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.     

COGNITIVE STYLE AND THE USEFULNESS OF INFORMATION*

The desirability of a merger/acquisition alternative depends in part on the perceptions of the decision maker. What sources of information are “useful” to the decision maker & Does the set of useful information remain constant for all decision makers; if not, do individuals using similar information sets have similar information processing characteristics? Do these sets vary as feedback is obtained during the decision process? To answer these questions, graduate students participated in a modified Delphi experiment, and the resulting data were analyzed by the two-way aligned-ranks nonparametric test. These test results affirm that in a merger/acquisition scenario, decision makers with different cognitive styles prefer different sets of information and these sets vary dynamically as feedback is incorporated in the decision-making process. Furthermore, information that contains worker and community welfare considerations is identified as “useful” five times more frequently by decision makers with a “feeling” cognitive style than those with a “thinking” style.     

Decision Making for Risk Management: A Comparison of Graphical Methods for Presenting Quantitative Uncertainty

Previous research has shown that people err when making decisions aided by probability information. Surprisingly, there has been little exploration into the accuracy of decisions made based on many commonly used probabilistic display methods. Two experiments examined the ability of a comprehensive set of such methods to effectively communicate critical information to a decision maker and influence confidence in decision making. The second experiment investigated the performance of these methods under time pressure, a situational factor known to exacerbate judgmental errors. Ten commonly used graphical display methods were randomly assigned to participants. Across eight scenarios in which a probabilistic outcome was described, participants were asked questions regarding graph interpretation (e.g., mean) and made behavioral choices (i.e., act; do not act) based on the provided information indicated that decision‐maker accuracy differed by graphical method; error bars and boxplots led to greatest mean estimation and behavioral choice accuracy whereas complementary cumulative probability distribution functions were associated with the highest probability estimation accuracy. Under time pressure, participant performance decreased when making behavioral choices.     

RANK GENERATION,PRESERVATION, AND REVERSAL IN THE ANALYTIC HIERARCHY DECISION PROCESS

Decision making has the objective of finding the best alternative or set of alternatives by considering a number of goals, objectives, criteria, competitors, and other important factors. The analytic hierarchy process is a decision aid used to assist a decision maker in sorting out the complexity of a decision problem and making use of his or her judgments. A decision maker must be assured that the arithmetic operations of any such decision process are the right ones—that they surface the correct ranking and values of the alternatives and preserve or alter ranks appropriately when new alternatives are added or deleted. In this paper it will be shown that with absolute measurement, rank always is preserved, with relative measurement, rank changes with nspect to scveral criteria only because of the structural dependence (involving both numbers and measurements) of criteria on alternatives. A discussion of the effect on rank of replicas and near replicas of the alternatives also is given.     

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.     

Preference-Order Recursion for Finding Relevant Pure,Admissible and Optimal Statistical Decision Functions*

A preference-order recursion algorithm for obtaining a relevant subset of pure, admissible (non-dominated, efficient) decision functions which converges towards an optimal solution in statistical decision problems is proposed. The procedure permits a decision maker to interactively express strong binary preferences for partial decision functions at each stage of the recursion, from which an imprecise probability and/or utility function is imputed and used as one of several pruning mechanisms to obtain a reduced relevant subset of admissible decision functions or to converge on an optimal one. The computational and measurement burden is thereby mitigated significantly, for example, by not requiring explicit or full probability and utility information from the decision maker. The algorithm is applicable to both linear and nonlinear utility functions. The results of behavioral and computational experimentation show that the approach is viable, efficient, and robust.     

Using Real‐Time Decision Tools to Improve Distributed Decision‐Making Capabilities in High‐Magnitude Crisis Situations*

Multi‐organizational collaborative decision making in high‐magnitude crisis situations requires real‐time information sharing and dynamic modeling for effective response. Information technology (IT) based decision support tools can play a key role in facilitating such effective response. We explore one promising class of decision support tools based on machine learning, known as support vector machines (SVM), which have the capability to dynamically model and analyze decision processes. To examine this capability, we use a case study with a design science approach to evaluate improved decision‐making effectiveness of an SVM algorithm in an agent‐based simulation experimental environment. Testing and evaluation of real‐time decision support tools in simulated environments provides an opportunity to assess their value under various dynamic conditions. Decision making in high‐magnitude crisis situations involves multiple different patterns of behavior, requiring the development, application, and evaluation of different models. Therefore, we employ a multistage linear support vector machine (MLSVM) algorithm that permits partitioning decision maker response into behavioral subsets, which can then individually model and examine their diverse patterns of response behavior. The results of our case study indicate that our MLSVM is clearly superior to both single stage SVMs and traditional approaches such as linear and quadratic discriminant analysis for understanding and predicting behavior. We conclude that machine learning algorithms show promise for quickly assessing response strategy behavior and for providing the capability to share information with decision makers in multi‐organizational collaborative environments, thus supporting more effective decision making in such contexts.     

