chemical systems.
28:03I want it to be the case, if you will, that we're at home in the universe, that life is spontaneous, natural and almost inevitable.
28:12And I think that that's the case.
28:14There is some proof that life inevitably emerged from the primordial seas of Earth four million years ago.
28:20If the basic chemicals in the sea are subjected to a violent electrical discharge, such as lightning, the result is a set of fundamental compounds, the chemical building blocks of all living things.
28:32Anti-chaos suggests that this is no accident.
28:36It's inevitable that individual chemicals will cluster together and organize themselves into more complex ones.
28:43Let's imagine the build-up and behavior of the imagined primordial soup.
28:51Life emerged about 3.8 billion years ago, just a hundred million years after the crust was cool enough to support liquid water.
28:58So we have to imagine that there were some organic molecules around, carbon, methane and so on, carbon dioxide, methane and so on, that were gradually built up into more complex kinds of organic molecules, creating this hoped-for primordial soup.
29:14Kaufman's theory involves the well-known chemical principle of catalysis, in which the catalyst persuades two molecules to combine more readily than if left to their own devices.
29:26Many of the functions of living organisms depend on this process, but in the primordial soup this process alone is not enough to explain how all the necessary ingredients for life were formed.
29:37Stuart Kaufman has come up with the idea of autocatalytic sets.
29:44In this process, catalysis takes place to produce a chemical which then combines with another to produce yet another sort of molecule, and so on, until the correct chemicals are present to make some more of the catalyst.
29:58Autocatalysis is a self-reinforcing reaction.
30:05If in fact life emerged as such collective autocatalytic sets of molecules, then it would appear to be the case that the roots to the formation of life are much more probable than we've expected.
30:14So we might hope to find it, for example, on other promising planets and other solar systems.
30:19In effect, life is the consequence of broad boulevards of possibility, not back alleys of thermodynamic improbability.
30:29The potential for life to order itself spontaneously doesn't, however, automatically explain everything.
30:35For instance, how did life, once formed, then evolve into relatively few distinct groups of organisms, each sharing many common features?
30:44There's been a very strong underlying pattern to all of evolution.
30:49The question is where does that order come from?
30:52Now, in the conventional view, that order is basically imposed by historical succession and by natural selection.
31:03So you have a process in which virtually anything is possible, you get random variations, and those structures which are useful, like the eye or the limb, the leaf, the flower, they are stabilised by natural selection.
31:21In other words, the Darwinian view.
31:24But if evolution is seen as a complex system in which order spontaneously arises from randomness,
31:30then the concept of natural selection becomes redundant.
31:34What we see as the origin of this order, the eye, the leaf, the limb, whatever it may be,
31:41is the intrinsic dynamics of developing organisms, a complex dynamic system.
31:49And therefore, the emphasis is shifted from natural selection as an external force to the robust dynamics of development as the generator of these forms.
32:00For instance, the eye we see as a highly probable structure, something that once you understand the principles of development in vertebrates,
32:09you realise that eyes are virtually inevitable.
32:12Studying artificial life, such as this computer-generated amoeba, is starting to suggest that Charles Darwin's theory of evolution by natural selection is at best incomplete.
32:24In fact, it may be too complicated.
32:27A set of very simple rules might well be enough to explain how complex life forms, real or artificial, come into existence.
32:36One of the things that we've learned in the last decade or so of the study of complex systems
32:40is that the most interesting complex behaviour comes out of not incredibly complicated systems,
32:46but out of systems which are composed of relatively simple parts, but many of them that interact with each other in fairly simple ways even.
32:54As a consequence, many of our formal models, the mathematical and computer models that we use to study complex systems,
33:02involve simulating or treating mathematically very, very simple parts that interact with each other in the context of some environment.
33:12And one of the systems that's been used the most is something called a cellular automaton.
33:17Cellular automaton consists of a grid of tiny squares.
33:22The action of any one of them is dependent on the action of the adjoining squares.
33:27This system of replicating loops is built up from a series of very simple rules programmed into each cell.
33:34Once a loop has surrounded itself with copies, there's no longer any space for it to reproduce, and it dies.
