Challenging Group Support Systems Research: The Case for Strategic Decision Making

Strategic decision making (SDM) often occurs in groups that can benefit from the use of group support systems (GSS). However, no comprehensive review of this logical intersection has been made. We explore this intersection by viewing GSS research through the lens of SDM. First, SDM is broadly characterized and a model of GSS-mediated SDM is produced. Second, we review empirical GSS research linking these findings to the characteristics of SDM. We conclude that GSS research has not produced sufficient knowledge about group history, heterogeneity, member experience, task type, time pressure, technology or tool effects, and decision consensus for a favorable evaluation of SDM in GSS groups. SDM in GSS groups challenges researchers to study the effects of group processes such as those just mentioned in a context that involves ongoing and established groups, political activity, and a multiplicity of tasks.     

Ethical Decision Making: A Comparison of Computer- Supported and Face-to-face Group

This study compares computer-supported groups, i.e., groups using group support systems (GSS), and face-to-face groups using ethical decision-making tasks. A laboratory experiment was conducted using five-person groups of information systems professionals. Face-to-face (FTF) and GSS groups were compared in terms of their decision outcomes and group members' reactions. The results revealed that computer-supported and face-to-face groups showed no significant difference in terms of the decision outcomes of choice shift and decision polarity. However, FTF groups reached their decisions more quickly and they were more successful in attaining group consensus than GSS groups. Subjects evaluated face-to-face communication more favorably than GSS interaction on most post-group measures related to perceived group processes and satisfaction. Despite these outcomes, some possibilities for using GSS technology in an ethical decision making context are examined.     

模糊群决策方法在中间商选择中的应用研究

企业在选择中间商时往往面临着候选伙伴评价指标信息获取的困难,为了使决策科学合理,采用群决策方法集成各领域不同专家的信息,通过语言变量把专家的主观判断转化为对应模糊数,建立相应的数学模型,并计算出用模糊数表示的候选者综合评价值。     

Multiple Criteria Decision Making Models in Group Decision Support

Use of multiple criteria decision making (MCDM) models to aid the group decision process was tested. Two multiple criteria group decision support systems (MCGDSS) were studied, one using the AHP/Tchebycheff method of Iz and the other using Kersten's NEGO system. These systems were compared with a commercial GDSS, VisionQuest. VisionQuest does not include multiple criteria tools. To make the study comparable, VisionQuest was augmented with an ad hoc linear programming model that could generate solutions with specified characteristics requested by the using group. The three systems were compared on the dimensions of solution quality and decision support effectiveness.One of the hypotheses was that MCDM models would force participants to examine criteria, preferences, and aspirations more thoroughly, thus leading to decisions of better quality. Subjects using the MCGDSSs were expected to have higher mean quality and effectiveness values. However, the quality and effectiveness values of the VisionQuest/ad hoc system were found to be better on the dimension of effectiveness. Explanations for this result are included in the paper.Another hypothesis was that the AHP/Tchebycheff method of Iz, a value-oriented system, would yield more effective group support than the goal-oriented NEGO system. However, the NEGO system was found to yield solutions with better quality measures than the solutions obtained with the AHP/Tchebycheff system.Observation of the groups using the MCDM systems indicate that both the AHP/Tchebycheff and NEGO methods can be revised to enhance their effectiveness. The primary difficulty encountered with the AHP/Tchebycheff method was in the large number of pairwise comparisons required by AHP. The NEGO method can be enhanced by including specification of desired attainment levels in the first stage of the method. Both MCDM techniques have potential to benefit group decision support by giving using groups a means to design better solutions.     

Monte Carlo Simulation Techniques in a Decision Support System for Group Decision Making

This paper describes a group decision support system based on an additive multi-attribute utility model for identifying a consensus strategy in group decision-making problems where several decision-makers or groups of decision-makers elicit their own preferences separately. On the one hand, the system provides procedures to quantify the DMs or group of DMs preferences separately. This involves assessing the DMs or group of DMs component utilities that represent their preferences regarding the respective possible attribute values and objective weights that represent the relative importance of the criteria. On the other hand, we propose Monte Carlo simulation techniques for identifying a consensus strategy. An iterative process will be carried out, where, after the simulations have been performed, the imprecise component utilities and weights corresponding to the different DMs or groups of DMs are tightened to output more meaningful information in the next simulations to achieve a consensus strategy. Finally, an application to the evaluation of remedial strategies for restoring contaminated aquatic ecosystems illustrates the usefulness and flexibility of this decision support tool.     

