Sometimes size does not matter

TL;DR

This paper introduces a framework for measuring cosmological fine-tuning probabilities using maximum entropy priors, addressing normalization issues and extending the concept of tuning to scientific modeling in general.

Contribution

It proposes a new method to evaluate tuning probabilities that accounts for normalization problems by employing maxent distributions, offering a broader perspective on fine-tuning.

Findings

Provides upper bounds for tuning probabilities

Addresses the normalization problem in Bayesian priors

Extends the concept of tuning beyond cosmology

Abstract

Cosmological fine-tuning has traditionally been associated with the narrowness of the intervals in which the parameters of the physical models must be located to make life possible. A more thorough approach focuses on the probability of the interval, not on its size. Most attempts to measure the probability of the life-permitting interval for a given parameter rely on a Bayesian statistical approach for which the prior distribution of the parameter is uniform. However, the parameters in these models often take values in spaces of infinite size, so that a uniformity assumption is not possible. This is known as the normalization problem. This paper explains a framework to measure tuning that, among others, deals with normalization, assuming that the prior distribution belongs to a class of maximum entropy (maxent) distributions. By analyzing an upper bound of the tuning probability for this class of distributions the method solves the so-called weak anthropic principle, and offer a solution, at least in this context, to the well-known lack of invariance of maxent distributions. The implication of this approach is that, since all mathematical models need parameters, tuning is not only a question of natural science, but also a problem of mathematical modeling. Cosmological tuning is thus a particular instantiation of a more general scenario. Therefore, whenever a mathematical model is used to describe nature, not only in physics but in all of science, tuning is present. And the question of whether the tuning is fine or coarse for a given parameter -- if the interval in which the parameter is located has low or high probability, respectively -- depends crucially not only on the interval but also on the assumed class of prior distributions. Novel upper bounds for tuning probabilities are presented.