# A Graphical Evolutionary Game Approach to Social Learning

## TL;DR

This paper introduces a low-communication, distributed graphical evolutionary game approach for social learning in networked systems, enabling agents to collaboratively detect states efficiently and accurately.

## Contribution

It proposes a novel game-theoretic learning method where agents communicate binary decisions, reducing communication complexity while achieving centralized detection performance.

## Key findings

- Steady state equilibria match centralized detector decisions
- Method reduces communication overhead
- Numerical experiments confirm effectiveness

## Abstract

In this work, we study the social learning problem, in which agents of a networked system collaborate to detect the state of the nature based on their private signals. A novel distributed graphical evolutionary game theoretic learning method is proposed. In the proposed game-theoretic method, agents only need to communicate their binary decisions rather than the real-valued beliefs with their neighbors, which endows the method with low communication complexity. Under mean field approximations, we theoretically analyze the steady state equilibria of the game and show that the evolutionarily stable states (ESSs) coincide with the decisions of the benchmark centralized detector. Numerical experiments are implemented to confirm the effectiveness of the proposed game-theoretic learning method.