English

Forecast Hedging and Calibration

Theoretical Economics 2022-10-14 v1 Computer Science and Game Theory Machine Learning Statistics Theory Machine Learning Statistics Theory

Abstract

Calibration means that forecasts and average realized frequencies are close. We develop the concept of forecast hedging, which consists of choosing the forecasts so as to guarantee that the expected track record can only improve. This yields all the calibration results by the same simple basic argument while differentiating between them by the forecast-hedging tools used: deterministic and fixed point based versus stochastic and minimax based. Additional contributions are an improved definition of continuous calibration, ensuing game dynamics that yield Nash equilibria in the long run, and a new calibrated forecasting procedure for binary events that is simpler than all known such procedures.

Keywords

Cite

@article{arxiv.2210.07169,
  title  = {Forecast Hedging and Calibration},
  author = {Dean P. Foster and Sergiu Hart},
  journal= {arXiv preprint arXiv:2210.07169},
  year   = {2022}
}

Comments

http://www.ma.huji.ac.il/hart/publ.html#calib-int

R2 v1 2026-06-28T03:34:24.765Z