Is Evolution Fact?

TRANSCRIPT: Lecture Presentation: Is Evolution Fact? – c. 1985 / (101)

 

So the title of this talk, “Is Evolution a Fact?”... so the idea of giving a talk on that arose in the fact that recently... I guess mainly as a result of the issue of evolutionist controversy, a number of prominent scientists have made the point that evolution is a fact, but then they go on to qualify this by saying that we don't really know so much about the mechanism of evolution, that is, there are still questions that remain to be settled as to exactly what the mechanism of evolution is but nonetheless we can understand that evolution per say is a fact. So I wanted to discuss this.

The first point to make is that the statement is that we don't know the mechanism. This assumes that there is a mechanism, that is, that evolution can be described in mechanistic terms, the origin of life can be described in mechanistic terms. So, apparently that is what is being regarded as a fact. There are, for example, various alternative theories on the origin of life, some of which definitely can be called evolutionary; but I could name a couple. For example, there's Whitehead’s process philosophy in which you have a sentient being, God, who's manipulating the actions of different organisms in such ways to get them to assume certain forms. So that's an example of actually a non-mechanistic theory of evolution. And there are quite a number of other examples of this, but when scientists say that evolution is a fact, they definitely do not have in mind any theories of this kind.

The basic paradigm of modern science is to explain everything in mechanistic terms – this means in terms of measurements, equations, mathematical formulas and so forth. And more specifically the ideas that the underlying mechanism behind any natural phenomenon would be the laws of physics. So evolutionary theory must also boil down to the action of the laws of physics. In fact, that is precisely what Darwin's contribution was in terms of providing an explanation of evolution. His idea was that by the processes of random variation and natural selection, you could explain the origin of all different life forms entirely in mechanistic terms. It would reduce down to the laws of physics. So that's essentially what I want to discuss. 

So, I'll just start the discussion by saying a couple of things briefly about what is involved in a mechanistic theory of the origin of life. Essentially, in modern science this kind of theory is broken down into two parts: one is called molecular evolution and the other is what you would call phylogenetic evolution. So this first slide illustrates one recent theory concerning molecular evolution.

The idea of molecular evolution is to start with, generally, a primordial earth in which there's no life, in which chemicals are arranged in a disorganized fashion within some kind of primordial ocean, and then by some kind of chemical processes based entirely on physical laws the first cell arises. So many people have given a great deal of thought of how this could happen. This particular outline of steps is a model which was devised by Manfred Eigen and elaborated at some length recently. And he has a series of stages of chemical reactions of increasing complexity, which he proposes will culminate in the production of the first self-reproducing cell. So that's the idea of chemical evolution. It’s significant that the basic principles that Eigen invokes are those of natural selection and some kind of random change. His idea's the first self-replicating molecules will arise within the primordial ocean – he chooses RNA molecules for this – and that gradually some of these molecules will happen to somehow generate certain protein sequences which will, in turn, catalyse the reproduction of those molecules. 

[5:22] 

So his basic application of the principle of natural selection is that if a protein arises from a molecule which catalyses the reproduction of that molecule to a greater degree than the reproduction of other molecules that may be in the solution, then the population of that kind of molecule will increase at the expense of all the others and will gradually replace the others. And so by a series of steps like this, you will get chemical systems of increasing sophistication and you'll wind up with the first living cell. So that's the chemical evolution idea. So, of course, the underlying principle here are the laws of chemistry and physics. 

Now, phylogenetic evolution is the discussion of everything from the first cell on up to all the different life forms in the world today. This is actually what Darwin mainly concentrated on. His idea is that if you started with one very primitive organism – perhaps something like an amoeba – then in due course of time by purely physical processes, you'd wind up with all forms of life. This diagram shows one reconstruction of, historically, how this process is supposed to go. This a phylogenetic tree. Much of the discussion of this kind of process is carried out in the context of palaeontology, the fossil record. This particular chart shows the origin of the plants, land plants, which supposedly radiated from a very kind of primitive vascular plant in the Devonian era and in due course of time gave rise to all the different forms of plants that we have today. 

So, much discussion about defects in the theory of evolution focuses on this fossil record and the interpretation of the fossils. I'm not going to concentrate on that in this talk, but I will make a couple of points just to set the scene. Namely that there's a great deal of room for various interpretations and there are many, many controversial points. 

So, for example, in this diagram, you'll see many dotted lines which represent supposed links between different orders of plants and so on, links of evolutionary descent, but there is no real evidence for it. They're quite conjectural and in fact, in this particular example, this was taken from a paper from a prominent botanist. Here we have another model of the same evolutionary sequence which he proposes. In this one, instead of having everything radiating from one source as you see here, on the basis of various evidence, he has various groups having independent ancestry through what he calls, question mark, independent algal sources in the Precambrian. So there's a great deal of controversy and uncertainty. 

Basically, in the fossil record, you have evidence that various kinds of life forms have existed in the past but the real question is, “Well, how did they come about?” So what we want to discuss is whether or not they could have come about by physical processes. So the approach I'm going to take is, interestingly enough, summed up by a Kurt Gödel actually, just recently. This also indicates skepticism about the validity of evolutionary theory is not limited simply to uneducated people as it's often charged. 

For example, Gödel, is one of the most prominent founders of mathematical logic as it's understood today. In the '30s, he came up with Gödel's theorem which is quite famous – it caused a revolution in that subject. So, this is a commentary on his views made by Hao Wang, who's a student of his, and he points out after making some other remarks, that 

...more generally Gödel believes that mechanism in biology is a prejudice of our time, which will be disproved. In this case, one disproval in Gödel's opinion, will consist in a mathematical theorem to the effect that the formation within geological time of the human body by the laws of physics or any other laws of a similar nature starting from a random distribution of elementary particles in the field is about as unlikely as the separation by chance of the atmosphere into its components. 

[10:56]

So this is a somewhat hyperbolic way of putting it, but what I would like to discuss here today are some lines of argument which point in the direction of what Gödel is saying. And we'll see, I don't know, I hope that we have the time, that some very interesting philosophical themes will very naturally arise in this discussion. 

So, the first thing to discuss then is what we mean by natural processes, what we mean by the laws of physics. So here is a simple illustration of two physical laws, to give an idea. On the top, we have a diagram illustrating Coulomb's law for the attraction or repulsion of two charged particles. The idea is that if x and y, you have particles with charge e1 and e2 and they're separated by a distance of r. So the force of attraction or repulsion between the two particles is given by this formula which we have right here, it's called the inverse square law. And the main point to make about it is that it's actually quite a simple formula – simple in that it's very succinctly written down in a symbolic form. Also, it's quite easy to conceptually understand if you imagine that somehow the attraction is being spread out over the area of a sphere, which goes as 4πr². So the point y gets attraction to x with diminishing the square of the distance. So that's one example of a physical law. 

Here we have another example, this is Hooke's law for elasticity. Here you have a spring which is being stretched and the amount of stretch is given by q and the pole by which this spring wants to pull back varies with the square of the stretch. So once again, you have a very simple relationship. Well, if you look at the various principles in physics, you will find that actually they are all based on a very few simple laws of this kind. 

So, physics has had quite a history. At the present time, the predominant theory in physics is quantum mechanics and quantum mechanics, in particular, is believed to give a full account of all chemical reactions. And the standard view in biology today is that living organisms are simply systems of chemicals interacting according to chemical reactions. So it follows then that the laws of quantum mechanics are sufficient to account then for all activities of living organisms; that's the current view in biology. So the idea of the theory of evolution then ultimately boils down to the idea that quantum mechanics should fully account for all the processes which give rise to life as we know it. So I wrote down an example of the laws of quantum mechanics that would be sufficient to describe a model of evolution. So this may look pretty complicated, but actually there's really not very much here. There are, let's see, six terms in this equation. 

