Jan 18, 2011
A parole board considers the release of a prisoner: Will he be violent again? A hiring officer considers a job candidate: Will she be a valuable asset to the company? A young couple considers marriage: Will they have a happy marriage?
The cached wisdom for making such high-stakes predictions is to have experts gather as much evidence as possible, weigh this evidence, and make a judgment. But 60 years of research has shown that in hundreds of cases, a simple formula called a statistical prediction rule (SPR) makes better predictions than leading experts do. Or, more exactly:
When based on the same evidence, the predictions of SPRs are at least as reliable as, and are typically more reliable than, the predictions of human experts for problems of social prediction.1
For example, one SPR developed in 1995 predicts the price of mature Bordeaux red wines at auction better than expert wine tasters do. Reaction from the wine-tasting industry to such wine-predicting SPRs has been "somewhere between violent and hysterical."
How does the SPR work? This particular SPR is called a proper linear model, which has the form:
P = w1(c1) + w2(c2) + w3(c3) + ...wn(cn)
The model calculates the summed result P, which aims to predict a target property such as wine price, on the basis of a series of cues. Above, cn is the value of the nth cue, and wn is the weight assigned to the nth cue.2
In the wine-predicting SPR, c1 reflects the age of the vintage, and other cues reflect relevant climatic features where the grapes were grown. The weights for the cues were assigned on the basis of a comparison of these cues to a large set of data on past market prices for mature Bordeaux wines.3
There are other ways to construct SPRs, but rather than survey these details, I will instead survey the incredible success of SPRs.
And that is barely scratching the surface.
If this is not amazing enough, consider the fact that even when experts are given the results of SPRs, they still can't outperform those SPRs (Leli & Filskov 1985; Goldberg 1968).
So why aren't SPRs in use everywhere? Probably, suggest Bishop & Trout, we deny or ignore the success of SPRs because of deep-seated cognitive biases, such as overconfidence in our own judgments. But if these SPRs work as well as or better than human judgments, shouldn't we use them?
Robyn Dawes (2002) drew out the normative implications of such studies:
If a well-validated SPR that is superior to professional judgment exists in a relevant decision making context, professionals should use it, totally absenting themselves from the prediction.
Sometimes, being rational is easy. When there exists a reliable statistical prediction rule for the problem you're considering, you need not waste your brain power trying to make a careful judgment. Just take an outside view and use the damn SPR.4
1 Bishop & Trout, Epistemology and the Psychology of Human Judgment, p. 27. The definitive case for this claim is made in a 1996 study by Grove & Meehl that surveyed 136 studies yielding 617 comparisons between the judgments of human experts and SPRs (in which humans and SPRs made predictions about the same cases and the SPRs never had more information than the humans). Grove & Meehl found that of the 136 studies, 64 favored the SPR, 64 showed roughly equal accuracy, and 8 favored human judgment. Since these last 8 studies "do not form a pocket of predictive excellent in which [experts] could profitably specialize," Grove and Meehl speculated that these 8 outliers may be due to random sampling error.
2 Readers of Less Wrong may recognize SPRs as a relatively simple type of expert system.
3 But, see Anatoly_Vorobey's fine objections.
4 There are occasional exceptions, usually referred to as "broken leg" cases. Suppose an SPR reliably predicts an individual's movie attendance, but then you learn he has a broken leg. In this case it may be wise to abandon the SPR. The problem is that there is no general rule for when experts should abandon the SPR. When they are allowed to do so, they abandon the SPR far too frequently, and thus would have been better off sticking strictly to the SPR, even for legitimate "broken leg" instances (Goldberg 1968; Sawyer 1966; Leli and Filskov 1984).
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