Mean-Field Type Games between Two Players Driven by Backward Stochastic Differential Equations

Alexander Aurell

doi:10.20944/preprints201809.0083.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 3 September 2018 / Approved: 5 September 2018 / Online: 5 September 2018 (04:59:41 CEST)

In this paper, mean-field type games between two players with backward stochastic dynamics are defined and studied. They make up a class of non-zero-sum differential games where the players' state dynamics solve backward stochastic differential equations (BSDEs) that depend on the marginal distributions of player states. Players try to minimize their individual cost functionals, also depending on the marginal state distributions. Under some regularity conditions, we derive necessary and sufficient conditions for existence of Nash equilibria. Player behavior is illustrated by numerical examples, and is compared to a centrally planned solution where the social cost, the sum of player costs, is minimized. The inefficiency of a Nash equilibrium, compared to socially optimal behavior, is quantified by the so-called price of anarchy. Numerical simulations of the price of anarchy indicate how the improvement in social cost achievable by a central planner depends on problem parameters.

mean-field type game; non-zero-sum differential game; cooperative game; backward stochastic differential equations; linear-quadratic stochastic control; social cost; price of anarchy

MATHEMATICS & COMPUTER SCIENCE, Numerical Analysis & Optimization

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Mean-Field Type Games between Two Players Driven by Backward Stochastic Differential Equations

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