Learning in rent-seeking contests with payoff risk and foregone payoff information
研究租金寻求竞赛中偏离纳什均衡是否源于收益学习的缓慢收敛，通过消除对手变动和收益概率两种噪声，发现仅在提供未实现收益信息并去除收益风险时，平均选择才接近风险中性纳什均衡预测，并提出混合学习模型拟合数据。
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.