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I agree, but only to the extent that we need to consider a cutoff somewhere for extraordinary bad luck. Maybe we only consider maximizing the well-being of the
by slaymaker1907 4y ago
I agree, but only to the extent that we need to consider a cutoff somewhere for extraordinary bad luck. Maybe we only consider maximizing the well-being of the 99.9% or the 99.99% of people like how we mostly ignore the maximum response time for a server in favor of looking at 95th percentile.
Do you have any evidence for the claim that minmaxing causes a steep drop off in overall expected utility? I'm not sure if research has been done about this with traffic lights, but I think that could be a good case study about this. Minmax for traffic light timings would mean minimizing the maximum wait time at a traffic light, though I could see how such a strategy might hurt the overall throughput of the intersection. In the extreme case, you have one granny trying to cross a 6-lane stroad during rush hour; in the pure throughput model, the road would never stop for granny and in the minmax model, she would only need to wait about as long as it takes her to cross the road. Obviously, different models would still allow granny to cross the road eventually and maybe with less of a throughput hit than pure minmax. All of this is speculation though and I'm curious what the throughput hit would be for minmax.
- wyager 4y ago> Do you have any evidence for the claim that minmaxing causes a steep drop off in overall expected utility? My evidence is just experience with optimization/utility theory. If you're looking for a paper describing this one takeaway out of thousands from this body of theory, I'm not sure what it's called. In general, any strategic constraint reduces EV, unless the constraint happens to already be a component of the optimal strategy. That doesn't tell you how steep the dropoff is, just that it exists. Determining the variance/EV tradeoff precisely depends entirely on the structure of the optimization space. However, any complex optimization space exhibits essentially the same behavior; crushing the outcome distribution to be super narrow (latency, wealth, ROI, test scores, etc.) is typically only possible at great cost to the median and average cases.