Data-Driven Behaviour Estimation in Parametric Games
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Date
2023-11-22
Publication Type
Conference Paper
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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.
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Publication status
published
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Book title
Journal / series
Volume
56 (2)
Pages / Article No.
9330 - 9335
Publisher
Elsevier
Event
22nd IFAC World Congress 2023
Edition / version
Methods
Software
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Date collected
Date created
Subject
Game theory; Multi-agent systems; Data-Driven Decision Making
Organisational unit
Notes
Funding
180545 - NCCR Automation (phase I) (SNF)