基于贝叶斯方法的地形辅助导航算法

提出并建立了一种基于贝叶斯方法的地形辅助导航(TAN)模型,推导了模型公式。针对该模型,应用最大后验估计方法,给出了一种近似的实现方法,即基于贝叶斯方法的地形辅助导航算法。仿真结果表明,该算法的均方根误差和圆概率误差等性能均优于SITAN(桑地亚惯性地形辅助导航)算法。

直接受益效应下新产品的扩散策略研究

论文介绍了直接受益效应下新产品的扩散方法,脱离了经典的Bass模型的研究平台,从网络动力学的角度分析了具有直接受益效应特征的新产品的扩散过程;通过贝叶斯等数学方法建立决策模型,为新产品扩散提供了一种新的途径;通过个体决策行为对社交网络中的新产品扩散现象进行描述,最后提出直接受益效应下新产品的扩散策略。

Biased Learning Creates Overconfidence

The aim of this paper is to develop a multi-period economic model to interpret how the people become overconfident by a biased learning that people tend to attribute the success to their abilities and failures to other factors.The authors suppose that the informed trader does not know the distribution of the precision of his private signal and updates his belief on the distribution of the precision of his knowledge by Bayer's rule.The informed trader can eventually recognize the value of the precision of his knowledge after an enough long time biased learning,but the value is overestimated which leads him to be overconfident.Furthermore,based on the definition on the luckier trader who succeeds the same times but has the larger variance of the knowledge,the authors find that the luckier the informed trader is,the more overconfident he will be;the smaller the biased learning factor is, the more overconfident the informed trader is.The authors also obtain a linear equilibrium which can explain some anomalies in financial markets,such as the high observed trading volume and excess volatility.

匹配不确定下考虑展厅现象的电子零售商渠道竞争策略

针对网络零售中普遍存在的商品匹配不确定问题,从电子零售商的视角提出两种竞争策略:建立虚拟展厅和以赠送退货运费险的形式提供退货服务。构建不同情形下的双寡头竞争模型,求解电子零售商和实体零售商的最优定价决策。通过对比均衡结果,得到如下结论:电子零售商建立虚拟展厅可以激励自身提高定价和增加需求从而增加收益,但会导致线下渠道的销售价格和需求同时降低,进而损害实体零售商的收益。退货服务在促使双方提高销售价格的同时,会导致线上渠道需求的降低和线下渠道需求的增加,从而使实体零售商获利。并且当购买退货运费险的费用较低时,电子零售商亦可从中获利。建立虚拟展厅和提供退货服务是一对互补策略,当建立虚拟展厅的成本较低时,可以同时采用。此外,具有低交通成本的消费者比例越小,电子零售商越倾向于建立虚拟展厅和提供退货服务。