Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/307524 
Year of Publication: 
2023
Citation: 
[Journal:] International Journal of Game Theory [ISSN:] 1432-1270 [Volume:] 53 [Issue:] 1 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2023 [Pages:] 159-195
Publisher: 
Springer, Berlin, Heidelberg
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.
Subjects: 
Mean field games
Learning in games
Finite state space
Finite action space
JEL: 
C73
C70
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
Document Version: 
Published Version

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