Probabilistic inference in very large universes
Feraz Azhar, Alan H. Guth, Mohammad Hossein Namjoo

TL;DR
This paper introduces a Bayesian framework for evaluating cosmological theories involving vast universes, emphasizing first-person probabilities and the Principle of Self-Locating Indifference to incorporate observer-specific information.
Contribution
It proposes a novel Bayesian method for assessing large-universe theories using first-person probabilities and the PSLI, addressing observer selection effects and subjective states.
Findings
First-person probabilities depend on observer-instants and subjective states.
Weighting universes by the number of observer-instants improves prediction accuracy.
The approach clarifies issues like Boltzmann brains and old evidence in cosmology.
Abstract
[Abridged] Some cosmological theories propose that the observable universe is a small part of a much larger universe in which parameters describing the low-energy laws of physics vary from region to region. How can we reasonably assess a theory that describes such a mostly unobservable universe? We propose a Bayesian method based on theory-generated probability distributions for our observations. We focus on basic principles, leaving aside concerns about practicality. (We also leave aside the measure problem, to discuss other issues.) We argue that cosmological theories can be tested by standard Bayesian updating, but we need to use theoretical predictions for "first-person" probabilities -- i.e., probabilities for our observations, accounting for all relevant selection effects. These selection effects can depend on the observer, and on time, so in principle first-person probabilities…
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Taxonomy
TopicsSpace Science and Extraterrestrial Life · Statistical Mechanics and Entropy · Cosmology and Gravitation Theories
