1,130,673 research outputs found
The Self-Organization of Meaning and the Reflexive Communication of Information
Following a suggestion of Warren Weaver, we extend the Shannon model of
communication piecemeal into a complex systems model in which communication is
differentiated both vertically and horizontally. This model enables us to
bridge the divide between Niklas Luhmann's theory of the self-organization of
meaning in communications and empirical research using information theory.
First, we distinguish between communication relations and correlations among
patterns of relations. The correlations span a vector space in which relations
are positioned and can be provided with meaning. Second, positions provide
reflexive perspectives. Whereas the different meanings are integrated locally,
each instantiation opens global perspectives--"horizons of meaning"--along
eigenvectors of the communication matrix. These next-order codifications of
meaning can be expected to generate redundancies when interacting in
instantiations. Increases in redundancy indicate new options and can be
measured as local reduction of prevailing uncertainty (in bits). The systemic
generation of new options can be considered as a hallmark of the
knowledge-based economy.Comment: accepted for publication in Social Science Information, March 21,
201
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 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
A framework for proving the self-organization of dynamic systems
This paper aims at providing a rigorous definition of self- organization, one
of the most desired properties for dynamic systems (e.g., peer-to-peer systems,
sensor networks, cooperative robotics, or ad-hoc networks). We characterize
different classes of self-organization through liveness and safety properties
that both capture information re- garding the system entropy. We illustrate
these classes through study cases. The first ones are two representative P2P
overlays (CAN and Pas- try) and the others are specific implementations of
\Omega (the leader oracle) and one-shot query abstractions for dynamic
settings. Our study aims at understanding the limits and respective power of
existing self-organized protocols and lays the basis of designing robust
algorithm for dynamic systems
"Meaning" as a sociological concept: A review of the modeling, mapping, and simulation of the communication of knowledge and meaning
The development of discursive knowledge presumes the communication of meaning
as analytically different from the communication of information. Knowledge can
then be considered as a meaning which makes a difference. Whereas the
communication of information is studied in the information sciences and
scientometrics, the communication of meaning has been central to Luhmann's
attempts to make the theory of autopoiesis relevant for sociology. Analytical
techniques such as semantic maps and the simulation of anticipatory systems
enable us to operationalize the distinctions which Luhmann proposed as relevant
to the elaboration of Husserl's "horizons of meaning" in empirical research:
interactions among communications, the organization of meaning in
instantiations, and the self-organization of interhuman communication in terms
of symbolically generalized media such as truth, love, and power. Horizons of
meaning, however, remain uncertain orders of expectations, and one should
caution against reification from the meta-biological perspective of systems
theory
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