Sequential multi-criterion decision making

In this paper we consider a simple sequential multicriterion decision making problem in which a decision maker has to accept or reject a series of multi-attributed outcomes. We show that using very simple programming techniques, a great deal of the decision making can be automated. The method might be applicable to situations in which a dealer is having to consider sequential offers in a trading market.     

Artificial Intelligence and Generalized Qualitative-Response Models: An Empirical Test on Two Audit Decision-Making Domains

Machine learning methods are currently the object of considerable study by the artificial intelligence community. Research on machine learning carries implications for decision making in that it seeks computational methods that mimic input-output behaviors found in classes of decision-making examples. At the same time, research in statistics and econometrics has resulted in the development of qualitative-response models that can be applied to the same kind of problems addressed by machine-learning models—particularly those that involve a classification decision. This paper presents the theoretical structure of a generalized qualitative-response model and compares its performance to two seminal machine-learning models in two problem domains associated with audit decision making. The results suggest that the generalized qualitative-response model may be a useful alternative for certain problem domains.     

FAMCDM: A fusion approach of MCDM methods to rank multiclass classification algorithms

Various methods and algorithms have been developed for multiclass classification problems in recent years. How to select an effective algorithm for a multiclass classification task is an important yet difficult issue. Since the multiclass algorithm selection normally involves more than one criterion, such as accuracy and computation time, the selection process can be modeled as a multiple criteria decision making (MCDM) problem. While the evaluations of algorithms provided by different MCDM methods are in agreement sometimes, there are situations where MCDM methods generate very different results. To resolve this disagreement and help decision makers pick the most suitable classifier(s), this paper proposes a fusion approach to produce a weighted compatible MCDM ranking of multiclass classification algorithms. Several multiclass datasets from different domains are used in the experimental study to test the proposed fusion approach. The results prove that MCDM methods are useful tools for evaluating multiclass classification algorithms and the fusion approach is capable of identifying a compromised solution when different MCDM methods generate conflicting rankings.     

On solutions that are better than most

A programmatic approach to solving decision problems by the use of ‘better than most’ (BTM) solutions is introduced. The BTM approach is contrasted to approaches that seek a solution whose outcome would be the best of all outcomes for feasible solutions, again according to criteria taken to represent the decision maker's objectives. This BTM approach is illustrated in terms of a decision problem in binary decision variables. This is followed by a discussion of (1) some of the probabilistic aspects of BTM solutions, including calculational experience, (2) a central BTM problem, akin to the problem of finding optimal methods in the more traditional approaches, (3) some ad hoc methods for overcoming this and other problems associated with the BTM approach, and (4) similarities and differences between the BTM approach and other approaches.     

On the effectiveness of cognitive feedback from an interface agent

The objective of this study was to determine the impact of task information (TI) provided by an interface agent during the idea evaluation and integration step of the problem formulation stage of the problem solving process. The effectiveness assessment was based on solving diagnostic decision problems in the domain of complex industrial machinery. Ten domain experts participated in this study. Decision support was provided by a case-based reasoning system. Findings suggest that TI provided by the interface agent had no effect on the decision maker's performance, nor on the associated cognitive effort. However, a verbal protocol analysis revealed that the ten subjects used the interface agent to verify their decision processes. The results and their implications are discussed with respect to current findings in the area of decision support systems.     

基于一致性度量的概率模糊语言多属性群决策方法

人们通常习惯用语言术语来表达他们的偏好，因此概率型语言术语集（Probabilistic Linguistic Term Set，PLTS）在决策过程中有着十分重要的作用。目前PLTS的研究刚刚起步，有关PLTS的相关研究没有关注到一致性度量的问题，对于PLTS的多属性群决策方法有待进一步研究。首先，给出了一种新的PLTS的集结方法，并且在已有的PLTS可能度公式的基础上，构建了PLTS的相似度量方法，在此基础上，进一步提出了基于PLTS一致性度量的多属性群决策方法。该方法在各决策者权重未知的情况下，考虑到各决策者之间的一致性。首先，定义PLTS的一致性度量公式，确定决策者权重；并根据PLTS的集结方法，集结各决策者的评价信息；最后，利用可能度公式对PLTS进行排序。通过案例分析验证了该方法的可行性和优越性。     

Decisional Conflict and User Acceptance of Multicriteria Decision-Making Aids*

Despite the development of increasingly sophisticated and refined multicriteria decision-making (MCDM) methods, an examination of the experimental evidence indicates that users most often prefer relatively unsophisticated methods. In this paper, we synthesize theories and empirical findings from the psychology of judgment and choice to provide a new theoretical explanation for such user preferences. Our argument centers on the assertion that the MCDM method preferred by decision makers is a function of the degree to which the method tends to introduce decisional conflict. The model we develop relates response mode, decision strategy, and the salience of decisional conflict to user preferences among decision aids. We then show that the model is consistent with empirical results in MCDM studies. Next, the role of decisional conflict in problem formulation aids is briefly discussed. Finally, we outline future research needed to thoroughly test the theoretical mechanisms we have proposed.     

