Predicting deliberative outcomes
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3408-3418, 2020.
We extend structured prediction to deliberative outcomes. Specifically, we learn parameterized games that can map any inputs to equilibria as the outcomes. Standard structured prediction models rely heavily on global scoring functions and are therefore unable to model individual player preferences or how they respond to others asymmetrically. Our games take as input, e.g., UN resolution to be voted on, and map such contexts to initial strategies, player utilities, and interactions. Players are then thought to repeatedly update their strategies in response to weighted aggregates of other players’ choices towards maximizing their individual utilities. The output from the game is a sample from the resulting (near) equilibrium mixed strategy profile. We characterize conditions under which players’ strategies converge to an equilibrium in such games and when the game parameters can be provably recovered from observations. Empirically, we demonstrate on two real voting datasets that our games can recover interpretable strategic interactions, and predict strategies for players in new settings.