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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Algorithmic Data Analytics, Small Data Matters and Correlation versus Causation
verfasst von : Hector Zenil
Erschienen in: Berechenbarkeit der Welt?
Verlag: Springer Fachmedien Wiesbaden
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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, which epitomize correlation and causation respectively, and then surveying classical results from algorithmic complexity and algorithmic probability, highlighting their deep connection to the study of automata frequency distributions. We end by showing that though long-range algorithmic prediction models for economic and biological systems may require infinite computation, locally approximated short-range estimations are possible, thereby demonstrating how small data can deliver important insights into important features of complex “Big Data”.