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) + ...w_n(c_n)

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, c_n is the value of the n^th cue, and w_n is the weight assigned to the n^th 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.