33:41As the process of reproduction continues, one ends up with an expanding colony of loops that spread out throughout this two-dimensional cellular automaton universe,
33:57consisting of a growing reproductive fringe surrounding an ever-expanding core or central region of dead loops.
34:05But it was surprising that what I ended up with was something which is very reminiscent of the growth of, say, a tree,
34:12where the central core of the tree consists of dead cells and only the bark and the layer right under the bark is alive, growing out and expanding.
34:23Or, for instance, the growth of a coral reef, where the outermost surface consists of living animals that are building their shells upon the shells of their dead ancestors.
34:32But is it life in all its color and richness? Hardly.
34:38By studying the animals and plants of the rainforests, it's been possible to create a whole universe inside a computer.
34:45In the artificial environment, digital lifeforms behave just like creatures of flesh and blood.
34:51The inhabitants of Tierra live by only two rules, reproduce and mutate.
34:57Well, I started studying the computer as if I were an ecologist, looking at the computer as an environment that could be inhabited by life.
35:05And I thought about what are the resources that these digital organisms would need.
35:11And I came to think of the computer's memory as the space that they would live in,
35:15and the central processing time as the analog of energy that would drive the organisms.
35:21So I wrote a computer program that could reproduce itself.
35:25And I designed a special computer for it to live in.
35:29And I would run this self-reproducing program in a computer that would mutate the programs.
35:34It would mutate it by flipping bits, so that the offspring programs wouldn't always be like the parent.
35:40The first creature to populate this world is red.
35:45It evolves from the primordial soup of the computer memory.
35:49As it does so, it occasionally mutates, so that a new kind of creature is created.
35:55If the mutation makes beings that breed faster than the original red creature,
36:00they start to take over this universe.
36:04Surprisingly, even in this digital world, many things occur that are recognizable,
36:11or are recognized from having worked in the rainforest.
36:15Right away, things appear that take advantage of the original program.
36:18These are parasites.
36:19They borrow information from the original program.
36:22And once the parasites become very common,
36:24then other things come along that take advantage of the parasites.
36:27They steal their energy.
36:29So we see a long succession of forms where whatever is successful
36:32becomes a target for some new form of parasite that takes advantage of it.
36:37All of this complex behavior results from just two simple instructions.
36:42Reproduce and mutate.
36:44As time passes in the computer universe,
36:47the creatures learn to trust one another,
36:49and, almost predictably, others then cheat on the society.
36:53This is disturbingly close to the way most life forms have evolved.
36:58Well, I consider this to be real evolution.
37:02The only thing that's not real is the medium that it's evolving in.
37:05But one thing we can learn from that is that regardless of what the medium is,
37:10whether it's the chemistry of carbon and hydrogen or the chemistry of bits,
37:15we still get the same sort of processes.
37:18The life process itself takes on these forms.
37:22In Tierra, moreover, the patterns of the digital extinctions match those in the real world,
37:28as displayed in the fossil record.
37:31They also follow the overall patterns of Pearback's sand piles.
37:36The arrival of a new creature is like the addition of another grain of sand to the pile.
37:41When there are too many animals, there's an extinction.
37:44Too many sand grains, there's an avalanche.
37:47As new animals or grains are arriving, the system is delicately balanced on the edge of chaos.
37:53Many natural systems are similarly poised.
37:56The most complex behavior that we observe in both the physical systems that we see in nature
38:02and also in the systems that we study in computers seem to occur at a point which we call the edge of chaos.
38:12It's a point when these systems are sort of just melting from a very ordered frozen regime to a disordered or random regime.
38:25Right at the transition point between the two is where we see them exhibiting their most complex behavior.
38:31Understanding the junction between order and disorder might, for instance,
38:36allow us to explain for the first time how once dominant economies can slip into decline and fall.
38:56It might help us comprehend the sometimes bizarre behavior of the stock market
39:03or understand how currencies can fluctuate wildly and seemingly randomly.
39:14Conventional economic theories cannot explain these events.
39:18The boundaries of anti-chaos might be able to.
39:22When you start to think from a complexity point of view,
39:26you begin to see the economy not as something that's static, that's reached an equilibrium,
39:31that's basically, in a sense, dead.
