基于演化博弈和多智能体仿真，以加强联盟管理、提升联盟服务商合作积极性为目标，研究汽车共享服务联盟协同策略.从演化博弈视角设计综合考虑服务商自身与联盟内其他成员历史信息的学习规则，构建汽车共享服务联盟的多Agent仿真模型，研究收益、成本及惩罚等不同参数设置对服务商协同策略的影响.依据仿真实验结果提出了不同收益、成本和惩罚参数场景下加强联盟合作的策略，为汽车共享服务联盟的协同策略提供决策支持.
Based on evolutionary game and multi-agent simulation, the cooperative strategy of automobile sharing service alliance studied to strengthen alliance management and enhance the cooperation enthusiasm of alliance service providers. From the perspective of evolutionary game, a multi-agent simulation model of automobile sharing service alliance is designed, which considers the learning rules of history information of the service provider and other members in the alliance. The multi-agent simulation model of automobile sharing service alliance is constructed. In addition, the influence of different parameter settings, such as revenue, cost and penalty on the strategy of service provider alliance is studied. Based on simulation experiment, we propose strategies of strengthening alliance cooperation under different parameters, accordingly providing decision support for the cooperation strategy of automobile sharing service alliance.
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