Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/27002
Title: When Nash meets Stackelberg
Authors: Carvalho, Margarida
Dragotto, Gabriele
Feijoo, Felipe
Lodi, Andrea
Sankaranarayanan, Sriram
Keywords: Algorithmic game theory;Integer programming;Bilevel optimization;Stackelberg game
Issue Date: 22-Dec-2023
Publisher: INFORMS
Abstract: This article introduces a class of Nash games among Stackelberg players (NASPs), namely, a class of simultaneous noncooperative games where the players solve sequential Stackelberg games. Specifically, each player solves a Stackelberg game where a leader optimizes a (parametrized) linear objective function subject to linear constraints, whereas its followers solve convex quadratic problems subject to the standard optimistic assumption. Although we prove that deciding if a NASP instance admits a Nash equilibrium is generally a Σp2 -hard decision problem, we devise two exact and computationally efficient algorithms to compute and select Nash equilibria or certify that no equilibrium exists. We use NASPs to model the hierarchical interactions of international energy markets where climate change aware regulators oversee the operations of profit-driven energy producers. By combining real-world data with our models, we find that Nash equilibria provide informative, and often counterintuitive, managerial insights for market regulators.
URI: http://hdl.handle.net/11718/27002
ISSN: 15265501
Appears in Collections:Journal Articles

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