The Impact of Role Training in a User-Driven Group Support System Environment

We tested the proposition that training groups to use roles would improve performance in a user-driven, GSS-supported meeting. Two methods of role training, fixed and rotated roles, were compared against a control procedure in which no formal role training was provided. In a repeated measures experiment, half of the groups completed four generate-creative tasks, while the remaining groups completed three generate-creative tasks followed by one choice-intellective task. Both role training methods were successful in reducing the time spent by groups orienting themselves to the technology, particularly in the choice-intellective task context; however, decision quality or quantity did not improve with role training.     

Distributed Decision Making: A Proposal of Support Through Cooperative Systems

In this paper we show that decision making processes evolve to be more and more distributed and asynchronous. In order to support decision-makers who are not at the same place at the same time we define cooperation processes and a set of models models able to support designers of Cooperative decision support framework. These models come from a more general architecture of a Cooperative Knowledge Based System and are based on the knowledge acquisition field. A definition of cooperative decision support framework is also given.     

A Practical Procedure for Group Decision Making under Incomplete Multiplicative Linguistic Preference Relations

The aim of this paper is to investigate a group decision making problem with incomplete multiplicative linguistic preference relations. We first define the concept of an incomplete multiplicative linguistic preference relation, and then develop a simple algorithm to extend each incomplete multiplicative linguistic preference relation to a complete multiplicative linguistic preference relation. Finally, we develop a practical procedure for group decision making under incomplete multiplicative linguistic preference relations, and give a numerical example to illustrate the developed procedure.     

Common Ground, Complex Problems and Decision Making

Organisations increasingly have to deal with complex problems. They often use multidisciplinary teams to cope with such problems where different team members have different perspectives on the problem, different individual knowledge and skills, and different approaches on how to solve the problem. In order to solve those problems, team members have to share their existing knowledge and construct new knowledge. Theory suggests that negotiation of common ground can positively affect team decision making on the solution of complex problems, by facilitating knowledge sharing across perspectives. In a small scale study with student groups, external representations supported by a specific negotiation ontology were used to facilitate negotiation by encouraging participants to make their beliefs and values explicit. Results showed that the external representations supported clarifying contributions to group members and increased group participation in discussions.     

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.     

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.     

Group Decision Support Using Fuzzy Cognitive Maps for Causal Reasoning

Cognitive maps have been used for analysing and aiding decision-making by investigating causal links among relevant domain concepts. A fuzzy cognitive map (FCM) is an extension of a cognitive map with the additional capability of representing feedback through weighted causal links. FCMs can be used as tools for both static as well as dynamic analysis of scenarios evolving with time. An FCM represents an expert's domain knowledge in a form that lends itself to relatively easy integration into a collective knowledge base for a group involved in a decision process. The resulting group FCM has the potential to serve as a useful tool in a group decision support environment. An appropriate methodology for the development and analysis of group FCMs is required. A framework for such a methodology consisting of the development and application phases is presented.     

A conceptual framework of anonymity in Group Support Systems

As the development and use of automated systems for collaborative work grows, the need for a better understanding of these systems becomes more important. Our focus is on one type of system, a Group Support System (GSS) and, in particular, on one important aspect of a GSS—anonymity. A conceptual framework for the study of anonymity in a GSS is presented, which describes the general classes of variables and their relationships. These variables include the factors that influence anonymity in a GSS, types of anonymity, and the effects of anonymity on a message sender, receiver, group process, and outcome. Each of these variables is discussed with working propositions presented for important group process and outcome measures. The objectives of this article are to highlight the importance and complexity of anonymity, to act as a guide for empirical investigations of anonymity, and to influence future GSS development and use.     

Artificial Neural Systems as a Research Paradigm for the Study of Group Decision Support Systems

Artificial neural systems (ANSs) have received interest recently because of their ability to accurately forecast and classify data. Group decision support systems (GDSSs) also have received much interest for their support of group communication and decision making. This paper explores the potential of the ANS as a methodology for modeling the many complex, interrelated research variables involved in the field of GDSS. As an illustration, multilinear regression, an ANS with backpropagation, and an ANS with a genetic algorithm were developed to classify 133 subjects into verbal or GDSS groups based on their responses to a questionnaire. The ANSs with backpropagation and the genetic algorithm achieved higher classification accuracies (81.8 and 90.9%, respectively) than was achieved with multilinear regression (75.8%). Therefore, an ANS may more accurately model the many interrelationships occurring with GDSS group behavior.     

