I’m always curious about how people turn complex utility functions into simple behavioral heuristics. For example, catching a ball in a sort is arg_max_(running trajectory){P(catch the ball(running trajectory) The way people solve it is “run to keep ball at constant angle”
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Robustness or antifragility (distinction is irrelevant here) both attack uncertainty potential of modernity by striving for locally Lindy simplicity. Waldenponding and other defensive postures are uncritical special cases of this. All sacrifice performance for robustness.
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Adaptability is the most interesting. You just try to think harder and faster. Get inside the OODA loop of the environment to pwn it rather than either dominate it or retreat from it. Those are the 3 broad strategies for uncertainty regulation: spend more, do less, think harder
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Each strategy has merits, but only the last one can make life steadily more interesting and continue the infinite game. The first two define winnable games and try to win them. If you fail, you’re destroyed. If you succeed, you’re in an arrested development cul de sac.
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In last one: You view life as an asymmetric guerrilla warfare challenge. Kissinger’s “The conventional army loses if it does not win. The guerrilla wins if he does not lose” principle. Big upside of this is that you stay interested and interesting in the world. Not win+exit.
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Any uncertainty regulation strategy that looks like win+exit is a kind of acting dead. You can’t actually win against the universe in the end. We all die. Do you want to explore as much of it as you can in the time you have? Or hit a win condition and sit around feeling empty?
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Gonna attach this thread on power output expectation, uncertainty, and stress herehttps://twitter.com/vgr/status/1305953459217117184 …
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End of conversation
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