title: Maximization, learning, and economic behavior.
abstract: The rationality assumption that underlies mainstream economic theory has proved to be a useful approximation, despite the fact that systematic violations to its predictions can be found. That is, the assumption of rational behavior is useful in understanding the ways in which many successful economic institutions function, although it is also true that actual human behavior falls systematically short of perfect rationality. We consider a possible explanation of this apparent inconsistency, suggesting that mechanisms that rest on the rationality assumption are likely to be successful when they create an environment in which the behavior they try to facilitate leads to the best payoff for all agents on average, and most of the time. Review of basic learning research suggests that, under these conditions, people quickly learn to maximize expected return. This review also shows that there are many situations in which experience does not increase maximization. In many cases, experience leads people to underweight rare events. In addition, the current paper suggests that it is convenient to distinguish between two behavioral approaches to improve economic analyses. The first, and more conventional approach among behavioral economists and psychologists interested in judgment and decision making, highlights violations of the rational model and proposes descriptive models that capture these violations. The second approach studies human learning to clarify the conditions under which people quickly learn to maximize expected return. The current review highlights one set of conditions of this type and shows how the understanding of these conditions can facilitate market design.
authors.affiliation: Industrial Engineering and Management, Technion, Haifa 32000, Israel;Warwick Business School, The University of Warwick, Coventry CV4 7AL, United Kingdom; and alroth@stanford.edu erev@technion.ac.il.
authors.forename: Ido
authors.initials: I
authors.lastname: Erev
authors.affiliation: Department of Economics, Stanford University, Stanford, CA 94305-6072 alroth@stanford.edu erev@technion.ac.il.
authors.forename: Alvin E
authors.initials: AE
authors.lastname: Roth
mesh_terms: D000223:Adaptation, Psychological / Q000502:physiology*; D002755:Choice Behavior / Q000502:physiology*; D003657:Decision Making / Q000502:physiology*; D025461:Feedback, Physiological / Q000502:physiology; D005716:Game Theory; D006801:Humans; D007858:Learning / Q000502:physiology*; D012054:Reinforcement, Psychology
keywords: contingencies of reinforcements; decisions from experience; experience–description gap; mechanism design; reinforcement learning