Well an ideal Bayesian would unashamedly use all available evidence. It’s only our flawed cognitive machinery that suggests ignoring some evidence might sometimes be beneficial. But the burden of proof should be on the one who suggests that a particular situation warrants throwing away some evidence, rather than on the one who reasons earnestly from all evidence.
I don’t think ideal Bayesian’s use burden of proof either. Who has the burden of proof in demonstrating that burden of proof is required in a particular instance?
In which case there’s some specific amount of distinguishing evidence that promotes the hypothesis over the less complicated one, in which case, I suppose, the other would acquire this “burden of proof” of which you speak?
Not sure that I understand (I’m not being insolent, I just haven’t had my coffee this morning). Claiming that “humans are likely to over-estimate the chance of a hard-takeoff singularity in the next 50 years and should therefore discount inside view arguments on this topic” requires evidence, and I’m not convinced that the standard optimism bias literature applies here. In the absence of such evidence one should accept all arguments on their merits and just do Bayesian updating.
If we are going to have any heuristics that say that some kinds of evidence tend to be overused or underused, we have to be able to talk about sets of evidence that are less than than the total set. The whole point here is to warn people about our evidence that suggests people tend to over-rely on inside evidence relative to outside evidence.
Agreed. My objection is to cases where inside view arguments are discounted completely on the basis of experiments that have shown optimism bias among humans, but where it isn’t clear that optimism bias actually applies to the subject matter at hand. So my disagreement is about degrees rather than absolutes: How widely can the empirical support for optimism bias be generalized? How much should inside view arguments be discounted? My answers would be, roughly, “not very widely” and “not much outside traditional forecasting situations”. I think these are tangible (even empirical) questions and I will try to write a top-level post on this topic.
Well an ideal Bayesian would unashamedly use all available evidence. It’s only our flawed cognitive machinery that suggests ignoring some evidence might sometimes be beneficial. But the burden of proof should be on the one who suggests that a particular situation warrants throwing away some evidence, rather than on the one who reasons earnestly from all evidence.
I don’t think ideal Bayesian’s use burden of proof either. Who has the burden of proof in demonstrating that burden of proof is required in a particular instance?
Occams razor: the more complicated hypothesis acquires a burden of proof
In which case there’s some specific amount of distinguishing evidence that promotes the hypothesis over the less complicated one, in which case, I suppose, the other would acquire this “burden of proof” of which you speak?
Not sure that I understand (I’m not being insolent, I just haven’t had my coffee this morning). Claiming that “humans are likely to over-estimate the chance of a hard-takeoff singularity in the next 50 years and should therefore discount inside view arguments on this topic” requires evidence, and I’m not convinced that the standard optimism bias literature applies here. In the absence of such evidence one should accept all arguments on their merits and just do Bayesian updating.
If we are going to have any heuristics that say that some kinds of evidence tend to be overused or underused, we have to be able to talk about sets of evidence that are less than than the total set. The whole point here is to warn people about our evidence that suggests people tend to over-rely on inside evidence relative to outside evidence.
Agreed. My objection is to cases where inside view arguments are discounted completely on the basis of experiments that have shown optimism bias among humans, but where it isn’t clear that optimism bias actually applies to the subject matter at hand. So my disagreement is about degrees rather than absolutes: How widely can the empirical support for optimism bias be generalized? How much should inside view arguments be discounted? My answers would be, roughly, “not very widely” and “not much outside traditional forecasting situations”. I think these are tangible (even empirical) questions and I will try to write a top-level post on this topic.