基于混合加权距离的毕达哥拉斯模糊TOPSIS多属性决策方法研究

针对决策信息为毕达哥拉斯模糊集的多属性决策问题，提出了一种基于混合加权测度的TOPSIS决策方法。在分析了现有距离测度方法不足的基础上，首先给出了一种新的毕达哥拉斯模糊距离测度——毕达哥拉斯模糊有序加权距离（PFOWD），并研究了该测度权重的确定方法；在PFOWD基础上，进一步提出了毕达哥拉斯模糊混合加权距离（PFHWD），同时探讨了其特征和与现有毕达哥拉斯模糊测度的关系；最后提出了一种基于PFHWD测度的毕达哥拉斯模糊TOPSIS多属性方法，并用实例验证了方法的有效性和可行性。     

区域制度变迁和中部地区经济发展的战略适应

我国区域制度变迁对中部地区的发展产生了深远影响。文章分析了区域经济新格局下中部地区的区域竞争环境，依据演化理论提出了环境变迁后中部地区区域发展的战略适应观点以及战略适应的现实路径，认为中部地区的战略适应路径在于区域制度创新和经济集聚战略，并就区域制度创新和经济集聚战略实施中的具体问题进行了探讨。     

语言多属性决策的目标规划模型

研究了只有部分属性权重信息、属性值以语言变量或不确定语言变量形式给出且决策者对方案有偏好信息的语言多属性决策问题.给出了语言变量和不确定语言变量的运算法则,以及不确定语言变量之间比较的可能度公式,定义了语言变量的偏离度概念.在属性值以1)语言变量和2)不确定语言变量,这两种形式给出的情形下,分别建立了一个基于偏离度的目标规划模型,并通过求解这两种模型分别获得相应的属性权重.然后对于情形1),利用语言加权平均(LWA)算子,对语言决策信息进行加权集成,继而对方案进行排序和择优;对于情形2),利用不确定语言加权平均(ULWA)算子,对不确定语言决策信息进行加权集成,并利用可能度公式构造可能度矩阵(互补判断矩阵),继而利用互补判断矩阵排序公式对决策方案进行排序和择优.最后进行了实例分析.     

Persuasion: An experimental study of team decision making

This paper studies persuasion within teams and investigates why teams commonly take, by some measures, better decisions than individuals. The analysis is based on data from electronic communication within teams of two players. Thanks to the experimental design, changes of an individual’s decision can be attributed to the content of the team partner’s message. The results for knowledge-related and strategic problems show that individuals’ decisions change upon receiving more informative and sophisticated arguments and remain the same otherwise. This individual behavior is an essential part of the information aggregation in teams and can explain the advantage of teams in decision making and in games.     

Aggregating classifiers via Rademacher–Walsh polynomials

Methods are proposed to combine several individual classifiers in order to develop more accurate classification rules. The proposed algorithm uses Rademacher–Walsh polynomials to combine M (≥2) individual classifiers in a nonlinear way. The resulting classifier is optimal in the sense that its misclassification error rate is always less than, or equal to, that of each constituent classifier. A number of numerical examples (based on both real and simulated data) are also given. These examples demonstrate some new, and far-reaching, benefits of working with combined classifiers.     

Strategic Learning and the Topology of Social Networks

We consider a group of strategic agents who must each repeatedly take one of two possible actions. They learn which of the two actions is preferable from initial private signals and by observing the actions of their neighbors in a social network. We show that the question of whether or not the agents learn efficiently depends on the topology of the social network. In particular, we identify a geometric “egalitarianism” condition on the social network that guarantees learning in infinite networks, or learning with high probability in large finite networks, in any equilibrium. We also give examples of nonegalitarian networks with equilibria in which learning fails.     

网络直播聚合犯罪的罪因、特征与治理措施

网络直播聚合犯罪愈演愈烈,主要集中于知识产权犯罪和色情犯罪领域,国内目前对该类犯罪的研究较少。网络直播聚合犯罪与社会结构方面的紧张、个人违法性意识方面的紧张存在关系,前者主要体现为：商品拜物教影响下的部分民众设定目标时日益以物质获得为参考系;阶层固化使得处于部分底层民众无法通过制度性手段实现目标。后者主要体现为网络直播聚合犯罪者实施犯罪,与其权衡犯罪成本与获利数额、心存侥幸有关。在对案例进行实证分析的前提下,归纳概括出此类犯罪的三个主要特征：犯罪主体呈现出低龄化、学历较低、无业状态较多的特点;分工的复杂性、网络的匿名性使得犯罪的隐蔽性较强;黑灰产业链业已形成并成熟。对此类犯罪的治理,应当坚持惩防并举,在惩罚方面应当注意网络直播聚合共同犯罪认定规则的变通适用,以及网络直播聚合犯罪立法扩张、司法限缩与严格执法之间的关系;在预防方面应当构建国家、社会两位一体的预防模式。     

时变视角下中美农产品期货波动溢出效应实证

由于金融市场间价格波动溢出效应具有时变性和动态性的特点,采用双变量BEKK模型研究了国内外农产品期货市场间时变波动溢出关系,得出结论：农产品期货的波动均显著地受到自身前期波动的影响,波动具有聚集性和持久性;美国玉米期货与中国玉米期货不存在波动溢出效应,但是美国大豆期货与中国大豆期货、美国豆粕期货与中国豆粕期货之间均存在双向的波动溢出效应。因此,中国大豆期货和豆粕期货在世界农产品期货市场上具有一定的权力,即定价权。     

全媒体时代主流媒体的数据化生存与发展

基于互联网连接的媒体平台,正在成为各类原生数据的源发地和汇聚中心,对大数据进行整合及应用的能力,将成为未来媒体平台运营的核心能力。建构在互联网平台之上的主流媒体必然要经历从信息总汇向数据总汇的转变,在这一过程中,媒体的业态、社会功能都将发生重大变化。伴随全媒体时代信息技术的快速发展,新闻传播呈现出新的特点和规律：传播主体大众化;传播容量海量化;传播方式交互化;传播手段多维化;传播时效全时化。在这样的背景下,主流媒体应不断进行数据化的探索和创新,基于5G网络,通过大数据技术运用,强化数据库建设,以此为基础,运用基于大数据的人工智能技术改变内容生产和分发方式,并以之作为内容监管的有力武器。未来基于大数据分析的综合业务推动自身产业结构优化。     

ESTIMATING A PARAMETER WHEN IT IS KNOWN THAT THE PARAMETER EXCEEDS A GIVEN VALUE

In some statistical problems a degree of explicit, prior information is available about the value taken by the parameter of interest, θ say, although the information is much less than would be needed to place a prior density on the parameter's distribution. Often the prior information takes the form of a simple bound, ‘θ > θ₁ ’ or ‘θ < θ₁ ’, where θ₁ is determined by physical considerations or mathematical theory, such as positivity of a variance. A conventional approach to accommodating the requirement that θ > θ₁ is to replace an estimator, , of θ by the maximum of and θ₁. However, this technique is generally inadequate. For one thing, it does not respect the strictness of the inequality θ > θ₁ , which can be critical in interpreting results. For another, it produces an estimator that does not respond in a natural way to perturbations of the data. In this paper we suggest an alternative approach, in which bootstrap aggregation, or bagging, is used to overcome these difficulties. Bagging gives estimators that, when subjected to the constraint θ > θ₁ , strictly exceed θ₁ except in extreme settings in which the empirical evidence strongly contradicts the constraint. Bagging also reduces estimator variability in the important case for which is close to θ₁, and more generally produces estimators that respect the constraint in a smooth, realistic fashion.