Approximations to Algorithmic Probability
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A Computational Theory of Everything
Physicists have long been searching for a so-called Theory of Everything (ToE). Just as quantum mechanics explains the smallest phenomena, from microwaves to light, and general relativity explains the classical world, from gravity to space and time, a ToE would explain everything in the universe, from the smallest to the largest phenomena, in a single formulation.
Science has operated under the assumption and in light of strong evidence that the world is highly, if not completely, algorithmic in nature. If the world were not structured, our attempts to construct a body of theories from observations, to build what we consider ordered scientific models of the world, would have failed. That they have not is spectacular vindication of the validity of the world’s orderly character. We started out believing that the world was ruled by magic, and by irrational and emotional gods. However, thinkers from ancient civilizations such as China and India and,...
- Gauvrit N, Zenil H, Tegnér J (2017) The information-theoretic and algorithmic approach to human, animal and artificial cognition. In: Dodig-Crnkovic G, Giovagnoli R (eds) Representation and reality: humans, animals and machines. Springer, New YorkGoogle Scholar
- Levin LA (1974) Laws of information conservation (nongrowth) and aspects of the foundation of probability theory. Probl Peredachi Inf 10(3):30–35Google Scholar
- Zenil H (2011) The world is either algorithmic or mostly random, 2011. Winning 3rd place in the international essay context of the FQXiGoogle Scholar
- Zenil H (2014a) Programmability: a Turing test approach to computation. In: L. De Mol, G. Primiero, (eds) Turing in context, Koninklijke Vlaamse Academie van België voor Wetenschappen en Kunsten (Belgian Academy of Sciences and Arts), Contactforum. Belgian Academy of Sciences and ArtsGoogle Scholar
- Zenil H, Delahaye J-P (2010) On the algorithmic nature of the world. In: Dodig-Crnkovic G, Burgin M (eds) Information and computation. World Scientific, LondonGoogle Scholar
- Zenil H, Kiani N, Jesper T (2017) Low algorithmic complexity entropy-deceiving graphs. Phys Rev E 96(012308)Google Scholar
- Zenil H, Soler-Toscano F, Kiani NA, Hernández-Orozco S, Rueda-Toicen A (2016) A decomposition method for global evaluation of Shannon entropy and local estimations of algorithmic complexity. arXiv preprint arXiv:1609.00110Google Scholar
- Zenil H. (2017) Algorithmic Data Analytics, Small Data Matters and Correlation versus Causation. In M. Ott, W. Pietsch, J. Wernecke (eds.), Berechenbarkeit der Welt? Philosophie und Wissenschaft im Zeitalter von Big Data (Computability of the World? Philosophy and Science in the Age of Big Data), Springer Verlag, pp. 453-475Google Scholar