Collaborative Research: Measuring and Modeling Collective Intelligence

协作研究：集体智慧的测量和建模

• 批准号: 0963404
• 负责人: Christopher Chabris
• 金额: $ 17.39万
• 依托单位: Union College
• 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 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0963404&HistoricalAwards=false
• 关键词: Collaborative; Research; Measuring; Modeling; Collective

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.

几十年来，人工智能研究的“圣杯”一直是设计出具有强大、集成、类人智能的计算机。尽管进行了大量的研究，但这个目标已被证明难以实现。但另一个目标现在才变得可行，因此一直是较少研究的主题：利用庞大的计算机网络创造新型智能实体，将人类和机器智能的优点结合起来。设计这种以人为中心的计算系统的一个关键是更好地衡量它们所展示的集体智慧的方法。这是这项研究的重点，它代表了麻省理工学院（牵头机构）、CMU和联合学院的研究人员之间的合作努力。pi将首先用已知的测量个体智能的方法进行类比，提出测量复杂人机系统集体智能的新方法。例如，它们将确定，作为人类个体智力特征的任务之间惊人的相关性模式，是否存在于人机群体中。接下来，将开发一系列经过统计验证的测试，以衡量人机群体中集体智慧的关键组成部分。然后，为了更好地理解集体智慧的“有效成分”，pi将使用已知的关于人群如何有效互动的知识来衡量人机群体中的微观行为。关键目标将是找到关键因素（如团队规模、技术支持或个人能力），这些因素有助于人机团队在广泛任务范围内的适应性。今天，大多数人和计算机都是更大的人机系统的一部分，必须处理各种各样的问题。这项研究将为管理和设计此类系统提供强大的新工具。例如，想象一下，可以对一个高层管理团队、一个产品开发团队或一组维基百科贡献者进行一个简短的“集体智力测试”。想象一下，这个测试可以预测团队在一系列重要任务上的未来表现。想象一下，测试还可以帮助建议团队做出改变，从而提高其灵活性。或者想象一下，新的协作软件工具的设计者可以使用一个测试来预测他们的工具将在多大程度上提高一个团队在许多不同任务上的效率。从最小的商业工作小组到我们最大的社会挑战，现在有许多新的机会让人和计算机一起解决问题，不仅更有效，而且更智能。这项工作将有助于为此建立更坚实的科学基础。更广泛的影响：就个体而言，衡量智力相对容易，但要提高智力或观察大脑内部产生智力的详细事件却很困难。在人机小组中，观察和改变可能决定小组集体智力的因素（如小组规模、组成和技术支持）要容易得多。因此，这是一个深刻的智力机会，不仅可以学习更多关于如何设计智能人机系统的知识，还可以获得对复杂系统中智能本质的新见解。因此，这项研究的结果不仅会引起计算机支持的协同工作、人机交互和人工智能研究人员的兴趣，而且还会引起认知科学、社会心理学和组织理论等更广泛领域的兴趣。