RESEARCH & Initiatives

Click to learn more about CCI’s different initiatives and areas of work:


New Examples of Collective Intelligence


Collective Intelligence in Today’s Organizations


Theories of Collective Intelligence


Apply to be a Visiting Scholar or Student at CCI
Creating New Examples of Collective Intelligence

Climate CoLab

Using new collaboration tools, this project is attempting to harness the collective intelligence of large numbers of people to address the problem of global climate change.

Combining Human and Machine Intelligence for Making Predictions

This project studies how human and machine intelligence can be combined to make predictions about future events such as product sales, political events, military actions, and business product development.

More about Combining Human and Machine Intelligence for Making Predictions

Combining Human and Machine Intelligence for Making Predictions

Progress in computing technology now allows machines to use vast amounts of data to make predictions that are often more accurate than those by human experts. Yet, humans are more adept at processing unstructured information and at recognizing unusual circumstances and their consequences.  Can we combine predictions from humans and machines to get predictions that are better than either could do alone?

This project involves using prediction markets and other methods to combine predictions from groups of people and artificial intelligence agents.  In our work so far, we have found that the combined predictions were both more accurate and more robust than those made by groups of only people or only machines. This combined approach may be especially useful in situations where patterns are difficult to discern, where data are difficult to codify, or where sudden changes occur unexpectedly.


Nagar, Y. & Malone, T. W.  Making Business Predictions by Combining Human and Machine Intelligence in Prediction MarketsProceedings of the International Conference on Information Systems ICIS 2011, Shanghai, China, December 5, 2011.

Nagar, Y., & Malone, T. W. (2012).  Improving predictions with hybrid marketsProceedings of the American Association of Artificial Intelligence (AAAI) Fall Symposium on Machine Aggregation of Human Judgment, Arlington, VA, November 2-4, 2012 (Published in on-line proceedings as AAAI Technical Report FS-12-06.

Principal Investigator
Thomas W. Malone

Graduate Students
Yiftach Nagar

Alexander (Sandy) Pentland
Tomaso Poggio
Drazen Prelec
Josh Tenenbaum


This project is exploring how to integrate ideas from argumentation theory and social computing to help large numbers of people enumerate the issues, ideas, and tradeoffs for complex problems with much greater signal-to-noise and much more systematic organization than existing (e.g. forum, wiki, or idea-sharing) technologies.

More about the Deliberatorium

Deliberatorium: Supporting Large-Scale Online Deliberation

Mark Klein; Ali Gurkan (Ecole de Paris); Luca Iandoli (University of Naples)

The Deliberatorium is a technology designed to help large numbers of people, distributed in space and time, combine their insights to find well-founded solutions for such complex multi-stakeholder, multi-disciplinary (“wicked”) problems as sustainability, climate change policy, complex product design, and so on.

See this video for an overview of the concepts underlying the Deliberatorium.

Go to this page for more details on this project.

Follow this link to access the system itself, which allows many authors to create deliberation maps collaboratively.

For additional information, contact Mark Klein

Selected publications

Klein, M., & Garcia, A. C. B. (2015). High-Speed Idea Filtering With the Bag of Lemons. Decision Support Systems, 78:39-50. .

Klein, M., & Convertino, G. (2015). A Roadmap for Open Innovation Systems. Journal of Social Media, 1(2).

Klein, M., & Convertino, G. (2014). An Embarrassment of Riches: A Critical Review of Open Innovation Systems. Communications of the ACM, 57(11):40-42.

How to Harvest Collective Wisdom on Complex Problems: An Introduction to the MIT Deliberatorium. CCI working paper, 2011.

Enabling Large-Scale Deliberation Using Attention-Mediation Metrics. Journal of Computer Supported Cooperative Work (CSCW)
October 2012, Volume 21, Issue 4-5, pp 449-473

Harnessing Collective Intelligence to Address Global Climate Change. Innovations, 2007. 2(3): p. 15-26.

See popular press articles in: Sloan Management Review, Nature, New York Times, MIT Technology Insider (page 11), Information Week, MIT Tech Talk, and The Independent (UK).

