Catastrophe, Chaos and ComplexityA workshop by

Professor Richard C. Lewontin (Harvard University)

Conducted in the Old Geology Lecture Theatre, University of Sydney

November 28th, 1997

Professor Richard Lewontin is Professor of Zoology at Harvard University, and is on the ScienceBoard of the Santa Fe Institute of Complexity Studies. Following his presentation of the 1997Templeton Lecture, he conducted a three-hour workshop exploring the theories of catastrophe,chaos and complexity in biology. While Professor Lewontin spoke for most of the time, theaudience and a panel of three invited experts, provided many comments, questions and responsesthroughout the workshop.

The invited guests were:

• David Green, Professor of Information Technology, Charles Sturt University in Albury.

• Keith Williams, Professor of Biology in the Department of Molecular Biology andDevelopment, Macquarie University, Sydney.

• Dr. Arran Gare, Department of Philosophy and Cultural Inquiry, Swinburne University inMelbourne.

The workshop was chaired by CHAST committee member, Peter Farleigh.

What follows is the transcription from the workshop, with apologies for any errors in the text dueto the less-than-perfect recording of the event.

Part 1: Catastrophe

Richard Lewontin: I gave the title "Catastrophe, Chaos & Complexity" just as a way of organisinga rather disorganised subject. And also because all three of those words at one time or another inbiology have been very trendy. And I want to ask where does this trendiness come from, how dothose notions get into biology. In order to motivate the whole affair I want to go back to what Isaid in my formal talk last night and in my answers to questions.

I want to emphasize a couple of points, which will lead into the issues of catastrophe, chaos andcomplexity. I want to assert that organisms are a particular part of the physical world that havecertain properties which stem from the place they occupy in the space of physical phenomena. Ihope you will understand that everything I say assumes that living organisms are in some sensenothing but physical systems. This is a totally materialist, realist point of view. But they occupy aregion of the space of physical systems, which makes for particular difficulties. In particularorganisms are intermediate in size. They are not as big as planets, they are not as small asmolecules or nuclei and they are internally heterogeneous. And the fact that they are internallyheterogeneous and intermediate in size, introduces another property, characteristic of suchsystems, and that is that the interactions or dynamical systems that are occurring within theorganism are not of greater magnitude in general then the coupling between the systems within theorganism and the systems outside the organism. That is to say - it is an error or an historical relicof the development of biology that one talks about the organism on the one hand, and the

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environment on the other and in some way illuminates them and sees the dynamical systemsoutside as somehow independent of the dynamical systems inside. What I want to talk about is thecoupling of dynamical systems inside and those outside. The coupling of multiple pathways ofcausation within the system and the consequence of its intermediate size. Physicists are not verygood with dealing with such systems. They work with wind tunnels and computers but outside ofthat they wouldn’t know what to do.

If I throw this key on the floor one would not want to try to write equations for what justhappened. There is an elastic collision, but noise is generated so there is some dissipation ofenergy. It came down through the air in a funny way. Whereas the laws of Newton are clearlyapplicable in some sense, they don’t do all that good a job. And of course every time you turn onthe tap the water comes out in a way that involves complex hydrodynamics.

The problem with objects that are discussed in the second volume of Newtown's Principia, aboutobjects which move through viscous fluids experiencing friction and are intermediate in size andbump and bash into one another and deform rather than springing apart - those are all problems wehave and that makes the study of biological systems rather more difficult. But more important thanthat is the internal heterogeneity of the system. That is to say, and we have a slogan here: "Livingorganisms are the nexus between a very large number of weakly determining causal pathways."That is the part of the space of physical systems that they occupy. And that has very seriousconsequences for the biologist as scientist, that the biologist would like to try to avoid if possible.That is because in what we consider the normal organism there are small perturbations in verylarge numbers of interactive causal pathways with the result that there are small outcomes of thoseperturbations, and the system has buffered properties such that perturbations are not propagated toall aspects of the system; some parts of the system may absorb the change so they become staticsystems.

And the result of that is that the biologists cannot understand biology by looking at the objects ofinterest unlike the astronomer, who doesn’t have much choice but to look. The astronomer has ofcourse the advantage of some physical knowledge and theory and learns about the system bywatching it. The biologist can learn virtually nothing by watching. And the reason is that thecausal pathways are many and weakly determining and no single one of them is normallyperturbed to such a degree to make a difference to the system, until the organism is sick. Anorganism is sick when it is dominated by one of the causal pathways. (My liver is destroying mylife. Or my kidneys, or I have an idée fixe and am obsessed.) So sickness occurs when what isnormally determined by a large number of causal pathways is dominated by only one. Now I feelvery strongly as an evolutionary biologist and a population geneticist and ecologist, that we cannotreally reconstruct or estimate the parameters of the dynamical systems which are operating in theevolution of an organism just by looking at it. The static observational data are not rich enough. Sowhat does the biologist do? Then he becomes an experimentalist. But what is an experiment? Anexperiment is where you take a system and you destroy it, so to speak, by taking one causalpathway, emphasising it, making it extreme in its effect, while holding all the others constant inthe hope that the response of the system to this unusual perturbation ceteris paribus will tell youhow that pathway is operating in the undisturbed system. But the problem with organisms is thatwe have a scaling problem. That is to say we do not, we sometimes can, but can't always, tell howthe organism actually operates by doing this trick.

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I tried to illustrate that last night usingdevelopmental genetics as an example. There is aclassic method in genetics that I inherited as astudent that you try to understand biochemicalpathways by studying mutations. So you had amutation which prevented the colour (preventedthe pigment development) in the eye ofDrosophila, called the white eye mutant and youlooked to try to find out the enzyme that wasblocked in that mutant and then you would say :"Now I know something about the formation ofeye pigment". You do indeed discover somethingabout pigment formation but there are many,many different points of intervention that affect

eye pigment. And it turns out that what you don't know is how to account for small variations inthe amount of pigment in Drosophila eye, from one individual to another, or between species.Because the perturbation you have performed is not related to the variation that is actually thecause, or one of the contributing causes, of natural variation. It is a pathological condition. Anawful lot of mutation studies in the history of developmental genetics have been studies on whatcould be called pathological changes. So the current problematic of developmental genetics, forexample, is to work out the signalling pathways among the genes that are concerned withdetermining the difference between the front and the back of an organism or between the ventraland dorsal surface. But that problematic does not include the question of why our noses aredifferent shapes from one another or, indeed, why a nose is nose-shaped. It's not even in thequestions. Why is a nose a particular shape? Shape is not in the problematic. What I am saying isthat when you go down an experimental path you necessarily make big perturbations and whenyou make those perturbations you push the system outside of its normal set of homeostaticbuffering channels.

If I plot the pH of the system against an amount of added acid it looks like Figure 1 and it’sbuffered in the region A. outside the buffering range the line looks like this, and this flat region(A) is what we call a buffered region where acid or base does not change the pH, and there may bemultiple buffer regions. Now organisms have morphogenic buffer regions in which all kinds ofperturbations in either the environment or the genotype have no effect whatsoever on thephenotype. [Jimmy] Rendell who used to be in the CSIRO here did very famous experiments toshow that genetic variation had no effect whatsoever on certain phenotypic aspects of the organismnor did environmental variation until you put enough genetic variation into it to push the systeminto the response region. Once you made the system respond you discovered that this geneticvariation was relevant. It wasn't that it was irrelevant it was just buffered out. As you move thesystem outside the zone of buffering you could observe this variation having its effect. So that isour experimental problem. Within the zones of buffering which have built up within the organismthrough evolution, an awful lot of the variation of causal pathways has little or no effect on thephenotype. Nevertheless the causal pathways are there inside the organism. So we are in an awfuldilemma, that if we perform experiments which succeed in pushing the system outside the bufferregion, it is then in a state which does not normally occur and which does not adequately describewhat is normally going on. And if we don't do that, we say there is nothing varying and that wehave nothing to study.

This is not a problem, I gather, which occurs in other regions of physical study. We assume thatthe scaling problems are not so severe in other areas, although I think they must occur to someextent.

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Bob Hunter: What you are talking about here is the macroscopic behaviour of the organism. Butyou could feasibly see, in that buffered region, a variation in, say, the concentration of someparticular metabolite.

Lewontin: When we say the system is buffered, we mean some aspect of the system is buffered,which means that that part of the system does not change; other parts may change but you have toknow where to look. And the problem is that we come back to the internal heterogeneity of theorganism. There are so many causal pathways you don't know where to look.

Keith Williams: Can I suggest that is changing? I think we are heading for a paradigm shift here,because the reason that you are stuck with that problem is that we did not know about the totalityof the system and so we were forced to focus on particular issues and, as you say, do dramaticthings to them in order to get some insights into what is going on. I think one of the things that ishappening in the world at the moment is that we are beginning to get a complete read-out of whatthe ingredients are. We have never had that before, so we are starting to get to the stage with theDNA sequencing programs where we understand all the informational components to theorganism. So we understand basically what the raw material to be worked on is. The problem atthe DNA level is, I think, that it is information but nothing more. And if you translate that to theprotein level, to actually what gets made, then you can start working in that middle region and seewhat happens as you move along that buffered zone. And the sort of thing we are seeing becausewe are looking at mass protein display of the codes is that you have got these whole networks ofchanges that occur. The lovely thing about that is that you do not need to make decisions aboutwhat the important componentry is. You can now start to look at holistic analysis and actuallywork in that domain.

Lewontin: I agree that that is the program of developmental biology. If I could caricaturedevelopmental biology, it is to make a completely connected graph of the signalling pathwaysamong all the genes. That is the program and the real issue is whether we can cash it out. Peoplesay, as you just have, that we are beginning to do that. I am less sanguine than you that you will beable to sort all those pathways out in a reasonable time with the resources available.

David Green: Can I take the point further? Some physical systems do have that property. Thinkabout water, ice and steam where you get water remaining as a liquid over a large range oftemperatures but you get to those particular temperatures where something dramatic happens.

