mlsanityData to 14 Oct 2026

Forecast Hedging and Calibration

arXiv 2210.07169 ↗First posted 13 Oct 2022econ.TH cs.GT cs.LGcited by 22

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Abstract (arXiv)

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.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Builds on 1 of its 39 references that are in the map. First posted on arXiv 13 Oct 2022.

Builds on

1 of 39 references.

References · 1

  1. 2022Calibrated Forecasts: The Minimax Proofcited by 17

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 6; 2024: 1; 2025: 5; 2026: 9.

22 papers.

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  1. 2023Smooth ECE: Principled Reliability Diagrams via Kernel Smoothingcited by 63
  2. 2022A Unifying Theory of Distance from Calibrationcited by 53
  3. 2023U-Calibration: Forecasting for an Unknown Agentcited by 46
  4. 2023Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agentscited by 25
  5. 2023Classifier Calibration with ROC-Regularized Isotonic Regressioncited by 19
  6. 2023The Relative Value of Prediction in Algorithmic Decision Makingcited by 14
  7. 2024Calibration Error for Decision Makingcited by 13
  8. 2025Simultaneous Swap Regret Minimization via KL-Calibrationcited by 11
  9. 2023Online Platt Scaling with Calibeatingcited by 8
  10. 2025In Defense of Defensive Forecastingcited by 5
  11. 2025Improved Bounds for Swap Multicalibration and Swap Omnipredictioncited by 4
  12. 2026An Efficient Black-Box Reduction from Online Learning to Multicalibration, and a New Route to Phi-Regret Minimizationcited by 3
10 more
  1. 2025Calibrated Multi-Level Quantile Forecastingcited by 2
  2. 2025Distribution free M-estimationcited by 1
  3. 2026Defensive Generationcited by 1
  4. 2026Brenier Isotonic Regressioncited by 1
  5. 2026Proper Calibeatingcited by 1
  6. 2026Blackwell Approachability and Gradient Equilibrium are Equivalentcited by 0
  7. 2026Regret, equilibrium, and learning in games: A guided tourcited by 0
  8. 2026Truthful Calibration Measures for Sequential Predictioncited by 0
  9. 2026A Unifying Perspective on Probabilities as Model Predictionscited by 0
  10. 2026Stabilizing Performative Feedback Loops with Minimal Model Deploymentscited by 0