CRII: CHS: Early Detection of Collective Misconceptions with Network-aware Machine Learning Tools

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This project builds theory, algorithms, and frameworks that can be used to design network-aware machine learning tools aimed at eliciting useful diversity and improving the accuracy of collective forecasting. Researchers in the social and economic sciences know that there is great capacity for collective intelligence to emerge from Web-based systems. Yet herding and homophily effects often restrain the wisdom of crowds, vastly limiting this potential. The research furthers the study of complex systems by introducing a new framework that improves our understanding of the mechanisms that govern decision-making under social influence. Advancing complex systems theory in this way greatly enhances the ability to predict when crowds will provide accurate decision-making support for complex problems and when they will fail miserably. Further, the research aids the development of opinion aggregation mechanisms that efficiently capitalize on diversity. The planned work will result in developments that make collective intelligence detection tools practical by providing early warning signs of shared misconceptions. To attain these goals, the research will apply a general framework that incorporates (1) network models that help understand the social processes that lead to observed decision patterns; (2) machine learning tools that draw from uncovered processes to identify signals that optimize the accuracy of collective judgment; and (3) evaluation testbeds that use simulation tools in addition to rich high-dimensional, real-world data about the various stages and performance of group decisions. This research will contribute to societally-relevant outcomes, including: (a) understanding decision-making in online investment and lending settings to enhance the economic growth of underserved market segments; (b) generating novel knowledge about the performance benefits of collective judgment, and (c) quantifying the link between limited opinion diversity and crowd misconceptions. The project will connect undergraduate students, including women and under-represented minorities, to authentic practice in science and engineering research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.