Learning About Unstable, Publicly Unobservable Payoffs

• 成果类型: Article
• 署名作者: Payzan-LeNestour, Elise; Bossaerts, Peter
• 署名单位: University of New South Wales Sydney; Utah System of Higher Education; University of Utah; University of Melbourne
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhu069
• 发表日期: 2015
• 页码: 1874
• 关键词: DECISION-MAKING; choice behavior; reinforcement; probability; INFORMATION; complexity; JUDGMENT; MODEL; neurobiology; Inattention

摘要

Neoclassical finance assumes that investors are Bayesian. In many realistic situations, Bayesian learning is challenging. Here, we consider investment opportunities that change randomly, while payoffs are observable only when invested. In a stylized version of the task, we wondered whether performance would be affected if one were to follow reinforcement learning principles instead. The answer is a definite yes. When asked to perform our task, participants overwhelmingly learned in a Bayesian way. They stopped being Bayesians, though, when not nudged into paying attention to contingency shifts. This raises an issue for financial markets: who has the incentive to nudge investors?