This equation on the top was the Schrodinger equation, which is the basic equation in quantum physics for describing how matter transforms with the passage of time. And one term in that which is called the Hamiltonian, the symbol H, is given in greater detail in part B and it consists of a few terms which describe different kinds of interactions with particles. For example, this first term describes free electromagnetic radiation and the next term describes kinetic energy, momentum of particles such as electrons and protons. The next term describes how particles interact with electromagnetic fields. Actually, then the next term describes something called spin, which comes up in quantum physics and the final term is that Coulomb's law, inverse square principle for attraction and repulsion. So, essentially, these formulas by themselves will suffice to describe how matter transforms in accordance with modern physics.

[16:17] 

So, however, to say what is happening in a physical model it's not enough just to have the equations describing the transformation of that model, you also have to have some things called initial conditions and boundary conditions. So here's a little diagram to explain what's involved there. This area here, this crosshatch, that represents a given volume of space; and as time passes, this diagram represents what's happening within that volume. 

So firstly, the volume’s represented two dimensionally so its boundary is this curve that goes around it; of course, in a real volume of space you have a surface going around it. So in order to predict physically what happens within this volume, as time passes, we need to know two things. One, you need to know the conditions that prevail at time zero, that is when you start your consideration. You need to know how you start out here in the beginning, those are called initial conditions; and then as time passes, you need to know what's happening on the boundary of the system. So if you know those two things and the physical laws, then in principle you can calculate what the final state of the system is going to be. 

So what we want to do is consider a model like this in which one can talk about evolution and the origin of life. So ideally, we'd like to talk about the most universal kind of model possible within the universe as a whole. It's instructive to consider the models of the universe that people are making nowadays. The most popular model of the present time is called the Big Bang theory and according to the Big Bang theory, if you go back, well, the time varies but maybe something like on the order of 16 billion years, you'll find that all the matter in the universe is compressed into one highly condensed plasma at extreme temperature. So the idea is that the universe begins with a very compressed mass of gas or particles, fundamental particles, at a very high temperature. 

The idea is then that it expands and as the expansion takes place, the gas cools off, galaxies and stars condense out and then around some of the stars, planets will form through gravitational contraction of the gas and then on some of these planets, oceans will form. And then in the oceans, due to chemical reactions and the bombardment of light from the central star, various processes of self-organization of matter will occur and you'll get a living cell and then everything evolved from that. So that is the general picture being considered for the present time.

The key thing to note about this is that the initial conditions are extremely simple because the initial state of the system is just a hot gas at uniform temperature. There's no diversity there. In fact, a uniform gas can be described by very simple thermodynamic equations, quite as simple as the other equations that I wrote down. And, of course, in a Big Bang theory, there are no boundaries. So there are no boundary conditions. 

[20:16]

So one might consider making a model of this for study. Unfortunately, you can't quite do that at the present time. There are problems with the Big Bang theory, namely that the theory hasn't been formulated yet as an actual physical theory. I won't go into the reason for that in detail.   Basically, to make a Big Bang theory, you have to combine two things, Einstein's general theory of relativity and quantum mechanics together. The general theory of relativity will describe how things expand and the quantum mechanics would describe how the particles behave; so you need both of them, but thus far, nobody knows how to put them together. 

So we can't quite do that at the present time, but we can consider a model of a primordial planet being bathed in solar radiation and we can formulate that. So just to see how it would work out I tried to do that. Once again, our initial conditions are quite simple. We have started out with a cloud of gas and we set up the initial motions within the cloud of gas so that this cloud naturally will contract gravitationally and the idea is that it should form a planet. Of course, actually there are many dynamical problems involved in actually proving that such a cloud would form a planet, that's another very much open question, but at least in principle this is the idea of how it's supposed to happen. 

So, we form a planet and here we have some boundary conditions. We have electromagnetic radiation coming in, we just postulate that as a source of light and that electromagnetic radiation will bathe the surface of this planet. So, ideally, as the planet collapses, it should differentiate,  oceans should form, and so on; and the electromagnetic radiation is coming in and one might expect to see these processes of evolution take place. 

So for the purpose of this analysis, the thing that we want to do is ask how complex our model is. The basic theme is that actually the model is quite simple. I've already mentioned the laws of physics, that you need to describe what happens in the model. This is a rough description of the initial conditions. So we can measure the complexity essentially, by the number of symbols it takes to describe this entire model in all its details; or to do that formally, we have to have an alphabet and a specific language in which we write these symbols and one uses a computer language for this purpose. So this brings us to the subject of what's called information theory. 

So I'm going to make some observations of the mathematical nature, but I'll try and make it quite qualitative so as to explain the concepts. I think that one can make a number of clear points about that. So the basic idea is that to describe this model symbolically, you can do it as it turns out in less than about a hundred thousand bits. Bits mean ones and zeroes, essentially, in the information theory field you break everything down to ones and zeroes and write it out in strings of these. So by coding the different symbols and formulae and so on this model, you can write down a complete description of it in about a hundred thousand bits. So, that gives an idea of the complexity of such a model. So that's the first step of the physical setting in which the process is supposed to occur. 

Now the next thing to consider is the question of what it is that you want to study, that is what is going to evolve within the system. So there we're talking about living organisms and so the theme that we'll pursue is to inquire into the information content, or the complexity of living organisms, to see if an estimate can be made. 

So here's some data, this is taken from Watson’s Molecular Biology of the Gene. This can give a certain idea of the complexity of life forms. The first thing we start to talk about are proteins, which are found within living cells. Proteins are long chains made up of 20 different types of amino acids which fold in a very complex way and they perform very remarkable functions within cells. A typical protein in a living organism may have say, on the average of 300 amino acids subunits in the chain; it varies quite a bit but that's an average figure. So to describe such a protein, that is to write a description of it in symbols, essentially you'd have to specify 300 different, say, numbers from one to 20 identifying those amino acids. So you can express that in bits also, which is a standard measure for information for these; and a typical protein, just to write down in the simplest form a description of it would take about 1300 bits – that gives an idea of what it takes to describe one protein.

[25:46] 

So, in a bacterial cell there could be, say, 2000 to 3000 proteins. The particular cell that we're using here is Escherichia coli which is studied quite a bit in biochemistry. So 2000 to 3000 proteins and with this many bits for a protein, you get about two and a half to 4 million bits to describe all those protein molecules. 

Apart from proteins in the cell, there's the DNA, which is believed to carry the hereditary information for the cell. And from this we can also get a certain idea of the complexity of the cell. For example, in E. coli, the amount of information needed to describe the genetic coding of the DNA comes to about five and a half million bits and to get an idea of how big that is, if you wrote this out on pages on a book of ordinary size using a 64 character alphabet, then it'll take about 330 pages to write down that much material. So that gives an idea of the amount of raw information, so to speak, it takes to describe these very large molecules within an E. coli bacteria. 

Now we can go to a mammalian cell just to go to a more complex organism. Watson estimates that a mammalian cell will have maybe 20 to 50 times as many structural proteins. So that comes to about 84,000 to 210,000 separate proteins of, on the average, 300 amino acids a piece. And also it's estimated that the amount of DNA in a typical mammalian cell is about 800 times as much as say an E. coli bacterium. So in pages, as before, that comes out to 264,000 pages of information to write that down.

So this is a very gross idea of the complexities of living cells. It's useful to consider first the actual structures that we're talking about. Here, for example, is an illustration of one protein molecule. They're quite intricate structures and these protein molecules perform rather remarkable functions within cells. I'll just give one example of some of these functions. There's a protein called DNA gyrase… [break]... then take another strand of DNA that's nearby, pass it through the break, and join the other two DNA strands up again so now that strand can be brought over to the other side. So that's a molecule that can do all of this. So, you can sort of appreciate that it'll take quite a complicated machine to do that. So there are many different remarkable enzymes like that and more are being discovered all the time. So the point here is you get an idea of how much it takes to describe all of these things. 

So another example, this is a diagram of one of the transfer RNA molecules. You may have seen descriptions of how from DNA you go into messenger RNA and then that is transcribed to form proteins. There's a genetic code in which the RNA has three-letter code units in a sequence along the strand of the molecule. And there's a process that translates those code units into amino acids and builds these protein chains. So this transfer RNA is one of the molecules that links a particular amino acid with a particular code symbol called an anticodon. 

