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"Calibeating": Beating Forecasters at Their Own Game

arXiv 2209.04892 ↗First posted 11 Sep 2022econ.TH cs.GT cs.LGcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 1 of its 31 references that are in the map. First posted on arXiv 11 Sep 2022.

Builds on

1 of 31 references.

References · 1

  1. 2013Relative Loss Bounds for On-line Density Estimation with the Exponential Family of Distributionscited by 59

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 5; 2024: 1; 2025: 3; 2026: 4.

14 papers.

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