Inverse Optimization with Prediction Market: A Characterization of Scoring Rules for Elciting System States
Abstract
Inverse optimization aims to recover the unknown state in forward optimization after observing a state-outcome pair. This is relevant when we want to identify the underlying state of a system or to design a system with desirable outcomes. Whereas inverse optimization has been investigated in the algorithmic perspective during past two decades, its formulation intimately tied with the principal’s subjective choice of a desirable state—indeed, this is crucial to make the inverse problem well-posed. We go beyond the conventional inverse optimization by building upon prediction market, where multiple agents submit their beliefs until converging to market equilibria. The market equilibria express the crowd consensus on a desirable state, effectively eschewing the subjective design. To this end, we derive a proper scoring rule for prediction market design in the context of inverse optimization.
Cite
Text
Bao and Sakaue. "Inverse Optimization with Prediction Market: A Characterization of Scoring Rules for Elciting System States." Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, 2025.Markdown
[Bao and Sakaue. "Inverse Optimization with Prediction Market: A Characterization of Scoring Rules for Elciting System States." Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, 2025.](https://mlanthology.org/aistats/2025/bao2025aistats-inverse/)BibTeX
@inproceedings{bao2025aistats-inverse,
title = {{Inverse Optimization with Prediction Market: A Characterization of Scoring Rules for Elciting System States}},
author = {Bao, Han and Sakaue, Shinsaku},
booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics},
year = {2025},
pages = {451-459},
volume = {258},
url = {https://mlanthology.org/aistats/2025/bao2025aistats-inverse/}
}