Modern socio-technical systems increasingly involve multi-stakeholder environments where actors simultaneously cooperate and compete. These coopetitive relationships exhibit dynamic trust evolution based on observed behavior over repeated interactions. While conceptual modeling languages like i* represent trust relationships qualitatively, they lack computational mechanisms for analyzing how trust changes with behavioral evidence. Conversely, computational trust models from multi-agent systems provide algorithmic updating but lack grounding in conceptual models that capture strategic dependencies covering mixed motives of actors. This technical report bridges this gap by developing a computational trust model that extends game-theoretic foundations for strategic coopetition with dynamic trust evolution. Building on companion work that achieved 58/60 validation (96.7%) for logarithmic specifications, we introduce trust as a two-layer system with immediate trust responding to current behavior and reputation tracking violation history. Trust evolves through asymmetric updating where cooperation builds trust gradually while violations erode it sharply, creating hysteresis effects and trust ceilings that constrain relationship recovery. We develop a structured translation framework enabling practitioners to instantiate computational trust models from i* dependency networks encompassing mixed motives of actors. Comprehensive experimental validation across 78,125 parameter configurations establishes robust emergence of negativity bias, hysteresis effects, and cumulative damage amplification. Empirical validation using the Renault-Nissan Alliance case study (1999-2025) achieves 49/60 validation points (81.7%), successfully reproducing documented trust evolution across five distinct relationship phases including crisis and recovery periods.

翻译：现代社会技术系统日益涉及多利益相关者环境，其中行动者同时进行合作与竞争。这类竞合关系基于重复互动中观察到的行为展现出动态的信任演化。尽管概念建模语言（如i*）能够定性表示信任关系，但其缺乏分析信任如何随行为证据变化计算机制。反之，多智能体系统中的计算信任模型虽提供算法更新机制，却缺乏扎根于捕捉行动者混合动机的战略依赖性概念模型。本技术报告通过开发一种计算信任模型来弥合这一鸿沟，该模型扩展了战略竞合博弈论基础并融入动态信任演化机制。基于前期已实现对数规范58/60验证点（96.7%）的配套研究，我们将信任构建为双层系统：即时信任响应当前行为，声誉追踪违约历史。信任通过非对称更新机制演化——合作行为逐步建立信任，而违约行为急剧削弱信任，从而产生滞后效应与信任天花板，制约关系修复能力。我们开发了结构化转换框架，使实践者能够从涵盖行动者混合动机的i*依赖网络中实例化计算信任模型。通过对78,125种参数配置的全面实验验证，确立了负面偏见、滞后效应与累积损害放大的稳健涌现特性。基于雷诺-日产联盟案例（1999-2025）的实证验证获得49/60验证点（81.7%），成功复现了涵盖危机与恢复期等五个不同关系阶段的文献记载信任演化轨迹。