An Integrated Group Decision-Making Method Dealing with Fuzzy Preferences for Alternatives and Individual Judgments for Selection Criteria

Organizations often require decisions to be made by a group, and decision makers often have fuzzy preferences for alternatives and individual judgments when attempting to reach an optimal solution. In order to deal with the fuzziness of preference of decision makers, this paper proposes an integrated fuzzy group decision-making method. This method allows group members to express fuzzy preferences for alternatives and individual judgments for solution selection criteria. It also allowed for the weighting of group members. The method then aggregates these elements into a compromise group decision which is the most acceptable for the group as a whole. This method has been implemented and tested. An example is presented to illustrate the method.     

A logistic regression-based pairwise comparison method to aggregate preferences

In a group decision making process, several individuals or a committee have the responsibility to choose the best alternative from a set. The problem addressed in this paper is how to aggregate personal preferences to arrive at an optimal group decision. New technologies allow individuals that may seldom or never meet to make group decisions. This paper proposes a methodology to obtain the group preference ordering in two steps. Firstly, each individual studies the problem isolated, and then, in a possibly virtual meeting, the group must agree on the preferences on some pairs of alternatives. Then, the group criterion is achieved by using a logistic regression model within the pairwise comparison framework proposed here. Properties of the procedure are studied and two illustrative examples are presented.     

Using Bids,Arguments and Preferences in Sensitive Multi-unit Assignments: A p-Equitable Process and a Course Allocation Case Study

Bonus distribution in enterprises or course allocation at universities are examples of sensitive multi-unit assignment problems, where a set of resources is to be allocated among a set of agents having multi-unit demands. Automatic processes exist, based on quantitative information, for example bids or preference ranking, or even on lotteries. In sensitive cases, however, decisions are taken by persons also using qualitative information. At present, no multi-unit assignment system supports both quantitative and qualitative information. In this paper, we propose MUAP-LIS, an interactive process for multi-assignment problems where, in addition to bids and preferences, agents can give arguments to motivate their choices. Bids are used to automatically make pre-assignments, qualitative arguments and preferences help decision makers break ties in a founded way. A group decision support system, based on Logical Information Systems, allows decision makers to handle bids, arguments and preferences in a unified interface. We say that a process is p-equitable for a property p if all agents satisfying p are treated equally. We formally demonstrate that MUAP-LIS is p-equitable for a number of properties on bids, arguments and preferences. It is also Pareto-efficient and Gale–Shapley-stable with respect to bids. A successful course allocation case study is reported. It spans over two university years. The decision makers were confident about the process and the resulting assignment. Furthermore, the students, even the ones who did not get all their wishes, found the process to be equitable.     

A Mixed-Data Evaluation in Group TOPSIS with Differentiated Decision Power

This main objective of this paper is to provide decision support for mixed data in group Technique for Order Preference by Similarity to Idea Solution (TOPSIS) with differentiated decision power. We use a signum function to compare the ordinal performance of alternatives on any qualitative criterion, or the partial information provided by decision makers. The proposed process for ordinal information is uniformly coherent with the traditional TOPSIS steps, preserving the characteristic of distance-based utilities. Ordinal weights are also considered herein, and the decision power of the group members is formulated by their weights under an agreement in the group. Two examples demonstrate that the proposed approach has some benefits and achieves robustness with two types of sensitivity analyses. Some discussions and their limitations to the approach are also provided.     

Ordered Weighted Disagreement Functions

In this paper a preference aggregation procedure is proposed for those cases in which decision-makers express their preferences by means of a ranking of alternatives. Among the most commonly applied methods for this purpose are those based on distance measures between individual and collective preferences, which look for the solution that minimizes the disagreement across decision-makers. Some models based on the minimization of the distance between rankings include weights to adjust the relative importance of the agents in the final decision, although in those cases, the weights are related with an a priori evaluation of the individuals and not with the behaviour of the agents in the group decision making process. In the model proposed here, a weighted disagreement function whose emphasis is on the ordered position of the individuals’ disagreement values is developed. In order to solve the problem, a mixed-integer linear programming model is constructed.     

Observation of a strategic household purchase decision

The present paper focuses on strategic household purchase decisions; i.e., major, complex buying decisions with long-term bindings of economic resources. An in-depth study of house buying in two-career households demonstrated preferences (goals) to be ambiguous, and consequences to be modestly understood and partly uncovered after the purchase. Only a few alternatives were considered, and they were from different broad need-satisfying categories such as purchase of apartment or house, or renting. No direct comparisons of alternatives were observed to take place. The purchase decisions were based on few, very crude decision criteria working as guidelines for judging whether or not the alternatives considered were acceptable, while the final choice seemingly was made according to an affect-referral decision rule.     

