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IV. Ecosmomics: An Independent Source Script of Generative, Self-Similar, Complex Network Systems

Niazi, Muaz, editor. Complex Adaptive Systems Modeling: A Multidisciplinary Roadmap. Complex Adaptive Systems Modeling. 1/1, 2013. A new periodical from the SpringerOpen Journal and BioMed Central project, which are all in full online. This posting is an initial, thorough overview of CAS features, along with guidance for paper writing and entries, by its editor who is a computer scientist at Bahria University, Islamabad with a doctorate from the University of Stirling, UK. See also by Niazi and Amir Hussain, University of Stirling, a 2013 Springer Briefs in Cognitive Computation volume Cognitive Agent-Based Computing: A Unified Framework for Modeling Complex Adaptive Systems.

Keywords: agent-based models, agent-based simulation, artificial life, biological networks, Boolean networks, citation networks, complex adaptive systems, complex networks, computer networks, emergence, epidemiological networks, gene expression networks, gene regulatory networks, individual-based modeling, metabolic networks, multiagent systems, network modeling, nucleic acid networks, protein interaction networks, self-adaptation, self-assembly, self-healing, self-organization, signaling pathway networks, social network analysis, social networks, social simulation, systems biology.

The CAS concept is married to the interaction of a set of perhaps simple but numerous entities, components or agents which interact and adapt on the basis of interactions. This interaction is known to give rise to interesting and emergent phenomena. While it is rather difficult to contain all aspects of CAS in a single definition, our current understanding is that CAS are often found in nature and in nature-inspired artificial systems in close relation with or inspired by life in some way. CAS concepts are tied to an abstract concept of a society. As such, it can be noted that typical research articles with a focus in modeling CAS are ornate with the following concepts: 1. A large number of agents (e.g. Genes, societies, humans, animals, insects, software agents, data packets etc.). 2. Focus on somewhat simpler individual agents. 3. Focus on the nonlinear interaction between agents and global phenomena resulting from these interactions. (6-7)

Nitzan, Mor, et al. Revealing Physical Interaction Networks from Statistics of Collective Dynamics. Science Advances. 3/e1600396, 2017. (arXiv:1801.05598) Mor Nitzan, Hebrew University of Jerusalem, with Jose Casadiego and Marc Timme, MPI Dynamics and Self-Organization contribute to deeper rootings of life’s interconnective propensities within nature’s active physical substrate (which in turn appears as increasingly animate and conducive).

Revealing physical interactions in complex systems from observed collective dynamics constitutes a fundamental inverse problem in science. Current reconstruction methods require access to a system's model or dynamical data at a level of detail often not available. We exploit changes in invariant measures, in particular distributions of sampled states of the system in response to driving signals, and use compressed sensing to reveal physical interaction networks. Testing various nonlinear dynamic processes emerging on artificial and real network topologies indicates high reconstruction quality for existence as well as type of interactions. These results advance our ability to reveal physical interaction networks in complex synthetic and natural systems. (Abstract)

Many complex systems in physics and biology constitute networks of dynamically interacting units. Examples range from gene regulatory networks in the cell and neural circuits in the brain to food webs in ecosystems and power grids, as well as other supply systems of engineering. These systems’ interaction networks fundamentally underlie their collective dynamics and function, thus rendering the knowledge of their interaction topology essential. For instance, identifying new pathways in gene regulatory networks and understanding long-range feedback in engineering systems require exact knowledge of their physical interaction networks. (1)

Nonnemacher, Marcel, et al. Signatures of Criticality Arise from Random Subsampling in Simple Populations Models. PLOS Computational Biology. Online October, 2017. MPI Biological Cybernetics and University of Tubingen integrative neuroscientists contribute new ways to understand cognitive performance as best served by critically balanced state between static and rigor. This significant appreciation which began hesitantly a decade ago (search Dante Chialvo), is now well evident.

