Sequential decomposition of repeated games with asymmetric information and dependent states

TL;DR

This paper introduces a linear-time sequential decomposition method to compute structured perfect Bayesian equilibria in repeated games with asymmetric information, correlated states, and signaling behavior, simplifying analysis of complex strategic interactions.

Contribution

It develops a novel linear-time methodology for computing structured perfect Bayesian equilibria in complex repeated games with private types and signaling.

Findings

The methodology computes equilibria efficiently in linear time.

Players' actions often reveal private information, leading to signaling behavior.

The approach simplifies the analysis of complex dynamic games with asymmetric information.

Abstract

We consider a finite horizon repeated game with $N$ selfish players who observe their types privately and take actions, which are publicly observed. Their actions and types jointly determine their instantaneous rewards. In each period, players jointly observe actions of each other with delay 1, and private observations of the state of the system, and get an instantaneous reward which is a function of the state and everyone's actions. The players' types are static and are potentially correlated among players. An appropriate notion of equilibrium for such games is Perfect Bayesian Equilibrium (PBE) which consists of a strategy and a belief profile of the players which is coupled across time and as a result, the complexity of finding such equilibria grows double-exponentially in time. We present a sequential decomposition methodology to compute \emph{structured perfect Bayesian equilibria} (SPBE) of this game, introduced in~\cite{VaAn15arxiv}, where equilibrium policy of a player is a function of a common belief and a private state. This methodology computes SPBE in linear time. In general, the SPBE of the game problem exhibit \textit{signaling} behavior, i.e. players' actions reveal part of their private information that is payoff relevant to other players.