A mathematical theory of complex specified information is introduced which unifies several prior methods of computing specified complexity. Similar to how the exponential family of probability distributions have dissimilar surface forms yet share a common underlying mathematical identity, we define a model that allows us to cast Dembski’s semiotic specified complexity, Ewert et al.’s algorithmic specified complexity, Hazen et al.’s functional information, and Behe’s irreducible complexity into a common mathematical form. Adding additional constraints, we introduce canonical specified complexity models, for which one-sided conservation bounds are given, showing that large specified complexity values are unlikely under any given continuous or discrete distribution and that canonical models can be used to form statistical hypothesis tests, by bounding tail probabilities for arbitrary distributions. Montanez GD (2018) A Unified Model of Complex Specified Information. BIO-Complexity 2018 (4):1- ˜ 26. doi:10.5048/BIO-C.2018.4
We are told to expect a lay-friendly version of the model soon as well.
See also: How can we measure specified complexity
Bill Dembski: Specification: The Pattern That Signifies Intelligence
Kirk K Durston et al. Measuring the functional sequence complexity of proteins
Winston Ewert at Evolutionary Informatics
Robert M. Hazen et al. Functional information and the emergence of biocomplexity (public access) A friend notes, “Functional information, as outlined by Hazen et al., can be a measure of specified complexity, where the specificity supplies the functional constraint.”
Could a signature of specified complexity help us find alien life?
A Tutorial on Specified Complexity