Part of International Conference on Representation Learning 2024 (ICLR 2024) Conference
Denizalp Goktas, Amy Greenwald, Sadie Zhao, Alec Koppel, Sumitra Ganesh
In this paper, we study inverse game theory (resp. inverse multiagent learning) inwhich the goal is to find parameters of a game’s payoff functions for which theexpected (resp. sampled) behavior is an equilibrium. We formulate these problemsas generative-adversarial (i.e., min-max) optimization problems, which we developpolynomial-time algorithms to solve, the former of which relies on an exact first-order oracle, and the latter, a stochastic one. We extend our approach to solveinverse multiagent simulacral learning in polynomial time and number of samples.In these problems, we seek a simulacrum, meaning parameters and an associatedequilibrium that replicate the given observations in expectation. We find that ourapproach outperforms the widely-used ARIMA method in predicting prices inSpanish electricity markets based on time-series data.