MANAGEMENT SCIENCE Vol. 58, No. 1, January 2012, pp. 159–178 ISSN 0025-1909 (print)  ISSN 1526-5501 (online) http://dx.doi.org/10.1287/mnsc.1110.1429 © 2012 INFORMS A Case-Based Model of Probability and Pricing Judgments: Biases in Buying and Selling Uncertainty Lyle A. Brenner Warrington College of Business Administration, University of Florida, Gainesville, Florida 32611, lbrenner@ufl.edu Dale W. Griffin Sauder School of Business, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada, dale.griffin@sauder.ubc.ca Derek J. Koehler Department of Psychology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada, dkoehler@waterloo.ca W e 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. Key words : probability; finance; asset pricing; decision analysis; prospect theory; value function; forecasting History : Received July 15, 2010; accepted May 31, 2011, by Brad Barber, Teck Ho, and Terrance Odean, special issue editors. Published online in Articles in Advance October 28, 2011. Introduction Judgments of probability offered by both experts and laypeople exhibit systematic biases, whether those biases are measured in terms of departures from normative standards or from actual outcomes (Tversky and Kahneman 1974, Griffin and Tversky 1992, Koehler et al. 2002). These biases in probabil- ity judgments have been taken as signature evidence for the operation of heuristic assessment processes. Much influential research in behavioral finance has attempted to tie these probability judgment biases to anomalies observed in the financial markets (e.g., Barberis et al. 1998, Daniel et al. 2001, Odean 1999). However, the question of whether biases found in judgments of probability are moderated in economic valuation tasks, such as the subjective evaluation of uncertain assets (asset pricing), has received little direct attention. Most related research has focused on whether biases found in probability judgment also exist—in a broad qualitative sense—in economic val- uation tasks (e.g., Bloomfield 1996, Camerer 1992, Fox et al. 1996, Fox and Tversky 1998, Kirchler and Maciejovsky 2002). Unlike most behavioral research on asset pricing, the research reported here uses experimental meth- ods to examine the causes and nature of biases in pricing and probability judgment. We first develop a psychological model of judgment that encompasses both probability judgment and asset pricing as sub- jective measures of uncertainty and use this model to develop hypotheses about the pattern of biases expected to be found for these measures. We exam- ine asset prices set in a simulated stock market set- ting where participants learn all cues and probability relationships through feedback and experience, and we evaluate how observed biases in probability judg- ment change when uncertainty is measured through the more familiar measure of pricing. Our model is grounded in an influential conceptu- alization of judgment that distinguishes between two reasoning systems (Kahneman and Frederick 2002, Sloman 1996, Stanovich and West 2000; for a recent 159