Behavior of Self-Motivated Agents in Complex Networks

TL;DR

This paper investigates how self-motivated agents, who independently choose strategies based on payoff comparisons, behave in complex networks using multi-agent simulations of the prisoner's dilemma game.

Contribution

It introduces a model of self-motivated agents that select strategies independently, contrasting traditional imitation-based models, and analyzes their behavior in various network structures.

Findings

Self-motivated agents can coexist with other strategies under certain conditions.

Network structure and participation rate significantly influence strategy dynamics.

Simulation results reveal stable coexistence states in complex networks.

Abstract

Traditional evolutionary game theory describes how certain strategy spreads throughout the system where individual player imitates the most successful strategy among its neighborhood. Accordingly, player doesn't have own authority to change their state. However in the human society, peoples do not just follow strategies of other people, they choose their own strategy. In order to see the decision of each agent in timely basis and differentiate between network structures, we conducted multi-agent based modeling and simulation. In this paper, agent can decide its own strategy by payoff comparison and we name this agent as "Self-motivated agent". To explain the behavior of self-motivated agent, prisoner's dilemma game with cooperator, defector, loner and punisher are considered as an illustrative example. We performed simulation by differentiating participation rate, mutation rate and the degree of network, and found the special coexisting conditions.