Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/307524 
Erscheinungsjahr: 
2023
Quellenangabe: 
[Journal:] International Journal of Game Theory [ISSN:] 1432-1270 [Volume:] 53 [Issue:] 1 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2023 [Pages:] 159-195
Verlag: 
Springer, Berlin, Heidelberg
Zusammenfassung: 
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.
Schlagwörter: 
Mean field games
Learning in games
Finite state space
Finite action space
JEL: 
C73
C70
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Dokumentart: 
Article
Dokumentversion: 
Published Version

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