Lewontin: So we have phase transitions. Sure, but on this question of whether developmentalgenetics can complete its program, I say it can’t complete its program if it doesn't ask all thequestions. Let me give an example: a great deal is known about the genes and certain signallingpathways for wing formation, (for example the UBX system) but the question is does that tell usanything about variation in wing size between individuals and between species? Are the geneticvariations in wing size connectable with variations in the UBX system and its proteins more thanin other genes? Now we have some information about that. Greg Gibson recently has found indeedthat flies with different size wings have somewhat different DNA sequences for UBX, so UBX issomehow involved, but it is not the only thing that is involved. There is quite a lot of geneticvariation out there that is not in UBX and we don’t know how to look for it. So I agree with youthat the program, as a program, is clear. The question is how much of genome can we explore andperturb in ways which will tell us how much of it is connected. That's the issue.

Keith Williams: The problem that we are facing at the moment is that the informational people, theDNA people, are trying to talk about function from an informational base and it might be that doesnot work. What I think is really exciting is that, and if you think of it like building a city likeSydney, if we want to work out how people build Sydney, you do need all the specs, you need the

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specifications how to make the chairs in this lecture theatre, and how to make a telephone boothand all of those things, and without all of that information you are not going to be able to build acity. But having all that information is pretty useless. What you need to be able to do is to startlooking at what actually happens. And this is where I think we are in a revolution because that iswhat looking at protein in a mass sense is. Because now we can start asking questions like `thatfly's got a bigger wing than that one', what has happened in terms of gene turn on, etc. to produceproteins that have brought about that change. I think that is a way forward. But you have tounderstand that protein chemistry is changing and you don't have to work on proteins asindividuals anymore. We are beginning to develop technologies to globally display how anorganism is composed. You then are still in the same sort of dilemma and if you think then abouthaving demonstrated what the organism is composed of, the analogy there might be it is a bit likehaving a chess set where you’ve described all the pieces and you have some idea where they areon the board, but you don't have the dynamic picture, you don't know how to play the game. I haveno idea how we get into learning how to play the game. But it is clear to me that there aretremendous changes happening because we are at least learning what all the componentry of thegame is, and I think that is a huge change.

Lewontin: Well you and I agree that we have got to know the circuitry, and we are also inagreement that there is a lot more to it than that. But you are more hopeful perhaps than I am.

Charles Birch: But what has all this got to do with catastrophe?

Lewontin: Well I am trying to get there. What I was trying to do is to describe the situation thatexists in biology and the problems of biology and our puzzle about it. Now let me get to chaos andcatastrophe.

Biologists in my view have not been happy with this situation. They do not really like the fact thatthe objects of their study are so heterogeneous and have such nasty interactive pathways and whatis on the inside is interacting with what is on the outside and in addition to which I claim thatstochastic elements within cells are real phenomena which have importance in many of the thingswe see in biology. Biologists who are not happy about that have been very receptive to theories orworld views or principles, I don't know what you want to call them, which would seem to tamethat complexity, and tame that stochasticity. I have lived through three of them and they wereothers before that. The first one that I can recall was catastrophe theory. Now let me tell you whatcatastrophe theory is. We have the three Cs. I am intrigues as to why it is catastrophe, chaos andcomplexity. I have been waiting for another C to come along.

If you take a dynamical physical system and describe it by a set of differential equations, it turnsout that the topology of that system can undergo radical shifts for certain values of the variables,not the parameters but the variables. As x changes through time, the topology of the object remainsthe same, it is compact. And then as x moves a bit more in time things start to break up. Sydney-siders know this better than anybody in the world because the famous example is the breakingwave. This is what surfers depend on, they depend on the catastrophic nature, the structuralinstability of the equations which would describe the movement of water off the Pacific as itcomes into the land. You get the thing building up, building up, building up and then at a criticalmoment it turns over and crashes and changes its entire shape. And René Thom’s catastrophetheory wasn’t a theory about the world. It was about the development of certain notions ofmathematical structure and stability and the way in which the topology of a system wouldsuddenly go into a new state from compact to breaking to pieces, as one or two variables werechanging continuously. Another example would be if I dropped this glass from a low height itwould come out as a glass, and I could drop it from different heights and there would be a point at

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which when I reached that height, if I dropped it its topology would change rapidly into bits andpieces. And that is a mathematical catastrophe, although the value of the variable here is simplythe kinetic energy, and as the energy increases suddenly it shatters.

Now what René Thom suggested to Conrad Waddington, who was always on the look out forsome way to tame biological complexity, was that a lot of what goes on in organisms, could bedescribed by catastrophe theory. For example, you begin with a cell and it becomes two cells andthen it begins to change shape, and the problem of developmental biology is a problem of shape.And that is the problem which no developmental biologists are working on. That is to say, thisthing is a shape and it goes like that, it didn't always go like that, things start to bud out and thingscome apart and organs separate, and what Thom suggested to Waddington was that what you areobserving is the unfoldings of topological catastrophes. The argument was that these systems werevery simple dynamically, they are not complicated, they are described by simple mathematical setsof equations. And the terrible complexity you see in the development of the organism is nothingbut the unfolding like the breaking wave or the breaking glass or the breaking stick. AndWaddington and other developmental biologists at the time thought well maybe this is the way todescribe things. What they liked about it was that it tamed the messiness and the apparent inabilityto predict the randomness, the stochastic elements. What appeared to be messy and radicallydifferent was nothing but the continuous unfolding of very simple systems and if you only knewthe equations you could then unfold the whole thing. That is to say what you are looking for is themagical formula. It is the ultimate Faustian problem, you know. If you could really know thesecret formula of the world you could unfold the whole course. And that was the catastrophetheory, and nobody that I know of currently works on catastrophe theory.

David Green: Isn't Brian Goodwin still doing it?

Lewontin: Oh, is Brian still doing it? I thought Brian had become a complexicologist. But Brianwas at the same meeting in Bologia that I was when Waddington introduced René Thom to thebiological world and Brian took it up.

Arran Gare: Wasn't it the other way about? That René Thom was inspired by Waddington's ideas,and he formalized it?

Lewontin: But surely notions of structural instability, and the basic elements of topology, musthave been known before. I don’t know. I’m just guessing.

Green: What he did was to prove a series of mathematical theorems which showed that thesestructures fall into several different categories which you can actually draw, so that all thesebiological forms fall into these different categories.

Lewontin: Right - but I am interested in that suggestion that Waddington pushed him into thisdirection in his topological studies. Is that so? I didn't know that.

Green: Yes. He says that in the latest edition of theoretical biology, the one edited by Goodwin. Inthe first paper he says that mathematics has got more from biology then biology has got frommathematics.

Lewontin: Well, it couldn't have got less. I say that as a mathematical biologist. So what we aretrying to fill you in on is how catastrophe theory came together with biology.

Intervention: I think that comment is also made in the preface to René Thom's book.

Lewontin: So speaking purely from the biological standpoint, I would say that, however Thom got

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his ideas, that was the original attraction; it would solve the complexity problem.

Hugh Murdoch: Catastrophe Theory as I understand it has no predictive power, it is onlydescriptive.

Lewontin: Well, if you knew to which regime the system belonged you ought to be able to unfoldthat using Catastrophe Theory. In that sense it would be predictive, but you would have to know towhich of the set of regimes it belonged.

David Green: But there is another problem as well - basically any model of the world has to bevalid. That is to say the assumptions have to satisfy what you see out in the real world, and as I seeit the big problem with Catastrophe Theory is that it is just not valid. Most of the examples that aretouted about this, for example one is the retreat or attack by a dog, it is fear versus rage - if youplot these on different axis you can chart as the animal gets more and more angry, or more andmore fearful, the situation evolves until it will either attack or retreat. And it flips from onebehaviour to the other. The problem is how do you map fear and rage? How do you measure thoseprecisely to map them onto that surface? The second problem is how do you map things onto thatgeometric surface? It involves, for example, probability notions - and so far as I know, no one hasever come up with satisfactory formulae for plotting these on the model.

Lewontin: You raise a problem that we will come back to. These are essentially metaphors, andthe problem is to catch the metaphor out in the real system.

Keith Williams: The other thing about that is, I think Hans Meinhart is one of the people who hasreally had a go at being predictive with some of the Drosophila modelling and he has actuallydone quite a good job. It is one of the rare cases in biology where he has actually told someDrosophila geneticists to go and do this and this is what will happen. The problem for a biologistis where you have this general theory that has some predictive power but the models that Hans issetting up, when I talk to him about trying now to understand what is going on, in terms of howyou form a discontinuity. There are a couple of ways that people have argued about doing that.Working with Dictyostelium it is a very simple system. Basically you just take one type of cell andyou end up with two types of cells. There is a decision between a bunch of cells whether you willbe A or B and there are two theories about how this happens. One is that the cells get together andmake decisions as to whether they will be A's or B's they do it in a neighbourly fashion. A quarterof the cells are A, so when a cell says it is A three cells around it say they will be B - and havingdecided what they will be they then sort out to form a pattern. So that is one of the models.

The other model is we will have a hierarchical situation where we will take all of the cells.Someone will make a decision about where the line will be drawn and all the ones to the left of theline will be A and to the right will be B. So there are two quite different models or views abouthow you make a pattern. One is positional, if you like: you decide where the line is going to bedrawn, and do it - whereas the other is local and it is followed by sorting out. The problem I havehad with the theoretical guys is that their models did not distinguish between the two. They wouldsay it is easy to switch between those two types, and I would come back and say well your modelis not much use to me if you can't distinguish between them. If it is so easy to fix your models sothat either can be correct, you are not giving me any guidance on how to go. I think that might bewhy biologists have walked away from Catastrophe Theory. It hasn't helped.

