# A prototype hybrid prediction market for estimating replicability of published work

arXiv: https://arxiv.org/abs/2303.00866 · HTML: https://mlsanity.com/paper/2303.00866/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 1 Mar 2023.

## Builds on (most cited first, 3 of 3)

- [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)
- [A Synthetic Prediction Market for Estimating Confidence in Published Work](https://mlsanity.com/paper/2201.06924/) (2021)

## Built on by (most cited first, 1 of 1)

- [An Audit on the Perspectives and Challenges of Hallucinations in NLP](https://mlsanity.com/paper/2404.07461/) (2024)

## Abstract (arXiv)

We present a prototype hybrid prediction market and demonstrate the avenue it represents for meaningful human-AI collaboration. We build on prior work proposing artificial prediction markets as a novel machine-learning algorithm. In an artificial prediction market, trained AI agents buy and sell outcomes of future events. Classification decisions can be framed as outcomes of future events, and accordingly, the price of an asset corresponding to a given classification outcome can be taken as a proxy for the confidence of the system in that decision. By embedding human participants in these markets alongside bot traders, we can bring together insights from both. In this paper, we detail pilot studies with prototype hybrid markets for the prediction of replication study outcomes. We highlight challenges and opportunities, share insights from semi-structured interviews with hybrid market participants, and outline a vision for ongoing and future work.