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.     

Consistency and Accuracy in Decision Aids: Experiments with Four Multiattribute Systems*

There have been a number of multiattribute decision aids developed to aid selection problems. Multiattribute value theory and the analytic hierarchy process are two commonly used techniques. Different systems can result in radically different conclusions if they inaccurately and inconsistently reflect the preference structure of decision makers, or if they are based on inappropriate theoretical models. This study examines the impact of the underlying theoretical model, the method in which preference information is elicited, and the structure of alternatives as influences on the results from using various decision aids. It was found that two systems based on the multiattribute value theory model were just as diverse in their conclusions as were results between AHP and the multiattribute value theory models. Therefore, accuracy of information reflecting decision maker preference is an important consideration. Feedback capable of assuring the decision maker that information provided is consistent is a necessary feature required of decision aids applied to selection problems. The study also found that the way in which information is elicited influenced the result more than did the underlying model. Exact numerical data for complex concepts such as attribute importance and alternative performance on attributes is not necessary, and elicitation procedures that are more natural for the user are likely to be more accurate.     

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.     

Decision Support by Interval SMART/SWING—Incorporating Imprecision in the SMART and SWING Methods

Interval judgments are a way of handling preferential and informational imprecision in multicriteria decision analysis (MCDA). In this article, we study the use of intervals in the simple multiattribute rating technique (SMART) and SWING weighting methods. We generalize the methods by allowing the reference attribute to be any attribute, not just the most or the least important one, and by allowing the decision maker to reply with intervals to the weight ratio questions to account for his/her judgmental imprecision. We also study the practical and procedural implications of using imprecision intervals in these methods. These include, for example, how to select the reference attribute to identify as many dominated alternatives as possible. Based on the results of a simulation study, we suggest guidelines for how to carry out the weighting process in practice. Computer support can be used to make the process visual and interactive. We describe the WINPRE software for interval SMART/SWING, preference assessment by imprecise ratio statements (PAIRS), and preference programming. The use of interval SMART/SWING is illustrated by a job selection example.     

Risk Averse Decisions in Business Planning

Second-order stochastic dominance is used to determine preferences among various investments for any risk-averse decision maker. On the other hand, when faced with choosing between different insurance policies or disaster plans, a risk-averse decision maker should use a type of stochastic dominance called variability ordering. In this situation, second-order stochastic dominance has been used in previous research and is incorrect.     

PREFERENCE MAPPING OF PERCEIVED INFORMATION STRUCTURE: IMPLICATIONS FOR DECISION SUPPORT SYSTEMS DESIGN

In a recent issue of this journal, Watkins [13] presented an approach for discovery of decision-maker perceptions of the complexity (dimensionality) of information items that might be supplied by a decision support system. Through use of multidimensional scaling and cluster analysis, relatively homogeneous groups of decision makers, sharing common perceptions of various information items, were formed. This prior research was referred to as a first step in suggesting that information reports could be tailored to groups of decision makers classified on the basis of common perceptions of information. The current research extends the prior study by evaluating decision maker preferences for information in a variety of decision-making scenarios in relation to the previously identified perceptions of the information. Based on the results of the study, conclusions are made which suggest that the tailoring of information to groups of decision makers should be based on both perceptions and preferences for information. Even so, it is demonstrated that the decision tasks have an impact on the preferences for information which may affect the attempt to tailor information to groups of decision makers.     

Combined emergency preparedness and operations for safe personnel transport to offshore locations

Long distances, sparse infrastructure, and adverse environmental conditions make the offshore emergency preparedness system in the High North a big and yet unsolved challenge. This applies in particular to the personnel transport between onshore bases and offshore facilities, which is usually conducted by helicopters. One of the issues to be solved is the sufficient coverage with emergency response units (RUs) in this sparse infrastructure environment. This paper proposes an answer to this issue by using sound logistical concepts, which involves connecting operations and preparedness. A mathematical model is introduced that combines a routing and a covering problem. On one hand, the shortest possible helicopter routes to offshore locations are sought, subject to being within the area covered by the deployed RUs. On the other hand, those RUs are placed so that a contingent helicopter ditching at any point on the chosen routes can be handled within given time limits. The combination of routing and covering forms a trade-off, which gives the decision maker the freedom to balance between the minimization of operational costs related to transport route distances and the long-term costs from response capacity requirements. A computational method that reduces the time to find a solution and allows decision makers to solve real life instances is presented. Computational experiments are conducted with the proposed model, based on prospective production sites in the Barents Sea.     