Also see Chapter IV in the book, Next Generation Democracy, by Jared Duval.

Nonlinear Negotiation

This project is investigating ways to help large numbers of individuals come to agreements about complex problems with many interdependent issues.

More about Nonlinear Negotiation

Nonlinear Negotiation: protocols for reaching agreements with complex contracts

Mark Klein; Miguel Angel Lopez Carmona (Universidad de Alcala, Spain); Peyman Faratin (Robust Links); Katsuhide Fujita (Nagoya University); Takayuki Ito (Nagoya University); Ivan Marsa Maestre (Universidad de Alcala, Spain); Shelley Zhang (University of Massachusetts Dartmouth)

We are defining novel software algorithms that help agents negotiate “complex” contracts with many interdependent issues.

See this video for an introduction to the concepts underlying this work.

See this paper for an overview of the project: Negotiating Complex Contracts.

Go to this page for more details on this project.

For additional information, contact Mark Klein

Marsa-Maestre, I., Klein, M., Jonker, C. M., Lopez-Carmona, M. A., & Aydoğan, R. (2014). From Problems to Protocols: Towards a Negotiation Handbook. Decision Support Systems, 60:39-54

Representative-based Multi-Round Protocol for Multiple Interdependent Issues Negotiations. Multiagent and Grid Systems (in press)

A Secure and Fair Protocol that Addresses Weaknesses of the Nash Bargaining Solution in Nonlinear Negotiation. Group Decision and Negotiation Journal (in press).

Using Iterative Narrowing to Enable Multi-Party Negotiations with Multiple Interdependent Issues. Sixth International Joint Conference on Autonomous Agents and Multi-Agent Systems (2007).

Multi-issue Negotiation Protocol for Agents: Exploring Nonlinear Utility Spaces. Twentieth International Joint Conference on Artificial Intelligence (2007).

Negotiating Complex Contracts. Group Decision and Negotiation Journal. Volume 12, Number 2 (2003).

Climate Plan Accelerator

In collaboration with other centers and groups at MIT, CCI has envisioned the MIT Climate Plan Accelerator (CPA), a framework for using collective intelligence, climate science-policy models and analysis, blended financing mechanisms, and impact measurement to scale governments’ and organizations’ ability to implement their climate goals.

Studying Collective Intelligence in Today’s Organizations

Sensible Organizations

This project is using new sensors embedded in wearable “social badges” as a kind of “information microscope” to systematically analyze organizations at a much finer grained level than has been done before.

Collaborative Innovation Networks

The goal of this research project is to help organizations increase knowledge worker productivity and innovation by studying Collaborative Innovation Networks (COINs).

Learn More about Collaborative Innovation Networks

Collaborative Innovation Networks

Peter Gloor, Tom Allen, Robert Laubacher, Detlef Schoder & Kai Fischbach (University of Cologne), Francesca Grippa (Northeastern), Ken Riopelle & Julia Gluesing (Wayne State), Christine Miller (Savannah College of Art and Design)

Wikipedia volunteers spend hours creating articles on topics close to their hearts, LEGO Mindstorm hackers pay their own tickets to Denmark to teach LEGO their most recent inventions, and Silicon Valley startup entrepreneurs all collaborate as creative swarms. They behave much like bees swarming to a new location. We call this process coolfarming, using the beehive as a metaphor to describe how to tap the creative potential of communities of innovators. A group of enthusiasts gets together to create something radically new and then recruit early adopters to try their innovation, thereby turning it into a cool trend. Coolfarming describes the genesis of an emergent trend—something new and fresh gets developed by a team of daring individuals, who then spread it to the rest of the world.

Coolfarming works by unlocking the creative potential of Collaborative Innovation Networks (COINs). COINs are made up of groups of self-motivated individuals linked by the idea of something new and exciting and by the common goal of improving existing business practices or creating new products or services for which they see a real need. The strength of COINs is based on their ability to activate creative collaboration and knowledge sharing by leveraging social networking mechanisms, which positively affect individual capabilities and organizational performance. Swarm creativity gets people to work together in a structure that enables a fluid creation and exchange of ideas. Patterns of collaborative innovation frequently follow an identical path, from creator to COIN to Collaborative Learning Network (CLN) to Collaborative Interest Network (CIN).

