An Approach to Stochastic Dynamic Games with Asymmetric Information and Hidden Actions

TL;DR

This paper develops a methodology for finding Bayesian Nash Equilibrium strategies in complex stochastic dynamic games with asymmetric information, private actions, and noisy observations, applicable even without common observations.

Contribution

It introduces a novel approach to compute BNE strategies in asymmetric information dynamic games, including cases with no common observations.

Findings

Methodology successfully computes BNE strategies in the proposed game class.

Existence of BNE strategies demonstrated in a specific game instance.

Approach extends to scenarios without common observations.

Abstract

We consider in discrete time, a general class of sequential stochastic dynamic games with asymmetric information with the following features. The underlying system has Markovian dynamics controlled by the agents' joint actions. Each agent's instantaneous utility depends on the current system state and the agents' joint actions. At each time instant each agent makes a private noisy observation of the current system state and the agents' actions in the previous time instant. In addition, at each time instant all agents have a common noisy observation of the current system state and their actions in the previous time instant. Each agent's actions are part of his private information. The objective is to determine Bayesian Nash Equilibrium (BNE) strategy profiles that are based on a compressed version of the agents' information and can be sequentially computed; such BNE strategy profiles may not always exist. We present an approach/methodology that achieves the above-stated objective, along with an instance of a game where BNE strategy profiles with the above-mentioned characteristics exist. We show that the methodology also works for the case where the agents have no common observations.