Slope Takers in Anonymous Markets
研究在瓦尔拉斯拍卖中，交易者通过统计学习估计线性残差供给斜率，并证明该学习过程收敛到线性贝叶斯纳什均衡，从而为匿名市场中的均衡选择提供依据。
We present a learning-based selection argument for Linear Bayesian Nash equilibrium in a Walrasian auction. Endowments vary stochastically; traders model residual supply as linear, estimate its slope from past trade data, and periodically update these estimates. In the standard setting with quadratic preferences, we show that this learning process converges to the unique LBN. Anonymity and statistical learning therefore support this commonly used equilibrium selection rule.