供应链视角下的增值税改革影响研究

构建了一个制造商和一个零售商所组成的两级供应链模型,研究增值税税率下调对供应链决策和社会福利的影响.结果表明,下调制造商、零售商增值税税率都会使产品零售价降低,提高零售商和制造商的利润.下调增值税税率给制造商带来的利润增量大于给零售商带来的利润增量.制造商税率下调导致批发价降低,零售商税率下调反而提高批发价.社会福利的变化趋势与潜在需求规模有关.当潜在需求规模较高时,社会福利总是随降税幅度的增大而增大,且税率降低后的社会福利大于税率降低前;当潜在需求规模中等时,社会福利随降税幅度的增大而减小,但税率降低后的社会福利仍大于税率降低前;当潜在需求规模较小时,社会福利随降税幅度的增大而减小,增值税税率下调损害了社会福利.     

生产率差异与企业对外直接投资进入模式选择

基于企业异质性视角,借鉴演化博弈理论探究企业通过OFDI“走出去”时的跨国并购方和标的方在全要素生产率上的差异对企业OFDI进入策略选择的影响机制。利用Hotelling模型构造资本密集型企业与劳动密集型企业OFDI进入策略选择博弈模型,讨论两类企业OFDI进入方式决策在并购双方生产率差异变化之下的变迁机理。研究发现:当并购方和被并购方生产率差异较小时,市场达到两类企业都倾向于跨国并购策略的演化稳定状态;当并购双方生产率差异较大时,企业的生产要素密集度及其结构对其OFDI进入模式决策具有重要的作用,知识或技术密集度较高的资本密集型企业与综合实力强势的部分劳动密集型企业采取跨国并购战略,而另一部分劳动密集型企业选择绿地新建方式进入国际市场。即企业OFDI进入策略选择因其所属的行业生产要素密集度及其结构不同而具有差异性。研究结论在一定程度上弥补了现有研究的不足,为以后的研究工作提供一定的理论参考。     

中国出境旅游的外交效应

2000年以来，中国出境旅游高增长、高消费，影响力不断增大，成为国家外交战略的重要内容，外交效应逐渐显现。通过辨析中国出境旅游外交效应的概念、表现形式和结果，基于10个中国主要出境目的国的旅游互动数据，采用DIF-GMM计量经济模型，实证检验了中国出境旅游外交效应。结果表明，中国通过有序推进ADS协议、加强经济援助、举办“旅游年”活动、实施旅游“制裁”，并积极参与国际制度建设等旅游外交行为，促进了与世界各国的友好交往，维护了自身核心利益，提升了外交软实力和国际影响力。国际旅游反作用于国际关系，对国际关系具有显著的正向促进作用，不仅是国际关系的结果，而且是其重要动因之一。中国出境旅游与政治、经济、社会、文化等联动，多方面提升了中国的国际影响力。     

基于区间直觉模糊不确定语言的中小企业电子商务网站建设决策方法研究

阐述中小企业在整个国民经济中的重要地位,指出电子商务对于中小企业具有天然的适应性和可行的推广性.揭示研究对象中蕴含着广泛的模糊性,应用区间直觉模糊不确定语言的必要性.介绍区间直觉模糊不确定语言的基本概念、基本运算、算术平均和加权平均、距离、期望函数、精度函数、期望函数值、精度函数值以及对象比较方法等内容.提出区间直觉模糊不确定语言决策步骤:设定方案集、指标集、专家集,确定评判矩阵,确定指标权重,计算加权平均,计算期望函数、精度函数,期望函数值、精度函数值,进行排序.以盛东商贸有限公司电子商务网站设计方案测评为例,碍出方案3优于方案2优于方案5优于方案1优于方案4的结论.     

Can People Predict Chaotic Sequences?

