Efficient Inverse Multiagent Learning
Authors: Denizalp Goktas,Amy Greenwald,Sadie Zhao,Alec Koppel,Sumitra Ganesh
Presentation: ICLR 2024,Spotlight

Abstract: In this paper, we study inverse game theory (resp. inverse multiagent learning) in
which the goal is to find parameters of a game’s payoff functions for which the
expected (resp. sampled) behavior is an equilibrium. We formulate these problems
as generative-adversarial (i.e., min-max) optimization problems, which we develop
polynomial-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 solve
inverse multiagent simulacral learning in polynomial time and number of samples.
In these problems, we seek a simulacrum, meaning parameters and an associated
equilibrium that replicate the given observations in expectation. We find that our
approach outperforms the widely-used ARIMA method in predicting prices in
Spanish electricity markets based on time-series data.