I actually think it probably (pending further objections) does have a somewhat straightforward answer with regards to the rather narrow, theoretical cases that I have in mind, which relate to the confusion I had which started this comment chain.
It’s hard to accurately convey the full degree of my caveats/specifications, but one simple example is something like “Suppose you are forced to choose whether to do X or nothing (Y). You are purely uncertain whether X will lead to outcome Great (Q), Good (P), or Bad (W), and there is guaranteed to be no way to get further information on this. However, you can safely assume that outcome Q is guaranteed to lead to +1,000 utils, P is guaranteed to lead to +500 utils, and W is guaranteed to lead to −500 utils. Doing nothing is guaranteed to lead to 0 utils. What should you do, assuming utils do not have non-linear effects?”
In this scenario, it seems very clear to me that a strategy of “do nothing” is inferior to doing X: even though you don’t know what the actual probabilities of Q, P, and W are, I don’t understand how the 1/n default will fail to work (across a sufficiently large number of 1/n cases). And when taking the 1/n estimate as a default, the expected utility is positive.
Of course, outside of barebones theoretical examples (I.e., in the real world) I don’t think there is a simple, straightforward algorithm for deciding when to pursue more information vs. act on limited information with significant uncertainty.
Good point! I think this is also a matter of risk aversion. How severe is it to get to a state of −500 utils? If you are very risk-averse, it might be better to do nothing. But I cannot make such a blanket statement.
I’d like to emphasize at this point that the DMDU approach is trying to avoid to
test the performance of a set of policies for a set number of scenarios,
decide how likely each scenario is (this is the crux), and
calculate some weighted average for each policy.
Instead, we use DMDU to consider the full range of plausible scenarios to explore and identify particularly vulnerable scenarios. We want to pay special attention to these scenarios and find optimal and robust solutions for them. Like this, we cover tail risks which is quite in line IMO with mitigation efforts of GCRs, x-risks, and s-risks.
I actually think it probably (pending further objections) does have a somewhat straightforward answer with regards to the rather narrow, theoretical cases that I have in mind, which relate to the confusion I had which started this comment chain.
It’s hard to accurately convey the full degree of my caveats/specifications, but one simple example is something like “Suppose you are forced to choose whether to do X or nothing (Y). You are purely uncertain whether X will lead to outcome Great (Q), Good (P), or Bad (W), and there is guaranteed to be no way to get further information on this. However, you can safely assume that outcome Q is guaranteed to lead to +1,000 utils, P is guaranteed to lead to +500 utils, and W is guaranteed to lead to −500 utils. Doing nothing is guaranteed to lead to 0 utils. What should you do, assuming utils do not have non-linear effects?”
In this scenario, it seems very clear to me that a strategy of “do nothing” is inferior to doing X: even though you don’t know what the actual probabilities of Q, P, and W are, I don’t understand how the 1/n default will fail to work (across a sufficiently large number of 1/n cases). And when taking the 1/n estimate as a default, the expected utility is positive.
Of course, outside of barebones theoretical examples (I.e., in the real world) I don’t think there is a simple, straightforward algorithm for deciding when to pursue more information vs. act on limited information with significant uncertainty.
Good point! I think this is also a matter of risk aversion. How severe is it to get to a state of −500 utils? If you are very risk-averse, it might be better to do nothing. But I cannot make such a blanket statement.
I’d like to emphasize at this point that the DMDU approach is trying to avoid to
test the performance of a set of policies for a set number of scenarios,
decide how likely each scenario is (this is the crux), and
calculate some weighted average for each policy.
Instead, we use DMDU to consider the full range of plausible scenarios to explore and identify particularly vulnerable scenarios. We want to pay special attention to these scenarios and find optimal and robust solutions for them. Like this, we cover tail risks which is quite in line IMO with mitigation efforts of GCRs, x-risks, and s-risks.