Learning in Rent-Seeking Contests with Payoff Risk and Foregone Payoff Information

Aidas Masiliūnas ()
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Aidas Masiliūnas: Department of Economics, University of Sheffield, 9 Mappin Street, Sheffield S1 4DT, UK.

No 2023002, Working Papers from The University of Sheffield, Department of Economics

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)
Pages: 75 pages
Date: 2023-01
New Economics Papers: this item is included in nep-exp and nep-gth
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Persistent link: https://EconPapers.repec.org/RePEc:shf:wpaper:2023002

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