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.

Project Report

Our research has shed light on a property of human groups called "collective intelligence." Essentially, it is like a group IQ. In psychology research, the most replicated result has been the moderate correlation in an individual’s performance on a wide variety of tasks, which we popularly know as IQ. Analogously, we found evidence that this same moderate correlation exists in groups which we call collective intelligence. Furthermore, we found that it is not individual intelligence that leads to the high collective intelligen ce of groups, but factors such as the social sensitivity of members, equality in conversational turn-taking, and a higher proportion of women. Beyond establishing its existence, our research now focuses on trying to understand how collective intelligence can be improved and what it reveals about real world teams. For instance, we have found that a one-hour test of CI predicts how well teams of students do in management simulations, social innovation projects, and curricular learning. We have also found that teams with a higher collective intelligence improve more rapidly in how they handle complex tasks overtime. Other studies focus on how a program to decrease prejudice among members in cross-cultural groups may improve their collective intelligence. Because the question of how to help groups of people work together better is of broad interest, we have developed an efficient online battery for measuring collective intelligence that can be done anywhere in the world in under an hour. With it, we have been able to explore collective intelligence in many settings: academia, management, entrepreneurship, the military, conflict resolution, and cross-cultural understanding. Another important contribution of our research is that it is shedding light on the role of gender in teams. In the news recently, there has been much discussion of women in the workplace and in fields like science, technology, engineering, and math (STEM). Our research has found that women, and we believe, their social sensitivity, make teams more collectively intelligent.

National Science Foundation (NSF)
Division of Information and Intelligent Systems (IIS)
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Ephraim P. Glinert
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Carnegie-Mellon University
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