Part 2: Chaos

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Lewontin: It is at the wrong level of abstraction. You can't cash it out. So let us go on briefly tothe next one of the three Cs, which is quite trendy now, that is Chaos theory. There is a lot ofmisunderstanding about chaos. Some very, very simple - extremely simple differential equations ina single variable describing the rate of change of the system with time, for example some variablex and you describe its rate of change with time, dx/dt is some simple function of x, let's say theratio of two cubics:

It turns out that for some choices of these constants, if you look at this not as a differentialequation but as a discrete time function you can calculate the state at time (t+1) as some functionof the state at time t. For lots of choices for these parameters (a,b,...a’,b’...), the state at time (t+1)if plotted over time, if you look at it as a statistician it would appear to be undergoing completelyrandom unpredictable behaviour. No matter how much information you were given, you wouldrequire infinite information, to predict what will happen. Because it never revisits the same pattern,it does not go through cycles. If you look at it forever and you look at the fluctuations, thatparticular set of states is never repeated. It is totally incoherent.

Now I could write down hundreds of such simple equations that have that quality that undergoesthis so-called chaotic behaviour. And also what you can show is that for some set of parametersthe system behaves in a perfectly nice way; it starts here and approaches some steady state and as Ichange the parameters continuously I reach a critical point where if a is a little bigger than somecritical value the thing just goes nuts.

It did not quite go nuts immediately. It went say smoothly from two to four and then it went nuts.And what I started out by saying is that biological systems show very nasty apparent stochasticvariation. We will take an ecological problem. The abundances of some particular organism overtime, for example: the number of aphids on roses. We can count the number of aphids onrosebushes. They have some correlation with some environmental variables like highest summertemperature, but it is only a correlation. You cannot predict the numbers from time to time becauseit is a stochastic relation and the problem is the stochastic variations are big; you have just got tothrow up your hands and say: "I don't know whether it is going to be an outbreak year next year ornot because there is so much stochastic variation." There are too many variables entering thesystem, and it's a mess. It's chaotic!

But what this system of equations does is it generates a chaotic sequence of states of the systembased on an extremely simple formula. It says that what appears to you to be chaotic is nothing butthe time dependent behaviour of a system that has a very simple underlying law:

3x3 + 9x2 + ..etc.

and therefore, if I could discover that law, then if you tell me what the state is I will be able to tellyou exactly what the subsequent states are. So this not a stochastic system. If I know the state atany time, t, and I know the law, I can then write down forever the complete set of subsequentstates. And I have turned it from a hopeless jumble of stochastic variations with no apparent lawinto a very simple law-like behaviour. If I were to do that, biologists would be out of their wits,because they would once again have accomplished what is supposed to be probably science, that isthe taming of this bewildering stochastic complexity and bringing it under a very simple law ofbehaviour, which gives you complete predictability.

Intervention: But isn't that predictability only possible if the initial state is known exactly?

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Lewontin: Indeed if I don't know exactly where I am at time t then I can't make predictions aboutthe subsequent behaviour of the system. To make predictions I have to know exactly what theinitial conditions are and if this initial situation is slightly different then the subsequent behaviourpattern is entirely different - and that is a very important point.

Let me put it this way - what you have said is a very good criticism of the usefulness of chaostheory in biology. Even if it were right, then precisely for the reason you say, we come down to anargument among biologists about whether these really are stochastic events and whether we knowthe initial state, and that is a very good criticism. But my criticism is that this is another attempt totame unpredictability and complex interactions by knowing the secret formula which can't work.First of all there are real stochastic events going on. I don't want to get into the question all theway down to the basement about whether stochasticity is ontological or not because for a biologistit does not matter.

For every science there is a level which is the basement for which stochasticity is good enough. Idon't have to answer the ultimate philosophical problem.So long as it is Brownian motion which satisfies Einstein'sequation it is good enough for me. So that is where chaosis. And everybody is talking about chaos. Why? Becausethey somehow feel better about it. They feel that all thisbewildering activity has been explained. Now chaos doesindeed exist, you can buy a little device in a toy storewhich will show you chaotic behaviour a little pendulum.It is not that you cannot make simple physical systems thatshow chaos. The question is: is that what genes in biologydo?

Keith Williams: One of the first systems which wasshown to be chaotic was looked at by Bob May wholooked at logarithmic growth, which is growth ofpopulations where you have two parameters, the growthrate of the population and the carrying capacity for theenvironment. What happens is that you get this famous S-

shaped curve, where the population grows rapidly at first, and then as it gets closer and closer tothe carrying capacity the rate drops off and then smoothly and asymptotically approaches thecarrying capacity.

Lewontin: Here is a plot of the numbers of individuals at time t as function of t, increasing at firstexponentially but then falling off to reach an equilibrium value and the equation to describe that isd n/dt = a log

Keith Williams: The reason I mention this is that it shows up a) what this sort of theory candemonstrate and b) what the practical limitations are: the issues you have to take into account. Butagain it comes back to the validity of the model. The first thing to point out is that if you assumethat growth is continuous in a system like that you will always get that curve. You only get thechaotic property which we can talk about if you have discrete growth. For example if you have apopulation which is reproducing annually or seasonally, say crops that are planted every year.Then what happens is that the pattern can vary and you get the onset of chaos. When you increasethe growth rate it first starts to do funny things, you get little blips in the curve, and you will getoscillations. Those oscillations will then become faster and more pronounced as you increase the

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growth rate. The populations with higher growth rate are obviously more pronounced until you getthe onset of chaos. Now a lot of the models you see talking about this are looking at an abstractsystem where the equations have been converted so that they always lie in the range of 0 to 1 soyou are dealing with continuous numbers. Populations, of course, are not continuous numbers.You either have an organism or you don't. And you find that when you get to this chaotic regionwhat almost inevitability happens is that very quickly the population crashes to zero, and so that iswhere the whole process stops.

Lewontin: I would like to add to this. Bob May's demonstration that population growth is chaoticdepends upon a particular form of the population growth equation. Now no one in the world knowswhether there is any such equation. That is to say we write down the population growth as a verysimple second order equation in numbers with a collision model in mind. For organisms toreproduce they need another organism of their own kind and if you reduce their reproduction youreduce the number of interactions. We don't have a universally agreed upon set of dynamical lawswhich control populations. We have a model we use all the time but I don't know if it is true. Itturns out to be the simplest linear equation I can write, the second order equation to describe thepopulation growth made out of ...... law. The chaotic behaviour depends entirely on whether it isthat form or some other form. So there again is the question of validity. Why are we even talkingabout the chaotic properties of that equation, when we don't even know if it is the equation forpopulation growth? Populations grow, that's all, and then they shrink. They grow and they shrink,and they may not follow any law, but it is so much more appealing to say yes we have a law ofpopulation growth and if I increase the value of R to the critical level suddenly you get amultiplication ......

John Bennett: How do we know that these big variations are not due to some error in thecomputer?

Lewontin: You don't need a computer to show this behaviour. The behaviour can be shownanalytically. This is not a computational problem. It has to do with the mathematics of bifurcation,so it is not rounding error.

Keith Williams: In fact that's how Lorenz discovered the butterfly effect by detecting round-offerrors in his computer. When he fed the results back in they had these round-off errors.

Lewontin: But the round-off errors led to the discovery that the chaotic regime existedindependent of the round off. It is a mathematical phenomenon.

Hugh Murdoch: This is the point that even a very simple equation can lead to chaotic behaviour.So anything in real life is likely to be much more complex.

Lewontin: Yes, things probably are a lot more complex, but then what do we accomplish byshowing that a simple equation if we turn the parameters will become chaotic? That's the bill ofgoods I am trying to present to you. That all the talk about chaos is precisely the attempt to say thatthe underlying laws are simple, very simple, and the parameters have just driven us into chaos.

Intervention: A lot of these things just come back to validity, don't they? That any model is anabstraction of the real world, but to achieve that you are making a lot of assumptions. To writesomething down as a mathematical formula you are assuming that it can be expressed in thoseforms as numbers, as quantities and that any other constraints or factors are just not entering intothe system.

Lewontin: If I avoid Complexity Theory for now and leave that for later and instead go on to afurther discussion of stochasticity would you agree with that? I want to talk about stochasticity and

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the questions raised about it. This is not a question of whether chance in an ontological property ofthe universe at all levels. I have nothing to say about that question, so much has already been saidabout that it is hard to say anything useful.

The fact is that cells have complicated machinery for the production of bits and pieces andinteractions. That machinery involves large molecules and some small molecules. These moleculesare manufactured by the cell, the large molecules are interacting with one another, and in theprocess they fold up during protein synthesis for example. A protein is not specified by its DNAsequence. What specifies the protein, if anything, is the amino-acid sequence, but that sequence ofamino-acids is not a protein. A protein is at the very minimum a folded sequence of amino-acids inthree dimensional space. And the folding of that polypeptide is not predictable from its amino-acidsequence. There exist multiple local free energy minima on the folding surface, ie. there aredifferent ways of folding the chain, each one of which represents a local minimum in free energy.The consequence of this is that we do not have any computer program which will predict thefolding of a polypeptide and to get people to work on this is a problem. We just don't know how todo it. You can't even do it after a little local unfolding. With these programs, if you join partiallyunfolded proteins you cannot put them back. The conditions in the cell in which the folding occursare critical and this has had a practical engineering application in the use of bacteria as factoriesfor making proteins like insulin.

It turns out you can take the gene for insulin and put it in a bacterium and the bacteria can be put ina vat and they will grind out insulin polypeptide, but they will not grind out insulin protein,because it will form the wrong folded intermediary and you have to search around for the rightsystem. It has been found, so it is now possible to make human insulin because we know how toarrange the external conditions in the vat to get the right folding. This folding is of moleculeswhich are of very low concentration and it is under the influence of other molecules that are invery low concentration. Most of the molecules that really matter to a cell are not present inAvogadro's number in the cell. They are present as one of this and two of that and nine of that andfifteen of that and some of them are over here and some over there, and some are inside a cellorganelle and some are outside. To do their business they have to be brought together in exactlythe right place. There are cell organelles that process them. So the metaphor of a factory is a goodone in a sense, although you must be careful of all metaphors. [Norbert Wiener said that the priceof metaphors is eternal vigilance. We must be vigilant, but I will use the metaphor of the factory.]