[30:29]   

So actually, this in itself brings up an interesting question about origins of life, which I'll just mention briefly. These processes whereby the protein is transcribed in the DNA molecule themselves require large numbers of highly specific and complex enzymes, which are themselves produced by that very process of transcription, which are encoded in the DNA. 

For example, for this transfer RNA thing to perform its function, it is necessary for the appropriate amino acid to be connected onto the molecule. The amino acid has to be one that's appropriate for the particular anticodon, which is at the other end of the molecule, because if the wrong amino acid is placed there then a mistake will be made in transcribing proteins. So there are enzymes called synthetases, which can recognize the shape of a particular transfer RNA molecule. It can also recognize the right amino acid to go with it and then fit the two together. So, without these proteins, how could the system work? – and yet the proteins are produced by the system. So you have a vicious circle and it represents quite an enigma, although various efforts have been made to try to provide an answer to this. But the main point that I want to make here is that these things are quite complex. I have a couple of other examples.

Here, for example, is an organelle which is found in that same E. coli bacterium. It turns out that this bacterium has a molecular motor built into cell wall, which rotates its spiral flagellum, and that acts like a screw on a propeller to drive the bacterium through the water – a rather fascinating story how they figured all these things out. 

This diagram represents a reconstruction of the motor. It looks rather similar in some ways to an electric motor. There's some kind of armature, then an axle, and this section here is a universal joint that is flexible and that connects to the spiral flagellum. So apparently the bacterium can run this motor forward or reverse and by doing this in the appropriate way, it can swim from environments that are unfavorable to ones that are favorable. Specifically, it can detect the presence of different kinds of chemicals; and if there's a chemical that the bacterium likes, it will swim in such a way that it goes towards higher concentrations of that chemical. So not only is there the motor, but there is a sensory system which is coordinated with the function of that motor. 

So it's interesting to consider how something like this would arise. As for the amount of information needed to describe it, they've studied this to some extent and I think that for this motor itself, some 22 different genes within the bacteria are necessary; and you can see that quite a few considerations would go into the description of such a thing. For example, it's not just enough to set up the motor so that it functions, you also have to describe how it's constructed within the cell and so many other things.

So this is an example of a very simple organelle within a bacterium. If you go to higher forms of life you'll find no end of highly complex structures. Here's something that I just took from random, from a recent Scientific American. Turns out that some species of butterflies have a sort of metallic iridescent appearance. And the reason for that turns out to be interesting. On the surface of the wing, they have little diffraction gratings which are supported on stilts. So these diffraction gratings diffract the light into the rainbow pattern. So you might consider, well, what does it take to give a description of that structure and engineering specifications? And you can go to other complex things. 

[35:25]  

Here's an eye. If you look at a single organ such as the eye of a human being, you find a rather incredible amount of information which is needed. I was talking with one anatomy student who was describing to me, I think it's called Descemet’s membrane, which is one of the membranes on the inner side of the cornea. He said a whole thesis had been written about it for a graduate school of medicine. So practically anywhere you look within a structure like this, you find incredible complexity. For example, in the retina there are many different cells and so on that if I start describing it, it would take a very long time to finish. 

So the basic point is that a great deal of information is needed to describe living organisms. So what we'd like to do is get a minimum estimate. The previous numbers that I gave, gave a very gross estimate of what you're dealing with. So we need a minimum estimate. So here's some idea of how you can get that. This is a little bit mathematically involved, but I’ll try and describe some of the concepts which are involved with it. So the basic question is, how do you add information? Suppose A and B represent two batches of information and you want to know how much information you have together. Well, it turns out that the additive process is something that you could sort of describe by a diagram like this. The two sets of information may have something in common – that's represented by this overlapping area – so when you want to add them together to get the total amount of information, you essentially, when you add B on the A, what you really want to consider is the information in B that is not already given to you by A. So that's the principle you want to take into account when combining pieces of information together. 

So in order to obtain an estimate, what we'll do is break down a living organism into many parts – different organs, organelles, and so on – and then consider combining them together to consider what the total amount of information is. Actually, I have a diagram here showing what we're considering. This strip represents a long sequence of symbols and some kind of code, which codes for the different kinds of structures that go into a living body. And as I indicated, there are many different highly complex structures that you would want to deal with. So in order to describe the amount of information, we pick a strip, a code-strip, and break it into pieces representing the various individual organs.

So here, these pieces are labeled Y₁, Y₂, Y₃ and it goes out to Y_n. For example, Y₁, Y₂,  and Y3 were individual proteins and by the time you got out to N here, you have hundreds in the order of 100,000 of them. So essentially you can go step by step – X_N means all the Y's up to Y_N, the whole group of them – so then X_N plus one means you add on to that group the next Y, Y_N+1. So the information in X_N is essentially equal to the information in X_N minus one plus the additional information needed for Y_N. So what's involved in estimating that? You can consider that if say Y_N is identical with one of the Y's that came before, then you don't need any additional information to describe it; so that's something called redundancy. But one can make an estimate that on the average a certain amount of additional information is to be expected to describe the successive structures, be they proteins or organs or whatever. So one estimate that I made to... say, if you take the genetic coding from an alien cell, which is estimated to be about 260,000 pages, so suppose you assume that on the average you need no more than six bits of information, additional per page, in order to specify that sequence. So that's a very low estimate but still, even if you make an assumption like that, it comes out that you have at least 6,000 bits of information that you're describing, 600,000.

[40:42]

So the basic idea here is that in the structures you find in living organisms, a great deal of information will be needed. So thus far we haven't said anything about evolution, but at this point we can begin to do so, because it turns out that you can then apply a mathematical theorem. So, this formula is what we want to talk about; I'll explain what the formula essentially means. 

We want to ask, given that we start our system with a particular initial condition that you were talking about and we let a certain amount of time elapse, say, a few billion years or something like that, we want to know the probability that a given structure will appear within the system so we can get an estimate of that. Here, this P(X_N) is that probability, it's the probability that X_N will appear within the system. So, this little c here, it's not too important. It's an artifact of the mathematics – it's a small constant of about 300 bits. 

The next thing we come to is the logT, T is the maximum number of configurations you can pack into your system. That's also a technical thing that you need, sort of the volume of the system. We can say that it's less than, say, 10⁸⁰. This is based on a common estimate that there are 10⁸⁰ particles in the universe, which I don't take too seriously. But at least it's given frequently that there are that many particles in the universe, so you couldn't have more configurations all at once than that. It would allow you one particle per configuration – that's the maximum. So, T is less than that.

The next thing, L(N), this is the amount of information needed to describe the model which we talked about before. That came out to about 100,000 bits and the final term L(X_N) is the amount of information needed to describe that particular configuration that we were asking about. We're asking if a certain configuration will arise within the system. So, if we substitute in the different numbers we have, we have P(X_N) is less than, well this constant, the logT comes out to 266, as it turns out, and you have the 1000 bits for the model. You subtract the 600,000 bits for the structure that we're interested in, but that very much dominates the whole sum. You have a very big negative number, two to a very large negative power and in powers of 10 it comes out to be less than 10^-150,000. So that gives the probability that a structure of this information content will arise within the physical system.

So this is a very small probability, in fact, extremely small. You can get an idea of how small it is by saying, since we're dealing here with a model of one planet, suppose you had a lot of planets and you ask, well, what is the probability that on at least one of them, this particular structure would arise? So say you had 10¹⁰⁰⁰ planets, that's a lot if you consider 10¹⁰⁰⁰ is one followed by 1000 zeroes. So the probability that on at least one of them, this structure would arise, would be 101,000 times 10^-150,000. So that's 10^-149,000, that would hardly make a dent in this figure. So it's an exceedingly small probability, essentially vanishingly small. 