AHP-Group Decision Making: A Bayesian Approach Based on Mixtures for Group Pattern Identification

This paper proposes a Bayesian estimation procedure to determine the priorities of the Analytic Hierarchy Process (AHP) in group decision making when there are a large number of actors and a prior consensus among them is not required. Using a hierarchical Bayesian approach based on mixtures to describe the prior distribution of the priorities in the multiplicative model traditionally used in the stochastic AHP, this methodology allows us to identify homogeneous groups of actors with different patterns of behaviour for the rankings of priorities. The proposed procedure consists of a two-step estimation algorithm: the first step carries out a global exploration of the model space by using birth and death processes, the second concerns a local exploration by means of Gibbs sampling. The methodology has been illustrated by the analysis of a case study adapted from a real experiment on e-democracy developed for the City Council of Zaragoza (Spain). Partially funded under the research project Electronic Government. Internet-based Complex Decision Making: e-democracy and e-cognocracy (Ref. PM2004-052) approved by the Regional Government of Aragon (Spain) as part of the multi-disciplinary projects programme.     

An Interactive Approach to Multiple Attribute Group Decision Making with Multigranular Uncertain Linguistic Information

The multiple attribute group decision making (MAGDM) problems having multiple sources of uncertain linguistic information assessed in different linguistic label sets are investigated. The existing linguistic labels in a linguistic label set are uniformly and symmetrically distributed, but in many real-life situations, the unbalanced linguistic information appears due to the nature of the linguistic variables used in the problems (Herrera and Herrera-Viedma, Proceedings of 4th international workshop on preferences and decisions, Trento, Italy, 2003). In this paper, we first define some unbalanced linguistic label sets, and then develop some transformation functions to unify the given multigranular linguistic labels in a unique linguistic label set without loss of information. Moreover, we utilize the uncertain linguistic weighted averaging operator to aggregate all individual uncertain linguistic decision matrices into a collective one, and define two similarity measures, one for measuring the similarity degree between each pair of uncertain linguistic variables, and the other for checking the consensus degrees among the individual uncertain linguistic decision matrices and the collective uncertain linguistic decision matrix. Finally, we develop an interactive approach to MAGDM with multigranular uncertain linguistic information and illustrate the developed approach with an application example.     

Decision making under conditions of conflict

A real-world application is employed to explain three general types of decision situations that can arise under conditions of conflict. In addition, meaningful connections and relationships among these areas of decision making are clearly pointed out. To permit useful decision technologies to be employed by practitioners for better understanding and for resolving a variety of actual decision problems, a range of flexible decision support systems is discussed. Subsequently, interesting research developments contained in the upcoming sequence of 12 articles on decision making under conflict are summarized and compared. The research articles not only present unique approaches to decision making involving multiple participants, each of whom may have multiple objectives, but suggest a variety of challenging research problems to be investigated in the future.     

Strategic Manipulation of Preference Information in Multi-Criteria Group Decision Methods

When group decisions involve the allocation of resources to group members, the members might have an incentive to strategically distort any information they provide in order to increase their share of resources. The paper compares several multi-criteria group decision methods with respect to this problem. We show, using a computational model, that strategic manipulation of preference information is possible in all of the methods considered, although to a different extent. Furthermore, when the solution a method generates under correct information is not Pareto-optimal, manipulation attempts might even improve the efficiency of outcomes.     

Information Collection Strategic Design in Experts-assisted Decision Making Paradigm

In an experts-assisted decision making paradigm, the information collection design becomes a strategic variable under a weak assumption that the final decision is dependent on the design used to collect information as well. As a result, the same information of the experts and the decision maker about the problem can potentially produce different final decisions for different information collection designs. The implication is that a decision maker can strategically select a design which serves his/her objective. This paper uses a Bayesian estimation methodology for combining experts' information with the decision maker's prior. An information collection process is designed by setting constraints on this model. Several designs are developed here using such controlled factors as a one-stage versus a two-stage decision process, experts' rank ordering, and group versus individual lobbying/consultation. An example is provided to illustrate the applicability of the concept. It is shown that the information produced in the process of producing a decision can also give insights into the impacts of the decision maker and the experts on the decision.     

Contrasting Single User and Networked Group Decision Support Systems for Strategy Making

The use of computers to support group work – as a Group Decision Support System (GDSS) – on strategy making has grown over the last decade. Some GDSS's have a facilitator managing the computer with the group viewing a public screen displaying the debate, problem definition, and agreements of the group as it negotiates strategies. Others involve members of the group in the direct input of data that forms part of the problem definition – data that is then used by the group employing electronic voting and other organizing devices. This paper discusses a real case relating to an organization seeking to reach important agreements about its strategy. The case involved the top management team and over 50 senior managers. The organization used a facilitator driven GDSS for some of this work, and a networked system for other parts. Some of the meetings were video taped, some were observed through one-way mirrors, and all of the participants were interviewed about their reactions to the different systems. This paper reports on some of the significant contrasts between the two approaches.