There is a temporal passage through the system of molecules one at a time, and the consequence ofthat is that cells do not do identical jobs even though they are right next door to one another andhave the same genes, and are surrounded by the same external medium. Because they do not haveexactly the same number of molecules in each case. Indeed the reductio is the DNA moleculewhich is present in only [depending on how you say it] one or two copies, and the cell has to havea very special complicated device for guaranteeing that those two molecules of DNA arereproduced as exactly the same two molecules when the cell divides. ...... the concentration ofDNA in the cell is quite interesting. You have this DNA molecule and there are two of them, ifthey are diploids, and when that cell divides into two, if everything is working all right, each ofthose cells will have exactly two DNA molecules, not more and not less, and there is verycomplicated machinery to guarantee that. You cannot rely on the laws of mass action, because thelaws of mass action are not working here.

There are lots of other molecules in a cell that are present in low concentration, for examplecertain vitamins like Biotin which are important for fungal growth, are present at an average of onemolecule per cell.

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Given that many molecules in cells are spatially located and they are in small numbers and it takesreal time to make them and to move them around, you get stochastic effects. I do not care whetherthe thermal model which has them jiggling around is based on a real ultimate stochasticity. It isirrelevant. The fact of the matter is that when I am dealing with systems of interacting molecules atlow concentrations I get stochastic processes because they are in different thermal states, they arewriggling around, they have varying bond energies. It comes down to quantum chemistry, it reallydoes. It is where quantum chemistry and the physical chemistry of molecules, like their vibrationalstates and rotational states really enter into the picture. The consequence of that can be large orsmall depending on what you are interested in.

Here is an example which I think perhaps I gave in the discussion last night of a bacterial colony inwhich it is not the case that all cells divide in phase with each other, and that is because they areparcelling out small numbers of molecules and each daughter cell does not get exactly equalnumbers. If there is a critical number involved that you have to have before you can divide thenyou have to wait different amounts of time.

We do not really have a very good idea of how much macro developmental events depend on thesestochastic events, but I want to give you one example. I mentioned it last night but did not give thedetails. I want to get a little technical here. This has to do with the formation of sensory bristles orhairs on the bodies of fruit flies. The hair on a fruit fly is a sensory apparatus and it consists of theresult of three cells. There is the cell which gives rise to the hair itself, the so-called trichogen cell.There is a collar out of which the bristle protrudes and that is the result of a secretion of anothercell called a tormogen cell and that cell has a nerve cell that grows out of it that connects it withthe central nervous system of the fly because it is a sensory apparatus. So there are three differentcells which give rise to this little sensory device. Now those three cells arise from two divisions ofa single stem cell. It divides into three cells, one will become the trichogen, the second becomesthe tormogen and the third is the nerve cell.

The cell division which gives rise to these three cells is occurring deep down in the sub-surfacelayers of the developing Drosophila. And up here is the integument, that is the hard coat or outerskin of the fly. It is hardening while the cell division is going on. And when these cells finishdividing they migrate as a cluster to the surface and if they make it to the surface before thatsurface is hardened they can succeed in making a bristle. And if they do not make it to the surfacebefore it hardens, they get excluded and they will not make a bristle. Now you have two processesthat are going on simultaneously. The process of hardening and the process of cell division andmigration, and they have obviously some constants and variability because it takes differentamounts of time for them to divide and to get there. And the consequence of all that is a lot ofvariation from place to place on the fly of whether a bristle gets there or not.

We have a famous mutation in Drosophila which is different from almost any other mutationknown. Most mutations in fruit flies either reduce the size of something or increase its size - youget too much or too little - but we have this one mutation which is called the ........? which actuallycauses variation on both sides of the organisms. Some flies have too many bristles and some flieshave too few. All have the same mutation, and the reason turns out to be that this mutation causesan additional division of the original stem cell so that it forms two or even three stem cells andeach one of those undergoes bristle formation.

Now each division takes time and if it takes too much time none of these cells gets included in thesurface. And if it does not take too much time, all of them may get included in the surface. Andthat is how stochasticity becomes involved. We have to cope with that kind of stochasticity inactual systems. That kind of process applies all the way up the scale. It is not interesting to me

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whether being struck by lightning, before producing any offspring is regarded as stochastic insome ontological sense. "The race is not to the swift, nor the battle to the strong, nor riches to thewise man, but time and chance happen to all." And that is true at every level of organisation inbiology. Of two female Drosophila individuals of identical genotype, one may be completelyfertile and the other completely sterile, and I do not know why that is; something bad happened onthe way. And it does not matter whether that is true. I want to emphasise that there are effectivelystochastic events in the development and life history patterns of cells, organisms and entirespecies. Some people think that is very important. The attempt of biologists to tame thatstochasticity and remove it from biological explanation is I think doomed to failure but I willregard it when we get to complexity.

Keith Williams: I think there are interesting things that come out of that because in the case of thehairs it does not really matter to the organism. The stochasticity can happen there because theorganism still survives. We can give chromosomal examples where it is absolutely crucial, becauseif you have too much stochastics going on, you do not have an organism. I think that, particularlywith complex organisms, much of what happens when you make proteins, for example, inprocesses like folding them up, you may have anti-stochastic processes or processes that minimizethe stochasticity. It may be you can use this to probe crucial functions versus ones that you canallow to be more sloppy.

Lewontin: There I want to disagree with you very strongly. Speaking now as a populationgeneticist, I think that we should not talk about differences in organisms which have no fitnessconsequences because I cannot find them. That is to say it is certainly true that having twochromosomes (instead of one may be lethal) but having the wrong number of sense sensillae,although allowing the organism to survive, I would guess does make a difference to thepopulation.

For a living what I do is sequence DNA. And for years we have been saying that, because of thenature of the DNA code, there are redundant positions where the code is degenerate and it does notmake any difference what the third position is like. If there is one thing I now know at the end ofmy career, it is that there is no such thing as a non-selective nucleotide position. Every DNAposition even if it is not translated, not transcribed, all show patterns of constraint, which cannot beexplained except by assuming that there is some kind of selective difference. And there is ourproblem: it is impossible to measure physiologically those differences because they make too littledifference to the outcome. You cannot measure it by counting eggs. The difference is too small.The only thing you can do is go back to the static data and say there is a pattern in DNA sequencedata which is clearly not random, so there must be something going on. I do not know what it isand I cannot tell a physiological story because the difference is small. Let me point out to you thatif it makes a difference of one 100th of 1% in the probability of survival on introduction into theorganism, whether it has four bristles or three, then that is quite enough to drive that systemevolutionarily within 100,000 generations, and that is not all that long.

Bennett: Are you suggesting that you can have a viable theory of evolution based on this. Some ofthese things survive and may be they would do so ......? (Inaudible)

Lewontin: I do not understand the question.

Bennett: Theories of why there are changes of species came to invoke cosmic rays and all kinds ofthings but you have described a possibility that you might get change due to the random effectsyou are talking about.

Lewontin: You are confusing it with the mutation issue. And certainly chemical stochasticity has

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to do with mutations. So-called spontaneous mutations have to do with mis-copying of DNA andthat probability is a consequence purely of free energy considerations for crystal structure. Youcannot have a perfect chance of reproduction. It would not correspond to a minimum free energy.So of course you have mutations. They must happen stochastically.

Intervention: I would like to ask you a question about what are considered non-random patterns ofwhat are naively considered neutral mutations. Could that possibly be due to non-randomicity inthe evolutionary processes of that organism.

Lewontin: No, when I say non-random I am not involving a stochastic process here with anergodic state. This is at a much simpler level than that. If I look at the DNA sequences of a lot ofdifferent genes in Drosophila, and I look at the variation among individuals of a gene species .....What I find that for almost every protein gene I have looked at, there is a region of 100 to 150 basepairs just off the end of translation but still with a transcriber which is totally conserved to aspecies ..... I do not need any notion of ergodicity to deal with that. Then why the hell doesn't that..... and that is true of gene after gene, not just one particular gene. There is some mechanicalsomething happening involving that 150 base pairs where maintenance of constancy not onlybetween individuals but across species boundaries has been extreme, so I can smell somethinggoing on here but I do not know what it is. But I can say what it could be for instance and we donot have any data about this: "How much of the constancy, how much of the lack of variation ofnucleotides can have nothing whatsoever to do with what amino-acids are produced even whenthose are coding for these, is entirely a consequence of the necessity to keep a certain two or threedimensional structure for the RNA molecule. That is how much amino acid composition constancyis not due to the necessity to have certain amino acids but because the nucleotide sequence has tobe maintained. We do not know anything about that.

Intervention: Is there any such thing as genetic apoptosis wherein if that 150 amino acid sequenceis not there, the molecule self-destructs and that perhaps is why it has to be there. If that 150 aminoacid sequence is quite resilient in all the genes that you are looking at...

Lewontin: (Interjects) No, no - you misunderstood what I said. I referred to 150 nucleotidepositions not translated in different genes and there is no similarity between what is maintained insay the dpp gene and what is maintained in the esterase gene. There is no motif - it is 150 basepairs or two hundred base pairs at the end of the gene and different genes have a different set.

Hunter: Are you saying that those base pairs have to be there so that the RNA molecule sitsagainst the gene in such a way as to read it properly.

Lewontin: Yes. I am saying it has something to do with the translation and transcriptionmechanism. But there is no common motif - that is what really drives me nuts! I did thesecompletely conserved regions and I searched the entire genetic database and I cannot find itanywhere else.

Intervention: Is that at the level of DNA – or is that motif at the level of DNA?

Lewontin: It is a motif at the level of AGCTCCAT, not at the level of secondary structure. Ofcourse you said exactly the right thing that what you need to have is good secondary structureprograms that will predict whether there is a loop or a hairpin.

Intervention: There is another thing. What implications does this have to do in the way themutation variation occurs at the molecular level. Is it by point mutation, or do you think that exonshuffling is involved?