Over the last ten years, our approach has been applied to dozens of organizations. We have studied their social networks through the lens of e-mail archives and other mechanisms to track organizational communication. The resulting analyses can show how to increase organizational effectiveness, creativity, productivity, and customer and employee satisfaction.
One result of our work is the software tool Condor (free for academic use) for Web mining, social network analysis, and trend prediction (available from the website).

Selected publications

Coolhunting: Identifying Trends Through Online Social Media Analysis

In this project, we study a wide range of methods for predictive analytics (coolhunting) mostly based on social network analysis and the emerging science of collaboration.

Developing Theories of Collective Intelligence

The Genome of Collective Intelligence

The Genome of Collective Intelligence project is developing a taxonomy of organizational building blocks or genes, that can be combined and recombined to harness the intelligence of crowds.

More about the Genome of Collective Intelligence

Genome of Collective Intelligence

Our current understanding of how organizations can be designed is based primarily on observations of the large hierarchical organizations that rose to prominence in the 20th century.   But over the past decade, the rise of the Internet has led to the emergence of surprising new forms of collective intelligence, including Wikipedia, Linux, Google, eBay, and many others.   This project involves categorizing and analyzing these new crowd-based ways of organizing work.

The project is identifying a set of design patterns (or “genes”) that can be combined and recombined to create systems that harness the intelligence of crowds.

Malone, T. W., Laubacher, R., & Dellarocas, C.  The Collective Intelligence Genome, Sloan Management Review, Spring 2010, 51, 3, 21-31 (Reprint No. 51303)

Malone, T., Laubacher, R., & Johns, T.  The Age of Hyperspecialization, Harvard Business Review, July-August 2011, 89(7/8): 56-65

Early work on this project included development of an online Handbook of Collective Intelligence, editable, like Wikipedia, by anyone interested.  This site is no longer actively being edited, but is included here for historical reasons.

The Future of Work 2.0, Interview with Prof. Thomas W. Malone by Harvard Business Review, July 29, 2011

Very Small Business: Rise of the Micro Job, On-stage interview with Prof. Thomas W. Malone, The Economist conference on Human Potential: The Great Unrest, New York, NY, September 27, 2012

Principal Investigator
Thomas W. Malone

Robert Laubacher
Chris Dellarocas (Boston University)
Tammy Johns

Project Alumni
George Herman
Richard Lai

Measuring Collective Intelligence

This project is using the same statistical techniques used in individual intelligence tests to measure the intelligence of groups.  We have found that, just as with individuals, there is a single statistical factor for a group that predicts how well the group will perform on a wide range of very different tasks.  The project also examines the factors that affect the “collective intelligence” of a group, such as its size, the collaboration tools it uses, and the gender and interpersonal skills of its members.

More about Measuring Collective Intelligence

Measuring Collective Intelligence

Psychologists have repeatedly shown that a single statistical factor—often called “general intelligence”— emerges from the correlations among people’s performance on a wide variety of cognitive tasks.  But no one had systematically examined whether a similar kind of “collective intelligence” exists for groups of people. In this work, we have found converging evidence of a general collective intelligence factor that explains a group’s performance on a wide variety of tasks.  This “c factor” is not strongly correlated with the average or maximum individual intelligence of group members, but it is correlated with the average social sensitivity of group members, the equality in distribution of conversational turn-taking, and the proportion of females in the group.

Our continuing work is investigating the factors that affect the collective intelligence of a group, such as its size, the electronic collaboration tools it uses, and the gender mix of its members.


Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., Malone, T. W. Evidence for a collective intelligence factor in the performance of human groups, Science, 29 October 2010, 330 (6004), 686-688; Published online 30 September 2010 [DOI: 10.1126/science.1193147]

Woolley, A., & Malone, T.  Defend your research:  What makes a team smarter?  More women, Harvard Business Review, June 2011, 89 (6): 32-33

Bear, J. B., & Woolley, A. W. (2011) The role of gender in team collaboration and performance. Interdisciplinary Science Reviews, 36(2), 146-153

Aggarwal, I., & Woolley, A.W. (2012) Two perspectives on intellectual capital and innovation in teams: Collective intelligence and cognitive diversity. In C. Mukhopadyay (Ed.), Driving the economy through innovation and entrepreneurship (pp. 495-502). Bangalore: Springer

Engel, D., Woolley, A. W., Jing, L. X., Chabris, C. F., & Malone, T. W. (2014) Theory of mind predicts collective intelligence. Proceedings of Collective Intelligence 2014, Cambridge, MA.

