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International Conference on Mathematical Optimization Theory and Operations Research

MOTOR 2019: Mathematical Optimization Theory and Operations Research pp 607–619Cite as

General Limit Value for Stationary Nash Equilibrium

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11548))

Abstract

We analyze the uniform asymptotics of the equilibrium value (as a function of initial state) in the case when its payoffs are averaged with respect to a density that depends on some scale parameter and this parameter tends to zero; for example, the Cesàro and Abel averages as payoffs for the uniform and the exponential densities, respectively. We also investigate the robustness of this asymptotics of the equilibrium value with respect to the choice of distribution when its scale parameter is small enough. We establish the class of densities such that the existence of the asymptotics of the equilibrium value for some density guarantees the same asymptotics for a piecewise-continuous density; in particular, this class includes the uniform, exponential, and rational densities. By reducing the general n-person dynamic games to mappings that assigns to each payoff its corresponding equilibrium value, we gain an ability to consider dynamic games in continuous and discrete time, both in deterministic and stochastic settings.

Supported by the Russian Science Foundation (project no. 17-11-01093).

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Authors and Affiliations

  1. Krasovskii Institute of Mathematics and Mechanics, Yekaterinburg, 620990, Russia

    Dmitry Khlopin

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  1. Dmitry Khlopin
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Correspondence to Dmitry Khlopin .

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Editors and Affiliations

  1. Krasovsky Institute of Mathematics and Mechanics, Ekaterinburg, Russia

    Michael Khachay

  2. Sobolev Institute of Mathematics, Novosibirsk, Russia

    Yury Kochetov

  3. University of Florida, Gainesville, FL, USA

    Panos Pardalos

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Khlopin, D. (2019). General Limit Value for Stationary Nash Equilibrium. In: Khachay, M., Kochetov, Y., Pardalos, P. (eds) Mathematical Optimization Theory and Operations Research. MOTOR 2019. Lecture Notes in Computer Science(), vol 11548. Springer, Cham. https://doi.org/10.1007/978-3-030-22629-9_43

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  • DOI: https://doi.org/10.1007/978-3-030-22629-9_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22628-2

  • Online ISBN: 978-3-030-22629-9

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