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Lewontin: I do not think exon shuffling is a live issue for the level of variation that we look atwhich is within individuals or even across a species. If that exon shuffling even happened, (it mayor may not have - I don’t know) but if it did, it would have been way back when. It is not a currentmechanism. However there is a question of how much mutational variation between individualsand between species is a consequence of single point mutations, as opposed to slippage ormisalignment on sorting or miscopying.

Part 3: Complexity

Lewontin: I guess we now want to move on to complexity, but there is another list of thingsbeginning with C.

David Green: Yes, like control theory, information theory, fractals, general system theory, gametheory. The common thing with all of them being that they explain some things but the claimshave been overblown, that is they have been touted as theories of everything, which of course theyare not.

Comment: Think of that C as creativity.

Lewontin: Well I guess the one that was promised in the title is complexity, and complexity not inits intuitive sense but in its technical sense (if it has a technical sense). And indeed that has beenthe problem. People have constantly talked about biological systems as being complex, and somesystems being more complex than others, but for a very long time there was no way of measuringunambiguously the level of complexity. Nor was there a theory of complexity. So a typicalargument would be: "You have to believe in evolution or how else would a complex system likean organism arise?" But I am not sure that I know what I mean by an organism being complex.And I certainly do not know a generally acceptable way to measure complexity. Many suggestionshave been made. As a participant of the Santa Fe Institute I have to look at this all the time.Murray Gell Mann, a very influential person in that Institute says he knows what complexity is -and it is wise not to disagree with Murray. At least it is easy to disagree with him, but it is hard toargue with him. The problem is that almost all definitions of complexity have generally started outfrom a decision about what is more complex than what. Then the definition of complexity hasflowed from that. In other words, if you define complexity in such a way that we are no morecomplex than a starfish, then you have failed, because your definition does not do the work for youthat you want it to do. The notion of complexity is that it is supposed to do some work for you.That means complexity must have increased during the course of evolution, but it is very hard toshow in what sense complexity has increased in evolution since the origin of the vertebrates. So itis hard to work out how we are more complex than, say, the fish.

Murray gets around this by arguing about the complexity that goes on in your head and of course ifyou do that then naturally you are more complex. Fish do not do maths (even if they do swim inschools). But not to be too flippant about it, the question is: "If we are going to have a theory ofcomplexity we have to know what the word is meant to do for us." Now complexity theory is themost recent (at least I think it is the most recent) of these various attempts to put some order inwhat appears to be a very complex situation.

Bennett: In the computer trade, complexity is defined as the number of multiplications you have todo to get an answer.

Lewontin: Computational definitions of complexity including bits of information, number of

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computations you have to perform, do not work with most of the things that biologists want to talkabout. They just do not apply. Although this is precisely the line along which Gell-Mann wants togo. He has tried to calculate how much you would have to compute in order to compute theorganism. But it does not really work for us. The real issue in the Theory of Complexity at thepresent time as introduced by Stuart Kauffman and people like him, is to try to find out or to claimthat there are laws of complexity. That is to say, if a very large number of elements are interactingand if the connecting graph of flows between the elements is large, then there ought to be a smallnumber of characteristics of that connectedness of graph flows (...the knee bone is connected to thethigh bone...). So this is the graph of the objects and the flows among them (Figure 3, andcomplexity theory as developed by the major school, which concerns itself with that theory, hasbeen to try to describe such networks by a very small number of descriptors compared to thecomplexity of the system. And to say that once you have a very large number of these elementssome general laws will be possible, namely laws relating the number of loops and so on.

Let me give you an example from the old complexity theorywe had in ecology which had quite a bit of currency for awhile, and that was a theory that complexity equals stabilitybecause for a long time it was claimed that an ecosystemmade up of a large number of species with complexinteractions would be more stable. And now we have twoproblems: (a) how do you measure the complexity of theecosystem and (b), what do you mean by stability? It turnedout that what was being said by Macarthur and others whobelieved this theory was that complexity for them simply wasthe number of species that had some connection, the number

of these paths in Figure 3. The greater the number of connections, the greater the complexity. Theclaim of this theory, which was not a very well worked out theory, was that if you had anecosystem with pathways of energy flow and growth rate dependencies, that is, with a very largenumber of these connections, then there would be resistance by the ecosystem as a whole toperturbations introduced by for example drastically reducing the number of that species. Becauseeven if you reduced drastically the number of that particular species, all the large number ofinteractions would not result in the loss of other species.

Now the first exact result ever given on this subject in ecology was in fact given by Bob May, whoshowed that if you define complexity as the number of such feeding interactions, and if you definestability as being when the species would be in stable equilibrium numbers (by perturbation innumbers or forced to extinction). Then under that exact definition of complexity and stability itturns out that the greater the complexity of the system the less stable they were. That is the way itcomes out. But that is because of the way we define complexity and stability. There is taxonomicstability, ie. whether a species persists or not. There is demographic stability, ie. whether thenumbers of individuals of that particular species tend to remain constant and equal in numbers.There is energy flow stability, ie. whether certain total flows of energy remain constant in thesystem. Each one of those will have a different outcome depending again on how you definecomplexity. Is it just the number of connections or does something about the strength of theconnections have to be included? It turns out that the intuitive notion that complexity equalsstability was purely an ideological proposition. This was the whig theory of biology, that we werenow living in an extremely complex interactive world that evolution had given us, and therefore itmust be more stable. It is like the theory of complex economic systems being more stable. Whereelse could such a notion come from? There was no analytic result that could have led to that

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conclusion, but there were all kinds of metaphors given. Like supposing you have a suspensionbridge with many cables, if you cut one of the cables the bridge does not fall down.

The newer attempt is to try to develop a theory of complexity that will somehow talk about generalsystems properties related to the number of these connections and their Boolean nature whetherthere is a positive interaction of this on that or whether a negative one and to try to simplify thesystem. So far as I understand it and I hope someone else can say more about it, the attempt to findsystems properties that are laws of complexity, the hope - let me explain what the hope is - thehope is that all this booming, buzzing confusion about organisms will sort itself out because it willturn out that their behaviour according to laws of complex organisation, only arise when you haveenough nodes and enough interactions, and would not arise if you only had two nodes and oneinteraction. Somehow having enough stuff interacting with enough stuff will give rise to new law-like properties. I must say I am sceptical but I would like someone else to take this up.

Arran Gare: One of the most delicate experiments along these lines was done by Alan Robinsonand Keith Pradonick? here about 17 years ago. What they did was to look at what happens whenyou form these networks at random, the idea being that when we look at a community in nature:Are they real communities or do they just happen to be the way they are? What they did was tothrow together some variable representative species in different combinations which had differentlevels of interaction and look at what happened. What they found was that the crucial thing waswhat they called positive feedback loops. So that if you have a situation where you have onespecies has a positive effect on another and that has a positive effect on a third and it goes round ina loop, then you have this feedback effect where if you have a small increase in one population itwill cause an increase all the way round, and of course in a real biological system that is going tobe unstable. Eventually there will be some limit and the whole system will collapse.

And that is exactly what they found. That is, when you form these structures at random, theygenerally collapse. That is to say one or more species would generally disappear, so any largeassemblage of species in their terminology would not be viable because it would collapse into asimple system. If I understand the current thinking about this, the issue is that, if the systems wehave are complex, if they have lots of species and lots of interactions, they are not stable andviable because they have lots of interactions – they have been selected because they happen to bestable.

Another scientist, Richard Levin, whom you certainly know, took another approach to this becausehe had a problem. He wanted to look at this in the real world. He said you couldn't make realmodels with pluses and minuses on the links, but let us look at the patterns of interactions that wecan identify. So he developed this idea called loop analysis, which looked at these patterns ofinteraction that you see in natural systems. He was concerned with rain forests, which are ofcourse, extremely rich in species, with lots of interactions which can be extremely difficult toidentify. It is not something, as far as I can see, which has been widely adopted or widely used,and that is because it is difficult to identify what all the interactions are.

But having said all that, there is still a problem of whether this sort of approach is a valid model ofreal ecosystems. There you have a couple of real issues that come in. It is not just whether youhave an interaction or pathway, but it is also the nature of them. Two important factors come in tocounteract this. One is that the organisms in communities are spread out in space so not all of theinteractions happen in one place or at one time. And that has a huge effect. The second factor itthat there are time delays in these interactions, and that means that instead of getting large effectsimmediately they will be spread over time, and also that the magnitude of interactions may be verysmall. The other thing that can happen is that you can have positive feedback loops counteracted

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by negative feedback loops, like a classic control structure. So there are quite a few complications.To try to straighten these out and to answer some of the questions referred to before - why are treestructures so common in biology? They are hierarchies. Is it because that is an inherent property ofnature, or is it just a function of the way we look at nature?

Hunter: You certainly would not choose the rainforest as the one you would start off on if youwere trying to look at this sort of thing unless you were deliberately trying to obfuscate thesituation. I am not suggesting that is all that Richard Levins is about, but it is a part of it, isn't it?The other thing is there is some empirical evidence in the build up of permaculture. That is adevice for getting a stable biological structure, is it not? As I understand it, that is an artificiallycreated system which ultimately becomes a stable ecological system.

Peter Farleigh: Permaculture is not a generally used word. It is perhaps confined to Australia.

Hunter: Is there some other word you can use more generally?

Professor Hill: In this area it seems to me that all these efforts to construct models tend to ignorethe importance of time and place. Certainly from working with insects and plant samples? there isa lot of absolutely vital characteristics, of insects for example, that only occur at one moment intheir life and which any of the constructed models would ignore. These events only occur atparticular locations. Richard referred to aphids on roses. Aphids are no more on roses thanaccountants aren't in Sydney. Accountants aren't in Sydney, they are in very specific locations inSydney and we tend to keep generalising this time and place. But also with respect to the roles .....they can audit? those things ... organisms in an ecosystem. There are multiple interactive.... andbio...... roles that those organisms are playing that are incredibly complex and our tendency is,because I think we live in a culture of tidiness, to keep trying to come up with tidy models. It is sofragmented and so little of that picture, and certainly with respect to permaculture and to variousapproaches to agriculture, I have worked with people designing agriculture systems for aboutthirty years and I can go to farmers who have designed agri-ecological systems where they do notseem to have pest problems and they do not seem to have soil fertility problems. And it is amazingthat they might be on a farm right next to an agricultural university and they cannot get a singleperson from that university to come and study their system because the university people are sointerested in looking at problems. (Laughter) And you ask that person: "why don't you think youhave pests? And they will give profound answers like "uhmm...." (Laughter) As Dick says, wedon't really know.

