Measuring credibility of compensatory preference statements when trade-offs are interval determined

This paper studies how an overall fuzzy preference relation can be constructed in the compensatory context of the simple additive difference model, when imprecision on the trade-offs has to be taken into account. Three credibility indices of preferences are analysed and illustrated by a numerical example. Arguments are presented supporting the use of the third index, for which an interesting transitivity property (which was an open problem) is proved.     

Three pseudo-paradoxes in ‘quantum’ decision theory: Apparent effects of observation on probability and utility

In quantum domains, the measurement (or observation) of one of a pair of complementary variables introduces an unavoidable uncertainty in the value of that variable's complement. Such uncertainties are negligible in Newtonian worlds, where observations can be made without appreciably disturbing the observed system. Hence, one would not expect that an observation of a non-quantum probabilistic outcome could affect a probability distribution over subsequently possible states, in a way that would conflict with classical probability calculations. This paper examines three problems in which observations appear to affect the probabilities and expected utilities of subsequent outcomes, in ways which may appear paradoxical. Deeper analysis of these problems reveals that the anomalies arise, not from paradox, but rather from faulty inferences drawn from the observations themselves. Thus the notion of quantum decision theory is disparaged.     

企业投资决策分析方法及应用

目的/意义针对投资决策中偏好信息为区间模糊数的多准则决策问题,提出了一种基于冲突最小化的群体决策方法。设计/方法首先,以决策整体最优为目标,构建极大熵规划模型求解决策准则权重;其次,以决策总冲突程度最小化为目标,构建优化模型求解决策成员权重;然后,建立决策方案的比较可能度矩阵和排序向量模型,获得问题决策方案排序结果。最后,通过一个投资决策实例进行应用,验证了方法的有效性和科学性。结论/发现该方法能够解决投资群体决策中的冲突问题,在投资决策中形成偏好最一致的决策方案。     

互联网金融风险与政府治理机制转型

目的/意义人工智能的飞速发展,一方面为政府治理提供了新的途径,但同时也带来了极大的挑战。因此,在人工智能背景下,如何优化政府智能决策模型、提高政府决策的效率和科学性就显得尤为重要。设计/方法从政府治理中智能决策的相关理论入手,对影响政府智能决策的内部因素和外部因素展开了研究,探讨了融合多源信息进行政府智能决策的问题。结论/发现构建了基于政府内外部信息的多源信息融合模型和政府智能决策系统,为智能化政务办公的发展提供强有力支撑。     

三支决策模型及其研究现状分析

目的/意义二分类群体决策问题广泛存在于社会民生和区域经济中的各个领域,例如：公务员面试、酒店推荐以及交通出行路线选择等。因此,需要提出合理的二分类群体决策方法,从而为政府和相关企业提供决策支持。设计/方法在考虑个体后悔规避行为的基础上,建立二分类选择过程,获得个体和群体关于备选方案的二分类向量;进一步建立二分类共识过程,协助个体调整偏好,从而获得具有较高共识水平的二分类。结论/发现从最小调整成本视角,建立了后悔规避行为下二分类群体决策方法,更加贴近现实决策情景,进一步丰富了群体决策方法的应用范围。     

高技术园区技术效率与规模指数——基于DEA的实证研究

文章运用数据包络分析法评价了53个国家级开发区的投入产出效率、投入剩余和产出亏空状况,同时以超效率DEA模型为基础对这些开发区的生产效率进行综合排名,最后通过线性回归探讨影响投入产出的纯技术效率和规模指数的外部因素。结果发现:(1)在众多的开发区中真正处于有效率生产状态的地区并不多,占20.75%,处于纯技术有效的开发区占39.62%;(2)导致大部分开发区无法处于有效的生产前沿面上进行生产的最直接的原因是规模过小;(3)开发区的投入产出纯技术效率受地区的开放程度、集聚程度和气候环境的影响较大,而规模指数则受开放程度、地区经济实力和政府政策的影响较为明显。     

Automatic locally adaptive smoothing for tree-based set estimation

Tree-based methods similar to CART have recently been utilized for problems in which the main goal is to estimate some set of interest. It is often the case that the boundary of the true set is smooth in some sense, however tree-based estimates will not be smooth, as they will be a union of ‘boxes’. We propose a general methodology for smoothing such sets that allows for varying levels of smoothness on the boundary automatically. The method is similar to the idea underlying support vector machines, which is applying a computationally simple technique to data after a non-linear mapping to produce smooth estimates in the original space. In particular, we consider the problem of level-set estimation for regression functions and the dyadic tree-based method of Willett and Nowak [Minimax optimal level-set estimation, IEEE Trans. Image Process. 16 (2007), pp. 2965–2979].     

Statisticians Can Matter

Statisticians fall far short of their potential as guides to enlightened decision making in business. Two important explanations are: (1) Decision makers are often more easily convinced by concrete examples, however fragmentary and misleading, than by competent statistical analysis. (2) The effective use of statistics in the process of decision making requires hard thinking by decision makers, thinking that cannot be delegated entirely to the statistical specialist. Modern developments in interactive statistical computing may help to reduce the force of these limitations on exploitation of statistics; used properly, computing can encourage, almost force, the student or business user of statistics to think statistically.     

Asymptotically Optimal Nonparametric Empirical Bayes Via Predictive Recursion

An empirical Bayes problem has an unknown prior to be estimated from data. The predictive recursion (PR) algorithm provides fast nonparametric estimation of mixing distributions and is ideally suited for empirical Bayes applications. This article presents a general notion of empirical Bayes asymptotic optimality, and it is shown that PR-based procedures satisfy this property under certain conditions. As an application, the problem of in-season prediction of baseball batting averages is considered. There the PR-based empirical Bayes rule performs well in terms of prediction error and ability to capture the distribution of the latent features.