The rise of large-scale recordings of neuronal activity has fueled the hope to gain new insights into the collective activity of neural ensembles. One attempt to interpret such data builds upon analogies to the behaviour of collective systems in statistical physics. Here, we connect “signatures of criticality”, and in particular the divergence of specific heat, back to statistics of neural population activity commonly studied in neural coding: firing rates and pairwise correlations. We show that the specific heat diverges whenever the average correlation strength does not depend on population size. To analyze these simulations, we develop efficient methods for characterizing large-scale neural population activity with maximum entropy models. Thus, previous reports of thermodynamical criticality in neural populations based on the analysis of specific heat can be explained by average firing rates and correlations. We conclude that a reliable interpretation of statistical tests for theories of neural coding is possible only in reference to relevant ground-truth models. (Abstract excerpts)

Novak, Miroslav, ed. Complexus Mundi: Emergent Patterns in Nature. Singapore: World Scientific, 2006. A collection from the international Fractals 2006 conference (check Google for paper abstracts). More than another theory or science of nonlinear dynamic systems, what is implied, upon reflection, is a new kind of animate natural genesis distinguished by its iterative, emergent similarity from cosmos to civilizations. Salient papers are (Scale Free) Structure of Genetic Regulatory Networks by L. S. Liebovitch, et al; Bruce West’s Modeling Fractal Dynamics; and Complexity in Nature and Society by Klaus Mainzer.

The dynamics of complex systems can clarify the creation of structures in Nature. This creation is driven by the collective interaction of constitutive elements of the system. Such interactions are frequently nonlinear and are directly responsible for the lack of prediction in the evolution process. The self-organization accompanying these processes occurs all around us and is constantly being rediscovered, under the guise of a new jargon, in apparently unrelated disciplines. (Back cover)
But, in general, the theory of complex dynamic systems deals with profound and striking analogies which have been discovered in the self-organized behavior of quite different systems in physics, chemistry, and biology. (Mainzer, 117)

Novak, Miroslav, ed. Emergent Nature. Singapore: World Scientific, 2001. Many papers on self-similar nonlinear dynamics being found in diverse systems such as heartbeats, peptide structures, ecosystems, solar magnetic fields, neural networks, and architecture.

Novak, Miroslav, ed. Thinking in Patterns. Singapore: World Scientific, 2005. Proceedings of the Fractal 2004 conference, an international biannual meeting which considers the many facets of a self-similar universe. Its website is www.kingston.ac.uk/fractal/. Typical papers are Selected Topics in Mathematics, Physics, and Finance Originating in Fractal Geometry by its founder, Benoit Mandelbrot, and Nicoletta Sala’s Fractal Geometry in the Arts.

The abundance of papers and the range of topics appearing here confirm the underlying similarity between subjects such as finance, road profiles, sound diffusion, image decompression, cognitive processes, biological aging, …epidermal ridges, fluctuations of sea levels, solar magnetic fields and arts across cultures. (Preface)

Oltvai, Zoltan and Albert-Laszlo Barabasi. Life’s Complexity Pyramid. Science. 298/763, 2002. A synoptic report on new research results and evidence about how nature is arrayed in an emergent scale where the same form and dynamics are in effect everywhere.

At the lowest level, these components form genetic-regulatory motifs or metabolic pathways (level 2), which in turn are the building blocks of function modules (level 3). These modules are nested, generating a scale-free hierarchical architecture (level 4). Although the individual components are unique to a given organism, the topologic properties of cellular networks share surprising similarities with those of natural and social networks. This suggests that universal organizing principles apply to all networks, from the cell to the World Wide Web. (763)

Paczuski, Maya. Networks as Renormalized Models for Emergent Behavior in Physical Systems. arXiv.physics/0502028. Now Director of the Complexity Science Group of the University of Calgary, physicist Paczuski offers a 2005 precursor glimpse of an independent, universal network here implied by the dynamic forms of solar coronas and earthquakes. This project is lately (2010) confirmed across vast realms from galaxies to Gaia, a grand salutary achievement if only we could get clear to admit an organic genesis universe.

