Can there be good reasons for judging one set of probabilistic assertions more reliable than a second? There are many candidates for measuring "goodness" of probabilistic forecasts. Here, I focus on one such aspirant: calibration. Calibration requires an alignment of announced probabilities and observed relative frequency, e.g., 50 percent of forecasts made with the announced probability of.5 occur, 70 percent of forecasts made with probability.7 occur, etc. To summarize the conclusions: (i) Surveys designed to display calibration curves, from which a (...) recalibration is to be calculated, are useless without due consideration for the interconnections between questions (forecasts) in the survey. (ii) Subject to feedback, calibration in the long run is otiose. It gives no ground for validating one coherent opinion over another as each coherent forecaster is (almost) sure of his own long-run calibration. (iii) Calibration in the short run is an inducement to hedge forecasts. A calibration score, in the short run, is improper. It gives the forecaster reason to feign violation of total evidence by enticing him to use the more predictable frequencies in a larger finite reference class than that directly relevant. (shrink)

It is bad news to find out that one's cognitive or perceptual faculties are defective. Furthermore, it’s not always transparent how one ought to revise one's beliefs in light of such news. Two sorts of news should be distinguished. On the one hand, there is news that a faculty is unreliable -- that it doesn't track the truth particularly well. On the other hand, there is news that a faculty is anti-reliable -- that it tends to go positively wrong. These (...) two sorts of news call for extremely different responses. We provide accounts of these responses, and prove bounds on the degree to which one can reasonably count oneself as mistaken about a given subject matter. (shrink)