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.     

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.     

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.     

Applying Multiattribute-Based Group Decision Making Techniques in Complex Equipment Selection Tasks

A practicing industrial engineer, even an experienced decision maker, needs help in the sense that there is a necessity to use more logical or analytical decision support tools, especially when dealing with control and instrumentation projects which are often worth millions of dollars. In this article we propose an algorithm which can support the process of group decision making relating to industrial automation, especially involving the selection of control and instrumentation equipment. The aim of this article is to look only at the algorithm's application and how it is applied. To this end, two test cases are used as examples: (1) selecting a local area network for installation in an academic environment; and (2) selecting an integrated control system for a real-world pulp and paper mill. Obtained results show that the algorithm leads to a satisfactory solution. A software form of the algorithm is being programmed for use as a 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.     

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.     

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.     

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.     

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.     

A Multi-Agent View of Strategic Planning Using Group Support Systems and Artificial Intelligence

The strategic planning process is dynamic and complex. Including a Group Support System (GSS) in the problem-solving process can improve the content quality of the strategic plan by allowing increased participation by more members of the organization. However, it can also add to the complexity of the problem by increasing the quantity of textual information that can result from group activity. Added complexity increases cognitive overload and frustrations of those participants negotiating the contents of the strategic plan. This article takes a multi-agent view of the strategic planning process. It considers group participants as multiple agents concerned with the content quality of the strategic plan. The facilitator agent is responsible for guiding groups in the strategic plan construction process as well as for solving process problems such as cognitive overload. We introduce an AI Concept Categorizer agent, a software tool that supports the facilitator in addressing the process problem of cognitive overload associated with convergent group activities by synthesizing group textual output into conceptual clusters. The implementation of this tool reduces frustrations which groups encounter in the process of classifying textual output and provides more time for discussion of the concepts themselves. Because of the large amount of convergent activity necessary for strategic planning, the addition of the AI Concept Categorizer to the strategic planning process should increase the quality of the strategic plan and the buy-in of the participants in the strategic planning process.     

A framework for thinking about Group Decision Support Systems (GDSS)

Strategic problem solving in organizations is a social process that disturbs established social relationships. Maintaining a negotiated social order is crucial to political feasibility and to emotional commitment from the participants in relation to a solution package. However, Group Decision Support Systems that attend overly to managing social order risk group think through bounded vision. This may be avoided if emotional commitment is also encouraged through participants experiencing the problem situation from multiple perspectives and in relation to alternative solution strategies. Commitment depends upon both means/ends rationality and procedural rationality. This acknowledges the balance in providing support to a group with respect to the negotiation of social order, with the more traditional group decision support for socially negotiating order out of the problem situation. This article argues that effective Group Decision Support Systems must attend to both aspects of creating order. OR modelling methods and the support that can be provided by modern micro-computers offer a new way forward—models can be toys that a group can play with together, enabling them to create knowledge as well as use it.     

多元化战略管理:决策程序及其理论依据

通过对多元化战略的理论回顾,总结多元化战略管理研究的理论流派,多元化战略管理理论对中国企业进行多元化决策的指导意义,并创造性地提出了多元化战略管理决策程序及其理论依据,试图初步建立起多元化理论与企业实践操作间联系的纽带。     

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。     

An Intelligent Distributed System for Strategic Decision Making

The decision-making process in strategic planning is often too complex to be handled by conventional methods. Strategic planning problems (building new plans, new product planning, etc.) belong to the class of problems called ill-structured by H. Simon. They involve a decomposition of the main problem into a set of subproblems, a reasoning process at the subproblem level, and then a coordinated and aggregated process to build a global solution. Because partial solutions are generated without having a complete view of the global objective, this type of decision-making process very often generates incoherent and contradictory hypotheses and actions. Therefore, the main problem is to find a way to achieve coherence and coordination among decisions made locally by different agents, at different levels.Recent advances in Artificial Intelligence, particularly in the field of multi-agent theory, offer great promises in modeling strategic planning processes. In this article we present a general framework called 'A Coherent Plan of Coordinated Actions (CPCA)' for building intelligent distributed strategic decision making systems which integrates advances in both distributed decision making and Distributed Artificial Intelligence. We then describe a multi-blackboard system, ARISTOTE, which is aimed at helping corporate managers address the feasibility and coherence of a plan of actions.     

