605 research outputs found
Measuring Complexity in an Aquatic Ecosystem
We apply formal measures of emergence, self-organization, homeostasis,
autopoiesis and complexity to an aquatic ecosystem; in particular to the
physiochemical component of an Arctic lake. These measures are based on
information theory. Variables with an homogeneous distribution have higher
values of emergence, while variables with a more heterogeneous distribution
have a higher self-organization. Variables with a high complexity reflect a
balance between change (emergence) and regularity/order (self-organization). In
addition, homeostasis values coincide with the variation of the winter and
summer seasons. Autopoiesis values show a higher degree of independence of
biological components over their environment. Our approach shows how the
ecological dynamics can be described in terms of information.Comment: 6 pages, to be published in Proceedings of the CCBCOL 2013, 2nd
Colombian Computational Biology Congress, Springe
Complexity and Information: Measuring Emergence, Self-organization, and Homeostasis at Multiple Scales
Concepts used in the scientific study of complex systems have become so
widespread that their use and abuse has led to ambiguity and confusion in their
meaning. In this paper we use information theory to provide abstract and
concise measures of complexity, emergence, self-organization, and homeostasis.
The purpose is to clarify the meaning of these concepts with the aid of the
proposed formal measures. In a simplified version of the measures (focusing on
the information produced by a system), emergence becomes the opposite of
self-organization, while complexity represents their balance. Homeostasis can
be seen as a measure of the stability of the system. We use computational
experiments on random Boolean networks and elementary cellular automata to
illustrate our measures at multiple scales.Comment: 42 pages, 11 figures, 2 table
Measuring the Complexity of Continuous Distributions
We extend previously proposed measures of complexity, emergence, and
self-organization to continuous distributions using differential entropy. This
allows us to calculate the complexity of phenomena for which distributions are
known. We find that a broad range of common parameters found in Gaussian and
scale-free distributions present high complexity values. We also explore the
relationship between our measure of complexity and information adaptation.Comment: 21 pages, 5 Tables, 4 Figure
The Past, Present, and Future of Artificial Life
For millennia people have wondered what makes the living different from the non-living. Beginning in the mid-1980s, artificial life has studied living systems using a synthetic approach: build life in order to understand it better, be it by means of software, hardware, or wetware. This review provides a summary of the advances that led to the development of artificial life, its current research topics, and open problems and opportunities. We classify artificial life research into fourteen themes: origins of life, autonomy, self-organization, adaptation (including evolution, development, and learning), ecology, artificial societies, behavior, computational biology, artificial chemistries, information, living technology, art, and philosophy. Being interdisciplinary, artificial life seems to be losing its boundaries and merging with other fields
Interaction dynamics and autonomy in cognitive systems
The concept of autonomy is of crucial importance for understanding life and cognition. Whereas cellular and organismic autonomy is based in the self-production of the material infrastructure sustaining the existence of living beings as such, we are interested in how biological autonomy can be expanded into forms of autonomous agency, where autonomy as a form of organization is extended into the behaviour of an agent in interaction with its environment (and not its material self-production). In this thesis, we focus on the development of operational models of sensorimotor agency, exploring the construction of a domain of interactions creating a dynamical interface between agent and environment. We present two main contributions to the study of autonomous agency: First, we contribute to the development of a modelling route for testing, comparing and validating hypotheses about neurocognitive autonomy. Through the design and analysis of specific neurodynamical models embedded in robotic agents, we explore how an agent is constituted in a sensorimotor space as an autonomous entity able to adaptively sustain its own organization. Using two simulation models and different dynamical analysis and measurement of complex patterns in their behaviour, we are able to tackle some theoretical obstacles preventing the understanding of sensorimotor autonomy, and to generate new predictions about the nature of autonomous agency in the neurocognitive domain. Second, we explore the extension of sensorimotor forms of autonomy into the social realm. We analyse two cases from an experimental perspective: the constitution of a collective subject in a sensorimotor social interactive task, and the emergence of an autonomous social identity in a large-scale technologically-mediated social system. Through the analysis of coordination mechanisms and emergent complex patterns, we are able to gather experimental evidence indicating that in some cases social autonomy might emerge based on mechanisms of coordinated sensorimotor activity and interaction, constituting forms of collective autonomous agency
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