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MattG comments on Open thread, 11-17 March 2014 - Less Wrong Discussion
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This actually assumes that the Bayesian model is accurate.
Under Black Swan Theory, you can't use past correct predictions to predict future correct predictions.
For example, most of the variance in the stock market is distributed over a few days in history. I could have calibrated on every day leading up to one of those days and felt confident in my ability to predict the stock market... but just one of those days could have wiped out my portfolio.
Calibration actually makes you necessarily overconfident in the Black Swan view of the world.
You don't need to assume that things are normally distributed to be a Bayesian.
People who calibrate themselves usually don't get more confident through the process but less confident.
Don't calibrate on a single variable.
But you do need to assume that somehow you can predict novel events based on previous data
Just going back to my stock market example, what variables would I have calibrated on to predict 9/11 and it's effects on the stock market?
I'm not arguing that you can predict the stock market. What you can do is calibrate yourself enough to see that it's frequently doing things that you didn't predict.