Information Preferences of Individual Agents in Linear-Quadratic-Gaussian Network Games

TL;DR

This paper analyzes how information disclosure affects individual agent payoffs in linear-quadratic-Gaussian network games, revealing conditions where full information benefits all agents and highlighting the strategic differences for central versus peripheral agents.

Contribution

It provides new conditions based on network structure and competition strength under which agents benefit from full information disclosure in LQG network games.

Findings

All agents benefit from information disclosure in star networks with symmetric, submodular or supermodular payoffs.

Central agents generally benefit more than peripheral agents from full information, unless competition is strong.

Central agents can be worse off in some cases under strong competition, leading risk-averse agents to prefer less information.

Abstract

We consider linear-quadratic-Gaussian (LQG) network games in which agents have quadratic payoffs that depend on their individual and neighbors' actions, and an unknown payoff-relevant state. An information designer determines the fidelity of information revealed to the agents about the payoff state to maximize the social welfare. Prior results show that full information disclosure is optimal under certain assumptions on the payoffs, i.e., it is beneficial for the average individual. In this paper, we provide conditions based on the strength of the dependence of payoffs on neighbors' actions, i.e., competition, under which a rational agent is expected to benefit, i.e., receive higher payoffs, from full information disclosure. We find that all agents benefit from information disclosure for the star network structure when the game is symmetric and submodular or supermodular. We also identify that the central agent benefits more than a peripheral agent from full information disclosure unless the competition is strong and the number of peripheral agents is small enough. Despite the fact that all agents expect to benefit from information disclosure ex-ante, a central agent can be worse-off from information disclosure in many realizations of the payoff state under strong competition, indicating that a risk-averse central agent can prefer uninformative signals ex-ante.