Typical = Random

Author:

Landsman Klaas1ORCID

Affiliation:

1. Institute for Mathematics, Astrophysics, and Particle Physics (IMAPP) and Radboud Center for Natural Philosophy (RCNP), Radboud University, 6525 XZ Nijmegen, The Netherlands

Abstract

This expository paper advocates an approach to physics in which “typicality” is identified with a suitable form of algorithmic randomness. To this end various theorems from mathematics and physics are reviewed. Their original versions state that some property Φ(x) holds for P-almost all x∈X, where P is a probability measure on some space X. Their more refined (and typically more recent) formulations show that Φ(x) holds for all P-random x∈X. The computational notion of P-randomness used here generalizes the one introduced by Martin-Löf in 1966 in a way now standard in algorithmic randomness. Examples come from probability theory, analysis, dynamical systems/ergodic theory, statistical mechanics, and quantum mechanics (especially hidden variable theories). An underlying philosophical theme, inherited from von Mises and Kolmogorov, is the interplay between probability and randomness, especially: which comes first?

Publisher

MDPI AG

Subject

Geometry and Topology,Logic,Mathematical Physics,Algebra and Number Theory,Analysis

Reference100 articles.

1. Brush, S.G. (1976). The Kind of Motion We Call Heat, North-Holland.

2. Sklar, L. (1993). Physics and Chance: Philosophical Issues in the Foundations of Statistical Mechanics, Cambridge University Press.

3. Butterfield, J., and Earman, J. (2007). Handbook of the Philosophy of Science, North-Holland. Volume 2: Philosophy of Physics; Part B.

4. Zalta, E.N. (2022). Boltzmann’s Work in Statistical Physics. The Stanford Encyclopedia of Philosophy, Stanford University. Available online: https://plato.stanford.edu/archives/sum2022/entries/statphys-Boltzmann/.

5. Von Plato, J. (1994). Creating Modern Probability, Cambridge University Press.

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