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Smooth Calibration, Leaky Forecasts, and Finite Recall

Author

Listed:
  • Dean P. Foster
  • Sergiu Hart
Abstract
We propose to smooth out the calibration score, which measures how good a forecaster is, by combining nearby forecasts. While regular calibration can be guaranteed only by randomized forecasting procedures, we show that smooth calibration can be guaranteed by deterministic procedures. As a consequence, it does not matter if the forecasts are leaked, i.e., made known in advance: smooth calibration can nevertheless be guaranteed (while regular calibration cannot). Moreover, our procedure has finite recall, is stationary, and all forecasts lie on a finite grid. We also consider related problems: online linear regression, weak calibration, and uncoupled Nash dynamics in n-person games.

Suggested Citation

  • Dean P. Foster & Sergiu Hart, 2015. "Smooth Calibration, Leaky Forecasts, and Finite Recall," Discussion Paper Series dp692, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
    • Handle: RePEc:huj:dispap:dp692
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    File URL: http://www.ma.huji.ac.il/hart/abs/calib-eq.html
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    Cited by:

    1. Juan I Block & Drew Fudenberg & David K Levine, 2017. "Learning Dynamics Based on Social Comparisons," Levine's Working Paper Archive 786969000000001375, David K. Levine.

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