So I'll illustrate this formula graphically. We can make a little diagram that illustrates what's going on. Essentially, what the formula does in this diagram, the x-axis here going to the right, measures information content and you imagine that at each point on this axis we have distributed, perpendicular to that, points representing all the different forms of that information content. So say right here, the forms that go this way would be those... [unclear]... the information content so these would be complex forms, and back here you'd be dealing with simple forms so that's what we're trying to represent in this picture. Actually, the number of forms of a given information content goes up exponentially with the information content, so actually, imagine the forms are very much denser out here that they are in here. 

[45:42]

So the vertical axis represents the probability that in our physical model, the form can arise, the particular form can arise. So in this diagram we consider two possibilities. One is that you have this peak representing very complex forms. Actually, the way the peak is spread out, in this case, we're considering a variety of possible highly complex forms, and this other peak at A represents a collection of simple forms. So this curved surface is the cut off on probability, which is given by that inequality. Essentially what it does, is it says that you cannot have the appearance of forms of complexity above a certain level and in fact the level is given to you by this constant L(N) + logT + c. So essentially what the information theory says is that there's a constant of nature which provides a limitation on what can evolve within the physical system. Anything more complex than that will not evolve because the probability will be exceedingly low, something of lesser complexity could arise. So that's an interpretation of what this formula means. I'll try and explain a couple of other points about it also. 

For example, it's important to emphasize here that we're not talking about sheer chance. Of course, anyone will admit that if you, say, tried to write down a large number of symbols purely by chance like the monkeys on the typewriter typing Shakespeare's plays, then the probability that you'll get a particular sequence is exceedingly remote; but we're not exactly dealing with chance here, we're also dealing with the action of natural law. So when certain laws are acting, the probability of something arising can be made larger. 

This is a simple example. We have this word configuration, so suppose we tried to spell that word by choosing letters from the alphabet at random – 13 letters – and just wrote them down, what would be the chance that we'd get the word? Well, there are 26 letters so the probability would be 26^-13 which is a small value but suppose we then introduced some law that modified how we chose these letters with the example I selected here. So, suppose we say the ratio of vowels and consonants is 6/13, because that's what it is in the configuration.

So we apply a rule. And then the probability comes out to be this number. And if you can see it has 5^-6 and 21^-7, it's much bigger numbers than this other probability. So by imposing the appropriate rule, we can increase the probability that the given event is going to occur, but it's still a very small figure. 

So there's a basic way to understand what we're talking about here; that is, that the law is that the system can be thought of as instructions which guide the formation of structures within that system. So if you have, say, N bits of instructions and M bits needed to describe the structure that you're looking for, then if N is bigger than M, you can get the structure, assuming you have the right instructions written down. But if it's smaller, then there are some missing instructions and they can only be made up by chance because, once you've exhausted the laws in the system, the only thing you have there is called chance. So what it amounts to is that for each additional bit of information that you need, it goes above the amount of information that's built into the model, the chance goes down by one half, The reason it’s one half is that a bit can be one of two things, one of two alternatives. So the chance that you'll be able to get one bit right if you choose at random is one half. So if you have a structure in which the number of bits needed to define it exceeds the amount of information built into the model by say, two bits, then one half by one half is the probability that you will get that.

[50:51]  

You can give an example of this. Imagine a maze in which there are many forks and passages in which if you take the wrong passageway, you'll go through a trap door into a lake full of alligators or something like that. So there's only one pristine passageway through this maze. So if you're not given any instructions, the chances are that you won't make it because you'll have so many different decision points at which you have to make the right choice. If you're given full instructions and you can go all the way through, assuming they're the right instructions, of course – if they're wrong, you won't make it. But suppose you're given instructions for going halfway, then halfway through the maze you'll do all right but then you depend on luck to make it the rest of the way. So if you've gone through half of the turns and there are, say, half of them left, then ½^N/2 is the probability that you make it the best.

 So that's the sort of thing that we're dealing with here. Our natural laws can improve the possibility of the given structures arising, but only if they provide the instructions needed to specify the structures and anything they leave out has to be made up by chance. So this is the basic calculation that you can make using information theory. There are many considerations that have to be made in order to make this rigorous and I won't try and go into them in detail. 

For example, one thing you want to consider is the idea of superfluous information. We've asked about the probability that one particular structure will arise. So let's say that that's a horse, you want to know the probability that a horse will arise within the physical model. So the thing is though, a complete description of a horse will have much superfluous information, the exact positions and different hairs and so on. So there'll be a lot of superfluous information there. So there are ways in which you can factor out superfluous information and I probably shouldn't go into it, but you get similar conclusions just with a more elaborate discussion. 

So I'd like then at this point to discuss some of the implications of this study. How can you interpret this? What are the implications for theories about the origin of life? So there are a number of possibilities you can consider. How could you get probabilities that are reasonably high within a physical system? Well one way would be to have complex boundary conditions, just to enumerate some of the possibilities, that is if you had information coming in from across the boundaries of the system, the specified forms that were going to develop there and that's one way you could do it. 

Actually, it's interesting, Francis Crick recently came out with a rather curious book in which he proposed such a thing. Actually, it's a rather old idea, but he proposed that the origin of the first cells could be accounted for by saying that extraterrestrial beings in spaceships sent a spaceship containing bacteria to the primordial earth. So that spaceship landed. So that's an example of information coming across the boundary. 

It's quite remarkable that Francis Crick came up with that theory. It illustrates the problem with that kind of an explanation though, because as soon as you postulate that some complex information comes in across the boundary, then you have to ask, well, where did that come from? For example, the problem with Francis Crick's theory is that he has to account for the origin of those intelligent extraterrestrials who supposedly made the spaceship. And even in the older panspermia hypothesis, which is a couple of hundred years old, in which it was merely proposed that spores would travel in outer space, and maybe they would seed the primordial earth and life would begin. Even there you then have to ask, well, where did the spores come from? – and really put off the question to a more remote answer. 

[55:13]

Of course, here in the analysis that we're presenting, it wouldn't be sufficient simply to send simple bacterial spores, you'd have to send in information for all the different kinds of complex structures which are later to arise. If you just sent information for bacterial spores, you could get bacteria but you wouldn't get horses, human beings, or whatever. So another possibility in explaining this is to suppose that you have complex initial conditions. So this has similar problems. The idea is that you can say at time zero at the beginning of your model, a lot of information was built into the system, which would then enable is to give rise to different kinds of forms. 

This question has arisen with respect to the Big Bang theory for example. Some people want to pose what they called the stacked deck theory, namely, within the initial state of matter in the Big Bang, the configurations of atomic motion were just such that later the different things you find in the universe would arise. Actually, they were merely thinking of explaining the origins and stars and galaxies, which in itself is hard with that theory – they weren't considering explaining the origin of life – but the idea was considered with quite a bit of disfavor among the scientists involved. And again, one of the reasons for this is that if you assume these highly complex initial conditions, well, how did they get that way? And that somehow seems unsatisfying. 

So another possibility is to say that the laws of nature really aren't so simple. This, of course, would go against current theories of physics in which they are simple. [break] Well, one possibility is to have complex laws and then another possibility is to say that the lifeforms come about purely by chance, that's another alternative. 

This one was introduced by Jacques Monod, interestingly enough. He made this point that, he says, "The thesis I shall present is that the biosphere does not contain a predictable class of objects or events that constitutes a particular occurrence, compatible indeed with first principles, but not deducible from these principles and therefore essentially unpredictable." And this was proposed by Jacques Monod. Essentially, what he's saying is that there's nothing in the laws of nature which would tell you anything about the origin of various life forms such as human beings. So, therefore, it's completely by chance. In other words, what he's saying is that maybe it has a probability of 10^-150,000, but that happened, just by chance. So that's another alternative. 

So at this point, of course, this is very much a qualitative overview of the whole subject, but I'd like to discuss some of the implications of these things. Essentially, what we have here is very fundamental paradox which presents an obstacle to explaining things empirically and that's the paradox of unity and diversity. So, modern science is based on the idea of explaining things in mechanistic terms, that is in terms of formulas, equations and so on. The idea of an explanation is a simplification. For example, if you have to describe something in a very elaborate, complicated way, if someone else can come along and describe it very briefly, you say he's explained something. 

