Abstract

Iterated prisoner’s dilemma (IPD) researchers have shown that strong positive reputations plus an efficient reputation evaluation system encourages both sides to pursue long-term collaboration and to avoid falling into mutual defection cycles. In agent-based environments with reliable reputation rating systems, agents interested in maximizing their private interests must show concern for other agents as well as their own self-reputations–an important capability that standard IPD game agents lack. Here we present a novel learning agent model possessing self-reputation awareness. Agents in our proposed model are capable of evaluating self-behaviors based on a mix of public and private interest considerations, and of testing various solutions aimed at meeting social standards. Simulation results indicate multiple outcomes from the addition of a small percentage of self-reputation awareness agents: faster cooperation, faster movement toward stability in an agent society, a higher level of public interest in the agent society, the resolution of common conflicts between public and private interests, and a lower potential for rational individual behavior to transform into irrational group behavior.

Highlights

  • Reputation provides a foundation for game theorists to analyze ways that past behaviors of social participants affect the behaviors and strategies of iterated prisoner’s dilemma (IPD) game opponents

  • Our first task was to analyze the results of IPD game simulations using cellular automata and without adding any self-reputation awareness component (SRAC) agents (Figs. 2, 3 and 4)

  • In this paper we described our proposal for a self-reputation awareness model in which agents are given the ability to calculate and interpret their self-reputation levels, and to adjust their IPD game strategies

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Summary

Introduction

Reputation provides a foundation for game theorists to analyze ways that past behaviors of social participants affect the behaviors and strategies of iterated prisoner’s dilemma (IPD) game opponents.

Results
Conclusion

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