MULTIPLE-CRITERIA DECISION MAKING INCLUDING QUALITATIVE FACTORS: THE POST-MODEL ANALYSIS APPROACH

As a method of solving multiple-criteria decision making problems with a single quantitative objective and multiple qualitative objectives, the post-model analysis (PMA) approach is proposed. The essence of PMA is to support the trade-offs between a quantitative objective and multiple qualitative objectives so that the decision maker can find a perceived most preferred nondominated solution. To this end, the optimal solution of a quantitative model is found first, without regard for qualitative factors. The solution is then evaluated in terms of qualitative objectives. When the initial quantitatively optimal solution is adjusted to allow improvement of qualitative goals, opportunity costs of achieving qualitative goals are incurred. In this process, an expert system and/or graphical display can be used. PMA therefore provides a way to incorporate quantitative models into knowledge-based expert systems.     

Incorporating preference information in interactive reference point methods for multiobjective optimization

In this paper, we introduce new ways of utilizing preference information specified by the decision maker in interactive reference point based methods. A reference point consists of desirable values for each objective function. The idea is to take the desires of the decision maker into account more closely when projecting the reference point onto the set of nondominated solutions. In this way we can support the decision maker in finding the most satisfactory solutions faster. In practice, we adjust the weights in the achievement scalarizing function that projects the reference point. We identify different cases depending on the amount of additional information available and demonstrate the cases with examples. Finally, we summarize results of extensive computational tests that give evidence of the efficiency of the ideas proposed.     

A system for decision support without explicit objective functions

This paper presents a conversational decision support system: Resource Allocation in Business Operation under Uncertain Worth (RAINBOW). The focus is placed on the loosely structured decision situation where the multiple objective functions are given only implicitly and should be locally approximated by the decision maker as the decision making process proceeds. Basically, RAINBOW supports the process of convergence to a preferred alternative by giving the decision maker information which will help him form consistent evaluations of the utility function, the objective functions and the solution for the decision problem.     

A comparative study of three methods of eliciting preference information

The study reported in this paper compared three methods of eliciting preference information from a decision maker and estimating weights with this information for use in a multiple objective decision making model. The design of the study included issues of implementing computerized interactive models vs more traditional question and answer techniques in the light of different decision models (in terms of level of complexity) and differing levels of decision maker experience. Results indicate that decision makers, regardless of prior exposure to computer terminals, are not intimidated by their use. Additionally, methods which required non-quantitative statements of preferences were preferred over techniques which requested numerical estimates of tradeoffs or marginal rates of substitution between objectives.     

Optimal Delivery Time Quotation and Order Sequencing

This paper examines the problem of quoting a common delivery time for several orders. The decision maker responsible for the quote must trade off the cost associated with a long delivery time against the cost of tardy orders that might be induced by a short delivery time. First, an aggregate cost model is investigated where it is assumed that some cost is incurred if the quoted delivery time exceeds a given threshold and the cost of order tardiness is dependent on the order. Both exact and heuristic solution procedures are proposed for this model. Finally, for the situation where the costs cannot be quantified, a bicriteria formulation of the problem is pursued and the set of nondominated solutions is identified.     

Probabilistic dominance criteria for comparing uncertain alternatives: A tutorial

This paper is a tutorial which demonstrates the current state-of-the-art methods for incorporating risk into project selection decision making. The projects under consideration might be R&D, IT, or other capital expenditure programs. We will show six decision making methods: 1. mean-variance (MV), 2. mean-semivariance, 3. mean-critical probability, 4. stochastic dominance, 5. almost stochastic dominance (ASD), and 6. mean-Gini. We will also describe the assumptions about the risk attitudes of the decision maker which are associated with each of the techniques. While all these methods have been previously applied elsewhere, this is the first paper which shows all of their applications in the project selection context, together with their interrelationships, strengths and weaknesses. We have applied all six techniques to the same group of five hypothetical projects and evaluated the resulting nondominated sets. Among the methods reviewed here, stochastic dominance is recommended because it requires the least restrictive assumptions. ASD and mean-Gini are recommended when stochastic dominance is not practical or when it does not yield definitive choices. MV, mean-semivariance, and mean-critical probability are shown to be flawed.     

