RESEARCH & Initiatives

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

CREATING

New Examples of Collective Intelligence

STUDYING

Collective Intelligence in Today’s Organizations

DEVELOPING

Theories of Collective Intelligence

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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.

Publications

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.

People
Principal Investigator
Thomas W. Malone

Graduate Students
Yiftach Nagar

Advisors
Alexander (Sandy) Pentland
Tomaso Poggio
Drazen Prelec
Josh Tenenbaum

Deliberatorium

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 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 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.

Collective Intelligence Design Lab

MIT’s Collective Intelligence Design Lab (CIDL) helps groups design innovative new kinds of collectively intelligent systems (superminds) to solve important problems.

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

http://www.ickn.org

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 ickn.org 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.

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.

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 Intelligence; Studying Collective Intelligence in Today’s Organizations; Developing Theories of Collective Intelligence. This 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 rjl@mit.edu.

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.