English

"Calibeating": Beating Forecasters at Their Own Game

Theoretical Economics 2026-03-20 v3 Computer Science and Game Theory Machine Learning Machine Learning

Abstract

In order to identify expertise, forecasters should not be tested by their calibration score, which can always be made arbitrarily small, but rather by their Brier score. The Brier score is the sum of the calibration score and the refinement score; the latter measures how good the sorting into bins with the same forecast is, and thus attests to "expertise." This raises the question of whether one can gain calibration without losing expertise, which we refer to as "calibeating." We provide an easy way to calibeat any forecast, by a deterministic online procedure. We moreover show that calibeating can be achieved by a stochastic procedure that is itself calibrated, and then extend the results to simultaneously calibeating multiple procedures, and to deterministic procedures that are continuously calibrated.

Cite

@article{arxiv.2209.04892,
  title  = {"Calibeating": Beating Forecasters at Their Own Game},
  author = {Dean P. Foster and Sergiu Hart},
  journal= {arXiv preprint arXiv:2209.04892},
  year   = {2026}
}

Comments

Corrected Appendix A.7 + new Appendix A.10. Included: Addendum and Errata to the published journal version (Theoretical Economics, 2023) and to arXiv previous version v2 (2022). Web page: http://www.ma.huji.ac.il/hart/publ.html#calib-beat

R2 v1 2026-06-28T01:05:19.105Z