A Synthetic Prediction Market for Estimating Confidence in Published Work
This addresses the need for faster and more robust scientific progress by providing a tool for evaluating published claims, though it appears incremental as it builds on existing replication frameworks.
The authors tackled the problem of estimating confidence in published scholarly work by developing a synthetic prediction market to assess credibility in social and behavioral sciences literature, demonstrating it with known replication projects.
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.