基于案例推理的直觉模糊决策方法

目前大多数决策方法的定权问题仅依靠决策矩阵而忽略了决策背景。针对上述问题,将案例推理方法融入到直觉模糊决策问题。首先根据直觉模糊数的内涵构建一种相似度公式,将案例推理模型跟相似度测度结合求得最优权重以及分类半径,进而进行择优排序。最后通过案例分析详细说明了本文方法的优势之处:根据决策数据又考虑专家意见,使得决策结果与现实情况更加一致,并提出一种解决面向直觉模糊决策问题的新思路。     

含非连续性信息多属性案例中的决策知识发现方法

医疗决策案例中非连续性属性信息大量存在,含该类信息的案例知识发现是多属性案例决策的关键和难点。该文研究了含非连续性属性信息案例中的决策知识发现,将条件概率和GAs融合技术整合到案例推理方法之中,开发了KNN的延伸方法--CRMGACP法。该方法的核心是基于Gas进行权重获取和基于融合条件概率的改进相似度算法进行案例知识获取。在某大型综合医院收集数据,获取有效数据300条,基于VC++开发实现的BC-CBRsys平台进行了实验研究,结果表明CRMGACP比其他常见方法具有更好的性能,在多个统计指标上展示出显著的优势。显然,改进的案例决策方法克服了含非连续性信息案例决策知识难以获取的问题,在临床决策领域具有广阔的前景。     

基于预期的报童问题研究

Koszegi与Rabin认为参照点往往由决策者的理性预期确定，本文选取报童的预期作为参照点，利用参照依赖偏好理论对报童问题进行了研究。研究发现，基于预期的报童的最优订货量不仅与货物销售的概率分布、价格等有关还与报童的损失厌恶程度有关。如果报童是损失厌恶的，报童的订货量要小于经典报童问题的订货量；反之，报童的订货量则大于经典报童问题的订货量，该结论与大部分实证结果是一致的。最后，用算例来验证了文中有关结论的正确性。     

CAPACITY AND PRODUCTION DECISIONS IN STOCHASTIC MANUFACTURING SYSTEMS: AN ASYMPTOTIC OPTIMAL HIERARCHICAL APPROACH

We present a new paradigm of hierarchical decision making in production planning and capacity expansion problems under uncertainty. We show that under reasonable assumptions, the strategic level management can base the capacity decision on aggregated information from the shopfloor, and the operational level management, given this decision, can derive a production plan for the system, without too large a loss in optimality when compared to simultaneous determination of optimal capacity and production decisions. The results are obtained via an asymptotic analysis of a manufacturing system with convex costs, constant demand, and with machines subject to random breakdown and repair. The decision variables are purchase time of a new machine at a given fixed cost and production plans before and after the purchase. The objective is to minimize the discounted costs of investment, production, inventories, and backlogs. If the rate of change in machine states such as up and down is assumed to be much larger than the rate of discounting costs, one obtains a simpler limiting problem in which the random capacity is replaced by its mean. We develop methods for constructing asymptotically optimal decisions for the original problem from the optimal decisions for the limiting problem. We obtain error estimates for these constructed decisions.     

Group decision making on water resources based on analysis of individual rankings

In problems to do with managing water resources multiple decision makers are involved, each acting in their own right and using different value systems. In the literature on management science, several procedures are proposed in order to establish a collective preference based on the aggregation of different individual preferences. However, the well-known methods that focus on a single winner have some inconveniences that should be addressed. This paper is focused on a group decision making procedure based on the analysis of individual rankings with the aim of choosing an appropriate alternative for a water resources problem. This alternative is found to be the best compromise from the points of view of all actors involved in the decision problem. The structure of the method is set out as is its application to the water resources problem. A comparison with other methods is presented and discussed.     

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.     

AN EMPIRICAL EXAMINATION OF THE BIASING EFFECTS OF FRAMING ON BUSINESS DECISIONS*

This paper presents results of three studies testing whether decision bias due to framing, found in consumer purchase contexts, also would occur in a business setting. In the first study, business-student subjects displayed framing-induced decision bias just as subjects had in an earlier study by Tversky and Kahneman [12]. When the consumer problem was recast into a business decision in the second study, student subjects trained to handle the problem still revealed bias, although to a lesser extent. In the third study, experienced business managers revealed the same pattern of decision bias as the student subjects. Implications of this bias for business decision makers are discussed.     

