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Entropy
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Information and Self-Organization III
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Information and Self-Organization III

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: 21 July 2024 | Viewed by 5505

Special Issue Editors

Prof. Dr. Juval Portugali

E-Mail Website
Guest Editor
Head of the City Center, Tel Aviv University Research Center for Cities and Urbanism, Tel Aviv University, Tel Aviv 69978, Israel
Interests: complexity theories; urban dynamics; information theory; spatial cognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Carlos Gershenson

E-Mail Website
Guest Editor
Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autonóma de México, Ciudad de México 04510, Mexico
Interests: self-organizing systems; complexity; information; artificial life; philosophy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The process of “self-organization” invokes a property that is far from equilibrium and refers to open and complex systems that acquire spatiotemporal or functional structures without specific ordering instructions from the outside. In domains such as physics, chemistry, or biology, the phrase “far from equilibrium” refers to systems that are “far from thermal equilibrium”, while, in other disciplines, the term refers to the property of being “away from the resting state”. Such systems are “complex” in the sense that they are composed of many interacting components, parts, elements, etc., and are “open” in the sense that they exchange matter, energy, and information with their environment. Here, “information” may imply Shannon information, as a measure of the capacity of a channel through which a message passes, pragmatic information, as the impact of a message on recipients, or semantic information, as the meaning per se conveyed by a message.

In the first Special Issue—“Information and Self-Organization”—(Haken and Portugali 2016), the aim was to deal with the different ways processes of self-organization are linked with the various forms of information. A prominent example was the concept of information adaptation, whereby Shannon information and semantic information condition each other. A study of such links has consequences on several research domains, ranging from physics and chemistry through the life sciences and cognitive science, including human behavior and action, to our understanding of society, economics, and the dynamics of cities and urbanization.

In the second Special Issue—“Information and Self-Organization II” (Haken and Portugali 2022) —the aim was to extend the discussion by adding studies exploring further aspects and domains of information and self-organization, such as principles of self-organization based on information theory, social neurology, and coordination dynamics, the ‘free energy principle’, or their conjunction.

In the two previous Special Issues, the discussion was mainly confined to conceptualizations within the domain of complexity theories. In the present Special Issue—“Information and Self-Organization III”— the aim is to further extend the discussion on information and self-organization to several new directions: Firstly, to questions of life (e.g., in line with Schrödinger’s What is Life?) and of artificial life. Secondly, to other conceptualizations such as Bohm’s implicate, explicate, and generative orders (including his notion of active information); and/or to Bohr’s complementarity principle and its implications to the domains of life, brain, coordination dynamics and society. Thirdly, as in the previous Special Issues, the aim is to explore the consequences of the above extensions on several research domains, ranging from physics and chemistry through the life sciences and cognitive science to our understanding of society and economy. However, in this Special Issue, there is a special reference to the dynamics of cities and urbanization.

Prof. Dr. Juval Portugali
Dr. Carlos Gershenson
Guest Editors

Manuscript Submission Information

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. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short 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 thoroughly refereed through a single-blind 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 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • self-organization in nature and society
  • synergetics
  • entropy
  • information theory
  • information adaptation (IA)
  • coordination dynamics
  • Schrödinger’s what is life
  • David Bohm’s notions of order (implicate, explicate and generative) and active information
  • Bohr’s complementarity principle
  • niche construction theory
  • allometry and urban allometry
  • cities and urbanism
  • language
  • society
  • economy

Related Special Issues

Published Papers (4 papers)

