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
Summary
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.
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