Learning to play public good games

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.

14 pages, 12 figures

References in corpus (10)

• Evolutionary games on graphs
• Co-evolution of strategy and structure in complex networks with dynamical linking
• Coevolutionary Dynamics: From Finite to Infinite Populations
• Fixation of strategies for an evolutionary game in finite populations
• Coexistence versus extinction in the stochastic cyclic Lotka-Volterra model
• Oscillatory Dynamics in Rock-Paper-Scissors Games with Mutations
• Fixation times in evolutionary games under weak selection
• How limit cycles and quasi-cycles are related in systems with intrinsic noise
• Evolutionary dynamics, intrinsic noise and cycles of co-operation
• Cycles of cooperation and defection in imperfect learning

Cited by in corpus (3)

• Deterministic limit of temporal difference reinforcement learning for stochastic games
• Intrinsic fluctuations of reinforcement learning promote cooperation
• Fixation and escape times in stochastic game learning