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A myopic adjustment process for mean field games with finite state and action space

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  • Berenice Anne Neumann

    (Trier University)

Abstract

In this paper, we introduce a natural learning rule for mean field games with finite state and action space, the so-called myopic adjustment process. The main motivation for these considerations is the complexity of the computations necessary to determine dynamic mean field equilibria, which makes it seem questionable whether agents are indeed able to play these equilibria. We prove that the myopic adjustment process converges locally towards strict stationary equilibria under rather broad conditions. Moreover, we also obtain a global convergence result under stronger, yet intuitive conditions.

Suggested Citation

  • Berenice Anne Neumann, 2024. "A myopic adjustment process for mean field games with finite state and action space," International Journal of Game Theory, Springer;Game Theory Society, vol. 53(1), pages 159-195, March.
    • Handle: RePEc:spr:jogath:v:53:y:2024:i:1:d:10.1007_s00182-023-00866-z
    DOI: 10.1007/s00182-023-00866-z
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    More about this item

    Keywords

    Mean field games; Learning in games; Finite state space; Finite action space;
    All these keywords.

    JEL classification:

    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General

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