Mean-field games of speedy information access with observation costs

TL;DR

This paper models how agents in a mean-field game actively control their information access speed and costs to optimize their rewards, providing a framework for understanding strategic information acquisition.

Contribution

It introduces a novel mean-field game model incorporating controlled, costly information access and develops a fixed point iteration for equilibrium computation.

Findings

Convergence to a unique mean-field equilibrium with entropy regularization.

Derivation of an approximate ε-Nash equilibrium for large populations.

Application example in epidemiology demonstrating the model's relevance.

Abstract

We investigate mean-field games (MFG) in which agents can actively control their speed of access to information. Specifically, the agents can dynamically decide to obtain observations with reduced delay by accepting higher observation costs. Agents seek to exploit their active information acquisition by making further decisions to influence their state dynamics so as to maximise rewards. In a mean-field equilibrium, each generic agent solves individually a partially observed Markov decision problem in which the way partial observations are obtained is itself subject to dynamic control actions, while no agent can improve unilaterally given the actions of all others. We formulate the mean-field game with controlled costly information access as an equivalent standard mean-field game on an augmented space, by utilizing a parameterisation of the belief state by a finite number of variables. With sufficient entropy regularisation, a fixed point iteration converges to the unique MFG equilibrium. Moreover, we derive an approximate $\varepsilon$-Nash equilibrium for a large but finite population size and small regularisation parameter. We illustrate our (extended) MFG of information access and of controls by an example from epidemiology, where medical testing results can be procured at different speeds and costs.