For example, take the trajectory of an object thrown through the air. If you have to list all the points that it follows, which would mean you have to list quite a bit of information, and somebody comes along and says, “Well, it follows the formula for parabola,” then this is considered to be an advance in understanding what was happening – it’s an explanation. But the idea that forms such as the forms of living entities may have a high information content means that this method of explanation is eventually going to meet with frustration because essentially, there's no simplifying explanation.

[1:00:14]

 So more than just a critique of evolution, we have here a critique of the power of the mechanistic approach to explain things. Essentially, truly complex forms cannot be explained, they simply are what they are, because an explanation would mean finding a shorter way of describing the form. And this has also brought out if you consider this idea of chance that Jacques Monod brings in. 

Actually, it's interesting, a number of other evolutionists have also brought in this concept. What do you mean by chance? It's a very complicated topic to discuss; I could go into the history of it for a bit. But essentially one ultimately evokes chance at the point where it says that something just happens, so, without any particular cause. So evoking chance to explain an event is essentially like saying well, that event just is what it is. So we're dealing with very complex structures which cannot be reduced down and described in a simpler form and essentially what that leaves us with on mechanistic terms is to say, “Well, it just is what it is and if you'd like, you can call that chance. You can say, well, it's chance. But essentially it means that there it is – there's no explanation.” So this is the problem that arises in a mechanistic attempt to describe the origin and development of lifeforms.

So what I'd like to end with here is to suggest, to make a few remarks on mechanistic science. In order to explain complex form, if you're going to do it at all, it's necessary to go beyond the mechanism, that is, you've reached the limits of what mechanistic science can do and it's here that we encounter some problems. Of course, the present tradition in science is strictly mechanistic, and there's a tendency among some people to think that, well, this is the all and all and that there can't be anything beyond it. 

So, for example, the evolutionists are saying that evolution is a fact. They're thinking when they say that, that some mechanistic explanation is possible even though we may not have the details of it worked out yet. But in fact there are going to be serious problems with finding the mechanistic explanation. So, a problem arises if one then dogmatically asserts that well, the explanation must be mechanistic. So that is essentially the reason for concern about such statements. 

I'll indicate very briefly that there are different non-mechanistic approaches to understanding the origin and nature of life. For example, let's see. I'm wondering what extent I should go into it, in fact, perhaps instead of doing math, if I see that it's quite a subject of discussion, I'll open this up for questions and maybe we'll naturally get into such discussions also, maybe that would be a good idea at this point. So any questions or comments any of you would like to raise?

Question:  Yeah, I... [unclear] but I'm curious as to a particular, well, mathematical situation, we talk about sets and a complement. In my understanding of mathematics, unless two items are complementary, that is a part of a whole, the disproof of one in no way is supporting evidence of the other. In other words, if they're just simply elements of a set, if they're not complementary, if you can show one thing not to be true, you in no way have shown the alternative would be true unless they are complementary in that fact. So what I'm wondering is what theory you are proposing as opposed to... You have not demonstrated as far as I can see that showing evolution not to be true is, in point and fact, supportive of some other theory. So I'm asking what evidence are you presenting in support of what theory or are you really here today to suggest that there are some weaknesses which I think any scientist is willing to admit with any theory that is credibly presented. I think any good scientist is certainly not going to claim that any theories which are designed to explain the natural world are in any way watertight and explain everything completely. Only in mathematics do we have a situation where we can completely define in that sense.

[1:06:16]

 Answer:  Yeah. Well, you're raising a good point. So I'll try and go through your points in order. First of all, it is quite true that if you disprove one thing you haven't necessarily proven some other independent thing. So I'll tell you what my basic idea here is. Various non-mechanistic approaches to explaining the origin of life have to stand on their own. It is not that within a mechanistic framework one can prove anything about something non-mechanistic. The mistake would be to say that within a mechanistic framework we can't prove anything about something non-mechanistic – therefore, it is somehow intellectually less viable to talk about such things. That's one point because sometimes it does happen, although of course as you say, a scientist who is at all reasonable will have to admit that there are so many deficiencies, either actual or potential in our present theories. 

Nonetheless, scientists very often do present a very dogmatic view of things and I would advocate the approach to science that you're describing as opposed to the dogmatic approach. Now, as for what this analysis points out, I think this does contribute something to an understanding of physical theories because of course, no doubt one can argue with different points in this analysis also. It's not going to be perfect, just as you say, but the analysis at least suggests very strongly that within our standard concepts, the physical theories which have been very prominent in science, there's a fundamental limitation to what could evolve, which I think is worth noticing. 

Like I mentioned, you get a constant of nature in this study, based on the physical laws, you can calculate this constant, which I did for the quantum model – t’is about 100,000 bits; and it says that nothing more complex than that is likely to appear – anything beyond that is essentially going to be a product of pure chance, which I think is worth considering as a fundamental limitation on the mechanistic viewpoint. So I wanted to offer that for people's consideration and also bring up this point that science sometimes is dogmatic; of course, one can try and analyze the reasons for that.

Sometimes it seems that maybe it's a matter of their insecurity. In the history of science, it's frequently happened that once a given theory is quite solidly established, that universal claims would be made for it. This happened: there's a story about Max Planck who was wondering whether to go into physics or be a pianist. He was advised to be a pianist because the subject of physics was closed, because in that day there was great confidence prevailing that we've really got physics figured out; but then, of course, everything changed during the next 30 or 40 years. 

So likewise, an open-mindedness is needed and as I've said, we've talked about the mechanistic approach in modern science. A non-mechanistic approach to things will necessarily be quite different from what we do in science as it stands today. For example, it may involve meditation, it may involve achieving higher states of consciousness by different internal processes, and so on and so forth. So one should consider the possibility that these may be viable ways of finding out about things which one cannot find out about by mechanistic or in empirical sciences, as we know. And I'm suggesting here that this question of the origin of life is one question where mechanistic science may reach a real impasse. So, I just wanted to offer that.

Q:  I think we're going back to our... [unclear] I think you seem to be implying that somehow the approach that you're suggesting is excluded by science; in other words, science chooses, if you will, a mechanistic approach, so it is perfectly legitimate to do what you suggested. But to expect that to be accepted within a mechanistic framework I think is unreasonable. In other words, no one is saying that someone cannot meditate to try and determine the origin of life, but then to take this non-mechanistic approach and expecting it to be accepted within a mechanistic framework seems unreasonable.

[1:11:26]

A:  I'm not saying that. As I pointed out, within the mechanistic framework, as long as you're playing that game, within that framework, you can't even talk about anything non-mechanistic but the positivists will point that out... 

Q:  And as you say, which is absolutely true, that within today's context, and I'm going to include quantum mechanics with your mechanistic approach, that it is not deterministic but it is mechanistic if you will. By definition science as we know it at the present day and as it has been for many hundreds of years and depending on whether you want to interpret the Greek philosophers, the Egyptians, the Babylonians in all of important history there are mechanistic aspects that would say science goes back in a mechanistic, some would say, vein from all the way back to as far as recorded history goes and then presumably even back further than recorded history. And the question that arises is, this is the tradition within which one can discuss a certain kind of thing. 

Certainly, the tradition of meditation and speculation and thoughts and all the other traditions which are not mechanistic also go back that far or even further, but does anyone suggest that these are incompatible in the sense of saying both cannot exist in the same human being or both cannot exist in the same world?

A:  Well, both can exist and both can go on. Merely, one should recognize the limitations of each and what I'm doing here... What I propose that this contributes is an idea of limitations of the mechanistic viewpoint for describing the origin of complex form. Admittedly, you may say that is negative, or that is as much as you can get from the mechanistic analysis per se. It, I think for some at least, points in a non-mechanistic direction for seeking explanations, but you don't have to do that. What I would suggest is that the upshot of this analysis for science is that of course, as we know, you can explain and deal with many things in mechanistic science as we have it, but you probably won't be able to explain the origin of life and the development of the different kinds of living beings.

