Are People Bayesian? Uncovering Behavioral Strategies
提出一种结合最大似然估计和隐分类的方法，从实验数据中找出最能解释受试者行为的决策规则集合，发现贝叶斯规则、代表性启发式（忽略先验）和保守主义（高估先验）是主要规则。
Abstract Economists and psychologists have recently been developing new theories of decision making under uncertainty that can accommodate the observed violations of standard statistical decision theoretic axioms by experimental subjects. We propose a procedure that finds a collection of decision rules that best explain the behavior of experimental subjects. The procedure is a combination of maximum likelihood estimation of the rules together with an implicit classification of subjects to the various rules and a penalty for having too many rules. We apply our procedure to data on probabilistic updating by subjects in four different universities. We get remarkably robust results showing that the most important rules used by the subjects (in order of importance) are Bayes's rule, a representativeness rule (ignoring the prior), and, to a lesser extent, conservatism (overweighting the prior).