Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/334871 
Year of Publication: 
2025
Citation: 
[Journal:] Optimization Letters [ISSN:] 1862-4480 [Volume:] 20 [Issue:] 1 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2025 [Pages:] 231-255
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
We propose a framework to compute approximate Nash equilibria in integer programming games with nonlinear payoffs, i.e. , simultaneous and non-cooperative games where each player solves a parametrized mixed-integer nonlinear program. We prove that using absolute approximations of the players’ objective functions and then computing its Nash equilibria is equivalent to computing approximate Nash equilibria where the approximation factor is doubled. In practice, we propose an algorithm to approximate the players’ objective functions via piecewise linear approximations. The numerical experiments on a cybersecurity investment game combined with a detailed analysis of the results show the computational effectiveness of our approach.
Subjects: 
Integer programming games
Algorithmic game theory
Integer programming
Piecewise linear approximations
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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