The Noonday Argument: Fine-Graining, Indexicals, and the Nature of Copernican Reasoning
Brian C. Lacki

TL;DR
This paper critiques typicality arguments like the Doomsday Argument, proposing a new framework called Weighted Fine Graining (WFG) that avoids solipsism and clarifies the role of indexical facts in cosmological reasoning.
Contribution
It introduces WFG, a novel approach for evaluating observations in cosmology that accounts for indexical facts and avoids the pitfalls of traditional typicality arguments.
Findings
The Doomsday Argument fails within WFG for self-observation scenarios.
Indexical facts influence credence weights but do not directly constrain physical theories.
The Copernican Principle cannot be used to infer properties of extraterrestrial intelligences.
Abstract
Typicality arguments attempt to use the Copernican Principle to draw conclusions about the cosmos and presently unknown conscious beings within it, including extraterrestrial intelligences (ETI). The most notorious is the Doomsday Argument, which purports to constrain humanity's future from its current lifespan alone. These arguments rest on a likelihood calculation that penalizes models in proportion to the number of distinguishable observers. I argue that such reasoning leads to solipsism, the belief that one is the only being in the world, and is therefore unacceptable. Using variants of the "Sleeping Beauty" thought experiment as a guide, I present a framework for evaluating observations in a large cosmos: Weighted Fine Graining (WFG). WFG requires the construction of specific models of physical outcomes and observations. Valid typicality arguments then emerge from the combinatorial…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSpace Science and Extraterrestrial Life · Statistical Mechanics and Entropy
