The effects of a group decision support system on culturally diverse and culturally homogeneous group decision making

Considerable research has been conducted over the last decade on the effects of using Group Decision Support Systems (GDSS) in group decision making. Studies have examined the influence of GDSS on such variables as group task, size, quality of decision, and satisfaction with the process. However, only a few studies have focused on the use of GDSS and its interaction with group composition. This study compared the effects of group decision making, both with and without GDSS technology, on culturally diverse and homogeneous groups. Group performance was measured by the number of non-redundant, realistic ideas and the quality of solution produced in response to posed problems. The GDSS used in the study was Vision Quest. Groups using it, regardless of cultural make up, developed a significantly higher number of non-redundant, realistic ideas than groups that did not. Furthermore, among groups using the GDSS, culturally diverse groups produced a significantly higher number of non-redundant, realistic ideas than homogeneous groups that used the GDSS. Implications for diversity management and group support research are suggested.     

Evolution in Groups: A Genetic Algorithm Approach to Group Decision Support Systems

Certain tasks undertaken by groups using Group Decision Support Systems (GDSS) can be viewed as search problems. These tasks involve arriving at a solution or decision where the problem is complex enough to warrant the use of computerized decision support tools. For these types of GDSS tasks, we propose to model the information exchange and convergence toward a solution by the group as a simple genetic algorithm. The simple genetic algorithm is a generalized search technique that is based on the principles of evolution and natural selection. Simply put, the best points in the current population are more likely to be selected and combined through genetic operators to determine new points. We propose that groups using GDSS to address certain tasks behave like a simple genetic algorithm in the manner in which possible solutions are generated, enhanced and altered in attempting to reach a decision or consensus.     

Extraversion and introversion in electronically-supported meetings

Laboratory experiments with Group Decision Support Systems (GDSS) have studied their effects on group and individual outcomes, such as equality of participation, time to decision, quality of decision, consensus, and satisfaction. Research models for GDSSs include individual member characteristics as independent variables in the group process and their outcomes. How individuals feel about group meetings will influence their perception about satisfaction and participation. This study examines how individual personality characteristics influence outcomes in an electronically-supported meeting. The Myers-Briggs Type Indicator was used to create mixed groups of introverts and extraverts. These groups then used a traditional face-to-face meeting and a GDSS to discuss ethical scenarios. All participants contributed more original solutions in the GDSS environment than the face-to-face context but provided more comments in the traditional environment. Overall, the extraverts had more comments in both environments.     

基于MAS的分布式群体决策支持系统框架体系结构的研究

论文分析了群体决策支持系统(GDSS)的现状,根据GDSS决策方式的两种分类,结合分布式人工智能(DAI)技术和多Agent系统(MAS)技术,提出了基于MAS的分布式群体决策支持系统的框架体系结构,并对群体决策支持系统中的协调作了初步研究。     

A Korean group decision support system

A Group Decision Support System (GDSS) can be used to improve communication in many languages. Heretofore, the vast majority of research with these systems has been conducted on American groups using English. Here, we demonstrate how groups of Korean students used a GDSS developed at the University of Mississippi to exchange comments in Korean and English anonymously and simultaneously. The study found no significant differences between the English and Korean systems in terms of self-assessed ratings of evaluation apprehension, production blocking, and process satisfaction. Participants rated both systems favourably, supporting our hypothesis that Korean groups can benefit from the use of a GDSS.     

GDSS中判断矩阵的两种集结方法

群体决策支持系统（GDSS）将通信技术、计算机技术和决策理论结合在一起,促进具有不同知识结构、不同经验、共同责任的群体在决策过程中对半结构化和非结构化问题进行求争。由于GDSS的理论研究尚不成熟,因而GDSS不能很好地解决定性决策问题。在GDSS中引入群体层次分析法（AHP）不仅能提高GDSS的定性决策能力,而且能促进对GDSS中决策思维方式的研究。文中定义了具有实际意义的矩阵“加法”、“乘法”“指数”运算,指出这些运算的性质;给出了判断矩阵“加法”凸组合及Hadamard“乘法”凸组合的概念,并证明了在群体AHP中判断矩阵的“加法”凸组合及Hadamard“乘法”凸组合不仅能消除主观因素的影响,而且还能保持和改善判断矩阵的一致性。从而得到了GDSS中判断矩阵的两种有效的集结方法。     