Q:  [unclear] ...scientists at some time in the future were actually able to create a living cell through experimentation of some sort or another, one can always argue that some external force intervened, but assuming that time disproves their theory, their mechanistic theory, we're actually able to create such a living cell, would you at that point be willing to accept that a mechanistic theory was a viable alternative to working out the questions of the origin of life?

A:  Well, if you consider, actually, there you're raising a different issue. The issue of whether or not a living organism is entirely a biochemical mechanism. This entire discussion has strictly dealt with the biochemical aspects of living organisms. We haven't even brought in the question of whether or not there's something more in a living organism, of course, as you'd expect I have a view that there is.

Q:  [unclear] 

A:  So the point he raised is if scientists were able to construct a living cell, would that...

Q:  [unclear] 

Q2:  They don't even have to do it, if they construct it through a basic set of mechanistic physical law, in other words, if they achieve that, just as we transplant a heart or something like that and we can explain how we did it and so on and so forth. In other words, using basic mechanistic science, would that be sufficient to convince you it's a practical problem within the realm of mechanistic science.

A:  Not for the origin of that cell. If you look at the analysis that I'm presenting here.

Q:  Let me get this clear, all right. Are you suggesting then that it might be possible to produce life but in this specific case here it didn't happen that way? 

A:  No, no. Notice the difference between scientists producing the cell in his laboratory by some chemical manipulations and the question we're considering. There, you're starting with the scientists. He's already a very complex being and in fact not only that…

[1:16:19]

Q:  All right here, you can't arise by chance but that's what I'm saying, that's a separate issue from whether it could arise at all by mechanistic means. In other words... 

A:  You're asking whether it can exist in the mechanistic terms then. 

Q:  What I'm saying is, you're saying this question cannot be dealt with from the standpoint of mechanistic science because... you're suggesting to look outside mechanistic science regarding the question of the origin of life. I'm saying, if in point of fact, we could through mechanistic science actually create a living cell, whether or not this actually tells us that this is the way it happened on the earth, wouldn't this at least indicate that life itself could be created through a mechanistic process and therefore is amenable to mechanistic thought? 

A:  In order answer to that question, you should look at the specific reasons that I did give in this presentation. Namely, I'm saying that the reason that a mechanistic explanation for the origin of the cell will not work is that in such a mechanistic explanation, in such a mechanistic model rather, you have to have the information for the cell built into the system or else you have to suppose that the arrangement arises purely by chance or some trade-off between the two as I described in terms of that form. So the point that I'm making then is, suppose the scientist were to build a cell, he's got the information. 

Q:  That's correct.

Q2:  No, that was my part. I understand what Peter's getting at but I think there's a more interesting way to put it because it corresponds with real research... [unclear] it may well succeed, and they are just jockeying around. They are trying to come up with plausible models of what conditions were actually like. So the question is really interesting here given the [unclear] you said earlier. If... [unclear] one of these guys... [unclear] or one of these guys using models of what really could've been going on manages to get a self-replicating molecule... [unclear] the real research is all about.

A:  Let me try and answer your question because it depends on how he gets it. So in answer to the first question, let’s say this: suppose you did start with a system and some simple initial conditions, an arrangement of gas in a flask – I don't know what it would be – and you let some things happen in there according to simple processes and indeed you got a halfway decent cell, then that would completely defeat everything I was suggesting here. But on the other hand, what he seemed to be suggesting was if you could by engineering techniques build a cell. 

Q:  I understand... [unclear] 

Q2:  I understand. No, I think I was suggesting the same thing... [unclear] I was not suggesting in any way that we're taking a little micromanipulators or something and putting little organisms in this and reconstructing it because that's already been done in the sense that we have already taken cells as such and taken the nucleus from somewhere else with DNA from somewhere else and put it into a cell and created a different kind of... [unclear] that's already been done. Nobody's suggesting manipulating an already existing material, what I was specifically referring to was in fact from the same model he's talking about. In other words, creating a living cell through a strictly, if you will, plausible chemical condition, physical condition. So when I was trying to avoid the question of asking the question of whether in fact these are the real conditions that existed, but only ask the question that you set up a set of conditions in which this production takes place regardless of whether these were the original conditions, because we go back to your original question which is eventually called the panspermia.

Perhaps the earth was seeded from somewhere in space, but maybe these conditions existed somewhere else or something similar. So I'm not arguing, I'm saying if any conditions ever that might exist anywhere could be set up any conditions you can set up to do it. In other words, you can do it, take chemicals and various energy sources and put all this stuff together and produce a living cell...

A:  Yes, if you could set up a model like that, which as I say, I’m differentiating from engineering the thing, but a simple model with something happening and a genuine living cell comes out, that would disprove what I'm saying here.

Q:  That's what I was asking. So you'd be willing to admit if somebody succeeds in that way then the... [unclear] is practical...

A:  If that's what they did do, they've proven it, yeah.

[1:21:22] 

Q:  I’ve got two words that you probably... [unclear] one is your hundred thousand... [unclear]

A:  What did you say? 

Q:  [unclear] ...we've got this system here and the question is how much information does it take to describe it. Well, depends on what level you want to describe it. One of the points that has come up in the discussion of the whole entropy question is, if you take the... [unclear] and you want to talk about how much entropy there is, well, that doesn't always have a straightforward answer. It depends on just exactly how you want to describe it. Information, the use of the word is rather interesting because it's no accident that it suggests something about... [unclear]. So I think that there'd be a lot of... [unclear]. The other problem that I see is this, I think it's incredibly improbable... [unclear] but that's not the issue. The issue is how probable is it that there could be something at all of that sort, whether a horse or whether something on a personal limb or who knows what and I think that the question... [unclear] 

A:  Okay, so I'll try and answer your questions. First of all, as for the physical model, there is no problem with that figure, 100,000 bits, because the point is that to describe, it's not that we're talking here about breaking down phase space into units and describing individually what's there. We're dealing with large scale ensembles. Now, let me make a few points about this. 

You might raise the objection, I'm saying the initial conditions are very simple but what about the exact positions of all the molecules, for example? So there are two ways you can go about this. One is if you deal with a statistical ensemble for different possible arrangements like Gibbs Canonical Ensemble and things like that, then the total description of all the possibilities is simple and that's what actually goes into the equation. Or another route you can take is you could say, suppose you start with something, a more complex description, and you say, okay, well let's start with that; but then you have to ask, well, which complex description do you take? 

Now the point is, and further examination of the equation shows you this, if you take a complex description with specific information built into it, what your structure is that you're talking about getting, you can get them; but if the information built in is not specific information to the structures that you're talking about, then you won't get them. So, essentially what it amounts to is, it comes back to that alternative that the information could be built into the initial state of the system. And in this case, we're saying it's built-in, in a complicated form. You're saying that by choosing the states of the atoms in the initial gas in just the right way, you can arrange it so that the ultimate structure comes out. But then one asks, how is it that they have to be arranged in just that way? Then one again comes back to the point that complex structure or form can be transformed but not ultimately explained because when you say it's transformed from this earlier state, that's also equally complex and we haven't explained that, it actually becomes even harder to explain.

I'll give you an artificial example from classical physics, it's pretty easy to understand. Say you have ball-bearings arranged in a square lattice, like at the center of the squares in a checkerboard and it goes out... [break] and it follows a certain path and let's say the path spells out a word. So you could say, well, isn't it rather improbable that it spelled out that word? Well, at least if we stick to classical physics, you can say though that if you specify the initial condition just right, then it could bounce in such a way as to spell out that word. But then the problem is that in that case, the exact direction of the particle in the initial state would have to be chosen and you'd ask well, really, why is that direction chosen? In other words, that's as remarkable as saying the word itself was spelt out.

