Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/334871 
Erscheinungsjahr: 
2025
Quellenangabe: 
[Journal:] Optimization Letters [ISSN:] 1862-4480 [Volume:] 20 [Issue:] 1 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2025 [Pages:] 231-255
Verlag: 
Springer, Berlin, Heidelberg
Zusammenfassung: 
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.
Schlagwörter: 
Integer programming games
Algorithmic game theory
Integer programming
Piecewise linear approximations
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