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Spotlight Poster

Efficient Inverse Multiagent Learning

Denizalp Goktas · Amy Greenwald · Sadie Zhao · Alec Koppel · Sumitra Ganesh

Halle B #208
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Tue 7 May 1:45 a.m. PDT — 3:45 a.m. PDT


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.

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