Paperin, Greg, et al. Dual-Phase Evolution in Complex Adaptive Systems. Journal of the Royal Society Interface. FirstCite, January, 2011. With coauthors David Green and Suzanne Sadedin, Monash University (Melbourne) researchers provide an overview of progress in the complexity sciences, of which they have been participants, and then propose, as the Abstract next, that by joining features such as emergence, networks and criticality, a novel way to explain nature’s fertile constancy across every scale can be achieved. Over the past decade and more, from physical to planetary phases, the same nonlinear phenomena has been found to instantiate and repeat through by way of the generic interactive agents, links and nodes, and so on, that compose a self-organizing CAS. This integral recurrence is conveyed via examples of stellar formations, landscape ecologies, population genetics, macroscopic evolution, river waterways, perceptive cognition, and economic cultures, indeed a universality. Circa 2011 going forward, it ought to be noted that this international discovery of such a breadth, depth, and common distillation would seem to portend, if to witness and allow, a fertile, animate, a creative materiality, a human genesis universe.

Understanding the origins of complexity is a key challenge in many sciences. Although networks are known to underlie most systems, showing how they contribute to well-known phenomena remains an issue. Here, we show that recurrent phase transitions in network connectivity underlie emergent phenomena in many systems. We identify properties that are typical of systems in different connectivity phases, as well as characteristics commonly associated with the phase transitions. We synthesize these common features into a common framework, which we term dual-phase evolution (DPE). Using this framework, we review the literature from several disciplines to show that recurrent connectivity phase transitions underlie the complex properties of many biological, physical and human systems. We argue that the DPE framework helps to explain many complex phenomena, including perpetual novelty, modularity, scale-free networks and criticality. Our review concludes with a discussion of the way DPE relates to other frameworks, in particular, self-organized criticality and the adaptive cycle. (Abstract)

Parker, Michael and Chris Jeynes. Maximum Entropy (Most Likely) Double Helical and Double Logarithmic Spiral Trajectories in Space-Time. Nature Scientific Reports. 9/10779, 2019. University of Essex and University of Surrey computational physicists post a unique mathematical procedure to explain how natural phenomena so often adopt into this dynamic patterning. By this view, a common affinity between genomes and galaxies can provide another glimpse of nature’s geometric universality.

The ubiquity of double helical and logarithmic spirals in nature is well observed, but no real explanation is offered for their prevalence. DNA and the Milky Way galaxy are examples, which we study using an information-theoretic complex-vector analysis to calculate the Gibbs free energy difference between B-DNA and P-DNA, and the galactic virial mass. We define conjugate hyperbolic space and entropic momentum co-ordinates to describe these spiral structures in Minkowski space-time, enabling a consistent, holographic Hamiltonian-Lagrangian system that is isomorphic and complementary to that of conventional kinematics. (Abstract excerpt)

Pastor-Satorras, Romualdo, et al. Epidemic Processes in Complex Processes. arXiv:1408.2701. With co-authors Claudio Castellano, Piet Van Mieghem, and Alessandro Vespignani, Spanish, Dutch, and Italian systems theorists contribute a 60 page synthesis and tutorial for ubiquitous network phenomena, along with its application to this specific manifestation. As an example of their generic presence, “epidemic” is seen not only for contagions or disease vectors, but also rumors, gossip, habits, and so on. It is noted that Daniel Bernoulli (1700-1782) conceived its first mathematical model, which was surpassed until the 21st century when, as if due to a collaborative humanity, this new realm of sophisticated nonlinear mathematics can achieves his goal.

In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and socio-technical systems. The complex properties of real world networks have a profound impact on the behavior of equilibrium and non-equilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. Here we present a coherent and comprehensive review of the vast research activity concerning epidemic processes, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, epidemiologists, computer and social scientists share a common interest in studying epidemic spreading and rely on very similar models for the description of the diffusion of pathogens, knowledge, and innovation. (Abstract excerpts)

Perez-Mercader, Juan. Scaling Phenomena and the Emergence of Complexity in Astrobiology. Gerda Horneck and Christa Baumstark-Khan, eds. Astrobiology. Berlin: Springer, 2002. Deep in the scientific literature a new universe is being described which arises by emergent, nested sequential stages. Perez-Mercader contends that these phases from biomolecules to human persons to galactic networks are distinguished by a universality whereby the same, invariant form and process recurs over and over.

Finally, among the main patterns we can identify a systematic presence of systems within systems, within systems: planetary systems, within galaxies, within clusters of galaxies, or bases within DNA molecules, within chromosomes, within cell nuclei, within cells, etc. (339)

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