Eliminative Reasoning in Astronomy and Astrophysics

I argue that a promising epistemology for astronomy and astrophysics (A and A) involves a new kind of eliminative reasoning. Unlike traditional conceptions that propose to eliminate rival theories or hypotheses, I argue that particular model assumptions should instead be weighed. Rather than a veridical comparison between theory predictions and individual observational results, Local Eliminative Reasoning has a web-like structure. Elimination results from a multi-step reasoning program that evaluates the introduction of a proposed assumption into the state space of previously or conditionally accepted evidence. Moreover, elimination is always and only a local endeavor. Model assumptions are evaluated within specific contexts.

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Publication Details

Journal
Philosophy of Physics
Published
2025-01-01
DOI
https://doi.org/10.31389/pop.178
Primary Topic
Bayesian Modeling and Causal Inference
Type
article
Field-Weighted Citation Impact
0.00
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article

Eliminative Reasoning in Astronomy and Astrophysics

Shannon Sylvie Abelson
Philosophy of Physics
Bayesian Modeling and Causal Inference
article

Eliminative Reasoning in Astronomy and Astrophysics

Shannon Sylvie Abelson
article en

Abstract

I argue that a promising epistemology for astronomy and astrophysics (A and A) involves a new kind of eliminative reasoning. Unlike traditional conceptions that propose to eliminate rival theories or hypotheses, I argue that particular model assumptions should instead be weighed. Rather than a veridical comparison between theory predictions and individual observational results, Local Eliminative Reasoning has a web-like structure. Elimination results from a multi-step reasoning program that evaluates the introduction of a proposed assumption into the state space of previously or conditionally accepted evidence. Moreover, elimination is always and only a local endeavor. Model assumptions are evaluated within specific contexts.

Philosophy of PhysicsVol. 3(1)
Society for Ethnomusicology (US)
Openalex Percentile: Top 95%
Bayesian Modeling and Causal Inference
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