[1:26:01] 

Q:  [unclear] ...of mathematics when you approach something that way. Why shouldn't it be that way? In other words, there is no absolute reason. If you've got to have some initial conditions and you don't specify the goal or you don't specify if this ball-bearing has to be going some direction, if it's not a single case, in other words, if it did it every time then you can ask yourself, why should it be that way? But if you have one case, why shouldn't it be that way? It could be that way as well as any other way. 

A:  So, the point I'm making. Yeah, why shouldn't it be? But you don't have an explanation. 

Q:  In other words, you don't need an explanation. 

Q2:  I think trajectory would be nice for probable... [unclear] 

A:  That's all well and good but the point I'm making is, is that you can't explain the particular form you get. So, I'm saying a complex form cannot...

Q:  It doesn't need an explanation.

A:  You say it doesn't need it, that's all right. You can say it doesn't need it, but I'm saying you don't have it. 

Q:  In other words, the fact that a horse does exist does not need to be explained as opposed to something else. The fact is a probability that it is greater than zero that a horse could be created by chance is sufficient to allow the fact that the horse exists. 

A:  So to sum up in a very crude way, what I'm saying then is that all we can say about the fact that a horse exists is that it exists.

Q:  No, you can say how it came into existence. You don't have to say that it would come into existence again if the same conditions existed, reviewing with a certain element of probability or a certain element of randomness in this, almost any evolutionist I suspect and any physicist and any chemist and any biochemist will tell you that the chances of it occurring again in just that way are infinitely smaller than the mathematic... 

A:  That gets to a second question. 

Q:  And so then the question is not whether or not such a thing would occur again, but only whether it was possible for it to occur once and the answer is yes, it's possible. 

A:  Which we know because it's there. 

Q:  That's right. And so that's all that's necessary to state is that it occurred.

A:  So if that is satisfactory, the most we can say about the horse being there is that we know it's possible for it to be there, on virtue of the fact that it's there. If that's satisfactory, okay, fine. So the point I'm saying is you won't be able to say more, but now...

Q:  No but that doesn't follow.

A:  I'm saying you won't be able to explain it, just like we can explain... Let me explain and then I'll get to the second question. You can explain the trajectory followed by a ball moving through the air is parabolic in terms of the law of gravity and acceleration and so forth, so if you're just given the parabola, given that law and so on, you can explain it and at least we think, well that's an explanation. I learned something about how that ball's moving. So the complex structures of the horse though, you can't do that. 

Q:  Sure you can, let's go back to the model that you presented. You specifically stated that in order for this particular ball that is bouncing through to describe a particular word, was very low probability. 

A:  I didn't say probability.

Q:  If it had a particular trajectory, then this thing could occur. So your explanation of it is, the ball had this particular trajectory at the beginning and that's how it occurred. In the same way, we can describe how the horse came to be through the system. Now, the question is why the system had the initial conditions, that can't be explained – it just had those conditions. 

A:  Okay. So you're saying you just had the initial conditions and later produced a horse?

Q:  That's right. No, I don't think any evolutionist suggests, wants to go back to the first causes and in effect be saying, why were these causes there? I think most evolutionists would be perfectly happy to say if you want to believe that those initial conditions simply existed because something or some non-thing created those initial conditions back as far as we can take it, then that's not within the realm of mechanistic science to answer that question. But we can go back to the starting point as it were, as far back as we can push it, say the Big Bang, if you will, and we can say maybe we don't know why it is matter in space, it just is. 

A:  Okay. So what it amounts to, of course, this isn't what they do... 

Q:  What we do know is that there is matter in space, then matter in space will behave in the following way, according to these laws and so on and so forth but there's nothing that says there has to be matter in space.

[1:31:11] 

A:  That's all well and good, but we conclude is it's like what Jacques Monod said, that there's nothing predictable within the biosphere in life that the living organisms and so on and so forth, of course, satisfy physical laws, but they're in no way predictable. So what that amounts to do then is to say that we really can't say anything more to explain why they're there. You know, if you trace it back to some earlier complex state, then you can't say why that is there. At some point you just have to throw up your hands and say, well, it just is that way. So you have to say that a very complex thing just is that way, well, that's okay. 

Q:  What's wrong with that? 

Q:  [unclear] 

A:  Well, some people are curious as to why you get horses.

Q:  [unclear] 

A:  So, the basic point then, that you're not going to know why you get horses or human beings or scientists. You're not going to know that. Let me answer your second question.

Q:  What do you mean by not going to know? That's what I don't understand.

Q2:  Relative to what?

A:  Like we can explain the parabola of the trajectory. We can give what many people feel is a satisfying explanation of how it is it's following a parabola. We're not going to have such an explanation of how it is we have human beings and horses and so forth. 

Q:  We can do exactly the same thing. Not in a precise way, not because it's a far more complex thing that can describe this molecular simple object. 

A:  So that's the question of what you can do in principle. Of course, in principle, we can't do this. Just let me give a little answer to the second question you raised because we've never gotten back to it, because you raised the point that anyone would agree that a particular horse is exceedingly unlikely to ever appear again and so forth. So what you can do, there's a concept which you can further bring in, the idea of traits of information shared in common among a variety of forms. So in this case, for this example, let's take all normal horses – just for one example, exclude defective ones – so we can talk about the amount of information they share in common, and you can deal with this mathematically. So the point that I'm making is that these estimates of information content I've used here, actually, are referring or are derived to express the amount of information in common in a large category of structures such as the set of horses. 

Q:  I know. What I suggest is... [unclear] 

A:  Very good, very good. So that's a good point. You can make the category larger. Now, what I'm saying is that if you make the category large enough for it to be probable you’ll get something falling within that category, then you've made it so large that practically it doesn't say much of anything at all. This is the basic argument that I would make. In other words, if your category, pardon me? 

Q:  I agree with that in a sense but I wouldn't... [unclear]

A:  Well, so in any case, just to try and sum up the conclusion we have, just to go back to that bouncing ball example, simple example. If the bouncing ball spells out one of Shakespeare's sonnets then you can say, well that's just because the initial condition happened to be just right for it to do that and that's perfectly satisfying. If that is satisfying, okay. Indeed. But there are some who might wonder why did it spell out that? So I'm saying, essentially, within the framework of mechanistic science, we won't be able to answer such questions. We may not want to; some people do. Like, I would be curious as to how we get horses, human beings, and so on.

[1:36:11]   

Q:  I personally feel that someone who asks that question doesn’t understand science. I mean because by asking why one of many possible choices that are based on some sort of random event occurred is to ask why I'm sitting in this chair here in this room at this time, which I can give a number of explanations, but basically it all goes back to things that are far too complex to calculate, but it doesn't mean they aren't real. In other words, I could equally well have been somewhere else at this time as opposed to right here, but I am here because of a series of events led to it; but I couldn't go back to my father and mother and say, did you know that your son would be in this position at this time... 

A:  All well and good.

 Q:  But it doesn't mean that event is any less real because of the fact that I can't do that. 

A:  So the basic point we're making, I'm trying to make, is that yes, of course, you'll say that we can't trace out these particulars. But in a broader form, anything that is predictable that you can trace out, as I tried to say to him, that will be a very broad category. Now take, for example, something like human intelligence or intelligence, at least the way we experienced it or understand, or human life with human type emotions, personality traits and so on, that's a broad category, including all individuals as human beings. The question is, can we say anything about that in terms of mechanistic science? 

I mean you say, I don't understand science, well, it's a matter of curiosity. I mean you can do with science what you can do with it, but still one is curious about various questions. Can you answer this or not? So the point I'm making is, I think you could argue that, say, if you took a broad enough category of forms, it'd be reasonably probable, let us say for an intelligent to describe intelligence or human personality, it would be so broad that would include many things that are totally alien to what we call human personality. In other words, the fact that you get such a thing as human personality is something that will have to be satisfied that mechanistic science won't tell us. We've got it, it's here, it's possible, it came about somehow but... 

