Smooth Calibration, Leaky Forecasts, and Finite Recall
Dean P. Foster and
Sergiu Hart ()
Discussion Paper Series from The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem
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.
Pages: 41 pages
New Economics Papers: this item is included in nep-for
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:huj:dispap:dp692
Access Statistics for this paper
More papers in Discussion Paper Series from The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem Contact information at EDIRC.
Bibliographic data for series maintained by Michael Simkin ().