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Entropy
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Complex Interdisciplinary Phenomena: Modeling and Analysis
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Complex Interdisciplinary Phenomena: Modeling and Analysis

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 7117

Special Issue Editor

Dr. Tomas Veloz

E-Mail Website
Guest Editor
1. Center Leo Apostel, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
2. Departamento de Matemáticas, Universidad Tecnológica Metropolitana, Las Palmeras 3360, 7800003 Ñuñoa, Chile
3. Fundación para el Desarrollo Interdisciplinario de la Ciencia, la Tecnología y las Artes, 8330307 Santiago, Chile
Interests: cognitive science; reaction networks; quantum interaction; artificial intelligence; theory of concepts
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims at proposing modeling frameworks and applications of collaborative processes of integration of knowledge and expertise originating from different disciplines, with emphasis on mathematical modeling and reasoning about these systems using concepts built upon information processing methods such as information theory, statistical physics, optimality, cybernetics, probabilistic inference, and others.

A pervasive problem in interdisciplinary science (IS) is how to coherently reason about complex causality. Concepts to deal with complex causality are generally non-reductionistic and thus require processing information of the whole of the system in question. Examples of such notions are emergence, resilience, and synergy. All of them are fundamental in main ISs such as sustainability, health, and urban sciences.

Ways of formal reasoning about such systemic concepts in ISs are now urgent given that black-box learning methods, which identify patterns with high precision at the cost of obscuring our understanding of the identification process, are increasingly applied to deal with world challenges.

Therefore, we are interested in works that advance ways in which we can formally model interdisciplinary phenomena  and in the ways to process information leading to reasoning that can be applied from such modeling frameworks for solving complex problems.

We welcome articles on the following (non-comprehensive) list of topics:

  • Information and statistical approaches to complex phenomena such as synergy, emergence, and resilience in interdisciplinary sciences.
  • Mathematical modeling of sustainability and resilience and other systemic notions, with emphasis on complex analysis of aggregated information.
  • Mathematical modeling of complex socially driven phenomena (urban, techno-social, political, economic, and their interactions) with emphasis on complex analysis of aggregated information.
  • Complex biologically driven phenomena (socio-ecological systems, epidemiology and health modelling) with emphasis on complex analysis of aggregated information.
  • Synergetic interactions and emergent phenomena in natural (e.g., physical, chemical, biological, ecological), human (e.g., psychological, social), and virtual (e.g., social/technological/human networks) systems.

Dr. Tomas Veloz
Guest Editor

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

  • interdisciplinary science
  • emergence
  • synergy
  • systemic modeling
  • resilience
  • sustainability

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Published Papers (3 papers)

