Dynamic Information Sharing and Punishment Strategies

TL;DR

This paper investigates how rational agents share information over time in a dynamic setting, demonstrating that cooperation can be sustained through punishment strategies in infinite horizon scenarios.

Contribution

It introduces a novel framework for analyzing information sharing with asymmetric information, proving conditions for cooperation and designing equilibrium strategies.

Findings

Agents do not share information in finite horizon games.

Cooperation can be maintained in infinite horizon games with punishment strategies.

An algorithm for computing equilibrium regions is proposed.

Abstract

In this paper we study the problem of information sharing among rational self-interested agents as a dynamic game of asymmetric information. We assume that the agents imperfectly observe a Markov chain and they are called to decide whether they will share their noisy observations or not at each time instant. We utilize the notion of conditional mutual information to evaluate the information being shared among the agents. The challenges that arise due to the inter-dependence of agents' information structure and decision-making are exhibited. For the finite horizon game we prove that agents do not have incentive to share information. In contrast, we show that cooperation can be sustained in the infinite horizon case by devising appropriate punishment strategies which are defined over the agents' beliefs on the system state. We show that these strategies are closed under the best-response mapping and that cooperation can be the optimal choice in some subsets of the state belief simplex. We characterize these equilibrium regions, prove uniqueness of a maximal equilibrium region and devise an algorithm for its approximate computation.