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Research

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12 pages, 1861 KiB  
Article
Cohesion: A Measure of Organisation and Epistemic Uncertainty of Incoherent Ensembles
by Timothy Davey
Entropy 2023, 25(12), 1605; https://doi.org/10.3390/e25121605 - 30 Nov 2023
Viewed by 737
Abstract
This paper offers a measure of how organised a system is, as defined by self-consistency. Complex dynamics such as tipping points and feedback loops can cause systems with identical initial parameters to vary greatly by their final state. These systems can be called [...] Read more.
This paper offers a measure of how organised a system is, as defined by self-consistency. Complex dynamics such as tipping points and feedback loops can cause systems with identical initial parameters to vary greatly by their final state. These systems can be called non-ergodic or incoherent. This lack of consistency (or replicability) of a system can be seen to drive an additional form of uncertainty, beyond the variance that is typically considered. However, certain self-organising systems can be shown to have some self-consistency around these tipping points, when compared with systems that find no consistent final states. Here, we propose a measure of this self-consistency that is used to quantify our confidence in the outcomes of agent-based models, simulations or experiments of dynamical systems, which may or may not contain multiple attractors. Full article
(This article belongs to the Special Issue Information and Self-Organization III)
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14 pages, 407 KiB  
Article
Deep Reinforcement Meta-Learning and Self-Organization in Complex Systems: Applications to Traffic Signal Control
by Marcin Korecki
Entropy 2023, 25(7), 982; https://doi.org/10.3390/e25070982 - 27 Jun 2023
Cited by 2 | Viewed by 1112
Abstract
We studied the ability of deep reinforcement learning and self-organizing approaches to adapt to dynamic complex systems, using the applied example of traffic signal control in a simulated urban environment. We highlight the general limitations of deep learning for control in complex systems, [...] Read more.
We studied the ability of deep reinforcement learning and self-organizing approaches to adapt to dynamic complex systems, using the applied example of traffic signal control in a simulated urban environment. We highlight the general limitations of deep learning for control in complex systems, even when employing state-of-the-art meta-learning methods, and contrast it with self-organization-based methods. Accordingly, we argue that complex systems are a good and challenging study environment for developing and improving meta-learning approaches. At the same time, we point to the importance of baselines to which meta-learning methods can be compared and present a self-organizing analytic traffic signal control that outperforms state-of-the-art meta-learning in some scenarios. We also show that meta-learning methods outperform classical learning methods in our simulated environment (around 1.5–2× improvement, in most scenarios). Our conclusions are that, in order to develop effective meta-learning methods that are able to adapt to a variety of conditions, it is necessary to test them in demanding, complex settings (such as, for example, urban traffic control) and compare them against established methods. Full article
(This article belongs to the Special Issue Information and Self-Organization III)
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Review

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29 pages, 2019 KiB  
Review
Self-Organisation of Prediction Models
by Rainer Feistel
Entropy 2023, 25(12), 1596; https://doi.org/10.3390/e25121596 - 28 Nov 2023
Viewed by 1612
Abstract
Living organisms are active open systems far from thermodynamic equilibrium. The ability to behave actively corresponds to dynamical metastability: minor but supercritical internal or external effects may trigger major substantial actions such as gross mechanical motion, dissipating internally accumulated energy reserves. Gaining a [...] Read more.
Living organisms are active open systems far from thermodynamic equilibrium. The ability to behave actively corresponds to dynamical metastability: minor but supercritical internal or external effects may trigger major substantial actions such as gross mechanical motion, dissipating internally accumulated energy reserves. Gaining a selective advantage from the beneficial use of activity requires a consistent combination of sensual perception, memorised experience, statistical or causal prediction models, and the resulting favourable decisions on actions. This information processing chain originated from mere physical interaction processes prior to life, here denoted as structural information exchange. From there, the self-organised transition to symbolic information processing marks the beginning of life, evolving through the novel purposivity of trial-and-error feedback and the accumulation of symbolic information. The emergence of symbols and prediction models can be described as a ritualisation transition, a symmetry-breaking kinetic phase transition of the second kind previously known from behavioural biology. The related new symmetry is the neutrally stable arbitrariness, conventionality, or code invariance of symbols with respect to their meaning. The meaning of such symbols is given by the structural effect they ultimately unleash, directly or indirectly, by deciding on which actions to take. The early genetic code represents the first symbols. The genetically inherited symbolic information is the first prediction model for activities sufficient for survival under the condition of environmental continuity, sometimes understood as the “final causality” property of the model. Full article
(This article belongs to the Special Issue Information and Self-Organization III)
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Other

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17 pages, 2283 KiB  
Perspective
Schrödinger’s What is Life?—Complexity, Cognition and the City
by Juval Portugali
Entropy 2023, 25(6), 872; https://doi.org/10.3390/e25060872 - 30 May 2023
Cited by 1 | Viewed by 1313
Abstract
This paper draws attention to four central concepts in Schrödinger’s ‘What is Life?’ that have not, as yet, received sufficient attention in the domain of complexity: delayed entropy, free energy, order out of order and aperiodic crystal. It [...] Read more.
This paper draws attention to four central concepts in Schrödinger’s ‘What is Life?’ that have not, as yet, received sufficient attention in the domain of complexity: delayed entropy, free energy, order out of order and aperiodic crystal. It then demonstrates the important role the four elements play in the dynamics of complex systems by elaborating on their implications for cities as complex systems. Full article
(This article belongs to the Special Issue Information and Self-Organization III)
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