基于CAS理论的决策支持系统研究

复杂适应系统理论的提出对于人们认识、理解、控制、管理复杂系统提供了新的思路。针对目前决策支持系统的分布式群体决策的发展趋势，文中提出一种基于自适应决策主体的决策支持系统模型，用于大型复杂决策系统的问题求解。给出了自适应决策主体的定义，并对其自适应行为作了描述。自适应决策主体在决策环境中不断适应的结果表现出全局性的突现行为，最终得到复杂决策问题的求解结果。     

基于CAS理论的决策支持系统研究

复杂适应系统理论的提出对于人们认识、理解、控制、管理复杂系统提供了新的思路。针对目前决策支持系统的分布式群体决策的发展趋势,文中提出一种基于自适应决策主体的决策支持系统模型,用于大型复杂决策系统的问题求解。给出了自适应决策主体的定义,并对其自适应行为作了描述。自适应决策主体在决策环境中不断适应的结果表现出全局性的突现行为,最终得到复杂决策问题的求解结果。     

关于决策支持系统发展综述

通过分析决策支持系统的定义和发展历史,对决策支持系统的逻辑结构、技术层次结构和物理结构进行了剖析,描述了其对应的逻辑关系,并结合最新的计算机技术和网络通信技术,讨论了决策支持系统的发展趋势.     

决策支持系统发展综述

决策支持系统的概念是在二十世纪八十年代中期引进我国,经过了长远的发展历程,逐渐从理论研究、系统开发和实际应用等多个方面取得了举世瞩目的成就,至今仍然呈现出积极的发展态势.决策属于系统处理的重要对象,支持就是系统的精髓,为此,决策支持系统的关键之处就是决策支持,系统属于一种承载着决策支持的重要平台.作为决策制定的核心,人正在用实际的行动驱使着这个系统的运行,决策支持系统建立的主要目的是形象化的模拟人的决策行为,从而提升决策的可靠有效性,及时体现出决策支持系统发展的智能化特征.     

通用综合评判决策支持系统的研究与实践

本文论述用层次分析法动态建立决策模型，用模糊综合评价技术处理评判数据，实现通用综合评判决策支持系统的原理和方法，并介绍了系统的特点和应用情况。     

决策支持系统相关技术综述

决策支持系统是现代化管理的重要手段,数据挖掘、数据仓库、在线分析又是它的三个主要技术,主要从特点、应用现状、涉及到的问题、技术基础等方面对其采用的三个技术作了详细介绍。     

决策支持系统与决策支持系统生成器NDSSG

本文对决策支持系统及决策支持系统生成器作探讨，特别对所研制的生成器ＮＤＳＳＧ的功能，内容作深入探讨。文章还对决策支持系统今后发展作了展望。     

综合决策支持系统中数据挖掘功能的设计与实现

通过对现有综合决策支持系统的架构分析,根据需要增加了一个中间件子系统部分,并进一步研究在该架构下如何利用数据挖掘技术来实现对决策主题数据的综合,即应该采取何种数据挖掘技术以及如何获取知识提供给综合决策支持系统;最后以灰色系统理论的建模法为例,给出了具体的理论、算法以及实现步骤与方法,可以为相关系统的设计与研究提供一定的借鉴和参考。     

多ES集成式决策支持系统的实现方法探讨

本文在文献［１］提出了多ＥＳ集成式决策支持系统（ＭＥＳＩＤＳＳ）的基本概念并对其总体结构设计进行探讨的基础上，从多领域知识库系统的组织方式、多ＥＳ动态合作的实现和系统的工作过程等方面对ＭＥＳＩＤＳＳ的实现方法做了探讨。     

决策支持系统发展综述及展望

全面研究和分析决策支持系统(DSS)的发展轨迹,对研究DSS未来的新理论、新模型、新技术、新应用具有十分重要的意义。对DSS的发展历程尤其是它的结构与支撑技术进行了全面的探讨。在对其进行深入剖析的基础上,指出需求和技术是DSS发展的两个主要动力,在大数据时代更是如此。同时分析了DSS在大数据时代所面临的新需求与新问题,并结合大数据和云计算相关技术,对未来DSS如何满足这些新需求、解决这些新问题进行了分析与展望。     

多人决策支持系统研究评述

对多人决策支持系统的研究状况进行回顾,总结了ＧＤＳＳ理论和实验研究工作及取得的成果,指出了ＧＤＳＳ研究中存在的问题和研究方向。ＤＤＳＳ和ＯＤＳＳ目前仍处于概念开发阶段。最后探讨了多人决策支持系统的发展前景。     