Coordination Structures and System Restrictiveness in Distributed Group Support Systems

Most GSS research has studied the impact of restricting group interaction to GSS-prescribed coordination structures with face-to-face groups, while Distributed GSS (DGSS) has been largely ignored. Due to the nature of mediated communication in asynchronous interaction, it is relatively difficult to coordinate distributed groups, and a special coordination structure must be arranged to overcome these difficulties. This study examines the effect of system restrictiveness of coordination structures in an asynchronous environment. A 2 × 2 factorial experiment was designed with two independent variables – sequential vs. parallel coordination mode, and with vs. without a leader – to construct coordination structures with varying degrees of restrictiveness. The study finds that less restrictive coordination structures are more appropriate to support asynchronously interacting distributed groups. Objective decision quality is equal for both parallel and sequential coordination mode, but is significantly better with a group leader. Groups with parallel coordination mode have a stronger belief that the decisions they made are of higher quality than those of groups with sequential coordination mode. In groups with a leader, communication effectiveness is better. Satisfaction with a decision process is higher in parallel coordination groups and in groups with a leader. There is also a significant interaction effect. Satisfaction with the decision process is higher in sequential coordination groups with a leader than sequential coordination groups without a leader.     

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 Role of Leadership in Group Support Systems Failure

This paper reports on a research case study in which the use of a Group Support System (GSS) by a multi-organizational alliance failed. The paper argues that the leadership style of the meeting champion may be a much greater moderating factor in GSS meeting success than previously thought. Transformational Leadership Theory is used to explain the results, and implications for both researchers and managers are drawn. Two themes emerge: first, the case shows where the concept of phony democracy may or may not occur. Second, the case illustrates conditions under which a GSS may generate, as opposed to mitigate conflict. For managers, it suggests that using a GSS may not be optimal if they choose to adopt a Transactional leadership style. For researchers, this work offers insights into boundary conditions affecting GSS usage, extending a paucity of research in negative GSSusage cases.     

A Fuzzy Multi-Criteria Decision Making Model for IS Student Group Project Assessment

To accommodate the criterion-referenced student group project assessment approach, this paper proposes a fuzzy group Multi-Criteria Decision Making (MCDM) model. The proposed model can be used to solve student group project assessment problem in particular and generic MCDM problems in general, where the criteria used are often different and can be scored with multiple preference formats. The proposed fuzzy group MCDM model supports seven different preference formats, including preference ordering, utility vector, linguistic term vector, selected subset, fuzzy selected subset, fuzzy preference relation, and normal preference relation. It has been firstly used in the context of Information Systems (IS) student group project assessment.     

Combining GDSS and Gaming for Decision Support

Both gaming and group (decision) support systems (GDSS) are frequently used to support decision-making and policymaking in multi-actor settings. Despite the fact that there are a number of ways in which gaming and GDSS can be used in a complementary manner, there are only sporadic examples of their combined use. No systematic overview or framework exists in which GDSS are related to the functions of gaming or vice versa. In this article, we examine, why, how and for what purpose GDSS can be used to enrich and improve gaming simulation for decision support, and vice versa. In addition to a review of examples found in the literature, four games are discussed where we combined gaming and GDSS for complex decision making in a multi actor context: incodelta, a game about transportation corridors; infrastratego, a game about a liberalizing electricity market; containers a drift, a game about the planning of a container terminal, and; dubes, a game about sustainable urban renewal. Based on the literature and these four experiences, a classification is presented of (at least) four ways in which GDSS and gaming can be used in a complementary or even mutually corrective, manner: the use of GDSS for game design, for game evaluation, for game operation and the use of gaming for research, testing and training of GDSS.     

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.