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

Berenice Anne Neumann ()
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Berenice Anne Neumann: Trier University

International Journal of Game Theory, 2024, vol. 53, issue 1, No 7, 159-195

Abstract: 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.

Keywords: Mean field games; Learning in games; Finite state space; Finite action space (search for similar items in EconPapers)
JEL-codes: C70 C73 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00182-023-00866-z

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