大历史的转折——60年前的北平“零和博弈”和谈

以七集电视连续剧《北平和谈》为影视分析对象,从博弈论的角度展现60年前北平和谈的那一幕大历史转折,这是一次"不许讨价还价"的和谈,也是最后的机会,但根本无法进行调和折中。《北平和谈》瞩目于和谈双方两个阵营中人物的情感世界,调动各种艺术表现手段,对历史史实作了相当情感化的处理。历史当然不能假设,但是,把合作性博弈的愿望转变为合作性博弈的行动,零和博弈负结局不是不可能实现的,或许中国近现代史也可以被改写。     

当前形势下上海文化体制改革的突破口与对策

上海文化体制改革在取得巨大成效的同时,也有自身的困境和难点,如文化建设目标不明确带来文化自信不足、文化观念上的束缚带来改革力度上的不足、计划经济时代的管理体制难以适应市场经济的发展等。当前文化体制改革突破口与对策,在于实施大部制、进一步理顺体制机制、建立新型宏观文化管理体制,以产权制度改革为重点建立现代企业法人制度和合格市场主体等一系列措施。     

能源需求子系统的建模及分析

将需求量、价格和利润看作一个系统中3个相互作用、相互依存和相互制约的因素,建立了能源需求子模型。对该模型进行了动力学行为分析,结果表明,以价格作为杠杆促进节能,可以抑制能源过度需求,保证能源系统的稳定。     

以礼仪教育为切入点,促进大学生道德建设

在认识理解“礼仪”及“礼仪教育”的基本含义基础上,阐述礼仪教育的社会功能及其与德育的关系,提出把礼仪教育作为加强德育实效性的切入点,有效解决知行统一的问题,引导大学生培养高尚的道德情操,形成正确的道德观、价值观、世界观,培养内在素质优秀,外在形象良好,德才兼备的大学生。     

提升网络意识形态话语权的五大着力点

网络意识形态话语权决定着意识形态主导权,影响着意识形态安全。随着新媒体的迅速发展和众多话语主体的相继出现,网络主流意识形态面临着信息碎片化、表达泛娱乐化、话语“平权化”、传播方式圈层化等不利影响,致使主流意识形态话语的权威性受到削弱。当前,必须把准提升网络意识形态话语权的着力点,重点解决好“谁来说”“说什么”“怎么说”“向谁说”“在哪里说”等问题。应通过打造话语主体、创新话语内容、融合话语媒体、增强话语导向、扩大话语空间等有力措施,不断提升网络意识形态话语的胜任力、说服力、传播力、向心力及国际影响力,为我国意识形态安全和政权安全筑牢根基,为实现中华民族伟大复兴的中国梦保驾护航。     

考虑公平关切及其谈判破裂点的供应链运作

将考虑谈判破裂点的Nash解作为公平参考点,构建了基于Stackelberg博弈的公平关切供应链模型,分别在批发价契约和回购契约中探讨了供应链的决策和协调问题,分析了谈判破裂点对公平关切供应链的影响.研究发现:当批发价分别为外生、内生变量时,谈判破裂点对最优订货量的影响是完全相反的.此外,在批发价契约和回购契约中,基于Stackelberg博弈的公平关切供应链均无法协调,继而文章采用价格补贴的方式进行联合契约设计使供应链达到协调.     

Density regression using repulsive distributions

Flexible regression is a traditional motivation for the development of non-parametric Bayesian models. A popular approach for this involves a joint model for responses and covariates, from which the desired result arises by conditioning on the covariates. Many such models involve the convolution of a continuous kernel with some discrete random probability measure defined as an infinite mixture of i.i.d. atoms. Following this strategy, we propose a flexible model that involves the concept of repulsion between atoms. We show that this results in a more parsimonious representation of the regression than the i.i.d. counterpart. The key aspect is that repulsion discourages mixture components that are near each other, thus favouring parsimony. We show that the conditional model retains the repulsive features, thus facilitating interpretation of the resulting flexible regression, and with little or no sacrifice of model fit compared to the infinite mixture case. We show the utility of the methodology by way of a small simulation study and an application to a well-known data set.     

Smoothed ranks for two or multi-sample location problems

Smoothed ranks are proposed for two or multi-sample location problems. The regular ranks in Wilcoxon's two sample test are replaced with smoothed ranks, and the shift parameter is estimated. Asymptotic properties of the smoothed rank estimator are shown and a hypothesis test is proposed. Moreover, the smoothed ranks are applied in the Kruskal–Wallis's r-sample test and the power of the test is computed using regular and smoothed ranks. Examples and Monte Carlo simulations show that the smoothed ranks perform similarly to the traditional rank based estimators under contaminated normal or non-normal populations.     

Local linear estimate of the point at high risk: Spatial functional data case

Abstract

The purpose of the present paper is to investigate by the local linear method a nonparametric estimator of the point at high risk of scalar response variable given a functional variable when the observations are spatially dependent. The main goal is to establish the almost complete convergence with rate of this estimator under some general conditions. A practical example on the climatological data shows the usefulness of our theoretical study.     

A fast algorithm for short term electric load forecasting by a hidden semi-markov process

The paper proposes an alternative algorithm to implement the current manual choosing mechanism of energy companies whose dedicated department has a library of electric load models and one best model is chosen manually everyday for daily forecast. The proposed algorithm is a combination of an estimation of change point, and a fast ECM algorithm based on the empirical probability function, as well as methods of a hidden markov chain. We train parameters of the proposed algorithm based on a historical dataset consisting of loads, exogenous information such as temperature, and the daily recommended best model which is unavailable sometimes. Simulations and a test on a real-world dataset show that compared with other state-of-art algorithms, the proposed algorithm is fast and efficient for short-term electric load forecasting. An implement to the proposed algorithm written in Matlab is provided in supplement file.