A Bayesian model of interpersonal choice was formulated that permits an economic agent to revise prior estimates of detection probability based on personal criminal experience. Within this model, a crime choice followed by no detection leads an optimizing agent to commit another crime in the next period. Conversely, if a decision is made not to commit a crime, a crime choice remains suboptimal for all subsequent periods. Imperfect information about the probability of detection implies that a crime choice has informational value for assessing probability of detection. As a result, deterrence requires a higher fine in a dynamic model of Bayesian learning than in a static model. Laboratory results with 30 students were inconsistent with theoretical stopping and continuation decision rules for crime choice. Subjects failed to update the detection probability according to Bayes rule. However, the deterrence effects of fines and an increase in prior mean detection probability were highly significant. 2 figures, 4 tables, 6 notes, and 17 references.