Mean-field Games for Bio-inspired Collective Decision-making in Dynamical Networks
This work provides a theoretical framework for bio-inspired collective decision-making in dynamical networks, but the contributions are incremental as they extend existing mean-field game theory to a specific three-state setup with applications.
The authors develop a mean-field game model for collective decision-making in large homogeneous populations across three states, deriving mean-field and stationary equilibria. They apply the model to honeybee swarms, virus propagation, and cascading failures in smart grids, simulating cyber-attack impacts on grid frequencies.
Given a large number of homogeneous players that are distributed across three possible states, we consider the problem in which these players have to control their transition rates, while minimizing a cost. The optimal transition rates are based on the players' knowledge of their current state and of the distribution of all the other players, and this introduces mean-field terms in the running and the terminal cost. The first contribution involves a mean-field game model that brings together macroscopic and microscopic dynamics. We obtain the mean-field equilibrium associated with this model, by solving the corresponding initial-terminal value problem. We perform an asymptotic analysis to obtain a stationary equilibrium for the system. The second contribution involves the study of the microscopic dynamics of the system for a finite number of players that interact in a structured environment modeled by an interaction topology. The third contribution is the specialization of the model to describe honeybee swarms, virus propagation, and cascading failures in interconnected smart-grids. A numerical analysis is conducted which involves two types of cyber-attacks. We simulate in which ways failures propagate across the interconnected smart grids and the impact on the grids frequencies. We reframe our analysis within the context of Lyapunov's linearisation method and stability theory of nonlinear systems and Kuramoto coupled oscillators model.