Learning in rent-seeking contests with payoff risk and foregone payoff information

Aidas Masiliūnas

Games and Economic Behavior, 2023, vol. 140, issue C, 50-72

Abstract: We test whether deviations from Nash equilibrium in rent-seeking contests can be explained by the slow convergence of payoff-based learning. We identify and eliminate two noise sources that slow down learning: first, opponents are changing their actions across rounds; second, payoffs are probabilistic, which reduces the correlation between expected and realized payoffs. We find that average choices are not significantly different from the risk-neutral Nash equilibrium predictions only when both noise sources are eliminated by supplying foregone payoff information and removing payoff risk. Payoff-based learning can explain these results better than alternative theories. We propose a hybrid learning model that combines reinforcement and belief learning with risk, social and other preferences, and show that it fits data well, mostly because of reinforcement learning.

Keywords: Experiment; Contests; Reinforcement learning; Foregone payoffs; Payoff risk; Nash equilibrium (search for similar items in EconPapers)
JEL-codes: C72 C91 D71 D81 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:gamebe:v:140:y:2023:i:c:p:50-72

DOI: 10.1016/j.geb.2023.02.007

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