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This has been on my mind for a few days. I'd love a criticism of my arguments outlined here: https://groups.google.com/forum/#!topic/fallible-ideas/9bcC5WN6bLs. I'll re-issue them here:

While studying genetic algorithms and genetic programming, I stumbled upon the concept of symbolic regression, developed by the father of genetic programming John Koza.

The idea is that instead of specifying the form of the function in advance (as other methods of regression may require), all you do is provide some data points and the program will figure out the equation from which all points can be derived. For example, given the (artificially very small) set of x-y coordinates (-1, 1), (0, 0), (1, 1) it may conjecture the solution f(x) = |x|. (For anyone who already noticed that f(x) = x^2 would also work, hold that thought.)

Here's how the program does it: It guesses random functions and eliminates those that are the worst fits. It then applies some genetic operators (such as random mutation and crossover at random points) to the remaining population of functions to introduce changes and lets them duplicate. This process continues until either a maximum amount of iterations have occurred, or until a function with perfect fitness is found. Koza found that using this process, perfect matches for quite complex data sets can be found rather quickly. (For anyone interested in the details, check out his book "Genetic Programming", chapter 7). Koza may even have done this for kicks using data from planetary motions to recreate Kepler's third law (it's not clear to me from his wording whether he actually did it; the point being I find it easy to agree that it could be done).

This process struck me as rather Popperian. I will explain what I mean with the help of Popper's "The Bucket and the Searchlight", which can be found as appendix I to his "Objective Knowledge" (to anyone familiar with Popper, this will surely all seem very familiar as well). Here Popper speaks of universal laws, specific initial conditions (which together make the explicans, ie the explanation), and explicanda (the things to be explained, usually problematic observations). He goes on to say that we conjecture the explicans, and that "the various methods of explanation all consist of a logical deduction; a deduction whose conclusion is the explicandum", ie we need to be able to derive the explicanda from the explanation. We criticize explanations by finding explicanda which cannot be derived from them even though we would have expected them to (these are problematic observations).

Here is why symbolic regression seems Popperian to me: the points we are given are the explicanda, and the function the program conjectures is the "universal law" (universal in the sense that the function always returns the same value for a given parameter, which parameter is the specific initial condition). Criticism is applied based on how many of the explicanda can be derived from the explanation.

I've been told that symbolic regression seems rather inductivist, however. (I realize that inductivism itself does not exist, so what I mean by "inductivist" is that it seems reminiscent of how inductivism was thought to work.) Presumably, this is because we seem to start with data/observations, and then try to construct a theory/function from them, whereas in the Popperian method, theories and problems usually precede observation. Let me first say I completely agree with this view in general, and that I am in no way advocating inductivism. In this particular case, however, I think this is a red herring, because the problems that symbolic regression deals with are first (admittedly broadly), "What's the explanation from which every point can be derived?", and second "Why can't these other points be derived from our best explanation so far?". The latter case can happen when given an additional explicandum, such as (2, 4), which cannot be derived from f(x) = |x|, and we need to instead conjecture g(x) = x^2 as a better explanation (my guess here is that the way humans do it, and the way you and I just did as we read this paragraph, is through some kind of symbolic regression as well). So we had to solve the problem of integrating the problematic new observation into a new, better explanation (better in the sense that g explains everything f does - it "saves the appearances" - plus it explains the problematic observation). I think this also means that Popper's concept of verisimilitude is basically used as the fitness function in the genetic program.

All this is to say that symbolic regression still seems Popperian and problem driven to me, even if at first sight it appears inductivist in the sense that it is "data driven". Hypotheses/explanations have their counterpart in functions, initial conditions have their counterpart in x-coordinates, and explicanda have their counterpart in x-y coordinates, and we are interested in deriving the explicanda from our function/theory, just like in science.

I would like to know if and where I am wrong, because if symbolic regression really does mimic Popperian epistemology (albeit only for mathematical formulae), maybe it has something to teach us about how humans think, and how this could be turned into a broader algorithm that works for more than just numbers.

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You say that the algorithm generates random functions. What is actually happening is that there is some set of functions that the algorithm has templates for and it fills in those templates with random numbers. So the algorithm is automating guesses according to some set of rules supplied by the programmer: the template.

The program isn't inductivist because induction refers either to processes for creating knowledge that are too vague to implement or to processes that are impossible:

Deduction vs Induction -- are they equally valid?

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The model here is not inductivist. Induction would be like normal, old-fashioned optimization techniques where one is guided by local features of the optimized function observed at more and more points, and you follow the lay of the terrain using your understanding of how things like this usually work, to find a peak value. Inductive observations are often local or avoidant, either based on getting more data near other data you have, or in avoiding parts of the solution space that you have already mapped. You are generally either fiddling with something that is OK, or purposely heading out to find a new vista. These techniques broadly model our conscious process of induction and they are much more directed that a lot of modern heuristics.

The point of most AI is to mimic human or other natural processes, and yes, the genetic algorithm can be looked at as mimicking science, but it is more like a post-paradigmatic era in the Kuhnian model.

It does not spend the quantity of time on an individual hypothesis that this elaboration seems to indicate. Instead, thousands of competing agents cycle around putting together bits and pieces of partially-effective-but-failed theories in search of something that works better than all of them. It also knows how to determine how well all approaches would work, and what basic form they need to take. This is like a large, established discipline being at an impasse, with a ton of old, dead theories, and fighting over how to put the existing ideas together to minimize the known anomalies. It is what Kuhn would picture as the death throes of a paradigm, and the start of a search for another.

It is not what Popper would envision in the uniform progress by making one single well-chosen and audacious choice at a time. Popper is talking about a single guess, based on a lot of evidence, and following down all of its consequences in search of a falsification. The algorithm makes thousands of guesses at a time and 'follows down' its effects on a single number.

It is also not the same kind of statistical paring-down. It never actually fully rejects any one option. It does not even keep a record of its recent failings. The same option may appear out of recombining its grandchildren over and over again, but then they will be recombined in a different way each time. For that reason, Popper's actual logic does not apply. You are not statistically increasing the odds of being right by paying less attention to falsified options. There is not a fund of falsified premises we try to avoid holding again, because checking against such a long list would defeat the efficiency of recombination.

Genetic algorithms are modeled on a different process, which is obviously genetics. The guesses are like oversimplified bacteria competing to find a long-term fit to a given environment. The closest human behavior to this model would be a sort of mass argument like the guessing side of charades where people make guesses, while those guesses are influenced by the guesses of others, at a manic pace. But the clue has been given, and all the giver can do is nod more or less emphatically at each guesser.

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The idea is that instead of specifying the function in advance we specify some data points and the programme will figure out the rest.

Nevertheless, the programmer will have specified the class of the functions from which the approximations are taken from, whether you are aware of this or not.

  • I think I understand, but what are the implications? That the program is not really creative after all? – weltschmerz Jan 18 at 20:34

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