A Comparative Evaluation of Labor Tour Scheduling Methods

This paper presents an initial study of relative performance for a number of the labor tour scheduling heuristic methods proposed in the literature. These heuristic methods were classified as either linear programming (LP) based or construction. Each of the methods was applied to a tour scheduling problem, subject to a variety of labor demand requirements distributions, with the singular objective being the minimization of total labor hours scheduled. Statistical analysis revealed that effective tour schedule solutions were generated by both LP-based and construction methods. Since the performances of the Keith [13], Morris and Showalter [18], and Bechtold and Showalter [5] methods were superior, their solutions were also compared across a number of secondary criteria. An overall analysis of the performances of these three methods resulted in the identification of a number of important managerial and decision-making issues. We conclude that service operations management should consider integrating these heuristic methods into a decision support system. Finally, suggestions for future research are provided.     

基于证据推理的不确定多属性决策方法

针对基本属性权重的不确定性,以及基本属性与广义属性评价集的不一致性等问题,提出一种基于证据推理的不确定多属性决策方法,将证据推理算法推广到更一般的决策环境中.根据决策矩阵的信息熵客观地获得属性的权系数;而对于基本属性与广义属性评价集不一致的情况,则通过对基本属性分布评价的模糊化及模糊变换,合理地实现到广义分布评价的统一形式;最后应用证据推理算法得到整个方案集的排序.实例结果表明,该方法是可行的、有效的.     

Escalation: The Role of Problem Recognition and Cognitive Bias

Escalation of commitment to a failing course of action is an enduring problem that remains central to the study of managerial behavior. Prior research suggests that escalation behavior results when decision makers choose to ignore negative feedback concerning the viability of a previously chosen course of action. Previous work has also suggested that certain cognitive biases might promote escalation behavior, but there has been little attempt to explore how biases other than framing affect escalation. In this article, we explore the extent to which decision makers actually perceive negative feedback as indicative of a problem and how this influences their decision to escalate. Although problem recognition and cognitive biases have been intensively studied individually, little is known about their effect on escalation behavior. In this research, we construct and test an escalation decision model that incorporates both problem recognition and two cognitive biases: selective perception and illusion of control. Our results revealed a significant inverse relationship between problem recognition and escalation. Furthermore, selective perception and illusion of control were found to significantly affect both problem recognition and escalation. The implications of these findings for research and practice are discussed. To improve problem recognition and reduce the incidence of escalation, practicing managers should implement modern project management practices that can help to identify and highlight potential problems while guarding against these two key cognitive biases that promote the behavior.     

Perceived Acceptability of Risk Analysis as a Decision-Making Approach

Three methods for making a consumer product safety decision were evaluated on scales relating to their perceived acceptability, logical soundness, completeness, and sensitivity to moral and ethical concerns. Two of the methods were formalized techniques: cost-benefit analysis and risk analysis. The third method involved abiding by standard industry practices. Other factors in the decision-making context were also varied. The results indicated that formalized techniques were preferred over the standard practices method. Within the formalized methods, cost-benefit analysis was judged less acceptable than a comparable method that did not involve making explicit value tradeoffs. All methods were judged more acceptable when they led to improved product safety. Knowledge of consequences did not exert direct effect on judgments, though it did interact significantly with other variables. The results are discussed in terms of judgmental processes that people apply when evaluating decision methods.     

Comparing methods for weighting decision criteria

Several metbods have been proposed to weight decision criteria. In this field study a decision group, like that used by public agencies, was formed to establish project priorities. The decision group used a variety of criteria weighting methods to study how these methods influence the magnitude of the weights assigned by members of the decision group. Direct criteria weighting methods (scaling, rank-weight, point assignment and odds procedures) and an indirect approach (weights derived from hypothetical projects) were considered. The study compared the methods in terms of the magnitude of the weights assigned, the variance in weights among members of the decision group, and the similarity of the decisions based on weights assigned by each method. The time to apply each method and the preferences of the decision makers were also determined. The linear rating scale that defined the meaning of scale increments, called an ‘anchored ratin scale’, was found to be favorably received and efficient and led to the most defensible decisions.     

Interactive fuzzy goal programming for multi-objective transportation problems

This paper presents an interactive fuzzy goal programming approach to determine the preferred compromise solution for the multi-objective transportation problem. The proposed approach considers the imprecise nature of the input data by implementing the minimum operator and also assumes that each objective function has a fuzzy goal. The approach focuses on minimizing the worst upper bound to obtain an efficient solution which is close to the best lower bound of each objective function. The solution procedure controls the search direction via updating both the membership values and the aspiration levels. An important characteristic of the approach is that the decision maker's role is concentrated only in evaluating the efficient solution to limit the influences of his/her incomplete knowledge about the problem domain. In addition, the proposed approach can be applied to solve other multi-objective decision making problems. The performance of this solution approach is evaluated by comparing its results with that of the two existing methods in the literature.