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> No. A good Bayesian should be able to come to decisions like, "I am 70% confident that Osama bin Laden is in that compound." That's not a decision though. Th
by bo1024 7y ago
> No. A good Bayesian should be able to come to decisions like, "I am 70% confident that Osama bin Laden is in that compound."
That's not a decision though. That's an assessment, what I would put in the same category as predictions. A decision would be whether or not to bomb the compound.
Your post seems to miss my point, that predictions and decisions are very different and one can be uncertain about predictions while being certain about decisions. For example, I completely agree with this:
> It is very rare for a good Bayesian to be absolutely confident of any prediction.
Here's a simple example to think through the difference. You have a sophisticated weather model that predicts 40% chance of rain today. You hate getting wet, so you take your umbrella. In fact, you would take your umbrella even if the chance were only 10%.
So you are really uncertain about whether it's going to rain (your forecast), but absolutely certain that taking your umbrella is the optimal decision given the information at hand.
- btilly 7y agoYou are right that I had not paid close attention to decisions. I see no particular reason why Bayesians should be better at being decisive. They should make better judgments given the available information. But they are not necessarily any better at making decisions and moving on.
- btilly 7y agoExpanding on this, the skill of figuring out the odds of bin Laden being in the compound is unrelated to the skill of figuring out how to handle both outcomes, and whether that is a worthwhile risk to take. So a good Bayesian can inform a good decision maker, but the Bayesian is not necessarily a good decision maker. Similarly in the book, one of the superforecasters made the point that listening to well-informed experts who might be bad at making decisions was very useful. Because the expert really did have a good grasp of the current situation and could explain it clearly, which was a great starting place for the Bayesian who lacked background. Preparing background and making predictions are both required, but the combination of skills need not start in the same brain.