Decisional Power in Group Decision Making: A Note on the Allocation of Group Members' Weights in the Multiplicative AHP and SMART

Many complex decisions are made in a group environment, where the decision is made jointly by a committee or group structure. The individual group members are often not equally qualified to contribute equitably to the decision process, or may have different saliences (desires) to influence the decision. A quantitative knowledge of the players' decisional power is useful for better understanding of the group decision process, and could even be used in weighted voting within the group structure. We adapt the REMBRANDT suite of decision models (multiplicative AHP and SMART) to measure decisional power in groups, and we generalise this to cater for the case where power itself is deemed to be multidimensional in nature, and the case of uncertain subjective judgements of power amongst group members.     

Some Intuitionistic Fuzzy Weighted Distance Measures and Their Application to Group Decision Making

Xu and Chen (J Syst Sci Syst Eng 17:432–445, 2008) introduced a new decision-making technique called the ordered weighted distance (OWD) measure, having been proved useful for the treatment of situation where the available information is represented in exact numerical values. In this paper, we consider the situations with intuitionistic fuzzy and interval-valued intuitionistic information, and develop some intuitionistic fuzzy weighted distance measures such as intuitionistic fuzzy ordered weighted distance (IFOWD) measure, interval-valued intuitionistic fuzzy ordered weighted distance (IVIFOWD) measure, intuitionistic fuzzy hybrid weighted distance (IFHWD) measure and interval-valued intuitionistic fuzzy hybrid weighted distance (IVIFHWD) measure. These developed weighted distance measures are very suitable to deal with the situation where the input data are represented in intuitionistic fuzzy numbers or interval-lvalued intuitionistic fuzzy numbers. Then we present a consensus reaching process for group decision making with intuitionistic fuzzy preference information based on the developed distance measures. Finally, a practical application of he developed approach to the problem of evaluating university faculty for tenure and promotion is given.     

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.     

Aggregation of Individual Preference Structures in Ahp-Group Decision Making

This paper presents a new procedure, to which we have given the name Aggregation of Individual Preference Structures (AIPS), whose objective is to deal with multiactor decision making when using Analytic Hierarchy Process (AHP) as the methodological support. This procedure incorporates ideas similar to Borda count methods and transfers to the case of preference structures the principle of aggregation employed in the two approaches traditionally followed in AHP-group decision making (aggregation of individual judgments and aggregation of individual priorities). The new aggregation method allows us to capture: (i) the richness of uncertainty inherent to human beings; (ii) the vision of each decision maker within the context of the problem; (iii) the interdependencies between the alternatives being compared and (iv) the intensities of the preferences that each decision maker gives to these interdependencies. From the preference structure distribution associated to each decision maker, this new approach (AIPS) provides the holistic importance of each alternative and ranking, as well as the most representative preference structure distribution for the group. The knowledge derived from these could be employed as an initial step in the search for consensus, which characterises the negotiation processes followed by the actors involved in the resolution of decisional problems. An earlier version of this paper was presented at the GDN2005 Conference held at Vienna. The work has been partially funded under Research Projects “Electronic Government. Internet-based Complex Decision Making: e-democracy and e-cognocracy” (Ref. PM2004-052) and “Internet-based Complex Decision Making. Decisional Tools for e-cognocracy” (Ref. TSI2005-02511).     

The Core of Consistency in AHP-Group Decision Making

This paper presents a new tool, the Consistency Consensus Matrix, designed to encourage the search for consensus in group decision making when using the Analytic Hierarchy Process (AHP). The procedure exploits one of the characteristics of AHP: the possibility of measuring consistency in judgement elicitation. Using two other tools, Preference Structures and Stability Intervals, we derive the Consistency Consensus Matrix that corresponds to the actor’s core of consistency. The performance analysis of the preference structure obtained from this matrix provides us with valuable information in search for knowledge. The new tool is illustrated by means of a case study adapted from a real-life experiment in e-democracy developed for the City Council of Zaragoza (Spain).     

Compatibility Analysis of Intuitionistic Fuzzy Preference Relations in Group Decision Making

Compatibility analysis is an efficient and important tool used to measure the consensus of opinions within a given group of individuals. In this paper, we give a compatibility measure between intuitionistic preference values and a compatibility measure between intuitionistic preference relations, respectively, and study their properties. It is shown that each individual intuitionistic preference relation and the collective intuitionistic preference relation is perfectly compatible if and only if all the individual intuitionistic preference relations are perfectly compatible. Based on the compatibility measures, a consensus reaching procedure in group decision making with intuitionistic preference relations is developed, and a method for comparing intuitionistic fuzzy values is pointed out, by which the considered objects are ranked and selected. In addition, we extend the developed measures, procedure and method to accommodate group decision making situations with interval-valued intuitionistic preference relations. Numerical analysis on our results through an illustrative example is also carried out.     

