= 27d567bc2d48d9f40e9ccc20ea370b15)
Wei_Dai comments on Why We Can't Take Expected Value Estimates Literally (Even When They're Unbiased) - Less Wrong
You are viewing a comment permalink. View the original post to see all comments and the full post content.
= 783df68a0f980790206b9ea87794c5b6)
Loading…= b715d02e85f7b3b4ae65ca3d3e450868)
Subscribe to RSS Feed
Powered by Reddit
= f037147d6e6c911a85753b9abdedda8d)
You are viewing a comment permalink. View the original post to see all comments and the full post content.
Comments (249)
I see this post as suggesting a way to better approximate Bayesian rationality in practice (since full Bayesian rationality is known to be infeasible), and as such we can't require that agents implementing such an approximation not exhibit preference reversals.
What we can ask for, I think, is more or better justifications for the "design choices" in the approximation method.
Nooo. If someone wants to improve their Bayesian correctness, they will avoid "approximation methods" that advise against the clearly beneficial action of giving money to the mugger. I read the post as proposing a new ideal, not a new approximation.
I'm surprised and confused by your comment. People have proposed lots of arguments against giving in to Pascal's Mugging, even within the standard Bayesian framework. (The simplest, for example, is that we actually have a bounded utility function.) I don't see how you could possibly say at this point that giving in is "clearly" beneficial.
Err, the post assumes that we have an unbounded utility function (or at least that it can reach high enough for Pascal's mugging to become relevant), and then goes on to propose what you call an approximation method that looks clearly wrong for that case.
Why clearly wrong?