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24 pages, 744 KiB  
Article
On the History of Ecosystem Dynamical Modeling: The Rise and Promises of Qualitative Models
by Maximilien Cosme, Colin Thomas and Cédric Gaucherel
Entropy 2023, 25(11), 1526; https://doi.org/10.3390/e25111526 - 8 Nov 2023
Cited by 4 | Viewed by 2721
Abstract
Ecosystem modeling is a complex and multidisciplinary modeling problem which emerged in the 1950s. It takes advantage of the computational turn in sciences to better understand anthropogenic impacts and improve ecosystem management. For that purpose, ecosystem simulation models based on difference or differential [...] Read more.
Ecosystem modeling is a complex and multidisciplinary modeling problem which emerged in the 1950s. It takes advantage of the computational turn in sciences to better understand anthropogenic impacts and improve ecosystem management. For that purpose, ecosystem simulation models based on difference or differential equations were built. These models were relevant for studying dynamical phenomena and still are. However, they face important limitations in data-poor situations. As a response, several formal and non-formal qualitative dynamical modeling approaches were independently developed to overcome some limitations of the existing methods. Qualitative approaches allow studying qualitative dynamics as relevant abstractions of those provided by quantitative models (e.g., response to press perturbations). Each modeling framework can be viewed as a different assemblage of properties (e.g., determinism, stochasticity or synchronous update of variable values) designed to satisfy some scientific objectives. Based on four stated objectives commonly found in complex environmental sciences ((1) grasping qualitative dynamics, (2) making as few assumptions as possible about parameter values, (3) being explanatory and (4) being predictive), our objectives were guided by the wish to model complex and multidisciplinary issues commonly found in ecosystem modeling. We then discussed the relevance of existing modeling approaches and proposed the ecological discrete-event networks (EDEN) modeling framework for this purpose. The EDEN models propose a qualitative, discrete-event, partially synchronous and possibilistic view of ecosystem dynamics. We discussed each of these properties through ecological examples and existing analysis techniques for such models and showed how relevant they are for environmental science studies. Full article
(This article belongs to the Special Issue Complex Interdisciplinary Phenomena: Modeling and Analysis)
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10 pages, 2195 KiB  
Article
Adaptive Dynamics Simulation of Interference Phenomenon for Physical and Biological Systems
by Tadashi Ando, Masanari Asano, Andrei Khrennikov, Takashi Matsuoka and Ichiro Yamato
Entropy 2023, 25(11), 1487; https://doi.org/10.3390/e25111487 - 26 Oct 2023
Cited by 4 | Viewed by 1612
Abstract
Biological systems have been shown to have quantum-like behaviors by applying the adaptive dynamics view on their interaction networks. In particular, in the process of lactose–glucose metabolism, cells generate probabilistic interference patterns similarly to photons in the two-slit experiment. Such quantum-like interference patterns [...] Read more.
Biological systems have been shown to have quantum-like behaviors by applying the adaptive dynamics view on their interaction networks. In particular, in the process of lactose–glucose metabolism, cells generate probabilistic interference patterns similarly to photons in the two-slit experiment. Such quantum-like interference patterns can be found in biological data, on all scales, from proteins to cognitive, ecological, and social systems. The adaptive dynamics approach covers both biological and physical phenomena, including the ones which are typically associated with quantum physics. We guess that the adaptive dynamics can be used for the clarification of quantum foundations, and the present paper is the first step in this direction. We suggest the use of an algorithm for the numerical simulation of the behavior of a billiard ball-like particle passing through two slits by explicitly considering the influence of the two-slit environment (experimental context). Our simulation successfully mimics the interference pattern obtained experimentally in quantum physics. The interference of photons or electrons by two slits is known as a typical quantum mechanical effect. We do not claim that the adaptive dynamics can reproduce the whole body of quantum mechanics, but we hope that this numerical simulation example will stimulate further extensive studies in this direction—the representation of quantum physical phenomena in an adaptive dynamical framework. Full article
(This article belongs to the Special Issue Complex Interdisciplinary Phenomena: Modeling and Analysis)
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13 pages, 685 KiB  
Article
Psychological Implicit Motives Construct as an Emergent Fractal Attractor from Intermittent Neurophysiological Responses: Simulation and Entropy-like Characterization
by Miguel Ángel Martín, Celia Vara and Carlos García-Gutiérrez
Entropy 2023, 25(5), 711; https://doi.org/10.3390/e25050711 - 25 Apr 2023
Viewed by 1544
Abstract
Implicit Motives are non-conscious needs that drive human behavior towards the achievement of incentives that are affectively incited. Repeated affective experiences providing satisfying rewards have been held responsible for the building of Implicit Motives. Responses to rewarding experiences have a biological basis via [...] Read more.
Implicit Motives are non-conscious needs that drive human behavior towards the achievement of incentives that are affectively incited. Repeated affective experiences providing satisfying rewards have been held responsible for the building of Implicit Motives. Responses to rewarding experiences have a biological basis via close connections with neurophysiological systems controlling neurohormone release. We propose an iteration random function system acting in a metric space to model experience–reward interactions. This model is based on key facts of Implicit Motive theory reported in a broad number of studies. The model shows how (random) responses produced by intermittent random experiences create a well-defined probability distribution on an attractor, thus providing an insight into the underlying mechanism leading to the emergence of Implicit Motives as psychological structures. Implicit Motives’ robustness and resilience properties appear theoretically explained by the model. The model also provides uncertainty entropy-like parameters to characterize Implicit Motives which hopefully might be useful, beyond the mere theoretical frame, when used in combination with neurophysiological methods. Full article
(This article belongs to the Special Issue Complex Interdisciplinary Phenomena: Modeling and Analysis)
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