# A Synthetic Prediction Market for Estimating Confidence in Published Work

arXiv: https://arxiv.org/abs/2201.06924 · HTML: https://mlsanity.com/paper/2201.06924/

## 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 (most cited first, 4 of 4)

- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)](https://mlsanity.com/paper/2003.12206/) (2020)
- [Predicting the Reproducibility of Social and Behavioral Science Papers Using Supervised Learning Models](https://mlsanity.com/paper/2104.04580/) (2021)
- [Design and Analysis of a Synthetic Prediction Market using Dynamic Convex Sets](https://mlsanity.com/paper/2101.01787/) (2021)

## Built on by (most cited first, 2 of 2)

- [A prototype hybrid prediction market for estimating replicability of published work](https://mlsanity.com/paper/2303.00866/) (2023)
- [Human-AI Collaboration for Estimating Scientific Replicability](https://mlsanity.com/paper/2605.27394/) (2026)

## 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.