You write: "There are no surprising facts, only models that are surprised by facts."

That's deterministic thinking. Surprising facts happen every once in awhile. Rarely, but occasionally.

But I agree with your general point. Surprise is an indication that you have a problem with your model, or that you have prior information that you have not included in your model.

Robin,

You ask, "would potatoes chips be a 'waste of taste', if some people eat too much of them? Is TV a "waste of time", if some people watch too much? Can we say that there is more of a tendency to buy too many lottery tickets than to do too much of any other thing one can do too much of?"

I think much of your question is better addressed to the Eliezer, who wrote the original entry with the "waste of hope" phrase. In any case, if someone buys so many lottery tickets that it interferes with other aspects of life (e.g., not being able to pay the rent or whatever), then, yeah, that seems like a problem. Maybe it's not a problem for such a person, but if it was someone I was close to, I'd be worried. Certainly there are people who have problems with food, drugs, maybe TV too, so I wouldn't single out gambling as being uniquely troublesome. I was just distinguishing your vision of the occasional lottery ticket (a small part of one's "portfolio of dreams") from something that's habitual, maybe harmful to one's other goals in life, and maybe not even so much fun.

Robin,

I think the concern is not with people who buy the occasional lottery ticket for fun but with addicts who gamble away a large proportion of their available money.

Eliezer ,

Just to be clear . . . going back to your first paragraph, that 0.5 is a prior probability for the outcome of one draw from the urn (that is, for the random variable that equals 1 if the ball is red and 0 if the ball is white). But, as you point out, 0.5 is not a prior probability for the series of ten draws. What you're calling a "prior" would typically be called a "model" by statisticians. Bayesians traditionally divide a model into likelihood, prior, and hyperprior, but as you implicitly point out, the dividing line between these is not clear: ultimately, they're all part of the big model.