Part-Time Bayesians: Incentives and Behavioral Heterogeneity in Belief Updating

Carlos Alós-Ferrer () and Michele Garagnani ()
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Carlos Alós-Ferrer: Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, CH-8006 Zurich, Switzerland
Michele Garagnani: Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, CH-8006 Zurich, Switzerland

Management Science, 2023, vol. 69, issue 9, 5523-5542

Abstract: Decisions in management and finance rely on information that often includes win-lose feedback (e.g., gains and losses, success and failure). Simple reinforcement then suggests to blindly repeat choices if they led to success in the past and change them otherwise, which might conflict with Bayesian updating of beliefs. We use finite mixture models and hidden Markov models, adapted from machine learning, to uncover behavioral heterogeneity in the reliance on difference behavioral rules across and within individuals in a belief-updating experiment. Most decision makers rely both on Bayesian updating and reinforcement. Paradoxically, an increase in incentives increases the reliance on reinforcement because the win-lose cues become more salient.

Keywords: Bayesian updating; incentives; reinforcement; heterogeneity; finite mixture models; machine learning (search for similar items in EconPapers)
Date: 2023
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