Multicriteria Group Decision-Making Method Using Vector Similarity Measures For Trapezoidal Intuitionistic Fuzzy Numbers

Based on the extension of the Jaccard, Dice, and cosine similarity measures, three vector similarity measures between trapezoidal intuitionistic fuzzy numbers (TIFNs) are proposed in the vector space and are applied to the fuzzy multicriteria group decision-making problem, in which the criteria weights and the evaluated values in decision matrix are expressed by TIFNs. Through the weighted similarity measures between each alternative and the ideal alternative, the ranking order of all the alternatives can be determined and the best one(s) can be easily identified as well. A practical example of the developed approaches is given to select the investment alternatives. The decision results of different similarity measures demonstrate that the three similarity measures have better similarity identification. The illustrative example shows that the proposed methods are applicable.     

Using the Analytic Hierarchy Process as a Two-phase Integrated Decision Approach for Large Nominal Groups

When decision makers who comprise a large nominal group face an unstructured decision problem and no simultaneous interactive communications are available, problem identification and consensus building are difficult, if not impossible. Few tools are available to assist decision makers in this situation. The Analytic Hierarchy Process (AHP) has typically been used to evaluate a set of alternatives after a decision problem has been structured as a hierarchy with various levels of criteria above the alternatives. With a group of decision makers, AHP has been used to evaluate those alternatives either by consensus building or by combining judgments or priorities using the geometric mean to aggregate their preferences. In this paper, we extend the use of AHP to a situation involving a large nominal group of dispersed decision makers where the entire hierarchy is not defined at the outset. In particular, we use the AHP as an integrative approach to identify the priorities of the various criteria and then use those priorities to screen and consolidate a large set of potential alternatives. This results in considering a reduced set of alternatives that will be affected by the more important criteria. The consolidated set of alternatives is evaluated by each individual in the group using AHP, combined using the geometric mean, and the results are synthesized to obtain the overall priorities of the alternatives. The approach is demonstrated and evaluated in a case study to select an alunmi anniversary gift to the U.S. Coast Guard Academy with a large nominal group of decision-makers dispersed throughout the United States.     

Aggregation and Disaggregation of Preferences for Collective Decision-Making

Group decisions are of longstanding interest to researchers from a wide spectrum of disciplines. Group Decision Support Systems (GDSS) can play a vital role in situations where multiple persons are involved, each having their own private perceptions of the context and the decision problem to be tackled. In such an environment the conflict between the members of the planning group is not an unusual situation. Multiple criteria decision aid (MCDA) methods may be a useful tool in coping with such interpersonal conflicts where the aim is to achieve consensus between the group members. This paper combines two well-known multicriteria methods, based on the notion of aggregation of preferences, in order to construct a consensus seeking methodology for collective decision-making.     

TOPSIS Based Approach to Scoring Negotiating Offers in Negotiation Support Systems

In this paper we analyze the possibility of applying the technique for order preferences by similarity to ideal solution (TOPSIS) to building the scoring system for negotiating offers. TOPSIS is a multiple criteria decision making method that is based on measuring distances between alternatives under consideration and two bipolar reference alternatives, a positive and negative ideal. Thus the criteria used for the evaluation of alternatives should be described using strong scales. However, in the negotiation, the issues are very often described qualitatively, which results in ordinal or even nominal variables that must be taken into consideration in offers’ evaluation process. What is more, TOPSIS may be applied to solving the discrete decision problems while the negotiation space may be defined by the means of continuous variables too. In this paper we try to modify the TOPSIS algorithm to make it applicable to negotiation support and, moreover, discuss the following methodological issues: using TOPSIS for a negotiation problem with continuous negotiation space; selecting the distance measure for adequate representation of negotiator’s preferences and measuring distances for qualitative issues. Finally, we propose a simple additional mechanism that allows for building the TOPSIS-based scoring system for negotiating offers and does not involve negotiators in time consuming and tiresome preference elicitation process. This mechanism requires from negotiators to construct examples of offers that represent some categories of quality and then by using a goal programming approach it infers all the parameters required by the TOPSIS algorithm. We also show a simple prototype software tool that applies the TOPSIS modified algorithm and may be used in electronic negotiation support.     

