Fine-Tuning in the Context of Bayesian Theory Testing

TL;DR

This paper discusses how fine-tuning in physics and cosmology can be understood through Bayesian theory testing, addressing normalization issues and explaining why some theories avoid fine-tuning problems.

Contribution

It formulates fine-tuning within Bayesian model selection, highlighting how physical theories circumvent normalization issues through parameter bounds or non-uniform distributions.

Findings

Normalizability issues are common in testing theories with free parameters.

Physical theories avoid fine-tuning problems by bounding parameters or using non-uniform priors.

Bayesian framework clarifies the interpretation of fine-tuning in physics.

Abstract

Fine-tuning in physics and cosmology is often used as evidence that a theory is incomplete. For example, the parameters of the standard model of particle physics are "unnaturally" small (in various technical senses), which has driven much of the search for physics beyond the standard model. Of particular interest is the fine-tuning of the universe for life, which suggests that our universe's ability to create physical life forms is improbable and in need of explanation, perhaps by a multiverse. This claim has been challenged on the grounds that the relevant probability measure cannot be justified because it cannot be normalized, and so small probabilities cannot be inferred. We show how fine-tuning can be formulated within the context of Bayesian theory testing (or \emph{model selection}) in the physical sciences. The normalizability problem is seen to be a general problem for testing any theory with free parameters, and not a unique problem for fine-tuning. Physical theories in fact avoid such problems in one of two ways. Dimensional parameters are bounded by the Planck scale, avoiding troublesome infinities, and we are not compelled to assume that dimensionless parameters are distributed uniformly, which avoids non-normalizability.