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A Synthetic Prediction Market for Estimating Confidence in Published Work

23 December 2021
Sarah Rajtmajer
Christopher Griffin
Jian Wu
Robert Fraleigh
Laxmaan Balaji
Anna Squicciarini
A. Kwasnica
David M. Pennock
Michael Mclaughlin
Timothy Fritton
Nishanth Nakshatri
A. Menon
Sai Ajay Modukuri
Rajal Nivargi
Xin Wei
C. Lee Giles
    OffRL
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Abstract

Explainably estimating confidence in published scholarly work offers opportunity for faster and more robust scientific progress. We develop a synthetic prediction market to assess the credibility of published claims in the social and behavioral sciences literature. We demonstrate our system and detail our findings using a collection of known replication projects. We suggest that this work lays the foundation for a research agenda that creatively uses AI for peer review.

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