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在不确定的多阶段动态博弈环境中发展了一种带惩罚项的销售薪酬合同模型,介绍了如何系统地通过观察调节合同参数引起的销售人员行为反应并运用贝叶斯推断学习基础销售量的方法,推导出了最优定额、产量和底薪的解析解;比较了信息共享和销售人员隐藏真实市场信息两种情况下厂商学习收益的差异,分析了厂商学习收益与地区销售环境、销售人员个性特征和技能水平、最初先验估计之间的关系,论证了两种情况下厂商都可通过对各阶段定额不同的设置方式来激励销售、获取新市场的需求信息从而优化生产计划和提高企业利润.
In the uncertain multi-stage dynamic game environment, we develop a sales compensation contract model with penalty items, and introduce how to systematically observe the salesman’s reaction caused by adjusting the contract parameters and use Bayesian inference to learn the basic sales Method, the analytic solution of the optimal quota, output and basic salary is deduced. The difference of learning profit under two situations of information sharing and sales staff hiding the real market information is compared, and the relationship between the learning gain and the sales environment, the salesperson’s personality Characteristics and skill levels, and initial a priori estimates. It is demonstrated that in both cases, firms can stimulate sales by setting quotas in different stages and obtain the demand information of new markets so as to optimize production plans and increase profits .