A Case-Based Model of Probability and Pricing Judgments: Biases in Buying and Selling Uncertainty

Abstract

We integrate a case-based model of probability judgment with prospect theory to explore asset pricing under uncertainty. Research within the “heuristics and biases” tradition suggests that probability judgments respond primarily to case-specific evidence and disregard aggregate characteristics of the class to which the case belongs, resulting in predictable biases. The dual-system framework presented here distinguishes heuristic assessments of value and evidence strength from deliberative assessments that incorporate prior odds and likelihood ratios following Bayes' rule. Hypotheses are derived regarding the relative sensitivity of judged probabilities, buying prices, and selling prices to case- versus class-based evidence. We test these hypotheses using a simulated stock market in which participants can learn from experience and have incentives for accuracy. Valuation of uncertain assets is found to be largely case based even in this economic setting; however, consistent with the framework's predictions, distinct patterns of miscalibration are found for buying prices, selling prices, and probability judgments. This paper was accepted by Brad Barber, Teck Ho, and Terrance Odean, special issue editors.