Anthropic's Scion is heavily inspired by Mononoke
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Компания Anthropic пересматривает свою систему контроля версий.
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33 дня назадLessWrong[This is the first piece in a series on the Parametric Parameter Assumption (PPA), a new model of anthropics. This particular piece is self-contained and for most people will contain all they need to know. TLDR: * With the right prior, the PPA avoids nearly every major anthropic paradox, including the Doomsday Argument, the Presumptuous Philosopher, and the inability to handle infinite universes. * The PPA divides the universe into two types of uncertainty: epistemic unknowns and randomness (a.k.a aleatoric unknowns). The first is treated SSAishly and the second is treated SIAishly. * This piece also introduces the "Basic Model" of anthropics, a single universe model that, paired with the SSA or SIA, exposes many different anthropic paradoxes simultaneously. None of these paradoxes persist in the PPA.] This is the first piece in a series introducing the Parametric Process Assumption (PPA), a new model of anthropics which allows us to resolve very nearly all anthropic paradoxes I am aware of. With the right prior, the PPA rejects both the Doomsday Argument and the Presumptuous Philosopher and can operate seamlessly over both finite and infinite universes. The motivating idea behind the PPA is that there are really two different ways to interpret a statement like "the probability of X": 1. The Frequentist approach, where probabilities represent the convergence of random trials. Flip many coins and about half will come up heads. 2. The Bayesian approach, where probabilities are beliefs. We don't know if the googolth digit of pi is even or odd, so we might as well say there's a 50% chance it's even. The first interpretation makes more sense for some types of questions, while the second approach makes more sense for others. But it turns out that if you take the standard framing for anthropic problems - where a universe is first produced and then you are assigned an observer identity within it - you will get different answers depending on whether you model the
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