Open access
Date
2023-11-22Type
- Conference Paper
ETH Bibliography
yes
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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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000660774Publication status
publishedExternal links
Journal / series
IFAC-PapersOnLineVolume
Pages / Article No.
Publisher
ElsevierEvent
Subject
Game theory; Multi-agent systems; Data-Driven Decision MakingFunding
180545 - NCCR Automation (phase I) (SNF)
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ETH Bibliography
yes
Altmetrics