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.     

Impact of Number of Interactions,Different Interaction Patterns,and Human Inconsistencies on Some Hybrid Evolutionary Multiobjective Optimization Algorithms*

We investigate the impact of the number of human–computer interactions, different interaction patterns, and human inconsistencies in decision maker responses on the convergence of an interactive, evolutionary multiobjective algorithm recently developed by the authors. In our context “an interaction” means choosing the best and worst solutions among a sample of six solutions. By interaction patterns we refer to whether preference questioning is more front‐, center‐, rear‐, or edge‐loaded. As test problems we use two‐ to four‐objective knapsack problems, multicriteria scheduling problems, and multiobjective facility location problems. In the tests, two different preference functions are used to represent actual decision maker preferences, linear and Chebyshev. The results indicate that it is possible to obtain solutions that are very good or even nearly optimal with a reasonable number of interactions. The results also indicate that the algorithm is robust to minor inconsistencies in decision maker responses. There is also surprising robustness toward different patterns of interaction with the decision maker. The results are of interest to the evolutionary multiobjective (EMO) community actively developing hybrid interactive EMO approaches.     

Decision Making with Uncertain Judgments: A Stochastic Formulation of the Analytic Hierarchy Process*

In the analytic hierarchy process (AHP), priorities are derived via a deterministic method, the eigenvalue decomposition. However, judgments may be subject to error. A stochastic characterization of the pairwise comparison judgment task is provided and statistical models are introduced for deriving the underlying priorities. Specifically, a weighted hierarchical multinomial logit model is used to obtain the priorities. Inference is then conducted from the Bayesian viewpoint using Markov chain Monte Carlo methods. The stochastic methods are found to give results that are congruent with those of the eigenvector method in matrices of different sizes and different levels of inconsistency. Moreover, inferential statements can be made about the priorities when the stochastic approach is adopted, and these statements may be of considerable value to a decision maker. The methods described are fully compatible with judgments from the standard version of AHP and can be used to construct a stochastic formulation of it.     

A Framework for the Analysis of Error in Conjunctive,Multi-Criteria,Satisficing Decision Processes*

This paper examines the impact of certain types of error on ad hoc, unstructured decision making that incorporates a satisficing strategy in a multi-criteria context using the conjunctive rule. The impacts of error in evaluation and of error in minimal or satisficing levels are systematically modelled for each of the decision criteria. Error functions, analytical expressions, and procedures are developed to obtain information such as the likelihood of correct decision making in the presence of evaluation and minimal criteria level error. These are applied to a specific research design, which is then analyzed. This highlights certain features of decision making in a satisficing context, such as the importance of keeping the number of decision criteria to a minimum. The results yield various insights, some of which confirm information obtained from previous analyses. A major advantage of the framework presented is that it can provide quantitative measurements which support ideas previously couched in qualitative terms only. Although the framework is applied in the context of decision making that uses the conjunctive rule in the case of multiple criteria, other satisficing strategies can be accommodated as well.     

HIGH-LEVEL DECISIONS: A LESSON FROM THE IRAN HOSTAGE RESCUE OPERATION

We use the analytic hierarchy process to analyze the role of subjective factors in decision making as illustrated in the Iran rescue operation. Essentially, we show that a decision maker and that decision maker's advisors may differ in their estimates on whether an action should or should not be taken depending on what intangible factors the leader holds as personally important, factors that the others would not necessarily include in their thinking-a serious point for which we provide a particularly effective methodology.     

Invisible Whispering: Restructuring Collaborative Decision Making with Instant Messaging

Organizational decision making is dominated by teams. When an important decision is required, a team is often formed to make it or to advise the individual decision maker, because a team has more resources, knowledge, and political insight than any one individual working alone. As teams have become geographically distributed, collaboration technology has come to play an important role in such collective decision making efforts. Instant messaging (IM) is an increasingly prevalent workplace collaboration technology that enables near‐synchronous text exchanges on a variety of devices. We examined the use of IM during face‐to‐face, telephone, and computer‐mediated team meetings, a practice we call “invisible whispering.” We introduce Goffman's characterization of social interaction as dramatic performance, differentiable into “front stage” and “backstage” exchanges, to analyze how invisible whispering alters the socio‐spatial and temporal boundaries of team decision making. Using IM, workers were able to influence front stage decision making through backstage conversations, often participating in multiple backstage conversations simultaneously. This type of interaction would be either physically impossible or socially constrained without the use of IM. We examine how invisible whispering changes the processes of collaborative decision making and how these new processes may affect the efficiency and effectiveness of collaborative decision making, as well as participation, satisfaction, relationships among team members, and individual attention.