Developing knowledge‐based decision support systems in public sector administration: A case study in the treasury department,ministry of finance,Malaysia

Since its introduction in the early 1970s the Decision Support System (DSS) concept has attracted a lot of attention from academics as well as users from business organizations. The literature abounds with accounts of systems that are claimed to have used the concept. The common characteristics of most of these systems are that they rely heavily on the use of quantitative models. In ill‐structured and higher level decision making quantitative models are used in conjunction with the qualitative models; in these cases a different approach and method in DSS development needs to be adopted. The objectives of this paper are twofold: to give a brief overview of the concept of DSS and Knowledge‐Based Systems and to describe the approach adopted and experience gained in conceptual analysis and modelling of the decision making process in an attempt to develop a Knowledge‐Based Decision Support System for Public Sector Management.     

Service-oriented intelligent group decision support system: Application in transportation management

In today’s ever changing consumer driven market economy, it is imperative for providers to respond expeditiously to the changes demanded by the customer. This phenomenon is no different in the transportation sector in which a service-oriented Group Decision Support System (GDSS) provides an important role in transportation enterprise to effectively manage and rapidly respond to the varying needs of the customer. In this paper, we explore the integration problem of service-oriented system and intelligence technology through the use of a GDSS. Initially, we analyze a service-oriented architecture and then, propose the design architecture of a service-oriented GDSS. Next, we put forward a general framework that integrates the intelligent techniques as a component into the architecture of service oriented GDSS. In addition, we illustrate how Artificial Intelligence techniques can resolve the conflicts of distributed group decisions. The paper is concluded by providing a number of applications in the railway management system that demonstrates the benefits of the utilization of a service oriented intelligent GDSS.     

Structural equation modeling the use of a risk assessment instrument in child protective services

Risk assessment instruments have been developed to help human service workers identify risk factors associated with abusive families. Many such instruments have been incorporated into management information systems in the form of Decision Support Systems (DSS). This paper reports findings from a case study that evaluated a risk assessment instrument as a decision support tool. Respondents considered “usefulness” of the information a more important factor than how easy the system was to use, the configuration of reports or whether the system was up or not. Focus groups were used to explore how to increase the usefulness of the information.     

群决策支持系统通信部件的研究与实现

群体间通信问题是群决策支持系统(group decision support system,简称GDSS)研究中所要考虑的一个重要问题.目前,实现群体间通信主要采用通用的群件产品,但这类产品协调通信慢,灵活性差,且不易集成,不适合应急GDSS的协同通信.为了寻找一种应急GDSS快速协调性通信的方式,该文研究了一种混合式GDSS网络通信结构,在重点分析GDSS协调性通信功能的基础上提出一种新的基于TCP/IP协议的GDSS 通信协议——GDSSTP(GDSS transmission protocol),给     

A model of decision-making with sequential information-acquisition (part 2)

While most real-life decision are of necessity made with less than perfect information, there is usually some opportunity to acquire additional information regarding the problem at hand before a final decision is made. It is, of course, the recognition of this fact which has led to the importance now attached to the field of Decision Support Systems. On the other hand, the formal analysis of the sort of decision problem for which Decision Support Systems can be useful appears to have lagged behind the developments in applications. In this paper we develope a model of decision-making in which there is available a variety of informational sources (experiments) which can reduce (though generally not eliminate) the uncertainty associated with the final decision. Since the informational sources are available only at some cost (either monetarily or in terms of time, or both), the decision-maker must solve two conceptually distinct problems: (1) developing an optimal information-gathering strategy, and (2) developing an optimal final decision strategy, conditional upon the information obtained during the information-gathering process. A theoretical framework is developed here for the analysis of this general problem, and fairly complete solutions are obtained for some interesting special cases; most notably the computer file search problem.     