EXPERIMENTAL RESULTS OF AN APPROACH FOR EVALUATING MULTIATTRIBUTE ALTERNATIVES

In this paper the results of an extensive simulation study are reported to provide empirical evidence on the Evaluation and Bound approach. Some rules for implementing the procedure interactively on a computer are also examined and compared experimentally. The results suggest that the Evaluation and Bound procedure, in conjunction with an appropriate strategy of implementing it, would reduce considerably the information requirement of the decision maker in the choice of a preferred multiattribute alternative.     

纵向沟通中隐性信息获取的激励兼容机制研究

下属隐性信息的获取是确保上级决策者有效决策的一个重要基础,现有的研究未能解决隐性信息获取的效率与预算平衡的两难问题。本文通过确认决策者的作用,在分析模型中引入决策者成本,并借鉴政府征收“所得税”的思路,不仅解决了隐性信息获取的真实性,而且解决了原有方案未能同时兼顾的预算平衡、利润最大化以及向下属转移损失等问题。     

纵向沟通中隐性信息获取的激励兼容机制研究

下属隐性信息的获取是确保上级决策者有效决策的一个重要基础,现有的研究未能解决隐性信息获取的效率问题与预算平衡的两难问题。本文通过确认决策者的作用,在分析模型中引入决策者成本,并借鉴政府征收"所得税"的思路,不仅解决了隐性信息获取的真实性,而且解决了原有方案未能同时兼顾的预算平衡、利润最大化以及向下属转移损失等问题。     

An Experimental Evaluation of Articulation of Preferences in Multiple Criterion Decision-Making (MCDM) Methods

This paper describes the results of a study done to determine how well multiple criterion decision-making methods perform in helping a decision maker arrive at a preferred solution to a multicriterion problem with conflicting objectives. The study used a factorial experiment and doctoral students as subjects. Two competing methods for solving multicriterion problems were compared along with the influence of problem complexity. The methods differed in the way preferences were articulated by decision makers. The results showed that there was no difference between the methods for several performance measures, disaffirming some prior expectations. From an actual use point of view, the study suggests that methodological improvements to existing techniques must be matched by efforts to improve information presentation and interpretation to facilitate preference judgments.     

基于粗集理论的属性权重确定方法

通常属性权重的确定方法是根据决策者的先验知识来确定。本文根据粗集理论中属性重要度的判断方法,提出将决策者先验知识给定的权重同粗集理论确定的属性重要度结合起来最终确定属性权重,即基于粗集理论的属性权重确定,实现主观先验知识同客观情况的统一,从而得出更加理想的权重确定结果。     

Data and Model Management in a Generalized MCDM-DSS*

Drawing upon the choice models developed in the multiple criteria decision making (MCDM) area, this paper proposes an architecture for designing an intelligent decision support system (DSS) that is intended to aid in making choices among multiple alternatives along multiple dimensions. It argues that effective support can be provided to the decision maker when the knowledge-based DSS is capable of dynamically selecting choice models appropriate to the domain and context of a particular problem being specified by the decision maker, and of properly applying them to the problem solution. Development of a prototype intended to partially represent application of the architecture is described. The paper concludes with suggestions for research extensions.     

Multiple Attribute Decision Making with Inexact Value-Function Assessment

A major issue in value-function assessment is the possibility of receiving an indefinite response from a decision maker to a question about preferences. The conventional treatment requires a single, definite response from the decision maker. This approach either assumes that response error is zero or accepts the final solution as only approximate, without knowing how approximate it might be. In this paper we examine the issue of indefinite responses, define it more precisely, and present a method for incorporating it into decision making. The method is adaptive in that it is iterative and interactive with the decision maker who, at each iteration, is provided with information regarding the potential advantage of answering more trade-off questions and/or answering questions more carefully to reduce response error. In contrast to other approaches that “force” rationality and decisiveness, we work with irrationality and indecisiveness on the part of the decision maker. The method is demonstrated for the case of concave, increasing value functions.     