一种基于Agent的决策支持系统生成器新框架

在已有ＤＳＳ框架的基础上,提出了一种基于Ａｇｅｎｔ决策支持系统生成器新框架,并讨论了该框架中各类Ａｇｅｎｔ的结构和实现方式。     

大系统模糊决策研究及其智能化决策支持系统IDSS

研究了模糊数学原理应用于大系统决策的理论和方法。设计了相应的多用途智能化决策支持系统ＩＤＳＳ。     

基于内联网的决策支持系统及其应用研究

本文提出了一种新的基于内联网的决策支持系统框架结构,通过EDUDSS原型系统说明了基于内联网的决策支持系统的实现过程,并揭示了其基本特征,证明了这种框架结构的合理性和实用性。     

An Intelligent Decision Support System for Investment Analysis

We present an intelligent decision support system that combines decision analysis and traditional investment evaluation and analysis. The system brings to the ordinary user expertise in both decision analysis and investment evaluation techniques, as well as domain knowledge about the market. The system supports the entire decision analysis cycle and provides facilities and tools for decision modeling, probability assessment, model evaluation, and sensitivity analysis. An example based on the Shanghai Stock Market is presented. We demonstrate how an investment decision model for the stock market is constructed by the system and how the optimal decision is obtained. Sensitivity and value of information analysis were also carried out by the system. Received 12 April 1999 / Revised 8 February 2000 / Accepted 2 March 2000     

基于知识的多专家决策支持系统

由于决策支持系统（ＤＳＳ）和专家系统（ＥＳ）的不同行为特征,使它们在信息处理中的决策和问题求解方面不能相互兼顾。本文交讨论和ＥＳ结合的可行性,提出一种基于知识的多专家决策支持系统的结构模式,及实现多专家协同求解和决策的方法。     

Understanding Data-Driven Decision Support Systems

It is important for managers and Information Technology professionals to understand data-driven decision support systems and how such systems can provide business intelligence and performance monitoring. Data-driven DSS is one of five major types of computerized decision support systems and the features of such systems vary across specific implementations. Different development packages also impact the capabilities of data-driven DSS and hence criteria for evaluating data-driven DSS development software are important to understand. Overall, this article builds on an historic foundation of prior decision support systems theory.     

Understanding Data-Driven Decision Support Systems

Abstract

It is important for managers and Information Technology professionals to understand data-driven decision support systems and how such systems can provide business intelligence and performance monitoring. Data-driven DSS is one of five major types of computerized decision support systems and the features of such systems vary across specific implementations. Different development packages also impact the capabilities of data-driven DSS and hence criteria for evaluating data-driven DSS development software are important to understand. Overall, this article builds on an historic foundation of prior decision support systems theory.     

An effective architecture for Decision Support Systems

Decision Support Systems (DSS) are, by nature, general- purpose systems, because they must support a variety of managers who have different decision styles and different problems. However, it seems that no effective general-purpose DSS have yet come into existence, although the components of DSS such as data base technology, modeling techniques, inexpensive graphic display etc., have progressed to the point where we should now be able to build effective DSS.This shortcoming seems to result from the following fact: Research on decision support has focused on data enlargement and model refinement, however, little attention has been paid to DSS architecture which integrates these components of DSS. It has not been well appreciated that DSS architecture itself facilitates learning about unstructured-problem solving and enables system evolution.In this paper, we propose a DSS architecture based on the study of unstructured-problem solving and considerations of the needs of managers as non-computer specialists. We illustrate this with a system realized using this architecture.     

Rationality in Information Systems Support to Decision Making

Management information systems serve business organizations by providing information for decision making. Various types of systems serve different types of decision contexts. The philosophic basis of information system support is discussed. The rational (or normative) philosophy is widely used, and appears in business theory in the form of agency theory and transaction cost analysis. While this approach has been valuable in some contexts, there are other contexts where the rational approach has limited in utility for real business decision making. Decision makers need to consider subjective factors to enable them to cope with the high levels of uncertainty, incomplete understanding, and imperfect data typical of dynamic open systems. There are alternative philosophies upon which to base decision-making that are appropriate for specific decision contexts. Churchman identified empirical, multi-perspective frameworks, dialectic, and cause-and-effect inquiring systems as alternatives to the rational (normative) system. A number of information system tools, such as decision support systems, expert systems, and group support systems can be supported by models based on philosophies other than rational models. A more empirically based philosophy, with decision-makers balancing hypothesis generation and observations of performance, is often more appropriate. The relationship between Churchman's inquiring systems and information system types are discussed.