But the Way of Bayes is also much harder to use than Science. It puts a tremendous strain on your ability to hear tiny false notes, where Science only demands that you notice an anvil dropped on your head.[...]
But if you try to use Bayes, it is math, and a single error in a hundred steps can carry you anywhere.
Hum… so basically Science works by coarsening the space of outcomes into three states (proven—falsified—uncertain), and then making “proven” and “falsified” into attractors for the scientific method. Since these are attractors, errors are correctable.
While Bayescraft keeps the full space of outcomes and does not create attractors, thus allowing greater precision (usefull for hypothesis formulation), but allowing errors to affect the result.
Yes, a perfect Bayesian making perfect updates is perfect, we all know that :-)
My point is that I can remember easily that things are false, or that they are true. But to remember that they are somewhere in between is much harder, unless it’s things I really care about. You have to keep track of the data, and compare it with new results.
But to remember that they are somewhere in between is much harder, unless it’s things I really care about.
It isn’t in between. Your knowledge of the question is in between. You would like it to be closer to one end or the other. You can apply a whole lot of heuristics without messing this part up.
Yes. And that’s what’s harder to remember. I “know” that Lincoln was assassinated, and I “know” that Charles de Gaulle didn’t die in Burma. But trying to remember what my estimate is as to whether it’s good or bad for overweight people to go on a diet… that’s a lot harder.
But the Way of Bayes is also much harder to use than Science. It puts a tremendous strain on your ability to hear tiny false notes, where Science only demands that you notice an anvil dropped on your head.[...]
But if you try to use Bayes, it is math, and a single error in a hundred steps can carry you anywhere.
Hum… so basically Science works by coarsening the space of outcomes into three states (proven—falsified—uncertain), and then making “proven” and “falsified” into attractors for the scientific method. Since these are attractors, errors are correctable.
While Bayescraft keeps the full space of outcomes and does not create attractors, thus allowing greater precision (usefull for hypothesis formulation), but allowing errors to affect the result.
Well, a Bayesian learner should eventually converge on the truth if the prior supports it, so that can be viewed as an “attractor” too...
Yes, a perfect Bayesian making perfect updates is perfect, we all know that :-)
My point is that I can remember easily that things are false, or that they are true. But to remember that they are somewhere in between is much harder, unless it’s things I really care about. You have to keep track of the data, and compare it with new results.
It isn’t in between. Your knowledge of the question is in between. You would like it to be closer to one end or the other. You can apply a whole lot of heuristics without messing this part up.
Yes. And that’s what’s harder to remember. I “know” that Lincoln was assassinated, and I “know” that Charles de Gaulle didn’t die in Burma. But trying to remember what my estimate is as to whether it’s good or bad for overweight people to go on a diet… that’s a lot harder.