Variance strikes back: sub-game--perfect Nash equilibria in time-inconsistent $N$-player games, and their mean-field sequel

TL;DR

This paper develops a novel framework for analyzing time-inconsistent, non-Markovian N-player games and their mean-field limits, characterizing equilibria through coupled backward stochastic differential equations.

Contribution

It introduces a two-layer game-theoretic approach to characterize sub-game--perfect Nash equilibria in complex time-inconsistent games and proves their convergence to mean-field solutions.

Findings

Characterization of equilibria via coupled backward stochastic differential equations

Analysis of mean-field game and its equilibria using McKean-Vlasov equations

Proof of convergence of N-player equilibria to mean-field limits

Abstract

We investigate a time-inconsistent, non-Markovian finite-player game in continuous time, where each player's objective functional depends non-linearly on the expected value of the state process. As a result, the classical Bellman optimality principle no longer applies. To address this, we adopt a two-layer game-theoretic framework and seek sub-game--perfect Nash equilibria both at the intra-personal level, which accounts for time inconsistency, and at the inter-personal level, which captures strategic interactions among players. We first characterise sub-game--perfect Nash equilibria and the corresponding value processes of all players through a system of coupled backward stochastic differential equations. We then analyse the mean-field counterpart and its sub-game--perfect mean-field equilibria, described by a system of McKean-Vlasov backward stochastic differential equations. Building on this representation, we finally prove the convergence of sub-game--perfect Nash equilibria and their corresponding value processes in the $N$-player game to their mean-field counterparts.