Effective complexity
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Effective complexity is a measure of complexity defined in a 2003 paper by Murray Gell-Mann and Seth Lloyd that attempts to measure the amount of non-random information in a system.[1] 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.[2]
References
See also
- Kolmogorov complexity
- Crude complexity
- Excess entropy
- Logical depth
- Total information
- Renyi information
- Self-dissimilarity
- Forecasting complexity
- Effective measure complexity