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This work proposes a multi-objective reinforcement learning framework that models the evolution of fairness as a dynamic trade-off between material payoff maximization and fairness-driven moral behavior, regulated by a fairness pressure coefficient.
The results reveal that reputation-modulated learning significantly promotes the emergence of cooperative behavior, and the discontinuous phase transition from full cooperation to full defection as the temptation increases.
Mechanistic analyses show that a moderate neighborhood size enables individuals to strike an optimal balance between information sufficiency and decision-making tractability, which allows them to detect reciprocal opportunities while avoiding the deterioration of decision quality due to information overload.
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