Previous studies suggesting that people predict chaotic sequences better than chance have not discriminated between sensitivity to nonlinear determinism and facilitation using autocorrelation. Since prediction accuracy declines with increases in the look-ahead window in both cases, a decline in prediction accuracy does not imply chaos sensitivity. To overcome this problem, phase-randomized surrogate time series are used as a control. Such series have the same linear properties as the original chaotic sequence but contain no nonlinear determinism, i.e. chaos. In the experimental task, using a chaotic Hénon attractor, participants viewed the previous eight days temperatures and then predicted temperatures for the next four days, over 120 trials. The control group experienced a sample from a corresponding phase-randomized surrogate series. Both time series were linearly transformed to provide a realistic temperature range. A transformation of the correlation between observed and predicted values decreased over days for the chaotic time series, but remained constant and high for the surrogate series. The interaction between the days and series factors was statistically significant, suggesting that people are sensitive to chaos, even when the autocorrelation functions and power spectra of the control and experimental series are identical. Implications for the psychological assessment of individual differences in human prediction are discussed.     

Optimal consumption and investment problem with random horizon in a BMAP model

In this paper, we consider the consumption and investment problem with random horizon in a Batch Markov Arrival Process (BMAP) model. The investor invests her wealth in a financial market consisting of a risk-free asset and a risky asset. The price processes of the riskless asset and the risky asset are modulated by a continuous-time Markov chain, which is the phase process of a BMAP. The possible consumption or investment are restricted to a sequence of random discrete time points which are determined by the same BMAP. The investor has only consumption opportunities at some of these random time points, has both consumption and investment opportunities at some other random time points, and can do nothing at the remaining random time points. The object of the investor is to select the consumption–investment strategy that maximizes the expected total discounted utility. The purpose of this paper is to analyze the impact of the consumption–investment opportunity and the economic state on the value functions and consumption–investment strategies. The general solution and the exact solution under the assumption that the consumption and the terminal wealth are evaluated by the power utility are obtained. Finally, a numerical example is presented.     

地下水脆弱性风险预警和评估的区间三角模糊多属性决策模型

应用基于逼近理想解排序法的区间三角模糊多属性决策模型,对三江平原六大分区地下水脆弱性进行了风险预警和评估.评估结果与前人吻合,可为有关决策部门采取相应降低环境风险的措施提供参考.实例验证表明,模型具有更高的计算精度和更好的评价效果,为有关环境风险决策部门对地下水风险预警和评估提供了新的思路和方法.     

On the poisson output of queueing systems with dependent service times

In this paper a method is stated to conclude that the output of given queueing system is POISSON from the steady-state probabilities and from the behavior of the queueing system at jump epochs, i.e. at epochs when the system state can be changed. The corresponding statement for queueing systems described by Markov processes with denumber-able state space will be generalized to systems which can have arbitrary service time distributions if the steady-state probabilities are insensitive with respect to these distributions.     

The multi-attribute grey target decision method for attribute value within three-parameter interval grey number

Based on the grey system theory and methods, the grey-target decision-making problem is discussed, in which the attribute values are grey numbers and the maximum probability of the value of grey number is known. Firstly, the optimal effect vector is the positive bull’s-eye and positive bull’s-eye distance of each scheme is defined. Subjectively or objectively weighting method is integrated to determine the index weight and integrated optimization model of index weight is established. Finally, the critical effect vector is the negative bull’s-eye and negative bull’s-eye distance of each scheme is defined, then relative bull’s-eye distance and comprehensive the bull’s-eye distance of grey target decision-making are given. An example is also presented to illustrate the usefulness and effectiveness of the methods obtained in this paper and provides a new idea for grey target decision-making method research.     

基于错误矩阵方程的解决城市交通拥堵决策研究

针对解决城市交通拥堵决策问题,首先给出了错误优化矩阵的概念,在此基础上引出错误矩阵方程的概念,利用消错理论中的错误优化矩阵方程,从错误优化的角度来研究并解决城市交通拥堵的决策方法.相应结合实际状况给出当前状态矩阵,从而进行下一步的求解,步步推理获得了决策人满意的方案集,为决策者提供最优建议.