拟半序意义下随机选择过程中的极大元定理

本文首先分析、比较半序、偏好与拟半序三者之间的关系,然后,在比半序、偏好更一般的拟半序意义下提出并论证了随机选择过程中的极大元定理,本文定理将统一和改进若干已知结果.     

不完全偏好信息下房地产投资的模糊优选

解决了不完全偏好信息下含有模糊决策元素的房地产投资方案的优选问题.分析形成了房地产投资方案的评价指标体系,并在Hamming距离与Euclidean距离测度的基础上,建立了不完全偏好信息下的模糊多属性决策模型,能处理决策元素为一般模糊数的决策问题,同时提供了所建非线性规划模型的交叉迭代解法.实际算例的比较结果表明,本文提出的决策模型及算法优于文献中关于偏好信息完全确知的经典模型与算法.     

决策支持下的不完全偏好信息多目标对策

针对多目标对策的不完全偏好信息,将理性策略概念和偏好规划理论引入到多目标对策研究中,把两人的多目标对策考虑为两个局中人同时面临的两个多目标决策问题,偏好信息用可行权重限定集合进行建模.多目标对策模型分为对策模型和决策模型两部分.在对策模型中,利用理性和偏好的共同知识,获得理性策略集.在决策模型中,局中人将策略选择作为使用私人偏好信息的多目标决策问题.最后,通过实例验算表明所提方法的有效性.     

基于属性关联的不完全风险偏好信息大群体应急决策方法

针对特大突发事件应急决策中大群体专家存在偏好信息不完全的问题,本文提出一种新的不完全风险性信息大群体应急决策方法。首先,利用最优离散拟合方法对决策者的风险偏好因子进行测度并据此对专家聚类;其次,根据不完全偏好矩阵进行属性关联测度,提出了基于风险偏好和属性关联的新的补值模型,得到完全偏好信息矩阵;然后,运用主成分分析方法提取属性主成分,并结合属性权重进行信息集结和方案择优;最后,通过台风“天鸽”事件验证所提方法的可行性和有效性。     

订单信息对双供应商公平偏好的实验研究

为了研究订单信息对双渠道进货供应商公平偏好的影响，本文考察由一个制造商和两个同质供应商组成的双渠道进货供应链，其中供应商按照价格折扣给制造商供货。为了获取更多优惠，制造商将会给一个供应商分配尽可能多的订单，因而引起小订单供应商的不满。这种小订单供应商对公平的偏好可能导致其拒绝供货。通过设计和实施实验室实验，本文对比订单是完全信息和不完全信息两种情境下被试的决策行为倾向。实验结果表明，在两种情境下被试决策都偏离理论预期，公平偏好是造成这一现象的主要因素。供应商在不完全信息下表现出更强的公平偏好。基于实验所观察到的现象，分别建立了完全信息和不完全信息下的行为模型，并通过参数估计考察了它们的有效性。研究结论表明，在双渠道进货时应重点关注小订单供应商的公平偏好，不要故意隐藏订单信息。     

随机偏好连接图的中心极限定理

我们研究了一类具有随机顶点和边的随机连接图模型, 其中顶点的随机性由一个Poisson 点过程所决定, 边的随机性由一个概率连接函数所决定. 我们得到了带偏好的随机连接图模型的关于所有随机边的长度和的一个中心极限定理.     

不完全市场下考虑损失厌恶的连续时间投资组合选择

在不完全市场条件下研究了一般情形下的损失厌恶投资者的连续时间投资组合选择模型. 面对市场风险, 投资者的偏好由一个S-型的价值函数定义. 通过把不完全市场转换为完全市场, 利用鞅方法和复制技术, 分别获得了投资者的最优期末财富以及最优投资策略. 最后讨论了一个分段幂函数的例子, 在模型系数为确定的常数情形下, 得到了最优解的显示表达式.     

完全与不完全的随机网

本文证明了鞍点连线网在小扰动下可以产生完全随机网和不完全随机网两种情况,并按照动力系统理论讨论了两类随机网的结构.     

