Smooth Calibration, Leaky Forecasts, and Nash Dynamics

Date : 


Joint work with Dean P. Foster

Abstract. How good is a forecaster? Assume for concreteness that every day the
forecaster issues a forecast of the type "the chance of rain tomorrow is
30%." A simple test one may conduct is to calculate the proportion of
rainy days out of those days that the forecast was 30%, and compare it to
30%; and do the same for all other forecasts. A forecaster is said to be
_calibrated_ if, in the long run, the differences between the actual
proportions of rainy days and the forecasts are small--—no matter what the
weather really was.  The classical result of Foster and Vohra (1998) is:
calibration can always be guaranteed by randomized forecasting procedures
(a short proof will be provided).

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. To construct it, we
deal also with the related setups of online linear regression and weak
calibration. Finally, we show that smooth calibration yields uncoupled
finite-memory dynamics in n-person games—"smooth calibrated learning"—in
which the players play approximate Nash equilibria in almost all periods.

We will also discuss a new "integral" approach to calibration.