北京市入境旅游与饭店旅行社的耦合协调度研究

基于2001—2010年北京市入境旅游和饭店旅行社的各指标数据,首先通过相关分析,筛选出与北京市入境旅游发展存在相关关系的饭店旅行社参量,然后借助熵值法,计算出各个参量的权重及评价值,最后建立耦合协调度模型,对入境旅游—饭店旅行社的耦合协调度进行了实证研究。结果发现,十年间,其耦合度一直处于拮抗阶段,但耦合协调度水平变化幅度较大,2003年达到低谷,属于中度失调,2010年达到最高值,属于中级协调。因此,北京市饭店旅行社的发展仍不能有效的支撑其入境旅游的发展,亟待提升和完善。     

基于熵的改进TOPSIS法在业务外包供应商选择中的应用

影响3PL选择的因素很多．其中交货提前期（T）、产品质量（Q）、变货可靠度（R）和产品价格（P）等4个因素是3PL选择的关键因素。本文就从这4个关键因素着手，从现代供应链管理要讲究战略的角度出发．引进基于熵的改进TOPSIS法，选择合适的3PL．和企业建立起长期的合作伙伴关系。     

基于熵权的信息工程监理风险评估

本文利用模糊评判方法对信息工程监理进行风险评估，对各风险因素分别从风险发生的概率和风险产生的影响两方面进行评估。采用熵权系数法对风险因素的权重进行客观计算，克服了专家评判的主观性。通过实例分析表明该方法是有效和实用的。     

Does china income FSDs follow Benford? A comparison between Chinese income first significant digit distribution with Benford distribution

Since Benford’s law is an empirical phenomenon that occurs in a range of data sets, this raises the question as to whether or not the same thing might be true in terms of the Chinese income distribution data. We focus on the first significant digit (FSD) distribution of Chinese micro income data from the 2005 Inter-Census sample, which corresponds to 1% of Chinese population and other micro income data from the China family panel studies (CFPS) and Chinese General Social Survey (CGSS). We use information theoretic-entropy based methods to investigate the degree to which Benford’s FSD law is consistent with the FSD of Chinese income data and our findings suggest consistency between the Chinese FSD income distribution and Benford’s distribution. The close connection between the two distributions has implications for the quality of the sample of Chinese micro data.     

从熵概念到耗散结构理论

本文介绍了从熵概念到耗散结构理论的发展及其具有广泛应用的现实意义,指出耗散过程和不可逆性在发展和进化中的建设性作用。     

基于“三”分析的上市商业银行经营绩效评价

安全性、流动性和盈利性被称为商业银行经营的“三性”原则。本文基于改进的熵值法,对我国14家上市商业银行的三性指标进行综合评价。评价结果表明,我同股份制商业银行的经营绩效要普遍优于圈有商业银行。     

Optimal investment of an insurer with regime-switching and risk constraint

We investigate an optimal investment problem of an insurance company in the presence of risk constraint and regime-switching using a game theoretic approach. A dynamic risk constraint is considered where we constrain the uncertainty aversion to the ‘true’ model for financial risk at a given level. We describe the surplus of an insurance company using a general jump process, namely, a Markov-modulated random measure. The insurance company invests the surplus in a risky financial asset whose dynamics are modeled by a regime-switching geometric Brownian motion. To incorporate model uncertainty, we consider a robust approach, where a family of probability measures is cosidered and the insurance company maximizes the expected utility of terminal wealth in the ‘worst-case’ probability scenario. The optimal investment problem is then formulated as a constrained two-player, zero-sum, stochastic differential game between the insurance company and the market. Different from the other works in the literature, our technique is to transform the problem into a deterministic differential game first, in order to obtain the optimal strategy of the game problem explicitly.