用忠诚机制破解团队合作中的搭便车难题，让努力付出者获益更多。
Computational Foundations for Strategic Coopetition: Formalizing Collective Action and Loyalty
- 引入忠诚度调节的效用函数，让忠于团队者更愿投入
- 实验显示忠诚度使努力差异提升15.04倍，六项行为目标全部达标
- 适用于人类团队与多智能体系统，可复现开源项目演化规律
混合动机多智能体场景中，个体努力惠及全体却自担成本，导致持续搭便车。经典理论表明纯自利下均衡为普遍逃避责任。尽管i*将团队视为复合主体，却缺乏可扩展的计算机制来分析协同行动问题的产生与解决。本技术报告拓展战略共竞的计算基础，构建团队层级动态模型，基于前作对依存性与信任动态的形式化（arXiv:2510.18802, arXiv:2510.24909）。提出双机制忠诚效用函数：忠诚收益（福利内化与贡献满足感）与成本容限（忠诚成员负担减轻）。通过依赖加权的团队凝聚力整合i*结构依赖，将成员激励与团队定位挂钩。框架适用于人类团队（心理认同）与多智能体系统（对齐系数与调整成本函数）。3,125种配置实验验证忠诚效应显著（中位努力差异15.04倍），六项行为目标均达阈值：搭便车基线（96.5%）、忠诚单调性（100%）、努力差异（100%）、团队规模效应（100%）、机制协同（99.5%）、边界结果（100%）。基于已发表的Apache HTTP Server（1995–2023）案例研究进行实证验证，60/60分达成，复现了形成、增长、成熟与治理各阶段的贡献模式。统计显著性p<0.001，Cohen's d=0.71。
原文摘要 · Abstract (English)
Mixed-motive multi-agent settings are rife with persistent free-riding because individual effort benefits all members equally, yet each member bears the full cost of their own contribution. Classical work by Holmström established that under pure self-interest, Nash equilibrium is universal shirking. While i* represents teams as composite actors, it lacks scalable computational mechanisms for analyzing how collective action problems emerge and resolve in coopetitive settings. This technical report extends computational foundations for strategic coopetition to team-level dynamics, building on companion work formalizing interdependence/complementarity (arXiv:2510.18802) and trust dynamics (arXiv:2510.24909). We develop loyalty-moderated utility functions with two mechanisms: loyalty benefit (welfare internalization plus intrinsic contribution satisfaction) and cost tolerance (reduced effort burden for loyal members). We integrate i* structural dependencies through dependency-weighted team cohesion, connecting member incentives to team-level positioning. The framework applies to both human teams (loyalty as psychological identification) and multi-agent systems (alignment coefficients and adjusted cost functions). Experimental validation across 3,125 configurations demonstrates robust loyalty effects (15.04x median effort differentiation). All six behavioral targets achieve thresholds: free-riding baseline (96.5%), loyalty monotonicity (100%), effort differentiation (100%), team size effect (100%), mechanism synergy (99.5%), and bounded outcomes (100%). Empirical validation using published Apache HTTP Server (1995-2023) case study achieves 60/60 points, reproducing contribution patterns across formation, growth, maturation, and governance phases. Statistical significance confirmed at p<0.001, Cohen's d=0.71.
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