Special Issue "Entropy Methods in Guided Self-Organization"


A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: closed (31 January 2014)

Special Issue Editors

Guest Editor
Dr. Mikhail Prokopenko
CSIRO ICT Centre, Adaptive Systems, PO Box 76, Epping, NSW 1710, Australia
Website: http://www.ict.csiro.au/staff/mikhail.prokopenko/
E-Mail: mikhail.prokopenko@csiro.au
Interests: guided self-organization; information theory; machine learning; complex networks

Guest Editor
Dr. Carlos Gershenson
Head of Computer Science Department, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, A.P. 20-726, 01000, México, D.F., Mexico
Website: http://turing.iimas.unam.mx/~cgg/
E-Mail: cgg@unam.mx
Interests: self-organizing systems, complexity, information, artificial life, philosophy

Special Issue Information

Dear Colleague,

The goal of Guided Self-Organization (GSO) is to leverage the strengths of self-organization while still being able to direct the outcome of the self-organizing process. GSO typically has the following features: (i) an increase in organization (structure and/or functionality) over some time; (ii) the local interactions are not explicitly guided by any external agent; (iii) task-independent objectives are combined with task-dependent constraints.

A number of attempts have been made to formalize aspects of GSO within information theory, thermodynamics and dynamical systems. However, the lack of a broadly applicable mathematical framework across multiple scales and contexts leaves GSO methodology incomplete. Devising such a framework and identifying common principles of guidance are the main themes of the GSO workshops.

Of particular interest are well-founded, but general methods for characterizing GSO systems in a principled way, with the view of ultimately allowing them to be guided toward pre-specified goals. In general, various entropy methods drawing from, and overlapping with, information theory, thermodynamics, nonlinear dynamics and graph theory are relevant, while quantifying complexity and its sources is a common theme.

The key areas of interest will include entropy methods for:

  • information-driven self-organization
  • quantifying complexity
  • self-organizing complex networks
  • adaptive behaviour
  • distributed computation
  • machine learning
  • computational neuroscience
  • swarm intelligence
  • cooperative and modular robotics

Dr. Mikhail Prokopenko
Dr. Carlos Gershenson
Guest Editors


Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs).

Published Papers (2 papers)

Entropy 2014, 16(4), 1985-2000; doi:10.3390/e16041985
Received: 25 October 2013; in revised form: 27 March 2014 / Accepted: 28 March 2014 / Published: 4 April 2014
Show/Hide Abstract | Download PDF Full-text (516 KB)

Entropy 2014, 16(2), 789-813; doi:10.3390/e16020789
Received: 16 December 2013; in revised form: 28 January 2014 / Accepted: 29 January 2014 / Published: 10 February 2014
Show/Hide Abstract | Download PDF Full-text (413 KB) |  Supplementary Files

Last update: 4 June 2013

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