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arXiv · 2201.06924

A Synthetic Prediction Market for Estimating Confidence in Published Work

Sarah Rajtmajer·Christopher Griffin·Jian Wu·Robert Fraleigh·Laxmaan Balaji·Anna Squicciarini·Anthony Kwasnica·David Pennock·Michael McLaughlin·Timothy Fritton·Nishanth Nakshatri·Arjun Menon·Sai Ajay Modukuri·Rajal Nivargi·Xin Wei·C. Lee Giles
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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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Sarah Rajtmajer, Christopher Griffin, Jian Wu, Robert Fraleigh, Laxmaan Balaji, Anna Squicciarini, Anthony Kwasnica, David Pennock, Michael McLaughlin, Timothy Fritton, Nishanth Nakshatri, Arjun Menon, Sai Ajay Modukuri, Rajal Nivargi, Xin Wei, C. Lee Giles. 2021-12-23. A Synthetic Prediction Market for Estimating Confidence in Published Work. https://arxiv.org/abs/2201.06924

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