39:35The economy, you begin to see, is something that's always unfolding.
39:39It's always reforming new patterns.
39:41It's forming into, think of the cities out there.
39:44They're forming patterns of settlement.
39:47Those patterns form and reform over decades and centuries.
39:51So we're starting to view the economy not as a sort of static system,
39:55that one can sort of study through a microscope like a dead specimen in a lab,
40:01but we're starting to see it almost as alive,
40:04something that's always unfolding, that's always changing,
40:09that is, in a sense, always evolving.
40:13And this point of view is starting to make us look at the economy
40:17very much as an evolving, complex system rather than a simple, static system.
40:25An example of how unpredictable markets can be
40:28was the battle between the two domestic video recorder systems in the late 1970s.
40:33Betamax was technologically slightly superior to VHS.
40:37Yet surprisingly, VHS sales started to outstrip those of Betamax.
40:42Traditional economics predicts that the Betamax system
40:46should then have enjoyed increased demand, more sales,
40:50and the market would have been pulled back into equilibrium.
40:53This did not happen.
40:55In other words, there was an outcome to the situation
40:58that classical theory couldn't predict.
41:02When I realised that in economics
41:04there could have more than one solution to the same problem,
41:07this absolutely horrified me.
41:09It horrified a lot of my colleagues
41:12and some of my former professors even.
41:15We were used to thinking in economics
41:17that there was only one solution to any given economic problem,
41:22at least that was the dominant mode of thinking,
41:25and that that solution in some way we could prove
41:29was the best of all feasible worlds.
41:32That is, that this solution that you see out there,
41:35that the economy has arrived at,
41:38is the best of all feasible worlds
41:40that England could do at the moment.
41:44Yet, here was I and other people thinking this way,
41:49showing that for any given economic problem
41:53there might be more than one solution.
41:55There might be two solutions, there might be a hundred solutions,
41:58there might be infinite number of solutions.
42:02And that would mean, and this was the real horror of the whole thing,
42:06that would mean that we might have ended up with a solution
42:12that was not the best of all feasible worlds.
42:15Ryan Arthur has found that economies, both national and international,
42:21operate rather like pare-back sand piles.
42:24An economic system builds towards a stable state.
42:28But to maintain that state, there are frequent major disturbances,
42:32financial avalanches.
42:34The system is finely balanced on the edge of chaos.
42:37Anti-chaos opens a window of order
42:54onto the seeming chaos of the stock market
42:57and makes it possible to build computer models
42:59of the way the market works.
43:01We've designed a system that predicts the behavior of financial markets.
43:08It works on a pretty simple principle.
43:10We go back through the historical record of financial data
43:13and we look for patterns in price movements
43:17and other factors that relate to economic markets.
43:22And we do a lot of tests to see whether those patterns reproduce.
43:26That is, do the patterns happen again and again
43:29and are they statistically significant?
43:32And we use those patterns when they occur to predict markets.
43:36We get data feeds from all over the world
43:39and our computers are looking at those data feeds all the time.
43:42They're looking for certain patterns.
43:44A certain set of prices goes up.
43:47A certain other set of prices goes down.
43:49The computer makes a prediction about what the markets are going to do
43:53and it does a computation to figure out
43:55how we should, what we should buy and what we should sell
43:58in order to make the most money based on those predictions.
44:02This is a day order for account prediction company.
44:04Friday 50 September 92.
44:07IMM British Pound Futures at market.
44:09Just checking the British Pound, stay with me.
44:11What's the British Pound?
44:13The mathematical tools that we were using developed out of the quest
44:20for finding order in chaos and for using that order to make better predictions.
44:26Even though chaotic systems are unpredictable over the long term,
44:30they're more predictable than traditional random processes over the short term.
44:35And we cut our teeth, as it were, analyzing systems that we knew were chaotic.
44:42It's not clear that that's really pertinent to the markets,
44:46but on the other hand the techniques do seem to give us better predictive power.
44:49That doesn't mean the markets are really simple chaotic systems,
44:52but it does mean these techniques have value.
44:55So far the trading is not real.
44:58The system is on trial and the money made is hypothetical.
45:02But the ideas seem to work.
