优化社会福利需超越成本与合作频率，关注整体福祉
Social welfare optimisation in well-mixed and structured populations
- 以最大化社会福利为单一目标，重构激励机制设计
- 发现最优福利对应的个体激励成本远高于单纯降本或促合作
- 适用于多智能体系统与人类社会的政策制定与评估
关于如何在自主、自利的代理之间促进合作的研究，长期聚焦于双目标优化：最小化总激励成本的同时最大化合作频率。然而，在此类约束下社会福利的最优值仍鲜有探讨。本文假设，实现最大社会福利并不必然出现在驱动代理达到期望合作状态所需的最低激励成本处。为此，我们采用以最大化社会福利为核心的单目标方法，基于经典演化博弈论模型，在随机混合与结构化群体中分析有限种群下的成本效率问题。通过解析模型与基于代理的模拟，揭示了奖励局部或全局行为模式等不同干预策略对社会福利及合作动态的影响。结果表明，以纯粹成本效率或合作频率为目标与追求最大社会福利之间存在显著的个体激励成本差距。总体而言，我们的研究指出，在多智能体系统和人类社会中，激励设计、政策制定与基准评估应优先考虑以福利为中心的目标，而非成本或合作频率等代理指标。
原文摘要 · Abstract (English)
Research on promoting cooperation among autonomous, self-regarding agents has often focused on the bi-objective optimisation problem: minimising the total incentive cost while maximising the frequency of cooperation. However, the optimal value of social welfare under such constraints remains largely unexplored. In this work, we hypothesise that achieving maximal social welfare is not guaranteed at the minimal incentive cost required to drive agents to a desired cooperative state. To address this gap, we adopt to a single-objective approach focused on maximising social welfare, building upon foundational evolutionary game theory models that examined cost efficiency in finite populations, in both well-mixed and structured population settings. Our analytical model and agent-based simulations show how different interference strategies, including rewarding local versus global behavioural patterns, affect social welfare and dynamics of cooperation. Our results reveal a significant gap in the per-individual incentive cost between optimising for pure cost efficiency or cooperation frequency and optimising for maximal social welfare. Overall, our findings indicate that incentive design, policy, and benchmarking in multi-agent systems and human societies should prioritise welfare-centric objectives over proxy targets of cost or cooperation frequency.
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