It often happens in science that our
theory of some area of reality is very precise, but the reality is too complex to work out precisely, or
analytically. This can be when we decide to use computer simulation of that reality to get at least a close approximation to the truth. When a phenomenon is determined by a precise process, then if we increase the complexity of our simulation, and if the simulation really is simulating the underlying reality, then the more computer power we apply, the closer we get to the truth--that is, our results approach that truth
asymptotically.
For example, if you want to predict the rotation of galaxies in space relative to each other, and of the stars within the galaxies, the theories of physics will do the job, in principle. But solving the equations directly the way one does in algebra or calculus is not possible with so many variables. However, you can use a computer to simulate the movement and get a very good approximation (we've discussed this
here, among other places). Thus, at each time interval, you take the position and motion of each object you want to follow, and those measures of nearby objects, and use Newton's law of gravity to predict the position of the objects one time interval later.
If the motion you simulate doesn't match what you can observe, you suspect you've got something wrong with the theory you are using. In the case of cosmology, one such factor is known as 'dark matter'. That can be built into models of galactic motion, to get better predictions. In this way, simulation can tell you something you didn't already know, and because the equations can't be directly solved, simulation is an approach of choice.
In many situations, even if you think that the underlying causal process is deterministic, measurements are imperfect, and you may need to add a random 'noise' factor to each iteration of your simulation. Each simulation will be slightly 'off' because of this, but you run the same simulation thousands of times, so the effect of the noise evens out, and the average result represents what you are trying to model.
Is life a simulation of life?Just like other processes that we attempt to simulate, life is a complex reality. We try to explain it with the very general theory of evolution, and we use genetics to try to explain how complex traits evolve, but there are far too many variables to predict future directions and the like analytically. This is more than just because of biological complexity however, in part because the fundamental processes of life seem, as far as we can tell,
inherently probabilistic (not just a matter of measurement error). This adds an additional twist that makes life itself seem to be a simulation of its underlying processes.
Life evolves by parents transmitting genes to offspring. For those genes to be transmitted to the next generation, the offspring have to live long enough, must be able to acquire mates, and must be able to reproduce. Genes vary because mutations arise. For simplicity's sake, let's say that successful mating requires not falling victim to natural selection before offspring are produced, and that that depends on an organism's traits, and that genes are causally responsible for those traits. In reality, there are other process to be considered, but these will illustrate our point.
Mutation and surviving natural selection seem to be probabilistic processes. If we want to simulate life, we have to specify the probability of a mutation along some simulated genome, and the probability that a bearer of the mutation survives and reproduces. Populations contain thousands of individuals, genomes incur thousands of mutations each generation, and reproductive success involves those same individuals. This is far too hard to write tractable equations for in most interesting situations, unless we make almost uselessly simplifying assumptions. So we simulate these phenomena.
How, basically, do we do this? Here, generically and simplified, but illustrating the issues, is the typical way (and the way taken by my own elaborate simulation program, called
ForSim which is freely available):
For each individual in a simulated population, each generation, we draw a random number based on an assumed mutation rate, and add the resulting number and location of mutations to the genotype of the individual. Then for each resulting simulated genotype, we draw a random number from the probability that such a genotype reproduces, and either remove or keep the individual depending on the result. We keep doing this for thousands of generations, and see what happens. As an example, the box lists some of the parameter values one specifies for a program like
ForSim.
Sometimes, if the simulation is accurate enough, the probability and other values we assume look like what ecologists or geneticists believe is going on in their field site or laboratory. In the case of humans, however, we have little such data, so we make a guess at what we think might have been the case during our evolution. Often these things are empirically estimated one at a time, but their real values affect each other in many ways. This is, of course, very far from the situation in physics, described above! Still, we at least have a computer-based way to approximate our idea of evolutionary and genetic processes.
We run this for many, usually many thousand generations, and see the trait and genomic causal pattern that results (we've blogged about some of these issues
here, among other posts). This is a
simulation since it seems to follow the principles we think are responsible for evolution and genetic function. However, there is a major difference.
