Overview of Modeling and Analysis of Incentive Mechanisms Based on Evolutionary Game Theory in Autonomous Networks

Abstract

This paper thoroughly investigated the Evolutionary Game Theory (EGT) based modeling and analysis of reciprocation-based incentive mechanisms. Unlike existing work which adopts replicator equation to analyze the stability of incentive mechanisms (actually, replicator equation is only applicable to describe deterministic selection in infinitely large and well-mixed population), we paid special attentions to the intrinsic heterogeneity in real autonomous networks: finite users, mutation probability and structured network graph, and proposed the unified framework to characterize the evolutionary dynamics. Specifically, through modeling and analyzing Prisoner's Dilemma (PD)-like game based and Public-goods game based incentive mechanisms, we show that although it is impossible for incentive mechanisms to get the whole network into static "absolute full cooperation (or reciprocation)" state, they can still drive the whole system into "almost reciprocation" state, that is, most of the system time would be occupied by the cooperation (or reciprocation) state.