Moral Preferences Co-Evolve With Cooperation in Networked Populations

Unravelling the evolution of cooperation is essential for advancing natural and artificial intelligence (AI) systems. Previous studies have investigated the impact of additional incentives, such as reciprocity and reputation, on cooperative behavior. However, a fundamental question persists: under what conditions do moral preferences evolve and does this evolution subsequently promote cooperation in networked populations of agents? To address this question, we propose a comprehensive framework to systematically explore the co-evolution of moral preferences and cooperative behavior in a networked population. In our framework, the population structure is modeled as a network, with nodes corresponding to AI agents. Moral preferences are modeled through a learning algorithm that adheres to social norms. Prosocial and antisocial behaviors lead to rewards or punishments, and learning agents receive morality scores based on their rewarding behavior toward others. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm in a networked population, showcasing faster convergence. We find that moral preferences enhance cooperation as long as the learning rate is moderate, even in the presence of dominant defectors. This surprising finding also holds for cooperation-inhibiting network structures, provided the critical benefit-cost ratio for cooperation is sufficiently high or below average. Interestingly, moral preferences also co-evolve with cooperation in the populations. Our work not only provides new design methodologies for network algorithms, but also highlights the insight that large-scale evolutionary computation can provide for evolutionary biology and emerging AI-agent populations.