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

Project: Research project

Project Details


Concepts of collective intelligence or the so-called "wisdom of crowds" play increasingly
fundamental roles in economic and political forecasting, as well as in addressing public policy
disputes and panel decisions about creative merit. Diversity of information and expertise among
crowd members has been identified as a key condition for the emergence of collective decisions
that are superior to any individual’s personal judgment. However, diversity is hard to attain:
there are systemic difficulties in mobilizing minority groups on top of which additional factors
related to social influence such as herding, groupthink, and conformity tend to further reduce
the variety of point of views. This project aims to design network-aware machine learning tools
that elicit useful diversity, counter herding and homophily effects in restraining the wisdom
of crowds, and improve the accuracy of collective forecasting. The proposed research will train
graduate and involve undergraduate students in state-of-the-art data science as they develop and
deploy novel research algorithms and software for effectively harnessing collective intelligence
at the intersection of modern computational and sociological research. The work will ultimately
deliver new technical tools that optimize the wisdom of networked crowds, helping designers
of online systems and policy makers leverage the benefits of collective intelligence.
The project aims to develop theoretical and analytical models as well as algorithmic tools for
diversity evaluation and collective intelligence enhancement under social influence. In specific,
the team will build 1) a model based on the exponential random graph formalization to evaluate
the role of homophily in restraining diversity and 2) a herding model using observational
learning that disentangles factors that lead towards lowest common denominator information
in a crowd. A better understanding of these two effects will inform feature design for a comprehensive
machine learning framework that predicts crowd performance, helps identify robust
collective intelligence signals, and aids the development of opinion aggregation mechanisms
that efficiently capitalize on diversity. These research goals are related to the PI’s prior work on
predicting outcomes in online crowdlending and -investment systems. The planned work places
a specific focus on developments that will make collective intelligence detection tools practical
by providing early warnings of collective misconceptions. With its theoretical, algorithmic, and
software components, the proposed research will produce deliverable assets that are intended to
benefit computer scientists, as well as the broader community of researchers and practitioners
in the social and economic sciences who are interested in harnessing the wisdom of crowds in
web-based systems. Research outputs will be accessible via the project web site (http://). The
tools and results will also be integrated into courses on complex networks and data science at
the PI’s institution, benefiting students from a variety of research groups and departments. Due
to the project’s focus on diversity and its explicit component about gender-related differences,
the proposed work is likely to attract students from underrepresented groups. The grant will
allow the PI to provide research opportunities to these undergraduates.
Effective start/end date3/15/182/28/21


  • National Science Foundation (IIS-1755873)


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