For any finite amount of data you won’t perfectly break even using a bayesian method, but it’s better than all the alternatives, as long as you don’t leave out some data.
What!? Are you saying that you can predict in advance that you’ll lose money? Surely that can’t happen, because you get to choose how much you want to pay, so you can always just pay less. No?
For simplicity, let’s imagine betting on a single coin with P(heads) = 0.9. You say “how much will you pay to win $1 if it lands heads?” and I say “50 cents,” because at the start I am ignorant. You flip it and it lands heads. I just made 40 cents relative to the equilibrium value.
So it’s not predictably losing money. It’s predictably being wrong in an unpredictable direction.
I read this to say that you can’t calculate a value that is guaranteed to break even in the long term, because there isn’t enough information to do this. (which I tend to agree with)
Perhaps he means that you break even once opportunity costs are taken into account, that is you won’t win as much money as you theoretically could have won.
What!? Are you saying that you can predict in advance that you’ll lose money? Surely that can’t happen, because you get to choose how much you want to pay, so you can always just pay less. No?
For simplicity, let’s imagine betting on a single coin with P(heads) = 0.9. You say “how much will you pay to win $1 if it lands heads?” and I say “50 cents,” because at the start I am ignorant. You flip it and it lands heads. I just made 40 cents relative to the equilibrium value.
So it’s not predictably losing money. It’s predictably being wrong in an unpredictable direction.
I read this to say that you can’t calculate a value that is guaranteed to break even in the long term, because there isn’t enough information to do this. (which I tend to agree with)
Perhaps he means that you break even once opportunity costs are taken into account, that is you won’t win as much money as you theoretically could have won.