A Synthetic Prediction Market for Estimating Confidence in Published Work
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 4 of its 28 references that are in the map. First posted on arXiv 23 Dec 2021.
Builds on
4 of 28 references.
References · 4
- 2017Deep Reinforcement Learning that Matterscited by 754
- 2020Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)cited by 322
- 2021Predicting the Reproducibility of Social and Behavioral Science Papers Using Supervised Learning Modelscited by 4
- 2021Design and Analysis of a Synthetic Prediction Market using Dynamic Convex Setscited by 3
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.
2 papers.