Mean field games without rational expectations

Ben Moll and Lenya Ryzhik

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Mean Field Game (MFG) models implicitly assume “rational expectations”, meaning that the heterogeneous agents being modeled correctly know all relevant transition probabilities for the complex system they inhabit. When there is common noise, it becomes necessary to solve the “Master equation”, in which the infinite-dimensional density of agents is a state variable. The rational expectations assumption and the implication that agents solve Master equations are unrealistic in many applications. We show how to instead formulate MFGs with non-rational expectations. Departing from rational expectations is particularly relevant in “MFGs with a low-dimensional coupling”, i.e. MFGs in which agents’ running reward function depends on the density only through low-dimensional functionals of this density. This happens, for example, in most macroeconomics MFGs in which these low-dimensional functionals have the interpretation of “equilibrium prices”. In MFGs with a low-dimensional coupling, departing from rational expectations allows for completely sidestepping the Master equation and for instead solving much simpler finite-dimensional HJB equations. We introduce an adaptive learning model as a particular example of non-rational expectations and discuss its properties.

Keywords: mean filed games; rational expectation; master equation; adaptive learning (search for similar items in EconPapers)
JEL-codes: J1 (search for similar items in EconPapers)
Date: 2026-03-05
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Published in Communications in Contemporary Mathematics, 5, March, 2026. ISSN: 0219-1997

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