Collaborative Research: Measuring Collective Intelligence

合作研究：衡量集体智慧

• 批准号: 0963285
• 负责人: Thomas Malone
• 金额: $ 53.82万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0963285&HistoricalAwards=false
• 关键词: Collaborative; Research; Measuring; Collective; Intelligence; Collaborative; Research; Measuring; Collective; Intelligence

The "holy grail" of artificial intelligence research for decades has been to design computers with robust, integrated, human-like intelligence. This goal has proven elusive, in spite of a massive amount of research. But another goal is just now becoming feasible, and so has been the subject of much less research: using vast computer networks to create new kinds of intelligent entities that combine the best of both human and machine intelligence. One key to designing such human-centered computing systems is better ways of measuring the collective intelligence they exhibit. That is the focus of this research, which represents a collaborative effort among researchers at MIT (lead institution), CMU and Union College. The PIs will first use analogies with what is already known about measuring individual intelligence to suggest new ways of measuring the collective intelligence of complex human-machine systems. For instance, they will determine whether the striking pattern of correlations across tasks that characterizes individual human intelligence even exists for human-machine groups. Next, a series of statistically validated tests will be developed to measure the key components of collective intelligence in human-machine groups. Then, to better understand the "active ingredients" of collective intelligence, the PIs will use what is already known about how groups of people interact effectively to measure micro-level behavior in human-machine groups. A key goal will be to find critical factors (such as group size, technological support, or individual capabilities) that contribute to a human-machine group's adaptability across a wide range of tasks.Most people and computers today are parts of larger human-machine systems that must cope with a wide range of problems. This research will provide powerful new tools for managing and designing such systems. Imagine, for instance, that one could give a short "collective intelligence test" to a top-management team, a product development team, or a collection of Wikipedia contributors. Imagine that this test could predict the team's future performance on a wide range of important tasks. And imagine that the test could also help suggest changes to the team that would improve its flexibility. Or imagine that designers of new collaboration software tools could use a single test to predict how well their tools would improve a group's effectiveness on many different tasks. From the smallest business work groups to our largest societal challenges, there are now many new opportunities for people and computers to solve problems together, not just more efficiently, but also more intelligently. This work will help build a firmer scientific foundation for doing this.Broader Impacts: With individual humans, it is relatively easy to measure intelligence, but it is difficult to increase that intelligence or to observe the detailed events inside the brain that give rise to it. With human-computer groups it is much easier to observe and change factors (such as group size, composition, and technological support) that are likely to determine the group's collective intelligence. Thus, there is a profound intellectual opportunity, not just to learn more about how to design intelligent human-computer systems but also to gain new insights into the very nature of intelligence in complex systems. The results of this research, therefore, will be of interest not only to researchers in computer-supported cooperative work, human-computer interaction, and artificial intelligence, but also more broadly to fields such as cognitive science, social psychology, and organization theory.

数十年来，人工智能研究的“圣杯”是用坚固，综合，类似的智能设计计算机。尽管进行了大量研究，但该目标仍然难以捉摸。 但是，另一个目标是现在变得可行，这是研究较少的研究的主题：使用庞大的计算机网络创建新型的智能实体，以结合人类和机器智能的最佳状态。 设计这种以人为本的计算系统的关键是衡量其展示的集体智能的更好方法。这是这项研究的重点，它代表了麻省理工学院（Lead Institution），CMU和联合学院的研究人员之间的合作努力。 PI将首先使用与测量个体智能有关的已经知道的类比，以提出测量复杂人机系统集体智能的新方法。 例如，他们将确定跨任务的引人注目的相关模式是否为人类机器群体而言，甚至存在人类智力的特征。 接下来，将开发一系列经过统计验证的测试，以衡量人机组中集体智能的关键组成部分。 然后，为了更好地理解集体智能的“主动成分”，PI将使用有关人群如何有效互动以测量人机群中的微观行为的已知知识。 一个关键目标是找到关键因素（例如小组规模，技术支持或个人功能），这些因素有助于人类机器人群体在各种任务中的适应性。如今，大多数人和计算机都是较大的人机系统的一部分，必须应对各种问题。 这项研究将为管理和设计此类系统提供强大的新工具。 想象一下，例如，一个人可以为顶级管理团队，产品开发团队或Wikipedia贡献者进行简短的“集体情报测试”。 想象一下，该测试可以预测团队在各种重要任务上的未来表现。 想象一下，该测试还可以帮助向团队提出更改，以提高其灵活性。 或者想象一下，新的协作软件工具的设计师可以使用单个测试来预测他们的工具如何改善小组对许多不同任务的有效性。 从最小的商业工作组到我们最大的社会挑战，现在，人们和计算机都有许多新的机会共同解决问题，不仅更有效，而且更聪明。 这项工作将有助于建立一个更牢固的科学基础。 借助人类计算机群体，可以更容易观察和改变可能决定集体的集体智能的因素（例如群体规模，组成和技术支持）。因此，有一个深刻的智力机会，不仅是为了了解如何设计智能人力计算机系统，还要了解复杂系统中智能本质的新见解。 因此，这项研究的结果不仅对计算机支持的合作工作，人力计算和人工智能的研究人员都感兴趣，而且对认知科学，社会心理学和组织理论等领域也更加广泛​​。