Unlike simulations in astronomy, life really does seem to involve random draws for probabilistic processes. In that sense, life looks like it is,
itself, a simulation of these processes. The random draws it makes are not just practical estimates of some underlying phenomenon, but manifestation of the actual probabilistic nature of the phenomenon.
This is important, because when we simulate a process, we know that its probabilistic component can lead to different results each time through. And yet, life itself is a one-time run of those processes. In that sense, life is a simulation but we can only guess at the underlying causal values (like mutation and survival rates) from the single set of data: what actually happened its one time through. Of course, we can test various examples, like looking at mutation rates in bacteria or in some samples of people, but these involve many problems and are at best general estimates from samples, often artificial or simplified samples.
But wait! Is life a simulation after all? If not, what is life?I don't want us to be bogged down in pure semantics here, but I think the answer is that in a very profound way, life is
not a simulation in the sense we're discussing. For the relevant variables, life is not based on an underlying theoretical process in the usual sense, of whose parameters we use random numbers to approximate in simulations.
For example, we evaluate biological data in terms of 'the' mutation rate in genomes from parent to offspring. But in fact, we know there is no such thing as 'the' mutation rate, one that applies to each nucleotide as it is replicated from one generation to the next, and from which each actual mutation is a random draw. The observed rate of mutation at a given location in a given sample of a given species' genomes depends among other things on the sex, the particular nucleotides surrounding the site in question (and hence all sites along the DNA string), and the nature of the mutation-detection proteins coded by that individual's genome, and mutagen levels in the environment. In our theory, and in our simulations, we assume an average rate, and that the variation from that average will, so to speak, 'average out' in our simulations.
But I think that is fundamentally wrong. In life, every condition today is a branch-point for the future. The functional implications of a mutation here and now, depend on the local circumstances, and that is built into the production of the future local generations. Life in fact does not 'average' over the genome and over individuals does
not in fact generate what life does, but in a sense the opposite. Each event has its own local dynamics and contingencies, but the effect of those conditions affects the rates of events in the future. Everywhere it's different, and we have no theory about
how different, especially over evolutionary time.
Indeed, one might say that the most
fundamental single characteristic of life is that the variation generated here today is screened here today and not anyplace else or any time else. In that sense, each mutation is
not drawn from the same distribution. The underlying causal properties vary everywhere and all the time. Sometimes the difference may be slight, but we can't count on that being true and, importantly, we have no way of knowing when and to what extent it's true.
The same applies to foxes and rabbits. Every time a fox chases a rabbit, the conditions (including the genotypes of the fox and rabbit) differ. The chance aspect of whether it's caught or not are not the same each time, the success 'rate' is not drawn from a single, fixed distribution. In reality, each chase is unique.
After the fact, we can look back at net results, and it's all too tempting to think of what we see as a steady, deterministic process with a bit of random noise thrown in. But that's not an accurate way to think, because we don't know how
inaccurate it is, when each event is to some (un-prespecified) extent unique. Overall, life is not, in fact, drawing from an underlying distribution. It is
ad hoc by its very nature and that's what makes
life different from other physical phenomena.
Life, and we who partake of it, are unique. The fact of local, contingent uniqueness is an important reason that the study of life
eludes much of what makes modern physical science work. The latter's methods and concepts assume replicable law-like underlying regularity. That's the kind of thing we attempt to model, or simulate, by treating phenomena like mutation as if they are draws from some basic underlying causal distribution. But life's underlying regularity is its
irregularity.
This means that one of the best ways we have of dealing with complex phenomena of life, simulating them by computer, smoothes over the very underlying process that we want to understand. In that sense, strangely, life appears to be a simulation but is even more elusive than that. To a great extent, except by some very broad generalities that are often too broad to be very useful, life isn't the way we simulate it, and doesn't even simulate
itself in that way.
What would be a better approach to understanding life? The next generation will have to discover that.
