Say that we have a set of options, such as (for example) wild animal welfare interventions.

Say also that you have two axes along which you can score those interventions: popularity (how much people will like your intervention) and effectiveness (how much the intervention actually helps wild animals).

Assume that we (for some reason) can't convert between and compare those two properties.

Should you then pick an intervention that is a compromise on the two axes—that is, it scores decently well on both—or should you max out on a particular axis?

One thing you might consider is the distribution of options along those two axes: the distribution of interventions can be normal on for both popularity and effectiveness, or the underlying distribution could be lognormal for both axes, or they could be mixed (e.g. normal for popularity, and lognormal for effectiveness).

Intuitively, the distributions [...]

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First published:
June 17th, 2024

Source:
https://www.lesswrong.com/posts/Z5sDqqGridJQfr4uC/fat-tails-discourage-compromise

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Narrated by TYPE III AUDIO.

“Fat Tails Discourage Compromise” by niplav

LessWrong (30+ Karma)