On the convergence of closed-loop Nash equilibria to the mean field game limit

By Daniel Lacker. The Annals of Applied Probability, 2018

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This paper continues the study of the mean field game (MFG) convergence problem: In what sense do the Nash equilibria of $n$-player stochastic differential games converge to the mean field game as $n\rightarrow\infty$? Previous work on this problem took two forms. First, when the $n$-player equilibria are open-loop, compactness arguments permit a characterization of all limit points of $n$-player equilibria as weak MFG equilibria, which contain additional randomness compared to the standard (strong) equilibrium concept. On the other hand, when the $n$-player equilibria are closed-loop, the convergence to the MFG equilibrium is known only when the MFG equilibrium is unique and the associated "master equation" is solvable and sufficiently smooth. This paper adapts the compactness arguments to the closed-loop case, proving a convergence theorem that holds even when the MFG equilibrium is non-unique. Every limit point of $n$-player equilibria is shown to be the same kind of weak MFG equilibrium as in the open-loop case. Some partial results and examples are discussed for the converse question, regarding which of the weak MFG equilibria can arise as the limit of $n$-player (approximate) equilibria.

Subfields: Demography, Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/ddb1f1dfed6e7301bf1708c6c17b23101f9ae55c

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From the master equation to mean field game limit theory: a central limit theorem (2018, background): “A law of large numbers has only recently come into focus [6, 31, 30, 19], clarifying how a MFG arises as the limit of a suitable sequence of n-player games as n→∞, in the presence of both idiosyncratic and common sources of noise.”