In working with students and trying to understand that, the nearest I can come to it is to havestudents become the damned insect. I worked for years with a weevil that goes in and out of theforest to the orchard and if we did not become that weevil we would not have understood afraction of what we did come to understand about its behaviour. In fact we got a little inkling ofwhy it located itself at different places at different times. But what I learned from this was that itwas very much a matter of time and place, and one had to think of the insect's relation to time andplace, not the human relation, in order to even come close to understanding what was going on.

Intervention: If I could just pick up on this question of place I could give another example. Thereare a lot of models of competition that say that there is competitive exclusion. They seem to be, byand large, discredited now by biological people, but there was this model where if you had twothings competing with one another, one would drive out the other. But it is very simple to showthat that does not have to be the rule, because if you spread things out over space you are notgetting everything interacting all in one place. One example of that is: what happens in rainforestswhere you have a lot of species. For example, considering the trees you have lots of tree species,hundreds of them. But you only find a few of them that are very common and very abundant, most

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of them are rare. But there are spatial properties that are important, at least I think they are. Peopletry to explain the maintenance of high diversity in rainforests. One reason I think is that you havetrees that produce seeds, and the seeds drop down on the ground nearby and it is only when a gapopening up that a new tree of that same species grows up. So what happens is that those trees forma clump, and what happens within the clump is that those species that are already there have acompetitive advantage over everything else, even if they grow slower and they may be inferior in alot of other ways. So the formation of those clumps, and you can show this in models, doesmaintain a very high diversity. That is exactly what people have gone and looked at distributionshave found. The rare species all have clump distributions. So that seems to be one mechanism formaintaining diversity.

Hill: If you are working in a complex rainforest like that, I don't think I have ever seen acompletely defoliated plant. You can have a tree right next to a leaf-cutting ant colony that prefersto feed on that tree, but there is an inter-relationship between them. There is a message goes outfrom the tree to the ant: "You've had enough, shove off, go feed somewhere else." And you can cutdown that rainforest, and clear a patch and put up a little plantation of grapefruit trees, orsomething, and that will be wiped out overnight by that same colony of batcher?? ants. It seemsthat the trees growing under stress give off a message: "I'm stressed - get in here and clean meout". And they do just that. This is not a very sophisticated theory. (Laughter) But there maybesome information on it that you can actually act on. Farmers are not too interested in mathematicalequations. What they want to know is - how can I make my farm viable in this ridiculousglobalised economy? And survive in the face of these bastards? And to wait until we have got it allsorted out mathematically will be no good to those fellows. I have got to work with themtomorrow.

I think I have got plenty of information to work with and practical ideas and we can catch up lateron how it works.

Arran Gare: We can jump about 20 steps ahead, we can deal with this big issue of complexity andcertainly in Australia I think we have got a fairly good record, not necessarily of putting it intopractice, but at least for setting up the basics for doing it. You know .... There is a pulp millTasmania, there is a spaceport in Cape York. How does the Government deal with it? This is thecutting edge of what we are talking about. And up until a couple of years ago all these things weredone on an ad hoc basis. How loud the people shouted generally, but the Australian Governmenthas tried to do something; they have tried to set up information systems which plot thedistributions of all species and combine the tools to try to predict in what areas the rare speciesmight be able to grow. They have translocation programs for identifying potential hazards with bigdevelopment projects. There is also every fortnight the entire continent is being examined by geo-satellite imaging. So we have a very good database now to look at some of these sorts ofprocesses.

Hill: We may be able to better monitor our extinction as it’s taking place in a very sophisticatedway, but at the same time we are encouraging monocultural coniferous plantations which any idiotcan predict will be attacked by insects.

Intervention: Let us take this in a different direction. Richard's direction has been to show theinadequacies of these developments in mathematics. But just going back to Waddington, thereason I was making that point about Waddington inspiring the work of Rene Thom was thatWaddington was trying to develop a different conceptual scheme. And that scheme was trying todo justice to the existence of living organisms.

Intervention: In your talk last night, you talked about the interaction between genes and their

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environment and also stochastic events. In the point of Waddington and later of Brian Goodwinand his school of process structures following Waddington, what they are trying to do is say that:'look, there is an organism that we are just forgetting about and we need to look at its imminentdynamics.' Now to do justice to the idea that there is something there and to get away from thisreductionism. What they argued for was the primacy of processes and forms rather than things thatinteract. So what they were looking at was how you could conceive these developmental processesleading from one form to another - the transformation of forms. So the mathematicians coming inwere providing a sort of refinement of this conceptualisation which perhaps might be useful or not.The primary importance of it was not to make predictions but to develop this alternative way ofconceiving of living organisms. So far as I understand it Brian Goodwin is still pursuing the samepath. At one of these discussions at the Santa Fe Institute there was what looked like a heatedexchange between Gell Mann and Goodwin over how radical Goodwin's ideas were. Goodwin wassaying that he was taking a different path; these people had just forgotten about the organism.

Now if this is the case then all these developments (I think that what happened was that chaoscame in because for Goodwin it was important because after Catastrophe Theory there developedDynamic Systems Theory, combining Chaos Theory and Catastrophe Theory to develop thedynamic theory of bifurcations and that might or might not prove useful). The whole idea ofcomplexity is whether or not it can add to this new way of conceiving organisms. So it is in thatcontext, or in relationship to that, that you really need to evaluate these ideas.

Lewontin: Well I guess the question is - what work is it mean to do?

Intervention: Goodwin's work is, as you know, about looking at the digits on frogs. Looking at thenumber of digits they develop and interfering with the processes of epigenesis to show the kind ofcanalisation that is taking place, how there is only a certain number of possibilities of differentstructures developing. It is really trying to understand what an organism is.

Lewontin: My problem is that I do not understand that approach. It is too hard ... Charles and I hada similar argument when he asked the question "What is Life?" and I rejected that question. I guessI do not understand the question: what is an organism? At least I do not understand what work isdone for me if I answer that question - why should I answer that question? Can you just talk moreabout it, because I do not understand it?

Intervention: Well I suppose it is getting down to pretty fundamental questions about what scienceis. Isn't it trying to understand what the board is and what its components are?

Lewontin: I am not sure. Look we are trying to avoid an obscurantist tone that says: "Well, anorganism is this thing and we cannot analyse it in any way." We may be anti-reductionist, but weare not at the same time experienced holists.

Panel: There is an analogy with the question: "What is intelligence? That has been booted aroundin psychology and computer science for generations. There is a whole area called artificialintelligence, but no one can yet say exactly what intelligence is. Turing proposed a definition thatif you are talking to a terminal and it is responding and it sounds intelligence to you, then it mustbe intelligence whether or not it is a machine or another person. However that definition by theboard about 20 years ago when software programs that responded to simple cues that soundintelligent and a lot of people were taken in by them. Similarly with life, there is a whole area nowof research called artificial life which, rather than trying to define what life is, is looking at whatare some of the properties of living systems - and trying to understand the consequences ofdifferent processes.

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Lewontin: But surely we don't wrest those out of their context. Let's go back to this topic ofartificial life. The problem of what intelligence is, at least in the psychological context, is not amatter of idle curiosity or simply solving some abstract question. The whole intelligence industryin psychology had a determined social function, and that function was the one I spent all last nighttalking about. Indeed, at least one famous psychologist in the intelligence business, Edwin Florey?,when asked what is intelligence, said: "It is what IQ tests measure". And in some sense that is areal truth. Whatever else you may be interested in, in psychology it has the function of classifyingindividuals, putting them in their place in the social scheme. The same thing is true of a lot of thework in AI and genetic algorithms. There is a real practical issue involved, namely can I learnfrom organisms a better way to make a machine that I can sell.

John Bennett: Quite unrelated to the intelligence of the psychologist, many things which arecalled AI, before that term was invented, in the computer game we used to do it as a process ofsolving problems as they were put to us. Now these bits are called AI but are quite unrelated tointelligence.

Lewontin: But the impetus is a practical one, namely to engineer something.

Arran Gare: One of the attractions for me in my current position is sharing environment andinformation sciences, working on both biology and computing at the same time. I think these twoareas are very closely related. As you were saying, the computer scientists are learning a lot frombiological systems, and you will see a lot of terms like genetic algorithms, evolutionarycomputation, cellular automata, neural networks ..... it goes on and on. Basically people arelooking at biological systems and therefrom learning how to solve computational problems. Thereason for that is that biological systems, animals have had to solve a lot of problems like how doyou find food in the environment. That is similar to a lot of computational problems.

Lewontin: You've just said the word I love. I can disagree with you about something! The claimthat organisms have solved problems during evolution has created the entire genetic algorithmbusiness and it has given, in my view, engineers a completely wrong idea of what goes on inevolution. I have worked with genetic algorithms for a while and on parallel machines with EdLewis and we were never able to solve the simplest kind of problem any better than you could doby sitting and ........ process. And the reason is that people were fooled by the metaphor and thatmetaphor is that the world poses problems and organisms have solved them. This is the notion ofthe solution of a problem at a distance. And this is very important.

The difference between an engineering problem and what organisms do is this. In the engineeringproblem, there is the problem and I can set it up and look at it and I am over here and I want tofind my way through a nasty space to get there. That is what an engineer has to do. I give you abunch of nuts and bolts and bits and pieces, and I say go make something that flies so you get ittogether and see if it flies. Engineers have been sold a bill of goods by bolder biologists that this islike the story of evolution, namely that for example, fish swim so they have solved the problem ofswimming. When did swimming become a problem? Swimming is a problem for us, swimming isa problem for oak trees. In that sense everything is a problem. If I could be an oak tree with all thethings an oak tree does and also swim in a sense I would be better off. This is not what happens inevolution. Organisms do not get confronted by problems at a distance which they then movetowards and try to solve. To put that in terms of potential surfaces it is the case in engineering thatyou have a surface that has multiple peaks and there is one you want and you have to find yourway across the field to find it. It is the problem of finding the global maximum amongst a numberof local maxima.

