Friday, March 03, 2006

Adversaries

David Molnar has an excellent article on the hidden assumption behind cryptography. In listing this assumptions he also hits upon some of the core contributions of theoryCS.

The first is a robust formal definition of efficiency, or tractability. In fact, the idea of a "reasonable" computing model works very well in cryptography, where ever since the days of RSA, the game has been to prevent attacks by a limited adversary, rather than an allpowerful one (which would be much harder).

Which brings up a second core contribution; the idea of worst-case performance, and an adversary that can do anything. One thing that I've often noticed when working with people in other areas is that they are surprised when I can prove that such-and-such technique is optimal, or approximately optimal, or efficient. The power of worst-case analysis is in the ability to make such sweeping guarantees, and it's so ingrained into our psyche that it can be surprising to have some one else note this explicitly.

But worst-case analysis has its limits, and David alludes to this with his question on why anyone would ever assume that an adversary was not worst-case:
This assumption makes fields that assume some kind of distribution on inputs or on errors seem extremely strange. Electrical engineering does this in the context of coding theory, and it's also common in statistical learning theory. These fields have enormous practical impact and relevance, which I acknowledge and appreciate. I just have a hard time with the shock of "well, why do we believe errors must be distributed like that?"
In fields like game theory and cryptography, your adversary is assumed malicious; this is often true, especially code hackers, and is a good modelling assumption (think "selfish agents" and the like). Theorems proved here are "pure"; you start off with a set of assumptions about your world, and the results have guarantees within this world.

But when you start getting into areas like machine learning, data mining, and data modelling in general, your "adversary" is Nature, or some unknown indifferent process that generates data. Time and again we see what we would formally think of as heuristics work very well "in practice" and often the explanation is that the algorithm is able to exploit some property of the data (something we can then prove after the fact). Whether it's expectation-maximization, its cousin k-means, ICP, or even the simplex method, we see the same story over and over again.

Worst-case analysis, applied straight up, does not help us in these domains. Worst-case analysis often provides bounds that are weak, and algorithms that are ineffective on many kinds of data sets. To use formal methods, you need to dig deeper into a problem, and try to understand the data sources themselves, and often you come up with hidden structure that can be exploited formally.

I can answer David's question by going all the way back to Gauss. According to Leonard Mlodinow, it was when Gauss was overseeing a surveying project that he discovered that the errors in measurement followed a bell curve (hence, Gaussian distributions). The central limit theorem tells us that all kinds of i.i.d error will end up looking Gaussian after a while. Many natural processes also generate power law or log normal distributions. The point is that if you're dealing with nature, then more often than not you're dealing with data that is stochastic rather than adversarial, and modelling it as such gives you a better handle on algorithm design.
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