国际气候基金的资金筹集与资金分配的理论探索——兼顾全球帕累托最优与财政收支平衡分析

通过构建数理模型，文章分析了国际气候基金应该如何筹集和分配资金，才能同时实现全球气候治理的帕累托最优与自身的财政收支平衡。为此，首先，构建全球气候治理的帕累托最优模型与市场均衡模型，并分别求得帕累托最优实现条件和市场均衡条件；其次，比较二者，求得使市场均衡结果实现全球帕累托最优的唯一的价格条件；紧接着，在该价格条件的基础上，纳入财政收支平衡的考虑，进而推导出兼顾全球帕累托最优与财政收支平衡下的国际气候基金的资金筹集与资金分配所需要遵循的唯一规则；在此基础上，给出了兼顾全球帕累托最优与财政收支平衡下的国际气候基金的资金筹集与资金分配的全部可选方案；最后，提出了逐步推进国际气候基金建设的政策建议。文章的创新有两点：对Baumol和Oates构建的公共外部性模型的假设条件进行修正，从而使本文的结论更加适合于全球气候治理分析；推导得出同时实现全球帕累托最优和国际气候基金收支平衡的唯一价格条件。     

艰辛的探索--新中国领导人关于资本主义经济发展的认识

系统考察了新中国领导人对资本主义经济发展的认识,从科学社会主义理论和中国社会主义经济思想史的角度,初步总结了中国共产党人在这个领域艰辛探索的经验和教训,论证了任何一种思想与理论的出现都有其历史背景与时代条件,其在一个国家的发屉与实现程度取决于它满足这个国家的需要程度以及它给这个国家创造的发展空间.     

以社会主义生产目的为目标的两大部类最优投资分配——以马克思扩大再生产理论为基础

文章以马克思扩大再生产理论为基础建立了一个最优控制模型,通过最优条件的变换得到了两大部类投资比例的最优算法。该算法表明,在没有发生革命性变革的情况下,两大部类投资比例是常数,这意味着文章推导出的最优解具有普遍意义。     

城市交通环境文化系统的多目标灰色局势评价

为科学评价城市交通环境文化系统,采用多目标灰色局势评价方法,对中国东北3个城市的交通环境文化系统进行综合评价。结果表明,该方法能较好反映实际交通环境文化系统的水平,可以使复杂的决策问题变得简明,提高决策精度。与其他方法相比,决策过程具有更大的优越性。     

考虑利他主义的古诺模型研究

针对完全利己和(或)完全利他可能不具有集体帕累托效率这些经济现象,利用文献[1]建立的GR博弈模型和GR均衡,建立了基于GR(黄金规则)的古诺模型,研究了GR均衡的集体帕累托效率性质与GR博弈中权重系统(描述厂商关心自己和他人的程度)之间的关系.结果表明,(1)对于N-厂商的情形,GR均衡集合与具有集体帕累托效率的策略组合构成的集合一致的充分必要条件是每个厂商都均等地关心自己和他人的利润;(2)对于两厂商的情形,GR均衡都具有集体帕累托效率的充分必要条件是,存在一个厂商均等地关心自己和他人的利润.这些结果一方面在古诺竞争的背景下加强了文献[1]的结果;另一方面表明.一个"平等地看待竞争对手利益"的古诺竞争将会获得集体有效率的竞争均衡.     

A Game Theory Approach for Assessment of Risk and Deployment of Police Patrols in Response to Criminal Activity in San Francisco

An efficient police patrol schedule must ensure the allocation of an appropriate number of officers sufficient to respond to the danger of criminal incidents, particularly in an urban environment, even when the available number of personnel is limited. This study proposes a framework that incorporates two game theory models designed for the allocation of police officers to patrol shifts. In the first step, the interactions of three factors between the criminal and the operation captain are modeled as a zero-sum, noncooperative game, after which a mixed strategy Nash equilibrium method is used to derive the risk value for each district to be patrolled. In the second step, the risk values are used to compute the Shapley value for all 10 districts, for three different threat levels. A fair allocation of police personnel based on the Shapley value is made with a minimum set of personnel deployment costs. The efficacy of the proposed method is verified using openly available data from the San Francisco City Police detailing incidents from the year 2016. The experimental results show that police planners can use this framework to quantitatively evaluate the criminal threat in each district when deciding upon the deployment of patrol officers for three shifts per day.     

Bayesian Inferences and Forecasts With Multiple Autoregressive Moving Average Models

The main objective of this paper is to develop convenient Bayesian techniques for estimation and forecasting which can be used to analyze multiple (multivariate) autoregressive moving average processes. Based on the conditional likelihood function and the least squares estimates of the residuals, the marginal posterior distribution of the coefficients of the model is approximated by a matrix t distribution, the marginal posterior distribution of the precision matrix is approximated by a Wishart distribution, and the predictive distribution is approximated by a multivariate t distribution. Some numerical examples are given to demonstrate the idea of using the proposed techniques to analyze different types of multiple ARMA models.     

Distance-based outlier detection for high dimension,low sample size data

Despite the popularity of high dimension, low sample size data analysis, there has not been enough attention to the sample integrity issue, in particular, a possibility of outliers in the data. A new outlier detection procedure for data with much larger dimensionality than the sample size is presented. The proposed method is motivated by asymptotic properties of high-dimensional distance measures. Empirical studies suggest that high-dimensional outlier detection is more likely to suffer from a swamping effect rather than a masking effect, thus yields more false positives than false negatives. We compare the proposed approaches with existing methods using simulated data from various population settings. A real data example is presented with a consideration on the implication of found outliers.     

Comparison of response adaptive randomization features in multiarm clinical trials with control

We investigate multiple features of response adaptive randomization (RAR) in the context of a multiple arm randomized trial with control, where the primary goal is the identification of the best arm for use in a broader patient population. We maintain constant control allocation and vary the length of time until RAR is started, interim frequency, the underlying quantity used to calculate the randomization probabilities, and a threshold resulting in temporary arm dropping. We evaluate the designs on five metrics measuring benefit to the internal trial population, the future external population, and statistical estimation. Our results indicate these features have minimal interaction within the space explored, with preference for earlier activation of RAR, more frequent interim analyses, randomizing in proportion to the probability each arm is the best, and aggressive thresholding for temporarily dropping arms. The results illustrate useful principles for maximizing the benefit of RAR in practice.     

Ridge regression versus ols by pitman's closeness under quadratic and fisher's loss

The existence of values of the ridge parameter such that ridge regression is preferable to OLS by the Pitman nearness criterion under both the quadratic and the Fisher's loss is shown. Preference regions of the two estimators under the above loss functions are found. An upper bound for the value of the Pitman's measure of closeness, independent of a deterministic or stochastic choice of the ridge parameter, is given.