局部凸Hausdorff空间内凝聚选择映象的极大元

在本文内,局部凸Hausdorff空间中凝聚选择映象极大元的两个存在性定理被证明,推广Mehta的最近结果.其中一个定理肯定地解答了Mehta提出的待解决的问题.     

一个随机开放经济中的财富偏好和财政政策

本文把财富偏好结合进入一个开放经济的随机模型.通过对生产技术、效用函数和随机冲击做了确定的假设,我们得到了许多显式解,包括居民的总消费、期望经济增长率和所有资产的均衡回报;进一步讨论了财政政策和财富偏好怎样影响经济增长和社会福利.     

An integrated model-based interactive approach to FMAGDM with incomplete preference information

Incomplete fuzzy preference relations, incomplete multiplicative preference relations, and incomplete linguistic preference relations are very useful to express decision makers’ incomplete preferences over attributes or alternatives in the process of decision making under fuzzy environments. The aim of this paper is to investigate fuzzy multiple attribute group decision making problems where the attribute values are represented in intuitionistic fuzzy numbers and the information on attribute weights is provided by decision makers by means of one or some of the different preference structures, including weak ranking, strict ranking, difference ranking, multiple ranking, interval numbers, incomplete fuzzy preference relations, incomplete multiplicative preference relations, and incomplete linguistic preference relations. We transform all individual intuitionistic fuzzy decision matrices into the interval decision matrices and construct their expected decision matrices, and then aggregate all these expected decision matrices into a collective one. We establish an integrated model by unifying the collective decision matrix and all the given different structures of incomplete weight preference information, and develop an integrated model-based approach to interacting with the decision makers so as to adjust all the inconsistent incomplete fuzzy preference relations, inconsistent incomplete linguistic preference relations and inconsistent incomplete multiplicative preference relations into the ones with acceptable consistency. The developed approach can derive the attribute weights and the ranking of the alternatives directly from the integrated model, and thus it has the following prominent characteristics: (1) it does not need to construct the complete fuzzy preference relations, complete linguistic preference relations and complete multiplicative preference relations from the incomplete fuzzy preference relations, incomplete linguistic preference relations and incomplete multiplicative preference relations, respectively; (2) it does not need to unify the different structures of incomplete preferences, and thus can simplify the calculation and avoid distorting the given preference information; and (3) it can sufficiently reflect and adjust the subjective desirability of decision makers in the process of interaction. A practical example is also provided to illustrate the developed approach.     

A goal programming model for incomplete interval multiplicative preference relations and its application in group decision-making

In decision making problems, there may be the cases where the decision makers express their judgements by using preference relations with incomplete information. Then one of the key issues is how to estimate the missing preference values. In this paper, we introduce an incomplete interval multiplicative preference relation and give the definitions of consistent and acceptable incomplete ones, respectively. Based on the consistency property of interval multiplicative preference relations, a goal programming model is proposed to complement the acceptable incomplete one. A new algorithm of obtaining the priority vector from incomplete interval multiplicative preference relations is given. The goal programming model is further applied to group decision-making (GDM) where the experts evaluate their preferences as acceptable incomplete interval multiplicative preference relations. An interval weighted geometric averaging (IWGA) operator is proposed to aggregate individual preference relations into a social one. Furthermore, the social interval multiplicative preference relation owns acceptable consistency when every individual one is acceptably consistent. Two numerical examples are carried out to show the efficiency of the proposed goal programming model and the algorithms.     

Eigenvector method,consistency test and inconsistency repairing for an incomplete fuzzy preference relation

In this paper, we extend the eigenvector method (EM) to priority for an incomplete fuzzy preference relation. We give a reasonable definition of multiplicative consistency for an incomplete fuzzy preference relation. We also give an approach to judge whether an incomplete fuzzy relation is acceptable or not. We develop the acceptable consistency ratio for an incomplete multiplicative fuzzy preference relation, which is simple and similar to Saaty’s consistency ratio (CR) for the multiplicative preference relation. If the incomplete fuzzy preference relation is not of acceptable consistency, we define a criterion to find the unusual and false element (UFE) in the preference relation, and present an algorithm to repair an inconsistent fuzzy preference relation until its consistency is satisfied with the consistency ratio. As a result, our improvement method cannot only satisfy the consistency requirement, but also preserve the initial preference information as much as possible. Finally, an example is illustrated to show that our method is simple, efficiency, and can be performed on computer easily.     

A Procedure for Decision Making Based on Incomplete Fuzzy Preference Relation

In this paper, we investigate the decision making problem based on fuzzy preference relation with incomplete information. We first introduce incomplete fuzzy preference relation and present some of its desirable properties. We then develop a system of equations. Based on this system of equations, we propose a procedure for decision making based on incomplete fuzzy preference relation, and finally, a numerical example is presented to illustrate the proposed procedure.     

基于决策者信任水平和组合赋权的不完全偏好复杂大群体应急决策方法

针对属性权重完全未知且专家偏好出现残缺值的复杂大群体应急决策问题,提出了一种新的决策方法。首先,设计了一套基于决策者信任水平的残缺值填充机制,对缺失的偏好信息进行补充。然后,将各方案的大群体偏好信息进行聚类,基于方案信息熵和群体偏好冲突水平构建组合赋权方法,对属性权重进行测算,进而得到各个方案的综合评价值。最后对该方法进行了实例验证,验证结果表明本文提出的方法具有良好的可行性和有效性。     

IP-MAGS: an incomplete preference-based multiple attribute group support system

In this research, we develop a group decision support system to deal with multiple attribute group decision-making problems, which involve getting incomplete judgements of individual preference and aggregating the judgements by means of the additive preference model. Dominance-based decision-making rules are built in and applied to obtain a group's preferred alternative. The proposed system, above all, allows for more various forms of incomplete judgements than prior systems that were designed to handle group decision problems. A user-friendly graphical interface enables users to easily encode their incomplete judgements. Further, the system helps individuals revise their preference judgements by referring them to their own decision results in comparison with the group's aggregated decision result. The system is a web-based application system, which enables bidirectional communications between individuals and the system. Any individual who is involved in a group decision-making problem is able to participate in the decision-making process from a remote site. Furthermore, we present a real-life case study on the selection of a branch office server that has been carried out using the proposed system.     

Preference Programming with incomplete ordinal information

This paper extends possibilities for analyzing incomplete ordinal information about the parameters of an additive value function. Such information is modeled through preference statements which associate sets of alternatives or attributes with corresponding sets of rankings. These preference statements can be particularly helpful in developing a joint preference representation for a group of decision-makers who may find difficulties in agreeing on numerical parameter values. Because these statements can lead to a non-convex set of feasible parameters, a mixed integer linear formulation is developed to establish a linear model for the computation of decision recommendations. This makes it possible to complete incomplete ordinal information with other forms of incomplete information.     

不同直觉偏好信息下的多属性决策方法

研究了属性值为实数且决策者对属性的偏好信息以直觉判断矩阵或残缺直觉判断矩阵给出的模糊多属性决策问题.首先介绍了直觉判断矩阵、一致性直觉判断矩阵、残缺直觉判断矩阵、一致性残缺直觉判断矩阵等概念,而后分别考虑关于直觉判断矩阵和残缺直觉判断矩阵的多属性决策问题,接着建立了基于直觉判断矩阵和残缺直觉判断矩阵的多属性群决策模型,通过求解这些模型获得属性的权重.进而给出了不同直觉偏好信息下的多属性决策方法.最后通过一个例子说明了该方法的可行性和实用性.     

Confidence models of incomplete preferences

This paper introduces a new class of representations for incomplete preferences called confidence models. Confidence models describe decision makers who behave as if they have probabilistic uncertainty over their true preferences, and are only willing to express a binary preference if it is sufficiently likely to hold. Confidence models provide a natural way to connect incomplete preferences with stochastic choice. This connection is characterized by a simple joint condition on an incomplete preference relation and a random choice rule.