糖心TV

Experience-Based Belief Distortion: When Experience is Information-Free

People overweight experience relative to descriptive and observational information, in games, portfolio choice, etc. However, little is known about how beliefs are biased by experienced payoffs. This paper offers a simple model of experience-based belief distortion, where the decision maker with good (bad) experience misinterprets bad (good) signals, and overestimates future good (bad) states. Two experiments were conducted to test the model predictions. The first experiment asked subjects to predict future prices after viewing some stock price charts, and experienced gain/loss was exogenously assigned. The second experiment further provided information about the outcome-generating processes to allow for Bayesian updating as a benchmark. Subjects who gained reported significantly more optimistic guesses than those who lost after viewing the same sequence; in belief updating, they overweighted new evidence in favor of the signal from which they gained.