Orley Ashenfelter

Orley Ashenfelter appears in the book as a Princeton economist and wine lover who turns a practical question about Bordeaux into a decisive test of expertise: can a formula built from a few measurable facts predict the future better than the opinions of people who taste, study, and trade fine wine? His answer is affirmative. By translating weather data into a forecast of mature-wine prices, Ashenfelter exposes both the predictive power of simple combinations and the emotional resistance that follows when human judgment is displaced by an algorithm.

Identity and Intellectual Role

Ashenfelter’s importance lies less in a personal biography than in the experiment associated with his name. He is introduced within the book’s broader examination of the illusion of validity, overconfidence, and the recurrent finding that statistical formulas can outperform experienced decision makers. His work supplies a concrete economic case for an argument also supported by studies of political forecasting and clinical prediction: knowledge and confidence do not automatically produce reliable forecasts.

The contrast is especially sharp because Ashenfelter is not portrayed as an outsider indifferent to wine. He is both an economist and a wine lover, someone able to approach Bordeaux with personal interest while also imposing statistical discipline on the problem. That combination allows the example to challenge the assumption that genuine expertise must be expressed through an intuitive, richly textured human appraisal.

The Bordeaux Forecast

Fine Bordeaux wines take years to reach their peak quality, and bottles from the same vineyard made only twelve months apart can differ in mature value by a factor of ten or more. Because investors purchase wine in anticipation of appreciation, a dependable forecast made when the wine is young would have substantial financial value. The difficulty is that the crucial judgment concerns the future: the quality and price of a wine before it has matured.

Ashenfelter began with a conventional account of what makes a Bordeaux vintage successful. Vintage differences were generally attributed to weather during the grape-growing season. Warm, dry summers improve the quality of the grapes, while wet springs increase the quantity of the harvest without greatly damaging quality. He converted this knowledge into a statistical formula using three features: the average temperature during the summer growing season, the amount of rain at harvest time, and total rainfall during the preceding winter.

The formula predicted the price of a wine for a particular property and at a particular age. Its reach was remarkable: it could forecast prices years, and even decades, into the future. More striking still, its forecasts were more accurate than the current prices of young wines themselves. The correlation between the formula’s predictions and actual prices was above .90, a level of accuracy that made the model more than an approximate rule of thumb.

Experts, Algorithms, and Resistance

Ashenfelter’s model belongs to a wider pattern identified by Paul Meehl: in many domains, simple statistical combinations match or exceed the judgments of experts. In the book’s account of Philip Tetlock’s study, 284 professional commentators generated more than 80,000 political and economic predictions, yet performed worse than a method that assigned equal probabilities to three possible outcomes. Even experts working in regions they knew best were not significantly better than nonspecialists. Against that background, Ashenfelter’s wine formula becomes a test of whether specialized cultural knowledge can overcome the limitations of intuition.

The model also challenged economic theory. If market prices are supposed to reflect all available information, including weather, young-wine prices should already contain the relevant facts. Yet the weather-based formula predicted later prices more accurately than those early market valuations. The result suggested that information may be available without being effectively combined, and that a transparent, simple rule can extract predictive value that confident human interpretation misses.

The reaction from the European wine community revealed that the dispute was not merely technical. The formula answered an apparent prayer by improving the ability to identify wines that would later become valuable, but French wine circles responded with hostility described by The New York Times as ranging from violent to hysterical. One oenophile called the findings “ludicrous and absurd,” while another objected that the method was like judging movies without actually seeing them. These objections defended the felt reality of expertise: wine, like cinema, seemed to require direct experience rather than measurement from a distance.

Narrative Significance

Ashenfelter’s episode dramatizes the book’s distinction between predictive validity and the appearance of understanding. Experts often construct coherent stories from the information that comes readily to mind, while formulas restrict attention to a small number of features whose relationship with outcomes can be tested. Complexity may succeed in an unusual case, but simple combinations more often preserve validity; human judges frequently feel entitled to overrule a formula because they possess additional information, yet they are wrong more often than not.

The wine example also exposes the moral and emotional dimension of algorithmic judgment. People tend to prefer the natural and personal over the synthetic and mechanical, even when the alternatives are equally effective. Resistance intensifies when decisions matter: a mistake attributed to a “blind, mechanical” equation can feel more horrifying than the same mistake attributed to a person. Ashenfelter’s formula therefore does more than predict Bordeaux prices. It becomes a cultural provocation, forcing readers to confront the possibility that an impersonal rule may be less vivid, less flattering, and more dependable than the expert whose authority feels human.