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Paper detail

A Synthetic Prediction Market for Estimating Confidence in Published Work

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.

preprint2021arXivOpen access
Sarah RajtmajerChristopher GriffinJian WuRobert FraleighLaxmaan BalajiAnna SquicciariniAnthony KwasnicaDavid PennockMichael McLaughlinTimothy FrittonNishanth NakshatriArjun MenonSai Ajay ModukuriRajal NivargiXin WeiC. Lee Giles
Information Retrievalcs.CYMachine LearningArtificial IntelligenceMultiagent Systems
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Sarah RajtmajerChristopher GriffinJian WuRobert FraleighLaxmaan BalajiAnna SquicciariniAnthony KwasnicaDavid PennockMichael McLaughlinTimothy FrittonNishanth NakshatriArjun MenonSai Ajay ModukuriRajal NivargiXin WeiC. Lee Giles

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