The Use of Ranking Veto Concept to Mitigate the Compensatory Effects of Additive Aggregation in Group Decisions on a Water Utility Automation Investment

The use of additive models for aggregating group decisions implies they have a compensatory effect in the process of aggregating all decision makers’ (DMs’) preferences. In this kind of model, the final result may produce some extremely undesirable alternatives for one or more DMs. Such alternatives may emerge with a higher ranking than desirable ones, thus generating conflicts and regrets. To overcome this problem the concept of ranking veto is introduced based on a reduction factor combined with the utility of the alternative in order to penalize conflicting alternatives and reduce disagreements in an additive model. A water utility problem was considered as a numerical application to illustrate the model. A decision group method based on MAUT, utility thresholds and a reduction factor is proposed to support group decision in selecting regions that will receive investments in automation over the next 4 years.     

The Secretary Problem Revisited - The Group Decision-Making Perspective

We consider a decision situation where there is an initial set of alternatives that may be augmented, a variant of the problem known in the literature as the "secretary problem". We focus the discussion on the special case of group decision-making, where a group or committee is charged with the responsibility of negotiating the decision. We investigate situations of explicitly stated (multiple) criteria and the simpler situation of no such stated criteria. The former case includes the congenial, the mixed, and the uncongenial cases, where the individuals agree on the direction of all, some, or none of the criteria, respectively. We offer a framework within which a group of individuals can be supported in such a decision process. In the case of explicit criteria, we provide the decision-makers with probability information of the likelihood of finding more preferred alternatives provided the initial set of alternatives is expanded. The framework is tested using a simulated real-world choice situation.     

Evaluation uncertainty of venture capitalists' investment criteria

This paper analyzes the decision process of venture capitalists. The study focuses on aligning the evaluation uncertainty in the decision criteria of venture capitalists with the progress of the process. The reasoning builds from the concept of search, experience and credence qualities, which was developed in the economics of information and allows the identification of the varying uncertainty of a single decision criterion compared to other criteria, along with uncertainty variations throughout the process. Exploratory empirical evidence suggests that in the early steps of the process in particular, management criteria are uncertain, while at the end of the process other criteria couple with uncertainty.     

A linguistic decision process in group decision making

Assuming a set of linguistic preferences representing the preferences of the individuals, a linguistic choice process is presented. This is developed using the concept of fuzzy majority for deriving a collective linguistic preference, and the concept of nondominated alternatives for deriving the selected alternatives in the linguistic choice process. The fuzzy majorities are equated with fuzzy linguistic quantifiers. The collective linguistic preference is derived by means of a linguistic ordered weighted averaging operator whose weights are defined using a fuzzy linguistic quantifier. In order to obtain the nondominated alternatives, we present a novel reformulation of Orlovski's nondominance degree under linguistic information.     

Evaluating a Framework for Multi-Stakeholder Decision Support in Water Resources Management

In this paper we describe a framework for multicriteria modeling and support of multi-stakeholder decision processes. We report on its testing in the development of a new water level management policy for a regulated lake-river system in Finland. In the framework the stakeholders are involved in the decision process from the problem structuring stage to the group consensus seeking stage followed by a stage of seeking public acceptance for the policy. The framework aims at creating an evolutionary learning process. In this paper we also focus on the use of a new interactive method for finding and identifying Pareto-optimal alternatives. Role playing experiments with students are used to test the practical applicability of a negotiation support procedure called the method of improving directions. We also describe the preference programming approach for the aggregation of the stakeholder opinions in the final evaluation of alternatives and consensus seeking.     

Developing a Group Decision Support System for Advertising Media Evaluation: A Case in the Middle East

The importance of advertising media evaluation as a multifaceted problem is well known by both academics and practitioners. Although previous studies tried to optimize media evaluation, there still are some gaps and problems to address, particularly in areas of flexibility of models/frameworks, decision making quality, tension management, and agility of the evaluation process. Most of previous studies are based on inflexible models/frameworks that have limitations on number of criteria/alternatives they can consider and type of data they can process. A great volume of the work used arbitrary decision making; arbitrary decision making regarding criteria and media importance may reduce effectiveness of advertising campaigns. Furthermore, the academic literature offers little guidance on group decision aggregation, and tension management during decision making is neglected. Media evaluation is a time taking process and any acceleration will reduce pre-campaign costs. The main aim of this paper is to illustrate how a group decision support system (GDSS) can assist media planners to overcome mentioned problems more systematically. For this purpose, we developed a GDSS that is an integration of three well-known multi-criteria decision making techniques. With a real world case study, we illustrate the performance of the proposed GDSS. Results of our quantitative assessments indicate that the GDSS is flexible, allows decision makers to express their opinions, reduces tension among decision makers, and saves time.     

Non-numeric multi-criteria multi-person decision making

We describe a decision making technique, ME-MCDM, for the evaluation and selection of alternatives using a non-numeric scale. Using this procedure each alternative is evaluated by an expert for satisfaction to a multi-criteria selection function. Each criterion can have a different degree of importance. The individual expert evaluations can then be aggregated to obtain an overall evaluation function. We apply this technique to the problem of proposal selection in the funding environment. In this environment the technique is augmented by some textual information which can also be used to help in the decision process.     

