基于OWGA算子的偏好信息集结法及其在群决策中的应用

研究了决策者的偏好信息以不同形式给出的群决策问题。首先，利用不同偏好信息之间的转换公式，将偏好次序型、效用值型、互反判断矩阵型三种偏好信息一致化为模糊互补判断矩阵型的偏好信息；其次。利用加权几何平均（OWGA）算子对一致化后的决策信息进行集结。再对方案的加权几何平均优势度进行综合集结，并以此进行方案的排序，提出了基于OWGA算子的群决策方法，该方法具有操作简便和计算量少的特点。最后。通过实例说明方法的可行性和实用性。     

基于分段策略评分的冲突主体偏好排序方法及其应用

冲突中各利益主体的偏好信息对冲突局势的演变和纠纷调解具有重要影响。现有的冲突偏好排序方法主要基于决策者对冲突局势或状态、策略权重和声明信息的主观判断和理解,缺乏科学的数据来源支撑。为准确获取冲突主体的偏好信息,本文提出了一种基于调查法的分段策略冲突偏好排序方法。首先,根据决策者类别将冲突策略集合进行分段,并通过问卷、调研等方法获取每个冲突主体对所有分段策略的重要度评分信息。在此基础上,计算决策者对各个冲突状态的综合偏好评分,进而得到状态偏好的排序结果。最后以医患纠纷为例,对比分析了传统策略权重法和分段策略评分法的偏好排序和稳定性分析结果,进一步验证了所提方法的有效性。     

基于图模型理论有序偏好下的冲突反问题研究

在冲突谈判中,能获知对手偏好是掌握谈判主动性的重要条件。本文基于冲突分析图模型理论构建了一种获取对手偏好的方法。该方法通过深入分析冲突分析图模型中Nash、GMR和SEQ三种稳定性定义,利用反向思维,建立求解对手偏好最少约束条件的数学模型。该方法能让决策者在预知冲突结局的前提下,得到对手的全部偏好信息。以“云南曲靖陆良县铬污染”冲突事件为例,通过对该事件引发的冲突进行建模和偏好分析,在已知冲突最终结局的前提下,运用数学模型,省环保厅可以得到陆良化工企业的所有偏好序,使其在冲突谈判中做到知己知彼,同时也验证了该方法的可行性和有效性。案例分析过程可以从战略层面为谈判中的一方提供参考。     

信息不对称下的超对策冲突分析反问题研究

在信息不对称且对手为信息劣势方的冲突中,对手对决策者的偏好信息容易存在误解,而正确分析对手误解的偏好将有利于决策者在冲突谈判中占据主动优势,并为后期诱导机制的设立提供决策支持。本文基于超对策理论,从冲突分析的反向视角出发,提出了求解对手误解偏好的具体方法,利用谈判中对手显露出的均衡信息,建立了求解对手误解偏好的0-1非线性规划模型。为便于求解,将该模型转化成等效的0-1线性规划模型,并进一步采用CB cuts方法及相应的算法求取该模型的所有最优解。通过双渠道冲突案例分析,进一步验证了该方法对于求解实际冲突中误解偏好的有效性。最后,根据求得的误解偏好概述了决策者如何选择诱导策略,以实现自己利益的最大化。     

基于前景理论的交叉效率集结方法北大核心CSCD

传统交叉效率方法往往采用相加平均的方式来集结效率,这不仅缺乏足够的理论依据,而且导致大量决策信息的遗失.针对这个问题,文章引入前景理论来研究决策者面临收益和损失时的主观价值感受,并分别在乐观型、中立型和悲观型决策偏好的引导下构建相应的效率集结方法.随后,引入距离熵的概念来衡量不同决策单元视角下评价结果的可靠性,以此修正交叉效率集结结果.该集结方法充分考虑了决策者的主观偏好,并在其引导下最大程度地保留了决策信息,从而获得最符合现实决策需求的效率评价结果.最后,通过案例分析来验证该方法的有效性.     