45:05The success of our computer system would put us at the top of performance of people
45:14who are trading out in the real market.
45:17It's always a bit difficult because when you pick the winners retrospectively,
45:22you're picking out what might have just been somebody who got lucky.
45:26It's easy, if you take five years worth of trading,
45:30there's always going to be somebody that's had a run of good luck.
45:33But our results put us up near the top of that heap.
45:39Understand economies? Make a fortune in stocks and shares?
45:43Can anti-chaos also save the planet?
45:52Here in the Arizona desert, the ecosystem is delicately balanced.
45:57Tom Vallone is studying a plot of land which has been subjected to an unusual experiment for the last 15 years.
46:05Typically the landscape is dominated by drought resistant shrubs and thin vegetation.
46:14At the start of the study, several large square plots of land were fenced off.
46:19A single rodent, the kangaroo rat, was removed from these areas and kept out by a system of gates
46:25which let all the other native creatures roam freely.
46:28Fifteen years later, these plots of desert land have been dramatically transformed.
46:34On the plots where we excluded kangaroo rats, two species of grass increased dramatically.
46:49One species increased tenfold, the second species increased threefold.
46:53The results were completely unexpected.
46:56When we first excluded the kangaroo rats, I don't think we would have been able to predict
47:01that the vegetation itself would change within those plots.
47:05We made simple predictions about the response of other granivores like the ants.
47:11But at that time, 15 years ago, we had no idea that the removal of kangaroo rats
47:16would change the vegetation type on those plots.
47:18Because ecosystems are so complex and sit just on the junction between chaos and anti-chaos,
47:25a small perturbation in the system, like adding one grain of sand to a sand pile,
47:30can set off an avalanche of change.
47:33What happens has proved almost impossible to predict.
47:37But anti-chaos is taking us closer to understanding these sorts of complex phenomena.
47:43And being able to predict is just the first part of man's ultimate ambition,
47:48to understand means to control.
47:50At first, heavier-than-air flight was a dream.
48:04Early attempts at flying vehicles imitated birds and were spectacularly unsuccessful.
48:13Then scientists discovered the principles behind aerodynamics.
48:17By applying them, we now have aircraft that can carry us a lot in great comfort,
48:23in extraordinary ways, or at high speed.
48:29Relatively simple systems such as the dynamics of flight, which are basically linear,
48:34respond well to our attempts at control.
48:37Complex non-linear systems have so far proved unconquerable.
48:42We understand, for instance, how rain clouds are formed and why rain falls.
48:47For the last 30 to 40 years, scientists have attempted to make rain.
48:52They've flown over drought-ridden areas, sowing chemical seeds into the air
48:57to try and persuade water vapour to become rain.
49:00But they've utterly failed to make nature bend to their will.
49:09Yet we are now a long way from blind belief in supernatural forces,
49:13which control the chaotic world on a whim.
49:16Science has given us a way to understand a universe which obeys simple laws.
49:21But in doing so, we seem to have lost sight of the complexity and unity of the world around us.
49:34Now anti-chaos may be leading us to the next stage,
49:37understanding how complex patterns arise,
49:40which would mean being able to explain why technologically advanced societies evolve, then die out.
49:53It might even mean that we're on the point of realising the dreams of scientists,
49:58to understand finally the laws that govern nature.
50:02Next Sunday at 7 o'clock, Equinox updates its investigation into the strange phenomenon of crop circles.
50:29If in the past pouquinho, the mind has been a real invention.
50:32Then we're looking at the new parti andba.
50:33The rule maps a mysterious world after theantiates of the nature of the world.
50:34The new parti andba.
50:35The new parti, which is describe in the past,
50:36the German principle of 2014,
50:38is the new parti andba.
50:40Which is an example of the ancient Part II,
50:42is a clandest of the tributary and the past.
50:43What kind of did you start with this?
50:44The new parti, if you look at the spectrum of the Artemis,
50:46of the Nazism,
50:49the space of the subject is a view in the state?
50:50The first part is on the future.
50:51The new andba may have and act of the future.
50:53The next cycle is the new part of this.
50:54The new part of our nation has come back down a little of the nature.
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