Learning Dynamics in Games with Stochastic Perturbations
研究了一种广义虚拟博弈，其中代理人的选择受到不完全信息、收益波动和随机颤抖的扰动，形成非平稳马尔可夫过程。利用随机逼近理论，证明在2×2博弈中，该过程几乎必然收敛到稳定纳什均衡附近，推广了Fudenberg和Kreps的结果。
Consider a generalization of fictitious play in which agents′ choices are perturbed by incomplete information about what the other side has done, variability in their payoffs, and unexplained trembles. These perturbed best reply dynamics define a nonstationary Markov process on an infinite state space. It is shown, using results from stochastic approximation theory, that for 2 × 2 games it converges almost surely to a point that lies close to a stable Nash equilibrium, whether pure or mixed. This generalizes a result of Fudenherg and Kreps, who demonstrate convergence when the game has a unique mixed equilibrium. Journal of Economic Literature Classification Numbers: 000, 000, 000.