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March 01, 2006

The scenic view

In my formal training in physics and computer science, I never did get much exposure to statistics and probability theory, yet I have found myself consistently using them in my research (partially on account of the fact that I deal with real data quite often). What little formal exposure I did receive was always in some specific context and never focused on probability as a topic itself (e.g., statistical mechanics, which could hardly be called a good introduction to probability theory). Generally, my training played-out in the crisp and clean neighborhoods of logical reasoning, algebra and calculus, with the occasional day-trip to the ghetto of probability. David Mumford, a Professor of Mathematics at Brown University, opines about ongoing spread of that ghetto throughout the rest science and mathematics, i.e., how probability theory deserves a respect at least equal to that of abstract algebra, in a piece from 1999 on The Dawning of the Age of Stochasticity. From the abstract,

For over two millennia, Aristotle's logic has rules over the thinking of western intellectuals. All precise theories, all scientific models, even models of the process of thinking itself, have in principle conformed to the straight-jacket of logic. But from its shady beginnings devising gambling strategies and counting corpses in medieval London, probability theory and statistical inference now emerge as better foundations for scientific models ... [and] even the foundations of mathematics itself.

It may sound it, but I doubt that Mumford is actually overstating his case here, especially given the deep connection between probability theory, quantum mechanics (c.f. the recent counter-intuitive result on quantum interrogation) and complexity theory.

A neighborhood I'm more familiar with is that of special functions; things like the Gamma distribution, the Riemann Zeta function (a personal favorite), and the Airy functions. Sadly, these familiar friends show up very rarely in the neighborhood of traditional computer science, but instead hang out in the district of mathematical modeling. Robert Batterman, a Professor of Philosophy at Ohio State University, writes about why exactly these functions are so interesting in On the Specialness of Special Functions (The Nonrandom Effusions of the Divine Mathematician).

From the point of view presented here, the shared mathematical features that serve to unify the special functions - the universal form of their asymptotic expansions - depends upon certain features of the world.

(Emphasis his.) That is, the physical world itself, by presenting a patterned appearance, must be governed by a self-consistent set of rules that create that pattern. In mathematical modeling, these rules are best represented by asymptotic analysis and, you guessed it, special functions, that reveal the universal structure of reality in their asymptotic behavior. Certainly this approach to modeling has been hugely successful, and remains so in current research (including my own).

My current digs, however, are located in the small nexus that butts up against these neighborhoods and those in computer science. Scott Aaronson, who occupies an equivalent juncture between computer science and physics, has written several highly readable and extremely interesting pieces on the commonalities he sees in his respective locale. I've found them to be a particularly valuable way to see beyond the unfortunately shallow exploration of computational complexity that is given in most graduate-level introductory classes.

In NP-complete Problems and Physical Reality Aaronson looks out of his East-facing window toward physics for hints about ways to solve NP-complete problems by using physical processes (e.g., simulated annealing). That is, can physical reality efficiently solve instances of "hard" problems? Although he concludes that the evidence is not promising, he points to a fundamental connection between physics and computer science.

Then turning to look out his West-facing window towards computer science, he asks Is P Versus NP Formally Indepenent?, where he considers formal logic systems and the implications of Godel's Incompleteness Theorem for the likelihood of resolving the P versus NP question. It's stealing his thunder a little, but the most quotable line comes from his conclusion:

So I'll state, as one of the few definite conclusions of this survey, that P \not= NP is either true or false. It's one or the other. But we may not be able to prove which way it goes, and we may not be able to prove that we can't prove it.

There's a little nagging question that some researchers are only just beginning to explore, which is, are certain laws of physics formally independent? I'm not even entirely sure what that means, but it's an interesting kind of question to ponder on a lazy Sunday afternoon.

There's something else embedded in these topics, though. Almost all of the current work on complexity theory is logic-oriented, essentially because it was born of the logic and formal mathematics of the first half of the 20th century. But, if we believe Mumford's claim that statistical inference (and in particular Bayesian inference) will invade all of science, I wonder what insights it can give us about solving hard problems, and perhaps why they're hard to begin with.

I'm aware of only anecdotal evidence of such benefits, in the form of the Survey Propagation Algorithm and its success at solving hard k-SAT formulas. The insights from the physicists' non-rigorous results has even helped improve our rigorous understanding of why problems like random k-SAT undergo a phase transition from mostly easy to mostly hard. (The intuition is, in short, that as the density of constraints increases, the space of valid solutions fragments into many disconnected regions.) Perhaps there's more being done here than I know of, but it seems that a theory of inferential algorithms as they apply to complexity theory (I'm not even sure what that means, precisely; perhaps it doesn't differ significantly from PPT algorithms) might teach us something fundamental about computation.

posted March 1, 2006 02:32 PM in Interdisciplinarity | permalink