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.

翻译：混合动机多智能体环境中普遍存在持续搭便车现象，因为个体努力使所有成员平等受益，而每个成员却需独自承担其贡献的全部成本。Holmström的经典研究证明，在纯粹自利假设下，纳什均衡将导致普遍懈怠。尽管i*将团队建模为复合行动者，但缺乏可扩展的计算机制来分析集体行动问题如何在竞合环境中产生与消解。本技术报告基于先前关于相互依赖/互补性（arXiv:2510.18802）与信任动态（arXiv:2510.24909）的形式化研究，将战略竞合的计算基础拓展至团队层面动态。我们构建了忠诚度调节的效用函数，包含两种机制：忠诚收益（福利内化与内在贡献满足感）与成本容忍度（忠诚成员的努力负担降低）。通过依赖加权的团队凝聚力整合i*结构依赖关系，将成员激励与团队层面定位相连接。该框架适用于人类团队（忠诚度作为心理认同）与多智能体系统（对齐系数与调整后的成本函数）。在3,125种配置上的实验验证表明忠诚度效应稳健（中位努力分化达15.04倍）。全部六项行为目标均达到阈值：搭便车基线（96.5%）、忠诚度单调性（100%）、努力分化（100%）、团队规模效应（100%）、机制协同（99.5%）及有界结果（100%）。采用已发表的Apache HTTP服务器（1995-2023）案例进行实证验证获得60/60分，成功复现了组建、成长、成熟与治理各阶段的贡献模式。统计显著性经确认为p<0.001，Cohen's d=0.71。