Premium
Learning and dynamic choices under uncertainty: From weighted regret and rejoice to expected utility
Author(s) -
Zagonari Fabio
Publication year - 2019
Publication title -
managerial and decision economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.288
H-Index - 51
eISSN - 1099-1468
pISSN - 0143-6570
DOI - 10.1002/mde.3002
Subject(s) - regret , outcome (game theory) , expected utility hypothesis , context (archaeology) , economics , mathematical economics , econometrics , bayesian probability , bayesian inference , computer science , artificial intelligence , machine learning , paleontology , biology
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.