Effective complexity
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Effective complexity is a measure of complexity defined in a 1996 paper by Murray Gell-Mann and Seth Lloyd that attempts to measure the amount of non-random information in a system.[1][2] It has been criticised as being dependent on the subjective decisions made as to which parts of the information in the system are to be discounted as random.[3]
See also[]
- Kolmogorov complexity
- Excess entropy
- Logical depth
- Renyi information
- Self-dissimilarity
- Forecasting complexity
References[]
- ^ Gell-Mann, Murray; Lloyd, Seth (1996). "Information Measures, Effective Complexity, and Total Information". Complexity. 2 (1): 44–52. Bibcode:1996Cmplx...2a..44G. doi:10.1002/(SICI)1099-0526(199609/10)2:1<44::AID-CPLX10>3.0.CO;2-X.
- ^ Ay, Nihat; Muller, Markus; Szkola, Arleta (2010). "Effective Complexity and Its Relation to Logical Depth". IEEE Transactions on Information Theory. 56 (9): 4593–4607. arXiv:0810.5663. doi:10.1109/TIT.2010.2053892. S2CID 2217934.
- ^ McAllister, James W. (2003). "Effective Complexity as a Measure of Information Content". Philosophy of Science. 70 (2): 302–307. doi:10.1086/375469. S2CID 120267550.
External links[]
Categories:
- Information theory
- Computational complexity theory
- Measures of complexity
- Theoretical computer science stubs