PROMETHEE Group Decision Support System and the House of Quality

Quality function deployment (QFD) is a multi-step method that monitors customer needs throughout a product development process. The House of Quality (HOQ) exercise undertaken in the first phase of QFD is considered as the most important, since customer needs must be accurately translated into a set of technical requirements for the final product. This paper provides a PROMETHEE group decision support system (GDSS) approach that integrates the design preferences of the QFD team. We highlight the selection and ranking of the technical requirements in the HOQ exercise, where a group of multidisciplinary decision makers (DMs) in a globally dispersed QFD team is required to input their individual preferences. Our approach advances the HOQ group decision making context in three important areas. First, it treats each criterion and DM as unique in terms of the preference function and threshold levels. Second, it seeks a multi-criteria approach for the HOQ process, where some DMs may play a more important role than others on a certain criterion. Third, sensitivity analysis through the Geometrical Analysis for Interactive Assistance (GAIA) plane provides valuable information about the conflicts, similarities, or independencies between the criterion and the DMs, respectively. A case on an automotive part illustrates the performance of the PROMOTHEE approach with GAIA.     

Multi-criteria Remote Asynchronous Group Decision Screening: An Experimental Study

A new visualization-based multi-criteria procedure for group decision screening is presented. The procedure supports a group in selecting a small number of alternatives from an initial list described by a collection of attributes which are used as screening criteria. We propose and experimentally study the Group Remote Asynchronous Screening Support (GRASS) procedure that can be used by a large number of participants who are not able to meet face to face. GRASS does not use any interaction between the participants and the group screening is based on the individual preferences expressed by them independently from each other. GRASS uses the concepts of Borda count, by applying the visualization of information to simplify the analysis of large lists of multi-criteria alternatives. Visualization is used to support the individual analysis of the variety of alternatives and the individual selecting of a small number of alternatives from the list for the subsequent scoring through Borda count. Visualization is carried via the Interactive Decision Maps / Reasonable Goals Method (IDM/RGM) technique. We first check a speculative supposition that participants are able to find a single best alternative by using the GRASS procedure. As it is not the case, we re-formulate our hypothesis and check whether the most preferred alternative is part of a short list of alternatives returned through GRASS. The experiment was carried out with senior applied mathematics students who managed to apply GRASS without any problem.     

Hybrid conjoint analysis: An estimation probe in new venture decisions

How venture capitalists select start-ups for financing has been an interesting topic for many researchers and practitioners. The underlying assumption is that people who make money investing in new businesses by assessing the proposals should be experienced enough to distinguish losers from winners. Our research study tested three models (self-explicated, conjoint and a hybrid—comprising the two previous ones—conjoint) in order to find out: 1. if these models could be applied to venture capital decision making and if so 2. to demonstrate the potential of conjoint analysis as a practical research method. 3. To test whether or not the characteristics of the entrepreneur, the product and the market replicate the venture capital decision.This research study confirms what normative literature on decision-making emphasizes: that in the first stage of an evaluation (screening), venture capitalists focus on a small subset of criteria in a non-compensatory process (i.e., an unacceptable value on one criterion cannot be offset by a high value of another one). The important criteria in this phase appear to be the entrepreneur's experience and the existence of a prototype for some decision-makers or unique features of the product for others. The screening step is more judgemental than analytic.In a second stage (the evaluation phase), however, venture capitalists end a detailed examination (due diligence process) by choosing the most preferred ventures through processes approximating compensatory rules; that is, a low but acceptable value on one criterion can be compensated by a high value on another. The most important criteria identified by the research in this second stage are criteria found in the previous stage, product gross profit margin and patent.Our research demonstrates agreement among venture capitalists in terms of one criterion to evaluate research proposals: managerial experience. As to the rest of the attributes tested, there was variation in the weights assigned to them.The findings of this pilot study also confirm the applicability of conjoint analysis as a research method in venture capital decision. The approach helps shed light on the decision rules applied, and permits the testing of previously researched criteria for predictive validity. The method has the advantage of retaining individual preferences and clustering them around venture capitalists' demographic and psychographic backgrounds (i.e., years of experience, type of education, life-style, and the like) or other types of information such as venture fund policies (size of the investment, type of industry, etc.).The major implication of the study for entrepreneurs is the importance of previous experience in the industry where they expect to develop their ventures, and a deep knowledge of the product (advantages over competition, technical, production, and cost feasibility) they are to produce and market. These are the factors that have the greatest influence on venture capitalists' evaluation of such projects.