title: Unifying complexity and information.
abstract: Complex systems, arising in many contexts in the computer, life, social, and physical sciences, have not shared a generally-accepted complexity measure playing a fundamental role as the Shannon entropy H in statistical mechanics. Superficially-conflicting criteria of complexity measurement, i.e. complexity-randomness (C-R) relations, have given rise to a special measure intrinsically adaptable to more than one criterion. However, deep causes of the conflict and the adaptability are not much clear. Here I trace the root of each representative or adaptable measure to its particular universal data-generating or -regenerating model (UDGM or UDRM). A representative measure for deterministic dynamical systems is found as a counterpart of the H for random process, clearly redefining the boundary of different criteria. And a specific UDRM achieving the intrinsic adaptability enables a general information measure that ultimately solves all major disputes. This work encourages a single framework coving deterministic systems, statistical mechanics and real-world living organisms.
authors.affiliation: Department of Biomedical Engineering, Wenzhou Medical College, Wenzhou 325035, China. kdg@wzmc.edu.cn
authors.forename: Da-guan
authors.initials: DG
authors.lastname: Ke
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical
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