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• W3082306865 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." @default.
• W3082306865 created "2020-09-08" @default.
• W3082306865 creator A5042310390 @default.
• W3082306865 date "2023-08-01" @default.
• W3082306865 modified "2023-09-23" @default.
• W3082306865 title "A myopic adjustment process for mean field games with finite state and action space" @default.
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• W3082306865 doi "https://doi.org/10.1007/s00182-023-00866-z" @default.
• W3082306865 hasPublicationYear "2023" @default.
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