Individual Priming in Virtual Team Decision-Making

Virtual teams have different interactions than face-to-face teams because they rely on information and communication technologies, which can impede or assist certain human cognitive processes. Past research has shown that although virtual teams exchange more information than face-to-face teams, poor decisions often result, because team members do not consider the unique information they receive from others. Drawing from cognitive psychology, our research explored a unique way to improve team decision-making through the use of cognitive priming. We proposed that priming group members to pay attention to others or to engage in counterfactual thinking would improve team members’ cognition and, therefore, team performance. Prior research with individuals and brainstorming teams has shown these forms of priming to improve performance; however, no research has attempted to use priming to improve the outcomes of virtual team decision-making, which requires deeper interaction and cognitive involvement than brainstorming. We performed two lab experiments using primes that have been found to improve the individual decision-making process. We found that priming had some impact, but it did not significantly improve decision quality. Various reasons are discussed to explain why priming techniques may not be as powerful in teams as in individuals, and future research ideas are suggested to build on our initial work on priming in virtual team decision-making.     

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.     

A Hierarchical Clustering Approach Based on Three-Dimensional Gray Relational Analysis for Clustering a Large Group of Decision Makers with Double Information

Two types of information, collectively referred to as double information, are usually required in management decision-making. The first is preference information expressed in a judgment matrix. The second is reference information expressed in a multi-attribute decision matrix. In this paper, we investigate large-scale group clustering problems with double information in group decision-making. We first establish a novel three-dimensional gray correlation degree index, which integrates the alternative decision-making vector, index vector and alternative preference vector, to fully excavate the correlation between decision makers with double information. We then develop a new clustering procedure combining three-dimensional gray relational analysis and the concept of hierarchical clustering. Moreover, a model for determining clustering centers is established on the basis of the maximum gray correlation degree within each cluster and minimum gray correlation degree among clusters. A heuristic algorithm for the model to identify the core decision maker in each cluster is proposed. Finally, we illustrate the applications of the developed procedures with a practical case. The rationality of the proposed method is demonstrated by comparing results with results obtained using other methods, including the traditional gray clustering method and hierarchical clustering method with single information; i.e., preference information or reference information.     

An investigation of skill requirements for business and data analytics positions: A content analysis of job advertisements

Presently, analytics degree programs exhibit a growing trend to meet a strong market demand. To explore the skill sets required for analytics positions, the authors examined a sample of online job postings related to professions such as business analyst (BA), business intelligence analyst (BIA), data analyst (DA), and data scientist (DS) using content analysis. They present a ranked list of relevant skills belonging to specific skills categories for the studied positions. Also, they conducted a pairwise comparison between DA and DS as well as BA and BIA. Overall, the authors observed that decision making, organization, communication, and structured data management are key to all job categories. The analysis shows that technical skills like statistics and programming skills are in most demand for DAs. The analysis is useful for creating clear definitions with respect to required skills for job categories in the business and data analytics domain and for designing course curricula for this domain.     

Career motives and entrepreneurial decision-making: examining preferences for causal and effectual logics in the early stage of new ventures

The influence of entrepreneurs’ career motives is examined on two alternative modes of decision-making logic; causation and effectuation. Based on Sarasvathy’s (Acad Manage Rev 26(2):243–288, 2001) seminal study, causation is defined as a decision-making process that focuses on what ought to be done given predetermined goals and possible means, and effectuation as a decision-making process emphasizing the question of what can be done given possible means and imagined ends. Analysis suggests that entrepreneurs who identify themselves with linear or expert career motives have a higher preference for causal decision-making logic. Entrepreneurs who identify themselves with spiral or transitory career motives have a higher preference for effectual decision-making logic. In addition, indications that prior start-up experience moderates the relationship between career motives and effectual decision-making logic for spiral-minded entrepreneurs is found. The overall results give ample support for the assumption that entrepreneurs’ career motives influence their decision-making.     

An Exploratory Study of Counterexplanation as an Ethical Intervention Strategy

The purpose of this exploratory study is to examine the use of an ethical intervention strategy – counterexplanation – on individuals’ ethical decision-making. As opposed to providing reasons to support a decision in the case of explanation, counterexplanation is the provision of reasons that either speak against or provide evidence against a chosen course of action. The number of explanations and/or counterexplanations provided by the participants is expected to have a significant effect on ethical evaluation and intention. The number of explanations is expected to be negatively related to ethical decision-making while the number of counterexplanations is expected to be positively related to ethical decision-making. The experiment, that made use of five ethical vignettes, manipulated four treatment groups – explanation, counterexplanation, explanation/counterexplanation, and counterexplanation/explanation. Participants were randomly assigned to one of the four reatments. They performed the requirements of their treatment before recording their ethical evaluations and intentions. As expected, larger numbers of explanations led to less ethical decision-making and larger numbers of counterexplanations led to more ethical decision-making. However, when both types of explanations are required, the order of counterexplaining before explaining is more desirable as it leads to more ethical decision-making. The study also reports that individuals with high social desirability bias (a tendency to present oneself in a culturally acceptable manner) may generate less counterexplanations. Implications of the findings are explained in the paper.     