Organisms do not find global maxima by passing around various local maxima to get there. Let me

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give an example: seals, who are quite good swimmers, began evolutionary life looking sort of likedogs, four footed, carnivorous, small bear-like animals. Question - when did swimming become anevolutionary problem to be solved by the dog-like ancestor of seals? It is a problem for any four-footed carnivore. The problem only comes into existence as a consequence of the actual currentlocal state of the organism. There is no problem at a distance. I think the whole problem-solutionmetaphor has really fooled engineers. They think that because organisms have "solved problems"then all you have to do is to see the way they did it and we will solve the problem. But the truth ofthe matter is, and I have said something about this before, the notion that the environment is anautonomous system presenting problems to which organisms adapt is bad biology. Theenvironment for the organism does not pre-exist. There are no environments without organisms.There is a real world without organisms, ie. a physical world without organisms, but theenvironment of an organism is precisely that construction or juxtaposition of bits and pieces of thephysical world which are relevant to that organism. And you cannot describe an environment foran organism you have never seen. Or rather to put it another way, you can describe a non-countable infinity of environments for such an organism. I could put the world together arbitrarilyin my head in any old way, but the only ones of those ways which are environments are those thatare organised around organisms.

It is the sensuous life activity of organisms that make a thing an environment, which means thatorganisms create the problems that they solve. That is to say that it is always a local hill kind ofprocess, but the hill itself has been built up in the first place and is continuously being built up, andthe right view of the matter is that you have a coupled system in which there is one equation, if Ican put it that way. The rate of change of the organism with time is some function of organism andenvironment:

dO/dt = f(O,E) where O is the organism and E is environment.

That is the classical Darwinian theory that organisms change in response to their environment andtheir current states. And then the environment changes with time and the classical way to write thatis as some function of the external forces.

dE/dt = g (E).

But that is not right. The environment changes in time because of the presence of the organismalso, so

dE/dt = g (E,O)

Every organism at every instance is in the business of recreating, changing or redistributing theenvironmental variables. There are no environments without organisms ...... Try it.

Valerie Morris: Can I give you another view? I can give an example of this with some recentinformation that has come out about the origin of the tetrapod limb. Organisms are creative. Inthemselves they are creative and opportunistic and they take advantage of what they have to dothings which they have never done before. There have been great arguments about how a fishchanged into an organism which has got four limbs. And a recent idea has come out that the fishwhich gave rise to the tetrapods, actually developed these little limb-like things for waddling abouton the bottom of streams through stuff that it could not swim through. And they used these thingsto push them apart. When they had done that they thought - my goodness! these are very usefulappendages, I can actually get up and put my head out of water. These things were created forother purposes and they found another use for them.

Lewontin: Yes. The notion of co-optation of features of an organism which was selected for

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another reason is a very important one. My problem is though that these are all stories. They arestories that we like but we do not have evidence for them. The other people tell stories we do notlike. People tell all sorts of stories and I am sympathetic to the stories. There is, for example, thestory about how wings developed. The problem is a tiny wing does not give you any advantage atall, so how can you develop an insect wing in order to solve the problem of flying. You know ifyou take ping-pong paddles and flap them about you don't get any lift. The answer given by JoePlimsol? is that they originally developed as heat balance devices that you could orient towards thesun as some insects (lepidoptera) do, and as they got a bit bigger they incidentally gave you somelift and then flying became a problem to be solved because they were doing a little of it anyway.Now that is a typical story - I don't know whether it is a true story. But at least it is in conformitywith what we know about the relationship between wing size and lift, and wing size and heatabsorption. The reason I bring it up, the reason this story is important is because it is a bad modelfor engineering if you do not know in advance what problem you are trying to solve. Because ifengineers really behave like organisms do, they would just sit around sticking pieces together andsee what happens. And when they got something, they would say that looks okay, let us try a littlemore of that. But that is not the way the problem is.

Arran Gare: But there is an approach developing in engineering coming out of this artificial lifeapproach which does exactly that. Take the analogy of the ant colony. How do ants know how tobuild their colony? How do they know how to pile up the eggs and so on, into the right places?The answer seems to be that they don't, rather they have various rules of behaviour that theyrespond to, and that those piles of eggs come out as consequences of lots of ants doing the samething - just following the rules. For example, take the rule: "if you are wandering around and yousee an egg lying by itself, you pick it up. If you see a pile of eggs and you are carrying an egg, youdrop it." Simple rules like that will create a pile of eggs. And that is one of the elements of thistheory of artificial life that people are using to understand how organisms actually work.

John Bennett: I think the point should be made that many of these descriptive terms like geneticprogramming and neural networks are chosen because they are nice names. Perhaps there might bea certain amount of parallelism. But neural networks do not tell you anything about nerves. It is amathematical technique, that works perfectly well, and it will give you some optimal solution to aprogram but we mustn’t be deceived into believing that computers are telling us anything about thefundamental nature of the world.

Arran Gare: Exactly. I have argued that in some of these biological analogies in computing weshould really be trying to mimic the biological processes more closely. Artificial neural networks,for those who don't know, are meant to be quasi-models of brains. You have neurons that interactand send signals between one another. The problem is that artificial neural networks that we builddo not work anything like a real brain at all. In fact, what happens is that if you train a neural netyou will converge to a particular behaviour. That is not what happens in a real brain. For example,very elegant work done by Walter Freeman has shown that ..... chaotic, real brains tend to be morechaotic, in that if you stimulate a brain it goes haywire with all sorts of signals being sent out. Andif you change that signal slightly, you can get an entirely different pattern of behaviour. And itseems like the brain is inherently chaotic, rather than tending to stabilize with one particularbehaviour it tends to diverge.

Bennett: The point I was making is that people should not be deceived by the words. As humpty-dumpty said, "Words mean what we want them to mean."

Intervention: Exactly. Genetic algorithms don't work exactly the way genes do either. You havethe same sort of problem there.

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Professor Hill: To bring it back to the practical, my students learned a lot more about insects bypretending to be insects, rather then pretending to be models of insects.

Peter Farleigh: You must have a funny looking class! (Laughter)

Intervention: We seem to have left complexity a fair way behind in this discussion. I was justwondering whether the discussion has more or less influenced our ideas like co-evolution andconvergence and interest in system properties rather than in components and pathways, and itseems to me that there is something in that that puts biological systems more at the heart of thingsthan as a metaphor for physical systems perhaps. I am just wondering if you have any commentsabout ideas like emergence that are in fact useful for biologists and give us a stronger focus in thiswhole perplexing field than we had.

Lewontin: I am not clear that the concept of emergence is very helpful. I think that the issue ofemergence is the wrong issue. I do not think the question is whether biological organisms are morethan the sum of their parts. It is the wrong discussion. The parts themselves do not exist inisolation. They become parts only as they are considered as parts of the organism. And thereforetheir properties do not exist in isolation. The issue is not whether the whole has properties differentfrom the sum of the parts but where do the properties of the parts come from? The properties of theparts do not exist until they come together; they are contextually defined. It is the dynamicinteractions between the bits and pieces that create their properties in the first place. I do not knowwhat a human being would be like in isolation from society. And to talk about society as beingmore than the sum of the individual behaviours, I think is senseless. Because there aren’t suchthings.

Intervention: But that is a relevant question though. The question emerges as the system changes.

Lewontin: I think you may be right, but the word "system" throws me.

Arran Gare: Can I make a couple of observations at this point? Let me give you two simpleexamples. We have been skirting around this. Take the idea of a gas. Thermodynamically it can bethought of as a lot of ping-pong balls bouncing around and exchanging energy. And therefore it ismeaningful to look at the average energy and call that the temperature. That's for simpleinteractions. But if you have non-linear interactions or different sorts of interactions you will getother sorts of properties emerging. For example, if you have a cloud of gas out in space or a cloudof dust, the main sorts of interactions are gravitational. So the effects are quite different fromcollisions. Instead of getting uniform structure it diverges and you get solar systems and galaxiesformed.

Similarly with a lot of chemical reactions if they are dissipative systems, such as Prigogine talksabout, then any irregularity gets enhanced which leads to larger level structures. We have notmentioned the idea of criticality which if you go back to those models with dots and lines joiningthem and you think of those objects as in interaction, and one of the crucial things here about thosesorts of processes is how well connected the entire system is. If you think of these as objects thatare interacting in some way, is the whole system interconnected, are they all interacting with oneanother? Are local interactions effecting the entire system?

You can see this in more simple processes, for example take the idea of a fire burning through alandscape: will the fire burn out the entire landscape or not? It depends on the connectivity of thefuel. If the fuel is very patchy, if you just have one grass tussock here and one there, the fire won'treach from one to the next and will just go out. If the tussocks are close enough together it will gothrough the whole environment. That manifests itself in terms of temperature. As the temperature

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goes up the fire gets hotter and effectively becomes more connected so the fire is more likely tospread, so you don't get small fires on coolish days, you get no fires at all. You get this criticaleffect, either you have fire danger or you don't. And that comes out in a lot of different things. Itcan be shown that this sort of model with dots and lines joining them (graphs, if you like) isinherent in a lot of different structures and processes, therefore that sort of criticality effectbecomes important.

Bennett: The question criticality in your fire example is entirely a matter of the way you choose todefine fire danger. It is entirely a matter of the words, you have a grey zone in between.

Intervention: No you don't have a grey zone. It is a yes/no process. You get to a point where eitherfire simply cannot spread, because any fire that starts cannot spread to the next element.

Hunter: That is what you call a percolation process.

Charles Birch: Can I go back to emergence and the question of consciousness. Do you recognizethe existence of consciousness in biological organisms and its emergence at one stage in theevolutionary process?

