Data-Driven Behaviour Estimation in Parametric Games

TL;DR

This paper introduces a data-driven method for estimating agents' utility functions in parametric games, capable of handling various observed behaviors and validated on real market data.

Contribution

It presents a unified, efficient inference technique for utility estimation from diverse behavioral observations in strategic games.

Findings

Method accurately estimates utilities from observed data.

Validated on Coca-Cola and Pepsi market data.

Efficiently finds parameters that best explain observed behaviors.

Abstract

A central question in multi-agent strategic games deals with learning the underlying utilities driving the agents' behaviour. Motivated by the increasing availability of large data-sets, we develop an unifying data-driven technique to estimate agents' utility functions from their observed behaviour, irrespective of whether the observations correspond to equilibrium configurations or to temporal sequences of action profiles. Under standard assumptions on the parametrization of the utilities, the proposed inference method is computationally efficient and finds all the parameters that rationalize the observed behaviour best. We numerically validate our theoretical findings on the market share estimation problem under advertising competition, using historical data from the Coca-Cola Company and Pepsi Inc. duopoly.