EAGER: The Climate CoLab: A System for Very Large-Scale Model-Based Group Problem-Solving
Massachusetts Institute Of Technology, Cambridge MA
Investigators
Abstract
The goal of this project is to develop a system that combines the strengths of both human and computer capabilities to help solve a complex societal problem: global climate change. The project will develop an on-line community called the Climate CoLab, in which many thousands of people around the world create, analyze, and ultimately select detailed plans for what humans can do about climate change. At the core of the system will be an evolving collection of user-created proposals based on computer simulations of the actions humans can take and the predicted impacts of those actions. Users will also be able to debate the pros and cons of different proposals and vote for the proposals and arguments they find most credible and desirable. By integrating three capabilities (computer simulation models, on-line debates, and electronic voting) in a novel way, the system lets a very large group of people define and evaluate alternative problem solutions while computers do the rapid calculations needed to assess key consequences of each alternative. By including relevant experts from different disciplines and members of the public who have novel points of view, the community can consider a wide range of plausible alternatives. And by involving policy makers and large numbers of citizens, the eventual political adoption of the most promising alternatives is facilitated. Intellectual merit: The primary intellectual contributions of this work will be generalizable lessons about how to design large-scale, on-line communities that use computational models to help solve difficult societal and managerial problems. Broader impacts: The project will help educate the general public about the issues involved in global climate change. In addition, by constructively engaging a broad range of scientists, policy makers, and concerned citizens, this system may help develop plans and policies that are actually better than any that would have otherwise been developed.
View original record on NSF Award Search →