A Package for Measuring Emergence, Self-organization, and Complexity Based on Shannon Entropy

Abstract

We present a set of Matlab/Octave functions to compute measures of emergence, self-organization, and complexity applied to discrete and continuous data. These measures are based on Shannon's information and differential entropy. Examples from different datasets and probability distributions are provided to show how to use our proposed code.

Human gut microbiota networks disturbance by parasites in indigenous communities: Effect on bacteria genera related to depression incidence subnetworks

“…In particular, adaptability is related to emergence which in turn the highest level of computational capabilities and it can be understood as new global patterns, which are not present in the system's components. More precisely, for continuous distributions, emergence interpretation is constrained to the average uncertainty a process produces under a specific set of the distribution parameters (e.g., the SD value for a Gaussian distribution), and so it can be measured using Shannon information as proposed by Santamiaría-Bonfil and co-workers (2017) 64 . Inhere we follow this interpretation of emergence and take it as Shannon Information.…”

Metrics of Emergence, Self-Organization, and Complexity for EWOM Research

“…In a recent paper, Santamaría-Bonfil et al [3] summarized both the discrete and continuous measures of emergence (E), selforganization (S), and complexity (C) which are applicable to any dataset or probability distributions [12], and rely on Shannon's information theory, as pioneered by the Santa Fe Institute [1]. A few ideas about the implications of using these concepts for analyzing EWOM data are necessary at this point.…”

“…santafe.edu/index.php/Complexity_of_Commerce_Agenda). As an extension of this matter, a welldeserved exposition would consist of providing a discussion on how the metrics of emergence, self-organization, and complexity [3] might benefit the research agenda of applied complexity and commerce/consumer studies. A warning note, however, should be stated beforehand.…”

“…Such illustration is not an analytical coverage. Also in section 2, is present the formalization of emergence, self-organization, and complexity, by summarizing the ideas of previous works [3,12]. Section 3, then, enumerates possible benefits and challenges for applied researchers.…”

A Multilayer Structure Facilitates the Production of Antifragile Systems in Boolean Network Models

“…To get back to the point about our complexity measure, we can interpret the regularity and the change from an information viewpoint. Regularity enables information to be preserved, and change allows new information to be explored [42]. In the context of RBNs used as GRN models, keeping and changing the node states which point out genetic information can be connected with stability to maintain existing functions and adaptability to flexibly adapt to a new environment.…”