4/ Due to network effects, each path A is more efficient at larger scales of deployment, but the efficiency is *uncertain* and not a deterministic function of efficiency at lower scales. So it could be that A is most efficient when 500 people use it, B at 750, but C at 1000.
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15/ The key to the practical solution is to guess the scaling phase transition points correctly across fractal levels, so you can switch between parallel/series gears and refactor options at the right time. The “series” option looks like “exploit” locally in time/space.
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16/ Throwing in some links that inspired this line of thinking. First
@TaylorPearsonMe Big Little Idea Called Ergodicityhttps://taylorpearson.me/ergodicity/Show this thread -
17/ Ole Peters 2019 Nature article which seems to have caused this current surge of interest, "The ergodicity problem in economics" https://www.nature.com/articles/s41567-019-0732-0 …
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Wright Meets Markowitz, via
@mengwong http://research.economics.unsw.edu.au/vpanchenko/papers/WriteMeetsMarkowitz.pdf …Show this thread -
Founder effect: non-ergodicity in nature https://en.wikipedia.org/wiki/Founder_effect …
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End of conversation
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