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A Synthetic Prediction Market for Estimating Confidence in Published Work

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arxiv 2201.06924 v1 pith:JLN2X2O6 submitted 2021-12-23 cs.CY cs.AIcs.IRcs.LGcs.MA

Sarah Rajtmajer , Christopher Griffin , Jian Wu , Robert Fraleigh , Laxmaan Balaji , Anna Squicciarini , Anthony Kwasnica , David Pennock
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Michael McLaughlin Timothy Fritton Nishanth Nakshatri Arjun Menon Sai Ajay Modukuri Rajal Nivargi Xin Wei C. Lee Giles
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classification cs.CYcs.AIcs.IRcs.LGcs.MA
keywords publishedworkconfidenceestimatingmarketpredictionsyntheticagenda
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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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