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

卷:140

Article

关键词: PROSPECT-THEORY;    FORM GAMES;    REINFORCEMENT;    FEEDBACK;    MODELS;    REGRET;    TIME;    ROBUSTNESS;    EXPERIENCE;    IMITATION;   

DOI  :  10.1016/j.geb.2023.02.007

来源: SCIE

【 摘 要 】

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.(c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/).

【 授权许可】

Free