Woolley, A. W., Aggarwal, I., & Malone, T. W. (in press. Collective intelligence in teams and organizations. In T. W. Malone & M. S. Bernstein (Eds.), The handbook of collective intelligence. Cambridge, MA: MIT Press

Engel, D., Woolley, A. W., Jing, L. X., Chabris, C. F., & Malone, T. W. (2014) Reading the mind in the eyes or reading between the lines? Theory of Mind predicts effective collaboration equally well online and face-to-face.  PLOS One 9(12). doi: 10.1371/journal.pone.0115212

Engel, D., Woolley, A. W., Aggarwal, I., Chabris, C. F., Takahashi, M., Nemoto, K., Kaiser, C., Kim, Y. J., & Malone, T. W. (2015) Collective intelligence in computer-mediated collaboration emerges in different contexts and cultures. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2015), Seoul, Korea.

Woolley, A. W., Aggarwal, I., & Malone, T. W. (2015). Collective intelligence and group performance. Current Directions in Psychological Science, 24, 420-424. doi:10.1177/0963721415599543.

Kim, Y. J.*, Engel, D.*, Woolley, A. W., Lin, J., McArthur, N., & Malone, T. W. (2017) What makes a strong team? Using collective intelligence to predict performance of teams in League of Legends. Proceedings of the 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2017). (*authors contributed equally) – Research featured on Nature News.

Chikersal, P., Tomprou, M., Kim, Y. J., Woolley, A. W., & Dabbish, L. (2017) Deep structures of collaboration: Physiological correlates of collective intelligence and group satisfaction. Proceedings of the 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2017).


Matrix Reasoning Challenge

Press and other media

This research has been featured in over 30 print and online publications around the world.  A sample of these media mentions is shown below and a comprehensive list is here.



Collective Intelligence in the Performance of Human Groups, Anita Williams Woolley, National Research Council Public Workshop, April 3-4, 2013.

Why interpersonal skills are more important that you think, Thomas W Malone, Techonomy Conference, Tucson, AZ, November, 13, 2013

We have developed an online battery of collective intelligence tests. To see what the battery looks like, click here

Principal Investigators
Thomas W. Malone
Anita Woolley (Carnegie Mellon University)
Christopher Chabris (Union College)

Postdoctoral Associate
Young Ji Kim

Graduate Students
Nada Hashmi

Other faculty collaborators
Ishani Aggarwal (Tilburg University)

Project Alumni
David Engel
Lisa Jing
Yiftach Nagar

Apply to be a Visiting Scholar or Student at CCI
CCI welcomes requests to visit the center by faculty, researchers, and students from other institutions.

Potential applicants should realize that the space available for visitors is quite limited, and only a few people can be accommodated at any time.

The characteristics we seek in visitors are:

Strong research record for visiting scholars or distinguished performance in coursework for students

Prior work related to collective intelligence in disciplines such as organization studies, strategy, and innovation, information systems management, computer science (especially social computing), social psychology, cognitive science, network science, or evolutionary biology.

Ability to contribute to a CCI research project

During their time at CCI, visitors will be asked to make a substantial contribution to at least one of CCI’s research projects; Creating New Examples of Collective IntelligenceStudying Collective Intelligence in Today’s OrganizationsDeveloping Theories of Collective IntelligenceThis contribution may be waived if the visitor’s own work is a close fit with one of the center’s projects.

Apply Now

Prospective visitors should send a CV, sample research paper, and cover letter outlining research interests to Robert Laubacher at

Requests will be reviewed on a quarterly basis (end of March, June, September, and December) by CCI’s leadership team, in consultation with its research staff.