基于网上拍卖的共同价值托投标分析

基于信息不对称条件下的共同价值模型,刻画了网上拍卖过程中可能存在的托投标行为,并运用博弈理论结合竞价关系求解了竞标者的赢标概率和最优竞价策略．研究发现,在共同价值模型下,参与竞标的人数与嬴者诅咒的发生存在联系;在最高出价者赢标的概率模型基础上,分别建立了是否存在托投标行为时的竞标者收益模型,并求解了竞标者的均衡竞价策略．     

考虑信息泄露的零售商需求预测信息共享研究

针对信息泄露导致零售商信息共享意愿降低的问题,基于竞争型制造商创新的视角,构建了考虑信息泄露的零售商需求预测信息共享模型,求解了供应链各主体的均衡决策,分析了信息泄露对零售商信息共享的影响。研究发现:虽然需求预测信息泄露能够增加两个竞争型制造商的创新投入,但会削弱博弈主导制造商的领导者优势。上游竞争型供应链中的信息泄露会削弱下游零售商的事前利润,零售商的信息共享价值降低。本文克服了Shamir关于供应链需求信息泄露局限于零售商的不足,并进一步考虑了制造商之间的竞争和创新。     

奖惩机制下零售商信息分享对闭环供应链的影响研究

研究奖惩机制下零售商的信息分享策略以及对闭环供应链的影响,建立了由制造商和零售商以及消费者组成的闭环供应链,其中制造商负责回收废旧产品并进行再制造。分别研究了集中式决策的情形和分散式决策下零售商信息分享和不分享的情形。研究发现,若政府在社会总福利目标中不考虑奖惩成本且回收难度较小时,零售商信息分享使社会总福利提高,反之,导致社会总福利降低;零售商信息分享总会使消费者剩余的期望值降低,但能够提高废旧产品的回收率。最后,针对零售商信息分享引起社会福利提高的情况设计了回收责任分担契约激励零售商分享信息。关键词:信息分享;奖惩机制;闭环供应链;社会福利;消费者剩余     

军事威逼博弈分析

本文通过构造一种三阶段动态博弈模型,对军事威逼行为进行了形式化分析,得出了在完全信息条件下军事威逼现象不会出现的结论;基于对挑战者与威慑者双边不完全信息条件下军事威逼模型博弈均衡的求解,探讨了军事威逼成功应满足的条件。     

需求信息泄露条件下供应链零售商订货策略研究

针对供应链订货过程中的需求信息泄露现象,通过对比供应商在泄露信息和不泄露信息情况下的收益,研究了供应链需求信息泄露的产生机理。建立了需求信息泄露条件下订货过程中零售商之间的信号博弈模型,通过模型求解得到了分离均衡和混同均衡的存在条件,并对不同均衡状态下零售商的最优订货策略和最优收益进行了分析。在此基础上,进一步讨论了零售商的订货策略选择问题。最后,通过一个数值算例说明了有关结论的有效性。     

Robustness in complex information systems: The role of information “barriers” in Boolean networks

In this supposed “information age,” a high premium is put on the widespread availability of information. Access to as much information as possible is often cited as key to the making of effective decisions. While it would be foolish to deny the central role that information and its flow has in effective decision‐making processes, this chapter explores the equally important role of “barriers” to information flows in the robustness of complex systems. The analysis demonstrates that (for simple Boolean networks at least) a complex system's ability to filter out, i.e., block, certain information flows is essential if it is not to be beholden to every external signal. The reduction of information is as important as the availability of information. © 2009 Wiley Periodicals, Inc. Complexity, 2010     

关于最大熵与信息熵之差的上界讨论

基于自信息函数为一可微凸函数这一事实,利用自信息函数所对应的Jenson离散型不等式,得到了有关最大熵与信息熵之差上界的一些结果.     

任意随机多元函数序列的一类强偏差定理

本文通过概率空间上的任意测度与另一任意测度相比较,研究了任意随机多元函数序列普遍成立的一类强偏差定理.利用网微分法与分析运算法,获得了若干任意信源的Shannon-Mcmillan随机逼近定理,并将已有的关于随机多地函数序列及离散信源的结果加以推广.     

A new structured quasi-Newton algorithm using partial information on Hessian

This paper presents a modified quasi-Newton method for structured unconstrained optimization. The usual SQN equation employs only the gradients, but ignores the available function value information. Several researchers paid attention to other secant conditions to get a better approximation of the Hessian matrix of the objective function. Recently Yabe et al. (2007) [6] proposed the modified secant condition which uses both gradient and function value information in order to get a higher-order accuracy in approximating the second curvature of the objective function. In this paper, we derive a new progressive modified SQN equation, with a vector parameter which use both available gradient and function value information, that maintains most properties of the usual and modified structured quasi-Newton methods. Furthermore, local and superlinear convergence of the algorithm is obtained under some reasonable conditions.     

Optimum calibration points estimating distribution functions

The calibration method has been widely discussed in the recent literature on survey sampling, and calibration estimators are routinely computed by many survey organizations. The calibration technique was introduced in [12] to estimate linear parameters as mean or total. Recently, some authors have applied the calibration technique to estimate the finite distribution function and the quantiles. The computationally simpler method in [14] is built by means of constraints that require the use of a fixed value t₀. The precision of the resulting calibration estimator changes with the selected point t₀. In the present paper, we study the problem of determining the optimal value t₀ that gives the best estimation under simple random sampling without replacement. A limited simulation study shows that the improvement of this optimal calibrated estimator over possible alternatives can be substantial.