I am extremely sympathetic to vNM, but think it’s not constructive. I think the world is too high-dimensional, and in some sense we are low compute agents in a high compute world. See here for a bit more background.
For example, there are lotteries L and M which are complex enough that a) I would express a strong preference if given enough time to parse it, b) the best option is not to actually choose between them but do something else.
For continuity, you can’t necessarily know which p it is.
If you want to extract someone’s utility function, this is an ~nlogn operation (using mergesort where each ordering step ellicits a numerical comparison). This line of research is interesting to me, but because of the expense it only works with enough buy in, which one may not have.
In practice, I think vNM works as an idealization of the values of a high or infinite compute agent, but because making it constructive is very expensive, sometimes the best action is not to go through with that but to fall back on heuristics or shortcuts, heuristics which you won’t be sure of either (again, as low compute agents in a higher complexity world).
Thanks, Nuño. I strongly endorse maximising expected welfare, but I very much agree with using heuristics. At the same time, I would like to see more cost-effectiveness analyses.
I am extremely sympathetic to vNM, but think it’s not constructive. I think the world is too high-dimensional, and in some sense we are low compute agents in a high compute world. See here for a bit more background.
For example, there are lotteries L and M which are complex enough that a) I would express a strong preference if given enough time to parse it, b) the best option is not to actually choose between them but do something else.
For continuity, you can’t necessarily know which p it is.
If you want to extract someone’s utility function, this is an ~nlogn operation (using mergesort where each ordering step ellicits a numerical comparison). This line of research is interesting to me, but because of the expense it only works with enough buy in, which one may not have.
In practice, I think vNM works as an idealization of the values of a high or infinite compute agent, but because making it constructive is very expensive, sometimes the best action is not to go through with that but to fall back on heuristics or shortcuts, heuristics which you won’t be sure of either (again, as low compute agents in a higher complexity world).
Thanks, Nuño. I strongly endorse maximising expected welfare, but I very much agree with using heuristics. At the same time, I would like to see more cost-effectiveness analyses.