基于GMCR-NPAWLAK混合模型的冲突分析研究

冲突分析图模型中,决策者的态度只有肯定和否定两种,实际问题中往往不止两种;新PAWLAK冲突模型(NPAWLAK模型)将冲突系统中决策者的三种态度扩展到决策争端的三种程度,符合实际情况,因而研究冲突系统中决策者的偏好排序和全局可行方案对决策者的策略选择具有重要意义。本文在NPAWLAK模型的基础上,引入冲突分析图模型理论(GMCR),提出GMCR-NPAWLAK冲突分析混合模型。该混合模型首先拓展和改进的策略优先排序法,实现了冲突系统中各决策者的客观偏好排序;同时,模型给出了全局可行方案的算法,该算法依据决策者的偏好排序分析结果找出系统的全局可行方案。最后,本文以某企业劳资关系的NPAWLAK冲突为例,对冲突系统进行建模和偏好分析,得到了冲突各方的偏好序列和全局可行方案,同时验证了混合模型的有效性。     

基于决策者风险偏好的模糊鲁棒性项目调度优化

不确定条件下模糊鲁棒性项目调度计划的生成受决策者风险偏好影响。本文研究模糊活动工期下考虑决策者风险偏好的鲁棒性项目调度优化问题，目标是合理安排活动开始时间，生成特定风险偏好下鲁棒性最大的进度计划。首先界定问题，构建优化模型；随后针对问题NP-hard属性和模型特点设计交替禁忌搜索启发式算法，求解得到不同风险偏好下满意的进度计划；最后用实例验证说明，并分析关键参数影响。结论如下：决策者风险偏好由规避转乐观时，项目冲突区间总和增多；截止日期、资源可用量较紧张时，风险偏好变化对冲突区间总和变化影响更大；风险偏好乐观时，截止日期变化对冲突区间总和变化影响更大。研究成果可为不同风险偏好决策者在不具历史数据的高不确定环境中制定合理前摄性计划提供决策支持。     

考虑决策者风险偏好的区间直觉模糊多属性群决策方法

提出了一种考虑决策者风险偏好且属性权重信息不完全的区间直觉模糊数多属性群决策方法。同时考虑相似度和接近度,确定每一属性的决策者权重。为了考虑决策者风险偏好对决策结果的影响和避免区间直觉模糊矩阵的渐进性,引入了决策者风险偏好系数,将集结后的综合决策矩阵转换成区间数矩阵。然后,为了客观地求出属性权重信息不完全环境下属性的权重,构建了基于区间直觉模糊交叉熵的属性权重目标规划模型,该模型不仅考虑了评价值的偏差,也强调了评价值自身的可信度。最后,通过研发项目选择问题的实例分析说明了所提方法的合理性和优越性。     

基于群体冲突的模糊偏好关系大群体决策方法

在大群体决策中,针对每一个决策者都有一个关于决策方案的模糊偏好关系的决策问题,提出了一种基于冲突的模糊偏好关系大群体决策方法。该方法首先考虑了复杂大群体的偏好差异,对决策者偏好进行聚类分析,形成不同的聚集,然后通过熵权法确定聚集的权重,集结成大群体模糊偏好关系,再对聚集内及聚集间进行冲突分析,通过一个迭代算子进行冲突消解,以达到一定冲突范围内的群体模糊偏好关系。最后通过一个算例说明了方法的有效性。     

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。     

A compromise weight for multi-criteria group decision making with individual preference

In a multi-criteria group decision making process, it is often hard to obtain a solution due to the possible conflict preferences from different participants and the undeterministic weights assigned to each criterion. This problem can be defined as to identify a set of weights for the given criteria to achieve a compromise of the conflict on different preferences. When such a compromise weight does not exist, we need to adjust (to reverse or to withdraw) some or all of the preferences from different participants. This paper describes a minimax principle based procedure of preference adjustments with a finite number of steps to find the compromise weight. At each iteration, we either find the weight or identify some ‘wrong’ preferences. We also define a consistency index for each participant to measure the distance between the individuals' preference and the final group decision. Corresponding theoretical work is referred to in support of the procedure, and numerical examples are provided for illustration. This study is further extended to the case of multiple assessments.     

