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

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