Algorithmic Data Analytics, Small Data Matters and Correlation versus Causation
Abstract
This is a review of aspects of the theory of algorithmic information that may contribute to a framework for formulating questions related to complex highly unpredictable systems. We start by contrasting Shannon Entropy and Kolmogorov-Chaitin complexity epitomizing the difference between correlation and causation to then move onto surveying classical results from algorithmic complexity and algorithmic probability, highlighting their deep connection to the study of automata frequency distributions. We end showing how long-range algorithmic predicting models for economic and biological systems may require infinite computation but locally approximated short-range estimations are possible thereby showing how small data can deliver important insights into important features of complex "Big Data".
Keywords
Cite
@article{arxiv.1309.1418,
title = {Algorithmic Data Analytics, Small Data Matters and Correlation versus Causation},
author = {Hector Zenil},
journal= {arXiv preprint arXiv:1309.1418},
year = {2017}
}
Comments
Predictability in the world: philosophy and science in the complex world of Big Data} edited by J. Wernecke on the occasion of retirement Prof. Dr. Klaus Mainzer, Springer Verlag. Chapter based on an invited talk delivered to UNAM-CEIICH via videoconference from The University of Sheffield in the U.K. for the Alan Turing colloquium "From computers to life". A minus sign missing was added