Learning to play public good games

TL;DR

This paper analyzes stochastic effects in multi-player public good games on networks, revealing noise-driven cycles in strategy dynamics that resemble experimental observations.

Contribution

It extends spectral analysis of fluctuations from two-player to multi-player networked public good games, highlighting noise-induced cyclic behaviors.

Findings

Noise can induce coherent cycles in strategy dynamics.

Spectral properties of fluctuations are analytically computed.

Noise-driven cycles resemble experimental behavioral patterns.

Abstract

We extend recent analyses of stochastic effects in game dynamical learning to cases of multi-player games, and to games defined on networked structures. By means of an expansion in the noise strength we consider the weak-noise limit, and present an analytical computation of spectral properties of fluctuations in multi-player public good games. This extends existing work on two-player games. In particular we show that coherent cycles may emerge driven by noise in the adaptation dynamics. These phenomena are not too dissimilar from cyclic strategy switching observed in experiments of behavioural game theory.