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CyberSEES:Type 2: Collaborative Research: Combining Experts and Crowds to Address Challenging Societal Problems

CyberSEES:Type 2: Collaborative Research: Combining Experts and Crowds to Address Challenging Societal Problems
Cyber​​SEES:类型 2:协作研究:将专家和大众结合起来解决具有挑战性的社会问题
批准号:
1442887
负责人:
Thomas Malone
金额:
$59.89万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2015
资助国家:
美国
项目状态:
已结题
起止时间:
2015-01-15 至 2018-12-31

项目摘要

项目成果

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中文摘要
翻译
从编写百科全书,到开发软件,再到折叠蛋白质,越来越多过去由小团体专家解决的问题现在正在由使用互联网的更大的“人群”来解决。然而,对于许多复杂的问题,群体带来的创造力、活力和多样性不足以解决问题。还需要各种只有专家才具备的专业知识。这个项目的重点是创建网络基础设施,将专家和人群这两种资源结合起来,解决复杂的社会问题。该项目将在许多人认为是人类之一的背景下解决这个问题?当今最重要的可持续发展挑战是:如何应对环境动态趋势。计划中的大部分工作包括在团队的CoLab上开发和测试想法,CoLab是一个专注于这个问题的软件平台和在线社区,它是在NSF和其他支持下开发的。更具体地说,该项目将调查专家和大众如何共同完成四个关键的问题解决活动:将整体问题分解成更小的部分,为这些部分生成潜在的解决方案,将这些部分整合成整体解决方案,以及评估解决方案。例如,该项目将研究(a)当群体从专家的“种子”想法开始时,他们是否会产生更好的想法,(b)如何将某些类型的专家知识编码到计算机模拟模型中,以便群体自己使用,以及(c)通过半专家、新手和软件工具的组合,专家评估可以在多大程度上接近。预计结果将包括:(a)适用于许多可持续性和其他挑战的开源软件,(b)社区可以有效使用该软件的一套流程和方法,以及(c)由数万人组成的多样化社区使用这种方法来解决与趋势环境变化相关的问题。所提议的工作的主要智力贡献将是更好的流程和计算机工具,以创建在线社区,结合专家和群体的最佳特征来解决复杂的社会问题。要研究的技术和组织方法的许多方面应该引起各个领域的研究人员的兴趣,包括集体智慧、计算机支持的协同工作、人机交互、计算机科学、工程、组织设计、心理学和公共政策。通过让学生和公众就人们如何解决复杂问题提出可信的想法,这个项目将有助于教育更广泛的社区了解所涉及的实际问题。如果成功,这项工作可能会导致开发出更好的方法来解决复杂的社会问题,而不是其他任何方法。结果的许多方面将适用于广泛的领域,如教育、医疗保健和业务问题,如战略规划和预算编制。
英文摘要
From writing an encyclopedia, to developing software, to folding proteins, more and more problems that used to be solved by small groups of experts are now being solved by much larger "crowds" of people using the Internet. For many complex problems, however, the creativity, energy, and diversity that crowds bring are not enough to solve the problems. Various kinds of specialized knowledge that only experts have are also needed. This project focuses on creating the cyber-infrastructure to combine these two kinds of resources--experts and crowds--in solving complex societal problems. The project will address this question in the context of what many people believe is one of humanity?s most important sustainability challenges today: how to deal with trending environmental dynamics. Much of the proposed work includes developing and testing ideas on the team's CoLab, a software platform and online community focused on this issue, which was developed with prior NSF and other support. More specifically, the project will investigate how experts and crowds can work together to perform four key problem-solving activities: decomposing the overall problem into smaller pieces, generating potential solutions for the pieces, integrating the pieces into overall solutions, and evaluating the solutions. For example, the project will study (a) whether crowds generate better ideas when they start with "seed" ideas from experts, (b) how certain kinds of expert knowledge can be encoded in computer simulation models that crowds can use on their own, and (c) to what degree expert evaluations can be approximated by combinations of semi-experts, novices, and software tools. The results are expected to include (a) open source software applicable to many sustainability and other challenges, (b) a set of processes and methodologies with which communities can effectively use this software, and (c) a diverse community of tens of thousands of people using this approach to address issues associated with trending environmental changes. The primary intellectual contribution of the proposed work will be better processes and computer tools for creating on-line communities that combine the best features of both experts and crowds to solve complex societal problems. Many aspects of the technical and organizational approaches to be studied should be of interest to researchers in a variety of fields, including collective intelligence, computer-supported cooperative work, human computer interaction, computer science, engineering, organizational design, psychology, and public policy. By engaging students and the general public to come up with credible ideas for what people can do about complex problems, this project will help educate a much broader community about the actual issues involved. If successful, the work will likely lead to the development of better approaches for complex societal problems than any that would otherwise have been developed. Many aspects of the results will be applicable to a wide range of domains such as education, healthcare, and business problems like strategic planning and budgeting.
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会议论文
VOSS: Collaborative Research: Is Larger Smarter? Investigating the Effect of Group Size on Collective Intelligence
EAGER: The Climate CoLab: A System for Very Large-Scale Model-Based Group Problem-Solving
Workshop on Collective Intelligence
SoCS: The Climate Collaboratorium: A Tool for Large-Scale Model-Centric Collective Decision-Making
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