Interval fuzzy preferences in the graph model for conflict resolution

A new analysis technique, appropriate to situations of high preference uncertainty, is added to the graph model for conflict resolution methodology. Interval fuzzy stabilities are now formulated, based on decision makers’ (DMs’) interval fuzzy preferences over feasible scenarios or states in a conflict. Interval fuzzy stability notions enhance the applicability of the graph model, and generalize its crisp and fuzzy preference-based stability ideas. A graph model is both a formal representation and an analysis procedure for multiple participant-multiple objective decisions that employs stability concepts representing various forms of human behavior under conflict. Defined based on a type-2 fuzzy logic, an interval fuzzy preference for one state over another is represented by a subinterval of [0, 1] indicating an interval-valued preference degree for the first state over the second. The interval fuzzy stabilities put forward in this research are interval fuzzy Nash stability, interval fuzzy general metarational stability, interval fuzzy symmetric metarational stability, and interval fuzzy sequential stability. A state is interval fuzzy stable for a DM if moving to any other state is not adequately desirable to the DM; where adequacy is measured by the interval fuzzy satisficing threshold of the DM and farsightedness, involving possible moves and countermoves by DMs, is determined by the interval fuzzy stability notion selected. Note that infinitely many degrees in an interval-valued preference are preserved in characterizing the desirability of a move. A state from which no DM can move to any sufficiently desirable scenario is an interval fuzzy equilibrium, and is interpreted as a possible resolution of the strategic conflict under study. The new stability concept is illustrated through its application to an environmental conflict that took place in Elmira, Ontario, Canada. Insightful results are identified and discussed.     

Multiple levels of preference in interactive strategic decisions

A multiple-level preference ranking structure is developed within the paradigm of the Graph Model for Conflict Resolution to study multi-objective decision making in conflict situations more realistically. In this structure, a decision maker may have multiple levels of preference for one state or scenario over another; for example, if state A is preferred to state B, it may be mildly preferred at level 1, more strongly preferred at level 2, …, or maximally preferred at level r, where r>0 is a fixed parameter. The number of levels, r, is unrestricted in this system, thereby extending earlier two-level (r=1) and three-level (r=2) structures. Multilevel versions of four stability definitions, Nash stability, general metarationality, symmetric metarationality, and sequential stability, are defined for the graph model with this extended preference structure, and the relationships among them are investigated. A specific case study, including multiple decision makers and multiple levels of preference, is carried out to illustrate how the new solution concepts can be applied in practice.     

Clusterwise aggregation of relations: The case of paired comparisons of cognac ads

This short paper takes up the problem stated by Gaul and Schader¹ in 1988 of simultaneous clustering and aggregation of relations—in this case precedences (preferences)—so that aggregate preferences could represent relatively homogeneous groups of preference relations. The paper indicates the existence of two distinct questions, similar to those asked in the case of the global aggregation problem, regarding intra-group agreement as to preferences represented in whatever form, and intra-group agreement as to a common (‘regular’) preference. In both these cases, though, fundamental computational problems arise. This paper presents a heuristic for obtaining an approximate solution. Requirements as to the properly optimal method are outlined.     

Fuzzy preference orderings in group decision making

In this paper, some use of fuzzy preference orderings in group decision making is discussed. First, fuzzy preference orderings are defined as fuzzy binary relations satisfying reciprocity and max-min transitivity. Then, particularly in the case where individual preferences are represented by utility functions (utility values), group fuzzy preference orderings of which fuzziness is caused by differences or diversity of individual opinions are defined. Those orderings might be useful for proceeding the group decision making process smoothly, in the same manner as the extended contributive rule method.     

Continuos social decision procedures

Classical social decision procedures are supposed to map lists of preference orderings into binary relations which describe society ‘preferences’. But when there are infinitely many alternatives the resulting plethora of possible preference orderings make it impossible to differentiate ‘nearby’ preference relations. If the preference information used to make social decisions is imperfect, society may wish to implement a continuous social decision procedure (SDP) so that nearby preference configurations will map into nearby social preference relations. It is shown here that a continuity requirement can severely restrict the admissible behavior of a social decision procedure. Furthermore, a characterization of continuous SDPs is presented which facilitates the examination of such procedures and their relation to various voting mechanisms.