This Project Grant from the National Science Foundation's Division of Computing and Communication Foundations, under the Computer and Information Science and Engineering federal grant program (CFDA 47.070), provides $269,376 to the University of Michigan from October 1, 2022 to September 30, 2025.
The grant supports research to develop computational models and algorithms that promote truth discovery and robust social learning on social media platforms. Specifically, the university researchers will analyze models of sequential and repeated social learning to identify conditions that allow crowds to quickly and reliably converge on the truth. They will also design optimization techniques to configure platform parameters in a way that encourages fast and accurate social learning in these settings. A key focus is augmenting social learning models to account for users' social embeddedness and incentives, as well as the reality of polarized online environments. Empirical research will further develop models for learning complex truths amid social influence. The work adds to the fundamental understanding of how societies learn and discover truth in digital contexts.
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