Can Bayesianism deal with the mail-order prophet scam? If so, how?
This scam uses a segmented mailing list and a large initial population to advertise predictions so that a 'winning' portion of recipients receive an apparently improbable series of correct predictions. Most recipients (the 'losers') receive an incorrect prediction at some point.
For clarity, here is the situation:
A Bayesian with no prior knowledge of the scam receives a letter advertising stock predictions via a new proprietary quantitative model. The letter predicts that by the end of the month, stock XYZ will be up. It also states that the firm will prove their legitimacy by sending 6 more predictions (one per month) that will correctly predict whether XYZ is up or down. Sure enough, the Bayesian receives each letter, and the predictions all end up correct!
Unbeknownst to the Bayesian, 63 other Bayesians also received the first letter, 31 others received the second, 15 the third, 7 the fourth, 3 the fifth, and 1 other received the sixth letter. Our unlucky Bayesian was the only one to receive the seventh.
The question is, assuming no knowledge of the scam or communication with the "losers" (for whom a stock prediction was wrong), should the Bayesian -- strictly adhering to Bayesian Epistemology -- believe that the firm's stock predictions are legit?
I don't know how to approach this from a Bayesian perspective and would appreciate any guidance from someone who's more familiar. My intuition is that the Bayesian should believe that the predictions are legit (which we observers know to be incorrect).
Also I feel I should disclose that I'm not a Bayesian (rather a Critical Fallibilist), and I'm looking for the best Bayesian answers to this problem (links to prior material welcome). It's often quite hard to find the best answers to a problem from a particular school, so please bear with me if I've missed something obvious.
I don’t think P(my epistemology) is a practically useful concept.
Various people have suggested paradoxes of Bayesian reasoning. The few I’ve looked in detail at, I don’t think hold water. But that’s the sort of thing to study.
Does critical fallibilism have anything to say about itself? If it gives itself a full bill of health, that’s no more useful than the statement “This sentence is true”, which can consistently be assigned any truth value.
Can Bayesianism deal with the mail-order prophet scam? If so, how?
This scam uses a segmented mailing list and a large initial population to advertise predictions so that a 'winning' portion of recipients receive an apparently improbable series of correct predictions. Most recipients (the 'losers') receive an incorrect prediction at some point.
For clarity, here is the situation:
A Bayesian with no prior knowledge of the scam receives a letter advertising stock predictions via a new proprietary quantitative model. The letter predicts that by the end of the month, stock XYZ will be up. It also states that the firm will prove their legitimacy by sending 6 more predictions (one per month) that will correctly predict whether XYZ is up or down. Sure enough, the Bayesian receives each letter, and the predictions all end up correct!
Unbeknownst to the Bayesian, 63 other Bayesians also received the first letter, 31 others received the second, 15 the third, 7 the fourth, 3 the fifth, and 1 other received the sixth letter. Our unlucky Bayesian was the only one to receive the seventh.
The question is, assuming no knowledge of the scam or communication with the "losers" (for whom a stock prediction was wrong), should the Bayesian -- strictly adhering to Bayesian Epistemology -- believe that the firm's stock predictions are legit?
I don't know how to approach this from a Bayesian perspective and would appreciate any guidance from someone who's more familiar. My intuition is that the Bayesian should believe that the predictions are legit (which we observers know to be incorrect).
Also I feel I should disclose that I'm not a Bayesian (rather a Critical Fallibilist), and I'm looking for the best Bayesian answers to this problem (links to prior material welcome). It's often quite hard to find the best answers to a problem from a particular school, so please bear with me if I've missed something obvious.