Inverse problems for mean field games

TL;DR

This paper investigates inverse problems in mean field games, demonstrating how to recover unknown costs from total costs, with results on unique identifiability for specific Lagrangians and cost structures.

Contribution

It introduces new inverse problem frameworks for mean field games, establishing conditions for recovering unknown costs from total cost data.

Findings

Unique identifiability of costs when Lagrangian is kinetic energy

Simultaneous recovery of running and terminal costs under certain conditions

Extension of results to general Lagrangians

Abstract

The theory of mean field games studies the limiting behaviors of large systems where the agents interact with each other in a certain symmetric way. The running and terminal costs are critical for the agents to decide the strategies. However, in practice they are often partially known or totally unknown for the agents, while the total cost is known at the end of the game. To address this challenging issue, we propose and study several inverse problems for mean field games. When the Lagrangian is a kinetic energy, we first establish unique identifiability results, showing that one can recover either the running cost or the terminal cost from knowledge of the total cost. If the running cost is limited to the time-independent class, we can further prove that one can simultaneously recover both the running and the terminal costs. Finally, we extend the results to the setup with general Lagrangians.