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Distributed Decision Making: A Multiparticipant Decision Support Systems Framework

Distributed Decision Making: A Multiparticipant Decision Support Systems Framework
分布式决策:多参与决策支持系统框架
批准号:
8921603
负责人:
Andrew Whinston
金额:
$22.82万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
1990
资助国家:
美国
项目状态:
已结题
起止时间:
1990-09-01 至 1993-02-28

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中文摘要
翻译
这项研究的动机是这样一个命题, 计算机在支持个人决策方面的成功, 重复进行分布式决策。 如果这件事失败了, 对组织有效性的积极影响可能是 与已经见证的那些一样重要和深远, 个人生产力。 首先需要的是一种理论, 组织代表和处理知识。 这个项目 涉及到这样一个公式化和语言形式化, 理论 该理论旨在体现结构和原则, 可以作为描述、研究、设计、测试 和管理人/计算机分布式决策系统, 以知识为导向的视角。 理论发展 必须借鉴和整合组织中的相关研究 计算机科学和决策科学。 疗效 理论和语言是在实验的基础上进行测试的。 具体 优先研究计划中的研究课题 包括对决策激励、目标驱动和目标- 更少的处理,角色间设计,事件触发处理, 并发问题处理,服务器,实体构造, 从经验、人机界面和参与者中学习 协同
英文摘要
This research is motivated by the proposition that the tremendous successes of computers in supporting individual decision making can be repeated for distributed decision making. If this comes to pas, the positive implications for organization effectiveness are likely to be as significant and far reaching as those already witnessed for individual productivity. What is needed first is a theory of how organizations represent and process knowledge. This project involves the formulation and linguistic formalization of such a theory. The theory aims to embody constructs and principles that can serve as a basis for describing, studying, designing, testing and managing human/computer distributed decision making systems from a knowledge oriented perspective. Development of the theory necessarily draws on and integrated related research in organization science, Computer science, and decision science. Efficacy of the theory and language is tested on an experimental basis. Specific research topics addressed within the prioritized research plan include the treatment of decision instigation, goal-drive and goal- less processing, inter-role design, event-triggered processing, concurrent problem processing, servers, entity constitution, learning from experience, human interfaces, and participant coordination.
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会议论文
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  • 项目类别:
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  • 资助金额:
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  • 财政年份:
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  • 财政年份:
    2009
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  • 财政年份:
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国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis