We propose to continue our theoretical and experimental research studying Quantal Response Equilibria (QRE), a statistical model of equilibrium behavior in games. The key feature of the equilibrium is that individuals do not always play best responses to the strategies of their opponents, but play better strategies with higher probability than worse strategies. Here we propose to apply QRE to three different areas of application: auctions, voting, and information aggregation. Several different series of experiments are planned, including large-scale experiments (100+ subjects) to test theoretical predictions about information aggregation and strategic voting in large groups. On the theoretical side, we propose to extend the model to dynamic settings to incorporate learning and repeated play, and to generalize the error structure to allow for correlation across moves. There is also a computational part of the proposal. Specifically, we plan to extend our current algorithms for computing QRE in several ways, to allow more efficient computation of equilibria in certain kinds of larger games and to allow for estimation in models with more general error structures.
