Dissertation/Thesis
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A Review of Methods for Estimating Algorithmic Complexity: Options, Challenges, and New Directions

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: A Review of Methods for Estimating Algorithmic Complexity: Options, Challenges, and New Directions
Συγγραφείς: Hector Zenil
Πηγή: Entropy ; Volume 22 ; Issue 6 ; Pages: 612
Στοιχεία εκδότη: Multidisciplinary Digital Publishing Institute
Έτος έκδοσης: 2020
Συλλογή: MDPI Open Access Publishing
Θεματικοί όροι: algorithmic complexity, Kolmogorov complexity, practical feasibility, Lempel–Ziv–Welch (LZW), Shannon entropy, lossless compression, coding theorem method, causality v correlation, block decomposition method, rebuttal to Paul Vitányi’s review
Περιγραφή: Some established and also novel techniques in the field of applications of algorithmic (Kolmogorov) complexity currently co-exist for the first time and are here reviewed, ranging from dominant ones such as statistical lossless compression to newer approaches that advance, complement and also pose new challenges and may exhibit their own limitations. Evidence suggesting that these different methods complement each other for different regimes is presented and despite their many challenges, some of these methods can be better motivated by and better grounded in the principles of algorithmic information theory. It will be explained how different approaches to algorithmic complexity can explore the relaxation of different necessary and sufficient conditions in their pursuit of numerical applicability, with some of these approaches entailing greater risks than others in exchange for greater relevance. We conclude with a discussion of possible directions that may or should be taken into consideration to advance the field and encourage methodological innovation, but more importantly, to contribute to scientific discovery. This paper also serves as a rebuttal of claims made in a previously published minireview by another author, and offers an alternative account.
Τύπος εγγράφου: Thesis
Περιγραφή αρχείου: application/pdf
Γλώσσα: English
Relation: Complexity; https://dx.doi.org/10.3390/e22060612
DOI: 10.3390/e22060612
Διαθεσιμότητα: https://doi.org/10.3390/e22060612
Rights: https://creativecommons.org/licenses/by/4.0/
Αριθμός Καταχώρησης: edsbas.F4B21EF4
Βάση Δεδομένων: BASE
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  Data: A Review of Methods for Estimating Algorithmic Complexity: Options, Challenges, and New Directions
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  Data: Entropy ; Volume 22 ; Issue 6 ; Pages: 612
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  Data: Some established and also novel techniques in the field of applications of algorithmic (Kolmogorov) complexity currently co-exist for the first time and are here reviewed, ranging from dominant ones such as statistical lossless compression to newer approaches that advance, complement and also pose new challenges and may exhibit their own limitations. Evidence suggesting that these different methods complement each other for different regimes is presented and despite their many challenges, some of these methods can be better motivated by and better grounded in the principles of algorithmic information theory. It will be explained how different approaches to algorithmic complexity can explore the relaxation of different necessary and sufficient conditions in their pursuit of numerical applicability, with some of these approaches entailing greater risks than others in exchange for greater relevance. We conclude with a discussion of possible directions that may or should be taken into consideration to advance the field and encourage methodological innovation, but more importantly, to contribute to scientific discovery. This paper also serves as a rebuttal of claims made in a previously published minireview by another author, and offers an alternative account.
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    Subjects:
      – SubjectFull: algorithmic complexity
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      – SubjectFull: Kolmogorov complexity
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      – SubjectFull: practical feasibility
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      – SubjectFull: Lempel–Ziv–Welch (LZW)
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      – SubjectFull: causality v correlation
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      – SubjectFull: block decomposition method
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      – SubjectFull: rebuttal to Paul Vitányi’s review
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