AN ADAPTIVE DECISION SUPPORT SYSTEM FOR ACADEMIC RESOURCE PLANNING

Institutions of higher learning are growing increasingly interested in the use of model-based approaches to their resource allocation problems. Recent modeling approaches, however, have failed to consider that resource allocation planning is not a well-structured decision process. Additionally, many decision makers are necessarily involved in the academic planning process and may assume dissimilar perspectives on the importance of achieving different goals and objectives. Furthermore, satisfactory allocation solutions can be expected to vary considerably from decision maker to decision maker as the individual's cognitive processes, perceptions, and evaluations are taken into consideration. This paper describes a decision support system (DSS) approach that attempts to adapt to a variety of academic decision makers with differing planning views in an environment of multiple conflicting objectives. This DSS, which was successfully tested on four academic decision makers in a large midwestern university, shows considerable promise for providing decision support to decision makers with varied problem-solving styles.     

考虑决策者后悔规避的风险投资项目选择方法

在风险投资项目选择过程中,决策者通常是有限理性的,会尽量避免选择可能会令其感到后悔的风险投资项目,而在涉及多个行业的风险投资项目选择过程中如何考虑决策者后悔规避的心理行为因素,这方面的研究特别需要关注。本文提出了一种涉及多个行业的考虑决策者后悔规避的风险投资项目选择方法。在该方法中,首先计算各风险投资项目在不同市场状态下的项目价值;然后,计算各组合市场状态发生的概率;进一步地,构建后悔函数刻画决策者后悔的心理感知,并通过计算不同行业的各风险投资项目的综合效用值得到风险投资项目的排序结果。最后,通过一个算例说明了本文提出方法的可行性与有效性。     

CONCEPTUAL LEVELS AND THE DESIGN OF ACCOUNTING INFORMATION SYSTEMS*

This article attempts to expand and clarify the discussions in the literature on complexity theory applied to accounting via two central assertions: (a) affecting the cognitive conceptual level of an individual through the financial reporting system can be accomplished not only by varying the immediate information load, but also, in the long run, by modifying the types of information and the mode of user-system interface. In other words, a properly designed accounting information system can have developmental cognitive effects in both the short and long run. (b) attempting to maximize the conceptual level of the decision maker is not always desirable. In certain types of environments, a high conceptual level actually impedes appropriate decision making. Therefore, the appropriate conceptual level must be related to the type of decision being made as well as to the type of decision maker.     

Concepts,Theory, and Techniques A NEW METHODOLOGY FOR ORDINAL MULTIOBJECTIVE DECISIONS BASED ON FUZZY SETS

Using the structural forms supplied by fuzzy set theory and approximate reasoning, a new method is presented for solving multiple-objective decision problems for which the decision maker can supply only ordinal information on his preferences and the importance of the individual objectives.     

管理者风险态度对系统能力受损后的应急采购决策影响研究

通信、电力和石化等大型运作系统核心能力受损后的应急期间内,管理者并非遵循风险规避的准则进行决策,为降低应急管理的成本,管理者是否应该采取激进或者保守的态度？为验证该观点,本文在文献[3]研究的基础上进行了拓展,在伙伴企业应急期间提供快速和正常两种能力支援的情境下,将管理者风险态度引入能力应急采购的决策问题。通过理论证明和数值仿真,本文得到了3个有益的应急管理结论:管理者的风险态度并不是越激进越好,"极端事件下采取双源供应"并非最优选择,应急期间管理者应首要考虑获得伙伴企业的能力恢复技术支援。     

Decision Making Under Risk: A Comparison of Bayesian and Fuzzy Set Methods

A classical decision problem is considered where a decision maker is to choose one of a number of actions each offering different consequences. The outcome from a choice of action is uncertain because it depends on the existing state of Nature. Also, the outcome, once an action and state of Nature are specified, may be a vector or a random vector. The decision maker employs both Bayesian methods and fuzzy set techniques to handle the uncertainties. The decision maker is also allowed to use multiple, possibly conflicting, goals in order to determine his best strategy. The Bayesian method produces a set of undominated strategies to choose from, whereas the fuzzy set technique usually produces a unique optimal strategy.     

Screening of multiattribute alternatives

A procedure for screening multiattribute alternatives is proposed. This procedure reduces the detailed data collection needs and the information burden on the decision maker. It is shown that the inferior decision alternatives can be excluded without specifying probabilities, importance weights, and utilities with precision.