A Synthetic Prediction Market for Estimating Confidence in Published Work
Computers and Society
2022-01-19 v1 Artificial Intelligence
Information Retrieval
Machine Learning
Multiagent Systems
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.
Keywords
Cite
@article{arxiv.2201.06924,
title = {A Synthetic Prediction Market for Estimating Confidence in Published Work},
author = {Sarah Rajtmajer and Christopher Griffin and Jian Wu and Robert Fraleigh and Laxmaan Balaji and Anna Squicciarini and Anthony Kwasnica and David Pennock and Michael McLaughlin and Timothy Fritton and Nishanth Nakshatri and Arjun Menon and Sai Ajay Modukuri and Rajal Nivargi and Xin Wei and C. Lee Giles},
journal= {arXiv preprint arXiv:2201.06924},
year = {2022}
}