论文标题
平滑校准，漏水预测，有限召回和纳什动态
Smooth Calibration, Leaky Forecasts, Finite Recall, and Nash Dynamics
论文作者
论文摘要
我们建议通过结合附近的预测来平滑校准评分，从而衡量预报器的良好程度。虽然只能通过随机预测程序来保证常规校准，但我们表明可以通过确定性程序来确保平滑的校准。结果，是否泄漏了预测，即提前已知：但是可以保证平滑的校准（而常规校准不能）。此外，我们的程序有有限的召回，是静止的，并且所有预测都在有限的网格上。为了构建该过程，我们还处理在线线性回归和弱校准的相关设置。最后，我们表明平滑的校准在N-Merion游戏“流畅的校准学习”中产生了未耦合的有限内存动力学，其中玩家在几乎所有时期内都会玩近似于NASH的平衡（相比之下，使用常规的校准，使用常规的校准，仅产生了游戏的时间，游戏的时间很近似于Equilibibriabia equilibibria）。
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 the procedure, 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 (by contrast, calibrated learning, which uses regular calibration, yields only that the time-averages of play are approximate correlated equilibria).
