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]

• Kolmogorov complexity
• Excess entropy
• Logical depth
• Renyi information
• Self-dissimilarity
• Forecasting complexity

• http://www.cs.brandeis.edu/~pablo/complex.maker.html