The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
On the improbability of algorithmic specified complexity
Abstract
An event with low probability is unlikely to happen, but events with low probability happen all of the time. This is because many distinct low probability events can have a large combined probability. However, some low probability events can be seen to follow an independent pattern. Algorithmic specified complexity (ASC) measures the degree to which an event is improbable and follows a pattern. We show a bound on the probability of obtaining a particular value of algorithmic specified complexity. Consequently we can say that high ASC objects are improbable.
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Algorithmic Specified Complexity (ASC) incorporates both Shannon and Kolmogorov complexity measures and quantifies the degree to which an event is improbable and follows a pattern.
“ACS incorporates both Shannon and Kolmogorov complexity measures, and it quantifies the degree to which an event is improbable and follows a pattern.”
From Using statistical methods to model the fine-tuning of molecular machines and systems (2020) · cites this paper as Ewert et al., 2013
Citations
Cites
On the Length of Programs for Computing Finite Binary Sequences
Chaitin, G. J. (1966) · Journal of the ACM · doi:10.1145/321356.321363 · Cited by 913
Flagged first, then by citations
Research works printed but not linked: 7
- cover, 2006, Elements of Information Theory
- chaitin, 1999, The Unknowable
- 10.1080/00207166808803030 · DOI
- 10.1109/TIT.1968.1054210 · DOI
- ewert, 2012, Algorithmic Specified Complexity, Engineering and Metaphysics
- 10.1017/CBO9780511570643 · DOI · Paper page
- solomonoff, 1960, A preliminary report on a general theory of inductive inference
Cited by
Sorted by citations
Using statistical methods to model the fine-tuning of molecular machines and systems
Thorvaldsen, S., & Hössjer, O. (2020) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2020.110352 · Cited by 15
“ASC is based on context and is measured in bits.”
Background
Metadata
- Authors
- Ewert, W., Marks, R. J., & Dembski, W. A.
- Affiliations
- Baylor University (US)
- Discovery Institute (US)
- Venue
- 45th Southeastern Symposium on System Theory
- Publisher
- IEEE
- Published
- 2013-03
- Pages
- 68-70
- ISSN
- 0094-2898 (ISSN-L)
- OpenAlex
- W2117491534
- Cited by
- 11
- References
- 1
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 1 · 2022: 2 · 2020: 3 · 2015: 1 · 2014: 1
- FWCI
- 0.52
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Southeastern Symposium on System Theory (SSST) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computability, Logic, AI Algorithms
- Physical Sciences › Mathematics › Theoretical Computer Science › Algorithms and Data Compression
- Physical Sciences › Computer Science › Artificial Intelligence › Diverse Interdisciplinary Research Studies
- Keywords
- improbability, probability bounds, low-probability events, pattern detection, algorithmic information theory
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