Today's post, Inductive Bias was originally published on April 8, 2007. A summary (from the LW wiki):
Inductive bias is a systematic direction in belief revisions. The same observations could be evidence for or against a belief, depending on your prior. Inductive biases are more or less correct depending on how well they correspond with reality, so "bias" might not be the best description.
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This still confuses me. 'Ball draws are completely unrelated and determined by completely separate processes' still contains information about how the balls were generated. It seems like if you observed a string of 10 red balls, then your hypothesis would lose probability mass to the hypothesis 'ball draws are red with p > 0.99.'
It seems like the problem only happens if you include an unjustified assumption in your 'prior', then refuse to consider the possibility that you were wrong.
My prior information is that every time I have found something Eliezer said confusing, it has eventually turned out that I was mistaken. I expect this to remain true, but there's a slight possibility that I am wrong.
Yes, I thought about this a bit too, but did't pay as much attention to being confused as you did. I'm not sure how to resolve it.