| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | March 9, 2018 |
| Latest Amendment Date: | March 9, 2018 |
| Award Number: | 1755873 |
| Award Instrument: | Standard Grant |
| Program Manager: |
William Bainbridge
IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | March 15, 2018 |
| End Date: | February 28, 2021 (Estimated) |
| Total Intended Award Amount: | $174,788.00 |
| Total Awarded Amount to Date: | $174,788.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
633 CLARK ST EVANSTON IL US 60208-0001 (312)503-7955 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
2240 Campus Dr Evanston IL US 60201-2952 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | HCC-Human-Centered Computing |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This research program studied collective intelligence emerging in online decision-making through the development of frameworks aimed at eliciting useful opinion diversity in crowds and improving the accuracy of collective forecasting under social influence. The project has led to nine peer-reviewed publications with graduate and undergraduate students, one submitted article, and two manuscripts that are awaiting submission. The project has resulted, e.g., in algorithmic tools that predict when connected crowds will provide accurate decision-making support in online lending and investing (https://github.com/LINK-NU/CRII-1755873). Furthermore, the work included investigations of collective decision-making dynamics in settings as varied as reputation systems on sharing economy platforms, email communication networks, news media networks, and digital music consumption. We explored how computational approaches leveraging network science and tools from machine learning can help both harness existing collective intelligence and provide early-warning signals of misconceptions. In addition to large-scale empirical analyses of observational data, our findings rely on simulations and user studies.
This research program contributed to the achievement of two primary societally relevant outcomes: (1) understanding and leveraging human behavior and activities on online funding platforms to enhance the economic growth of under-served market segments, and (2) generating novel knowledge about the performance benefits of collective decision-making. This knowledge empowers policy makers, planners, and designers to make effective decisions regarding the use of latent collective behaviors that enhance crowd efficiency and lay the foundations for a crowd-aware system design. Broad dissemination efforts included more than ten presentations of the work by students and the PI at international conferences for experts in the field, resulted in a dedicated project page (https://link.soc.northwestern.edu/research/network-aware-machine-learning/), and supported the work of underrepresented students.
Last Modified: 03/14/2021
Modified by: Emoke-Agnes Horvat
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