Young Wu

Brandon Han, Young Wu, Shiyun Cheng et al.

We introduce the game changer problem, where an external designer modifies a game's reward matrix to make a target pure action profile the unique equilibrium, subject to the constraint that all entries of the reward matrix come from a finite set. We give simple feasibility characterizations for two-player zero-sum games and general-sum games, and the discrete reward structure yields exact optimality and enables efficient dynamic programming algorithms, providing a sharper alternative to prior continuous reward redesign formulations based on linear programming.

Jason Jisen Li, Young Wu, Yancheng Zhu et al.

The mean mechanism is known to be non-incentive-compatible, namely, rational players are incentivized to misreport their values. Despite this game-theoretic issue, the mean mechanism is prevalent in practice due to its other desirable properties. We give a full characterization of pure Nash equilibria--how the players will misreport--for the affine mechanism, of which the mean is a special case. Furthermore, we characterize both complete-information and Bayesian games under the affine mechanism. Our results highlight the inevitability of extreme exaggeration in such games.