Q:  I think I understand what you're saying though let me try and say what I agree with... [unclear] and what bothered me... [unclear] I don't actually disagree about those but the kinds of question that you're considering here is one which absolutely... [unclear]. Also, a question which might write very well that would read... [unclear] and furthermore, if you wish, there might be just as forthcoming... [unclear] In fact, maybe noticed the... [unclear] but what bothered me about the way in which you were putting this was this, there was a suggestion at first that somehow there was a question here, which is a mechanistic science term, I thought it was... [unclear] but mechanistic science takes itself to be grappled with and it's shown that it is going to fai...l [unclear]. Whereas I said, it's not a question that mechanistic science takes itself to be grappled with. 

A:  Well, it's okay. Perhaps some people don’t. I suspect though that many people, they think of, say, the question of explaining such complex forms and so they would hope that you actually could explain it in the sense where they could discuss it; and, of course, in our actual experience in science, we don't explain such things in fact. The kind of things that are discovered in the scientific realm never give such an explanation. But still, I suspect that many people, including even scientists, have a hope that science is going to explain such things. And as for the theory of evolution, I made some comments to the effect that it can be a dogmatic idea that well, this explains everything and this does arise, which may tend to make you think that, well no other explanation in this area or no other approach to explanation is going to give the answers. So one needs to have a broad-mindedness... 

Q:  I agree that scientists take themselves to be answering all the needs, the questions... [unclear]

[1:40:27] 

A: Yeah?

Q:  Sir, if you're... [unclear] theory of evolution is saying everything grows by chance, Darwin's theory of evolution proposes an adaptive life process in which we find first, the source of variability which is spontaneous mutation which is known to occur... [unclear] and the process of selection, natural selection, which culls out unviable forms and which causes and propagation... [unclear] selective process but I think if we were to propose theory of mutations without natural selection then these objections would be perfect, this is not a one-sided theory this is a two-sided theory... [unclear]. And as far as the selection, which is anything but... [unclear] So why a horse? Well, we have mutations... [unclear], but we have conditions in which the... [unclear], in which there was an environmental niche in which the rest of them were horse-like things... [unclear] and less than horse... [unclear] so well, therefore, we have of horse.

Now, given that horses exist, you've evolved in the environmental conditions such that in addition to everything else, anything else developing has to exist well enough in the world, which has horses. Grass has to survive being trampled on and other creatures have to learn to survive, well and done, if that's their niche. And so we don't have a random process here. 

Also, you can... [unclear]. Now, in the beginning of life we're again not talking about random processes, we're talking about life rising out of lawful and natural processes and presumably, not all of which we have... [unclear]. If we want to set a condition to see whether life will arise spontaneously and of course the very existence of these natural laws are indeed a very marvelous thing. But remember what we have here, to set up a proper experiment, any less than this would demonstrate nothing... [unclear] negative contrary. What we want to do is set up an ocean filled with wonderful organics that suits such as pretty well demonstrated original that we had and leave it to sit there... [unclear] of all kinds of vigorous stirring energies with 2 billion years and if even one viable pops up and it spreads like crazy and then you'll have an environment in which you have spreading... [unclear]. 

A:  Okay, can I try to answer some of the points? I have a feeling you're saying we really can predict horses after all. I understand.

Q:  I want to say something further on that. Right now, we have horses, in the past we didn't have horses. We had something that was before horses. In the future, horses do not die out, they maintain the bloodline of something else. In the past 5,000 years have seen a tremendous change in dogs, and right now we have horses, horses are what we have, but.. [unclear] it’s amazing that we have horses, because what interests us is to determine the processes by which such things as horses and men and other living beings…

[1:45:23] 

A:  Okay, so let me try and answer the points you're raising. The first point that you made was that essentially I'm only dealing with chance here whereas, of course, mutation occurs randomly, that's a chance phenomenon, but natural selection is a lawful process – it's not random. 

Q:  Both are lawful processes... [unclear] just like the cloud themselves change randomly... [unclear] but if you look at the clouds, you the looker are finding a selective process and you see the patterns which are interesting, just like... [unclear] sooner or later you'll praise the possible patterns, but the viewer will consider some significant and some not, while natural selection considers some configurations by their viability significantly and selects them and propagates them. 

A:  Let's talk about natural selection just for a very short time because, you see, an important point to know about natural selection is that it's a derived principle. In other words, natural selection should occur because of the underlying physical laws as an automatic consequence of those laws. In other words, the idea, I'll give you an example of natural selection. If an organism has a particular trait which enables it to do better in its environment in terms of reproductive success, its population will eventually crowd out all the populations of lesser organisms. That's the idea of natural selection and given all the environmental factors that have to be there at the time and so forth. 

So the point is, this is occurring due to the underlying physical laws and as I say, we're taking a mechanistic model of life. We're talking within that context. So then this must be due to the underlying physical laws. So my question is, will natural selection tend to give us a horse?

Now, that's an interesting question. We have examples of natural selection doing something like the peppered moth and so on, which you may know of, which are very simple examples, but will natural selection actually produce a horse? That's the question that we want to pose. So what we're doing here is going to a level beneath the level at which one talks about very complicated interactions between horses and trees and grass and hills and so on, which would be very hard for us to deal with. So we're going to the level of the underlying natural laws. 

So I'm not just considering chance here. I'm considering those underlying natural laws. As I was explaining before if you have laws that are acting that can increase the probability of things. The idea of natural selection is that it will increase the probability of something that wouldn't occur by chance with any probability at all. The idea is that natural selection, that process, can cause this to come about with a fairly high probability. So, to analyze that we're considering the underlying natural laws. What we're showing that in order to obtain a highly complex form like a horse, the probability is exceedingly remote. In other words, the laws of physics as we know them, cannot provide that selection. That's the conclusion of the analysis. One might think, well maybe they can, but the conclusion of the analysis is that in fact, those laws can't do it. 

Q:  What you're going about... [unclear] it's very much as if... [unclear] you were setting up a question, what is the chance of organic molecules in a... [unclear] spontaneously bringing themselves together into the origins of a horse? Now, that's exceedingly uncommon. 

A:  That's not what I'm talking about. 

Q:  However, at this point, things happen one step at a time, but the question at that later stage is what is the chance that the horse will arise out of the... [unclear] or whatever it was?

A:  Like I was saying, I'm not talking about the chance of that just molecules purely at random make a horse, I'm talking about the whole process, how the whole system works, that's what I'm talking about. 

Q:  The chances of a horse rising out of... [unclear] conditions where you... [unclear] the steps larger, faster and many, many other things may approach a certainty. 

A:  Yeah, it may. That's the thing, you don't know. So the point that I'm making is that by this analysis you can see that it won't be probable. Whereas, if you just talk about what horses might do and what mutations might affect their genes, that's an exceedingly complicated problem, it may, but this indicates, no.

Q:  Let me give you an example which I think will demonstrate what I mean. We in Europe and in North American continent have this thing called the wolf which is a predatory hairy animal with a certain appearance as so and it fills a certain environmental niche and... [unclear] it developed out of something... [unclear] it had to develop out of that material. Now, Australia and as you know, we innately have marsupials, nevertheless it's remarkable that Australia produces marsupial wolves, wolves that look remarkably like our wolves. They are different in terms of structures because they were built out of a marsupial background because they were filling a niche very similar to our wolves. 

A:  So the question is how did that come about? So this is an analysis... 

Q:  [unclear] 

A:  Yes, very good. That's the theory of evolution by natural selection. The question is, will you really get wolves and so on. Here's an approach of analyzing which suggests a certain answer. As far as speculating about what might've happened with wolves, it's exceedingly fuzzy. One might hope to be able to do that. You know, like the case of the peppered moth where the color changes, there is an area where you can come up with an explanation. You say, well the birds are going to eat the moths which stand out on the bark of the trees and their population ratios will change. There we can actually talk in what I would call a scientific manner. When you're talking about the wolves, it's all completely vague. So, we don't really know whether selective pressures could really do that or not. I know you're thinking maybe selective pressures could do that. That's the theory. The question is, could they really? So here's an analysis, which suggests, no.