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

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. To do so, the analysis moves from the relevant behavioural models suggested by psychology (i.e., weighted probabilities applied to regret and rejoice theory), and by updating probability estimations and outcome preferences according to the learning models suggested by neuroscience (i.e., adaptive learning aimed at reducing surprises), and 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 obstinate and lucky individuals are better off in the short-run (i.e., a low density of events in the reference period), but they do not learn, and this is true to a greater extent in a simple context; in contrast, reactive and unlucky individuals are worse off in the short-run, but they learn and are better off in the long-run (i.e., all individuals are equally lucky or unlucky), and this is true to a greater extent in a complex context. The expected-utility model explains real behaviours in the long-run whenever unlucky events are more likely than lucky events.