A Synthetic Prediction Market for Estimating Confidence in Published Work

Abstract

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.

Cite

Text

Rajtmajer et al. "A Synthetic Prediction Market for Estimating Confidence in Published Work." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21733

Markdown

[Rajtmajer et al. "A Synthetic Prediction Market for Estimating Confidence in Published Work." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/rajtmajer2022aaai-synthetic/) doi:10.1609/AAAI.V36I11.21733

BibTeX

@inproceedings{rajtmajer2022aaai-synthetic,
  title     = {{A Synthetic Prediction Market for Estimating Confidence in Published Work}},
  author    = {Rajtmajer, Sarah Michele and Griffin, Christopher and Wu, Jian and Fraleigh, Robert and Balaji, Laxmaan and Squicciarini, Anna Cinzia and Kwasnica, Anthony and Pennock, David M. and McLaughlin, Michael and Fritton, Timothy and Nakshatri, Nishanth and Menon, Arjun Manoj and Modukuri, Sai Ajay and Nivargi, Rajal and Wei, Xin and Giles, C. Lee},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {13218-13220},
  doi       = {10.1609/AAAI.V36I11.21733},
  url       = {https://mlanthology.org/aaai/2022/rajtmajer2022aaai-synthetic/}
}