A systematic comparison of metamodeling techniques for simulation optimization in Decision Support Systems

Simulation is a widely applied tool to study and evaluate complex systems. Due to the stochastic and complex nature of real world systems, simulation models for these systems are often difficult to build and time consuming to run. Metamodels are mathematical approximations of simulation models, and have been frequently used to reduce the computational burden associated with running such simulation models. In this paper, we propose to incorporate metamodels into Decision Support Systems to improve its efficiency and enable larger and more complex models to be effectively analyzed with Decision Support Systems. To evaluate the different metamodel types, a systematic comparison is first conducted to analyze the strengths and weaknesses of five popular metamodeling techniques (Artificial Neural Network, Radial Basis Function, Support Vector Regression, Kriging, and Multivariate Adaptive Regression Splines) for stochastic simulation problems. The results show that Support Vector Regression achieves the best performance in terms of accuracy and robustness. We further propose a general optimization framework GA-META, which integrates metamodels into the Genetic Algorithm, to improve the efficiency and reliability of the decision making process. This approach is illustrated with a job shop design problem. The results indicate that GA-Support Vector Regression achieves the best solution among the metamodels.     

航空公司收益管理决策支持系统

收益管理决策支持系统作为航空公司一项重要的辅助决策工具,在我国的研究与应用刚刚起步。该文结合我国实际对航空公司收益管理系统的功能进行了论述。     

A model of decision-making with sequential information-acquisition (Part 1)

While most real-life decisions are of necessity made with less than perfect information, there is usually some opportunity to acquire additional information regarding the problem at hand before a final decision is made. It is, of course, the recognition of this fact which has led to the importance now attached to the field of Decision Support Systems. On the other hand, the formal analysis of the sort of decision problem for which Decision Support Systems can be useful appears to have lagged behind the developments in applications. In this paper we develop a model of decision-making in which there is available a variety of informational sources (experiments) which can reduce (though generally not eliminate) the uncertainty associated with the final decisions. Since the informational sources are available only at some cost (either monetarily or in terms of time, or both), the decision-maker must solve two conceptually distinct problems: (1) developing an optimal information-gathering strategy, and (2) developing an optimal final decision strategy, conditional upon the information obtained during the information-gathering process. A theoretical framework is developed here for the analysis of this general problem, and fairly complete solutions are obtained for some interesting special cases; most notably the computer file search problem.     

基于Multi-Agent的群体决策支持系统结构研究

在针对传统的群体决策支持系统框架的基础上,本文结合Internet平台和Agent技术,提出了一种基于Multi-agent的群体决策支持系统结构框架,重点介绍了框架的结构和系统流程.     

基于讨论原理模型的群体决策支持系统的分析与改进

讨论原理模型(DiscussionRationaleModel,DRM)在群体决策支持系统中的应用具有重要意义。以系统化的决策过程为背景,对基于DRM的GDSS进行了分析,并指出了其不足之处。进一步提出了一种改进方法,将讨论原理模型与多准则决策方法相结合来支持方案的选择,以支持群体决策过程,具有重要的理论和实际意义。     

基于GIS技术的空间决策支持系统的设计

本文通过对“智能化的空间决策支持系统”的研究与探讨,分析了GIS技术的优缺点及其与SDSS的结合途径,介绍一个基于GIS技术的空间决策支持系统原型的设计。     

A framework for decision support systems evaluation

In this paper, we present a framework for the Decision Support Systems evaluation problem. Using the Gorry-Scott Morton's framework for information systems, we develop several evaluation methods that structure the evaluation process. The framework determines the best methods of evaluation that are suitable to the characteristics of the Decision Support System concerned. Finally, we use the framework to evaluate two widely used Decision Support Systems.     

基于面向对象模型库的商业决策支持系统

目前,管理信息系统(MIS)已经在我国商业企业中得到了广泛的应用。但目前商业中的管理信息系统仍然还停留在基本事务处理的阶段,为决策者提供决策支持的功能比较弱。解决这一问题的途径是设计一套基于MIS的商业决策支持系统。决策支持系统是利用各种数据、信息、知识,特别是模型技术,辅助各级决策者解决结构化、半结构化决策问题的人机交互系统。该系统采用了模型/方法库、数据库、会话控制系统三模块的总体架构。模型/方法库的设计和管理是该系统的核心和难点所在。该文提出了利用面向对象技术实现模型/方法库的构造,参考数据库管理模式来管理模型库。预测是决策的基础。由于不同的模型针对不同的商业经营数据─主要表现为离散随机型的时间序列值─的预测效果不同,文章提出了一种思路,通过比较各预测模型的预测误差,选择出最优的预测模型,解决了多模型预测的自适应问题。该文所提出的模型的定义方法、模型/方法库的构造思路,基本上是一种通用的模型设计思路,对于其它决策支持系统的设计也有一定的参考作用。