Lewontin: I do not like this question. I have a lot of trouble with this question of consciousness.(And not just at the moment) - it always gives me trouble. Let me ask you, do you think a tree hasconsciousness?

Charles Birch: No

Lewontin: You know that? Then how about starfish?

Charles Birch: Now you are trying to put me on the spot.

Lewontin: No I’m not. I’m trying to illustrate my problem that I have with the question of theevolution of consciousness.

Charles Birch: Let me ask you a question. Are human beings conscious?

Lewontin: By every sense of the word and what we understand by it, yes.

Charles Birch: Then when do you think this consciousness arose? This is the emergence problem.Something totally new came into existence all of a sudden.

Lewontin: I have no trouble with that. Novelties arise in evolution all the time. The vertibralcolumn was new. Our invertebrate ancestors didn’t have it. The vertebrate ear ossicles are new,our ancestors do not have them. There are plenty of novelties. They are appearing all the time Iagree. I don’t know what that tells us about the problem of emergence. This is simply theoccurrence of something that has not appeared before. Perhaps I am misunderstanding it, but myunderstanding of the usual problem of emergence is that it occurs when I bring together a lot ofbits and pieces. Does that ensemble of things have a property that could not be ascribed to any ofthe bits and pieces?

Intervention: Such things occur in the physical world. An individual water molecule has noviscosity but in the liquid state it has a viscosity. Would you call that an emergent phenomenon ina sense?

Lewontin: It is a macroscopic phenomenon which only applies at a certain macroscopic level. Ifthat is what one means by emergence, then emergence occurs all the time. But then that is

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unproblematic. But again, because it is unproblematic, for example you and I can engage inconversation and none of our cells is engaged in conversation. We can understand that. But thatexample does not confront what complexity theory and other similar theories are trying to sayabout emergence, which is not simply that there are appropriate characteristics of objects involvedin different collections, but which can only apply to those. It seems to be talking about somethingelse. Obviously consciousness is not something possessed by a cell. If that is what you mean byemergence, then sure consciousness emerges. But what I do not understand, Charles, in youroriginal presentation of progress, why it is important to know or to be able to establish a momentin which consciousness arises?

Bennett: I don't think it is important at all. But the traditional convention and wisdom of this is todo that. For example, an analogy is of the combination of hydrogen and oxygen gases which ifthey are brought together in a certain combination, you have the property of wetness emerging.....that is the famous example. Well now how do you think about that? There is a quality in H2O thatis not to be found in the separate gases.

Lewontin: Absolutely. First of all we have to be aware that there are problems of types here. Thereare properties of ensembles which individuals cannot have just because they are ensembles. Anensemble has a mean. The Liberal Party has properties that no individual member of that Party canhave, and that is a matter purely of the problem of types. Sets have properties that members of theset do not have, on account of their logical nature.

Peter Farleigh: It seems that emergence is just a word for a problem that does not help us.

Lewontin: But the practical issue being brought up here or the larger issues I thought, relating tothe problem of complexity, was: Is there some number of interacting elements or some forms ofpositive or negative feedback which are necessary for any system that we would want to postulatehas the property of consciousness. I am trying to understand what the question is. Are thereorganisms later in evolution which have what we would call consciousness whereas those earlierin evolution do not? We have consciousness.

Bennett: Well then, the next question is: Is there a continuum?

Lewontin: If I knew what consciousness was I could begin to think about that but as I do not knowwhat it is, I can define it only by extension. You have consciousness, I have consciousness. Andthat is it. I do not know how else to define it. Now you could make up lots of definitions ofconsciousness post hoc which do the work for you which you want them to do. Namely, they havethe property that your definition by extension is made good. In other words, all the things whichyou think have consciousness in the first place will have it, and those things which you do notwant to include will not have it. But that is a post hoc definition in my view. So I do not want toengage in that discussion. It is the same as the problem of complexity. I do not want to definecomplexity because the definition does not do any work for me. If it turns out that certainensembles of interacting elements with positive and negative feedback have a greater resistance tobecoming extinct as a whole, then that would be very interesting. But what complexity theory hastried to do is to define complexity in some clear way with a continuous measure and to be able totalk about Laws of Complexity and that’s what I find confusing.

Intervention: What is Gell-Mann's position on complexity?

Lewontin: It has to do with the number of computational operations involved.

Intervention: If you associate emergence with the situation where the whole is more than the sumof the parts, then if you say that the whole is never more than the sum of the parts you are back at

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old fashioned reductionism, and that essentially never got us anywhere.

Charles Birch: No, his idea is entirely different. And I absolutely with him 100% as far as I amconcerned. You have got to think in terms of the qualities of the parts in the context of thecomplex situation.

Lewontin: Yes. The parts do not have properties.

Intervention: I have been listening to all these theories and philosophies about looking at theBuddhist position of do not go out in the world and do no harm today - chaos and complexitytheories have caused harm in a sense by limiting the possibilities of what we can truly perceive.Then how can we look at developing more theories that will do no harm, because if you ask theaverage person if a tree has consciousness, or if one ourselves has consciousness even today in theexchange between people, if you ask them to talk about what is happening even in this encountertoday where people are speaking, there is energy in the earth and a transmission of electricity, orwhatever you would like to call it. How can there be a development of a theory, is it possible orappropriate particularly since science, and the theories that are developed in science, have such amajor impact on people’s place in the world. How can you get that, perhaps, to embrace otherphilosophies and traditions? You talked about what the IQ test measure. How can we step backand consider embracing a more humble theory that embraces what we do not know?

Lewontin: My first question is - do no harm to whom? I think the one thing that moral philosophyhas taught us, after a lot of water has flown under the bridge, is that there are no ways of doing noharm to anyone. There are no theories of justice, which deny contradictions. So as a politicalperson I would say my first question is - do no harm to whom? Do no harm to British Petroleum orits stockholders? Do no harm to the Aboriginal residents of such and such a place? Because to dono harm to someone will do some harm to someone else. It is a political issue - it is always apolitical struggle. I do not think it is a philosophical struggle at all.

Intervention: But if you look at the system, the interactions on each other then the system is madeup of the parts. Are they independent of the whole or are they in the context of the whole? Theinter- relation of the parts may be valuable for the parts once.... So to do no harm I guess is toacceptable to itself?? In the context of the participating whole.

Lewontin: But then you have to know so many details. We come back to the original problem,which is - is it a matter of knowing all the details of all the interactions? It is the ecologicalproblem. It is the problem of environmentalism. We cannot stop the world and get off. Everyintervention we make changes the roles. If I say - reduce the rate of using fossil fuels that is anintervention and it is changing certain rates. Now the question is - if I do that, how will it percolatedown to all other things? The question we are trying to answer in biology is whether I need toknow all the details, and write them all out to find out the answer, or whether there are somegeneral principles that enable me to find an answer. I do not know the answer to the question - Ireally don't. I’m fairly certain that any perturbation you care to make will have an effect. Youcannot talk in terms of let us stop changing the environment. Whatever our slogan is going to be itcannot be that. Because, first of all, the environment does not exist. There is a world out there andwe are constantly remaking it with every breath we take. So the issue is not to retain it but to try toput some pressure on the direction in which it is being remade.

Intervention: Maybe what we need is to be going with some theory which is not so general but is aprospectively simple theory that does not need to solve everything. In other words it does not haveto be a general theory. Because of the nature of the involvement of these other things.

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Lewontin: But you have got to have a theory of how the whole thing works. To say that you aregoing to perform an action which will not have any perturbations elsewhere is to assume that youknow all about the system. I have to admit I do not have the faintest notion what the effect will beof any particular change in the use, for example, of fossil fuels. Because included in all that, is notonly what is going to happen to the CO2 in the atmosphere but a huge number of other economicissues and labour struggles and government policy and who is going to get elected. It is a reallycomplex system, it really is.

So being humble, my reaction is to say I just do not know the answer. How can you invent asystem where you have some confidence that you know what the effect of these perturbations willbe? Most of the time you just shut your eyes to most of it. You behave in a very narrow way. Youchoose a target to which you want to do no harm and you are not ready to say whether you aredoing harm to any other aspects of the system. Because you don't know. The amount of analysisyou would need to do is unavailable to you.

Peter Farleigh: Last question - make it brief.

Intervention: Can I get Professor Lewontin comments on psychologists who attempt to studyhumans and attempt to generalise from human behaviour particular laws that govern biologicalbehaviour?

Lewontin: I am sorry - don't they usually do the reverse? Don't they study animal models?

Intervention: Not necessarily.

Lewontin: Well, there is a lot of laying on to animals of human behaviour, but that is not so muchwhat psychologists do. Evolutionists do that. The psychologists study animal behaviour and try togeneralise it to human behaviour.

Intervention: But isn’t that all rather futile? That’s what I’m asking. Given the fact that humanslive in an environment that is much more chaotic relative to that of, say, the cell whoseenvironment is chaotic, but less chaotic.

Lewontin: I'm sorry. I'm beginning to lose consciousness....!

Peter Farleigh: Time for lunch.

CLOSE OF WORKSHOP

Peter Farleigh: I must first say thank you to you all for coming. My second thank you is to ourthree guests - Dr Arran Gare, Professor Keith Williams and Professor David Green. Thank you fortravelling here and for your input and participation. Thank you to Richard. It has been a veryhectic three days. He had just arrived on the plane on Wednesday to be greeted by the Herald. Thenext day we had to get him to the Radio Station for interviews at 8.00am for 2FC and 2BL. Inbetween that Robyn Williams grabs him for an interview on The Science Show! When we got himback to the hotel, The Australian was waiting to interview him. He had a lecture that night to giveand the workshop this morning. I must thank Richard and Mary- Jane for their energy and goodhumour throughout.

Lewontin: Thank you. This has been a homecoming for us. We were delighted to have theopportunity to be here. I want to thank the members of CHAST and everyone else who wasinvolved. I cannot say I would like to come back every 35 years. That would be impossible. Thankyou again.