Analysis of the Problem of Prediction in Physics from a Computational and Foundational Approach

Juan Ferret, University of Texas at El Paso

Abstract

In this paper, we analyze the problem of prediction in physics from a computational and foundational viewpoint. Besides showing that assumptions like Laplace determinism, statistical determinism, etc., can be naturally explained by this computational analysis, we use the concepts of Kolmogorov complexity and algorithmic randomness from Algorithmic Information Theory, as well as the novel more physics-oriented variants of these notions, to show that the dynamical evolution of a system can be practically predicted with the dynamical laws, the initial, and final conditions of the system.