The Kahneman-Tversky-Slovic Thesis, Heuristics for Inference, Behavioral Economics, (Allegedly) Irrational Human Inference & All That

A persistent thesis of some cognitive scientists and cognoscenti is that ordinary people are rather lousy at factual inference and are prone to all sorts of fallacies in making judgments about factual questions, particularly factual questions that depend on statistical data. A couple of people (Kahneman & Tversky) even won a Nobel prize for questioning the inferential sagacity of plain Janes and Joes. See A. Tversky & D. Kahneman, "Judgment under uncertainty: Heuristics and biases," 185 Science 1124-1131 (1974) See also D. Kahneman, P. Slovic & A. Tversky, Judgment under uncertainty: Heuristics and biases (1982). From the very start, however, some observers were skeptical of the views of the skeptics, wondering, for example (but not only), whether the skeptics' experiments for testing inferential performance were properly designed. Now an interesting new study raises further questions about the Kahneman-Tversky-Slovic Thesis, about the hypothesis of the irrationality of human inference, and about one of the central tenets of "behavioral economics." See Thomas L. Griffiths & Joshua B. Tenenbaum, Predictions in Everyday Cognition," 17 Psychological Science No. 9 200[6?]). The abstract for the article reads this way:

Human perception and memory are often explained as optimal statistical inferences that are informed by accurate prior probabilities. In contrast, cognitive judgments are usually viewed as following error-prone heuristics that are insensitive to priors.We examined the optimality of human cognition in a more realistic context than typical laboratory studies, asking people to make predictions about the duration or extent of everyday phenomena such as human life spans and the box-office take of movies. Our results suggest that everyday cognitive judgments follow the same optimal statistical principles as perception and memory, and reveal a close correspondence between people's implicit probabilistic models and the statistics of the world.

What is perhaps so remarkable is not how badly ordinary people draw inferences about the world they inhabit but how astonishingly well just plain folks figure out what's what in the world.

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