An Extended PROMETHE II Multi-Criteria Group Decision Making Technique Based on Intuitionistic Fuzzy Logic for Sustainable Energy Planning

The implementations of Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) category to complex multi-criteria group decision making (MCGDM) scenarios have been included in thousands areas. Outranking methods such as PROMETHEE II are also greatly employed in energy planning application. In MCGDM methods if decision makers (DMs) are not able to treat precise data in order to define their preferences, the intuitionistic fuzzy set (IFS) theory enables them. IFS attributes are connected with the degree of membership and non-membership, and can be used to draw uncertainty in group decision-making situations. In this paper, a new version of the PROMETHEE II method is proposed, aiming at solving MCGDM problems. Linguistic variables are expressed in the membership function and non-membership function of IFS which are used to assess the weights of all criteria and the ratings of each alternative with respect to each criteria. Conditional normalized Euclidean distance measure is adopted to measure deviations between alternatives on intuitionistic fuzzy set. Then, a ranking algorithm is applied to indicate the order of superiority of alternatives. Finally, a practical example is given to an application of sustainable energy planning to verify our proposed method. Additionally, a comparative analysis is done among the proposed PROMETHEE II method and the intuitionistic fuzzy technique for order preference by similarity to ideal solution (IF-TOPSIS) method and elimination and choice translating reality method (IF-ELECTRE).     

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.     

The Effect of a Quantitative Decision Aid – Analytic Hierarchy Process – on Group Polarization

The objective of this paper is to examine group polarization in subjects who have access to a quantitative decision aid, comparing to subjects who do not. Group polarization indicates the opinion of a group involved in a decision process will tend to be more extreme in the direction of the norm than the initial opinions of its members. Most of previous studies were focused on the theoretical explanation of this effect. Two theories are generally proposed to explain it: social comparison (SC) and persuasive arguments (PA). As we know, no study has been worked on how it can be dealt with. In this study, we use Analytic Hierarchy Process (AHP) as a quantitative decision aid in a group process to examine the changes of this effect. Two hundred and forty undergraduate students participated in this study. Results show that persuasive argument was significantly reduced by the use of AHP, while no significant effect was found in group polarization and social comparison. The implications of the findings are further discussed.     

Angel group members' decision process and rejection criteria: A longitudinal analysis

This paper investigates business angel group members' decision-making from project submission to the final decision. Using a Canadian group's archival data on 636 proposals, we provide a detailed longitudinal analysis of the decision process. The rejection reasons generally refer to market and execution risk; this finding holds for every step of the process for proposals that pass the pre-screen. Angel group members focus more on market and execution risk than agency risk, similar to venture capitalists. Inexperienced entrepreneurs are rejected for market and product reasons. Decision-making by the studied angel group members differs from that generally described for independent angels.     

A Proposal of Toolkit for GDSS Facilitators

Most group decision meetings are perceived to be extremely unproductive in terms of efficiently utilizing the participants’ time and effectively achieving the group decision meeting objectives. Indeed, group decision meetings consume a great deal of time and effort in organizations. These problems occur frequently because effective guidelines or procedures are not used. To overcome these problems, many group decision support systems (GDSS) imbed some facilitation mechanisms and are currently being used with the help of a human facilitator who guides the group members through the decision process. We propose in this paper a toolkit for GDSS facilitators that we integrate in our proposed architecture for distributed GDSS. Based on a model of the decision making processes group facilitation tasks are automated, at least partially in order to increase the ability of inexperienced facilitator to monitor and control the group decision meeting process.     

Uncertain Power Average Operators for Aggregating Interval Fuzzy Preference Relations

In this paper, we investigate group decision making problems based on interval fuzzy preference relations. We define an uncertain power weighted average (UPWA) operator and an uncertain power ordered weighted average (UPOWA) operator, on the basis of the power average operator of Yager (IEEE Trans Syst Man Cybern A 31:724–731, 1988) and the uncertain geometric mean. In the situations where the weights of experts are known, we develop a method based on the UPWA operator for group decision making with interval fuzzy preference relations; and in the situations where the weights of experts are unknown, we develop a method based on the UPOWA operator for group decision making with interval fuzzy preference relations.     

Procedural Instrumentality and Audit Group Judgment: An Exploration of the Impact of Cognitive Fallibility and Ability Differences

The issue of auditor judgment prowess and resultant decision-making success has been an important topic in the behavioral auditing area for many years and has generated a voluminous research literature. However, relatively little literature exists on how differences in individual group member cognitive heuristics (fallibility) and ability impact the group process, and are impacted upon by the group process. This issue is important since so much of audit firm decision-making has its origins in audit group deliberations (Hunton 2001). Accordingly, understanding circumstances that give rise to either more flawed (‘process losses’), or better (‘process gains’), group decision-making outcomes are important even though the literature generally recognizes the superiority of group over individual decision-making (e.g., Rich et al. 1997). The model developed here is intended to develop a better understanding of cognitive factors that impact positively or negatively on audit group process. We then develop a four stage model of group decision-making, during which the differing assets and liabilities (cognitive, ability, expertise) of audit group members are combined. The four stages are diversity, controvery, insight and resolution. These are then described at length.