Learning and dynamic choices under uncertainty: From weighted regret and rejoice to expected utility

摘要

This paper identifies the globally stable conditions under which an individual facing the same choice in many subsequent times learns to behave as prescribed by the expected-utility model. The analysis moves from the relevant behavioural models suggested by psychology, by updating probability estimations and outcome preferences according to the learning models suggested by neuroscience, in a manner analogous to Bayesian updating. The search context is derived from experimental economics, whereas the learning framework is borrowed from theoretical economics. Analytical results show that the expected-utility model explains real behaviours in the long run whenever bad events are more likely than good events.