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Collective Intelligence


This Mission approaches how we can optimize human-AI group decision making. How can we create super-intelligent groups that operate effectively in dynamic environments? How can highly polarized groups make decisions the whole group will implement?

Goals include defining benchmarks to determine what is superintelligence and character types of tasks and groups, and how to evaluate performance of different types of groups and tasks.  Engineering goals include developing modeling and CAD tools for group design, create a library of group configurations, and establish predictive models to evaluate performance.

During Advances in the quest to understand intelligence, a one-day conference held at MIT on Nov. 4, 2022, Tom Malone gave an overview of this Mission and its goals. 

  • Photo of Abdullah Almaatouq
    Douglas Drane Career Development Professor in Information Technology, MIT Sloan School of Management.
    Assistant Professor, Information Technology, MIT Sloan School of Management.
  • David Rand
    Professor, MIT Sloan School of Management
    Associate Professor, Department of Brain and Cognitive Sciences
    Institute for Data, Systems, and Society
    • Computational Cognition
    • Behavioral Science
  • Daniela Rus portrait
    Deputy Dean of Research, MIT Schwarzman College of Computing
    Director, Computer Science and Artificial Intelligence Laboratory
    Andrew and Erna Viterbi Professor, Department of Electrical Engineering and Computer Science
    • Machine Learning
    • AI Robotics
  • photo of Adam Berinsky
    Mitsui Professor of Political Science, MIT Department of Political Science
    Director of the MIT Political Experiments Research Lab (PERL)
  • Guangyu Robert Yang portrait
    Silverman (1968) Family Career Development Assistant Professor, Brain and Cognitive Sciences
    Assistant Professor, Electrical Engineering and Computer Science
    Associate Investigator, McGovern Institute
    • Computational Neuroscience
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