IV. Ecosmomics: Independent, UniVersal, Complex Network Systems and a Genetic Code-Script Source

Strogatz, Steven, et al. Fifty Years of “More is Different.”. Nature Reviews Physics. April, 2022. As a response to this anniversary (Science 177/393,1972), the veteran Cornell University systems theorist asks eight complexity thinkers such as Sara Walker, Corina Tarnita, and Oriol Artime for their views going forward. Some responses cite emerging patterns, broken symmetry, information flows, and new singularities. So into the 2020s, Anderson’s complexity prescience has now become a revolutionary florescence undertaken by a vast worldwise faculty.

August 1972 saw the publication of Philip Anderson’s essay ‘More is different’. In it, he crystallized the idea of emergence, arguing that “at each level of complexity entirely new properties appear” — that is, although, for example, chemistry is subject to the laws of physics, we cannot infer the field of chemistry from our knowledge of physics. Fifty years on from this landmark publication, eight scientists describe the most interesting phenomena that emerge in their fields.

Many emergent phenomena are sustained through networks of interactions. Focussing, for instance, on biophysical phenomena, we have biomolecular interactions emerging from the human interactome, or electrochemical neural connectivity patterns emerging from the human connectome. In general, though, these networks do not operate in isolation: they are coupled to each other by means of structural or functional interdependencies, and they are organized in multiple contexts of interactions, also knownas layers. These layers may include links that are spatial, temporal, informational or combinations thereof. (Oriol Artime, Manlio de Domenico)

Thurner, Stefan, et al. Introduction to the Theory of Complex Systems. Oxford: Oxford University Press, 2018. Senior Medical University of Vienna system physicists Stefan Thurner, Peter Klimek and Rudolf Hamel provide a wide-ranging technical survey to date which covers scaling, networks, evolutionary processes, self-organized criticality, non-equilibrium statistical mechanics, information theory, and future advances, see next quote.

The chapter is a mini outlook on the field. The classic achievenments in complexity science are mentioned, and we summarize how the new directions might open new doors into a twenty-first-century science of complex systems. We do that by clarifying the origin of scaling laws, in particular for driven non-equilibrium systems, deriving the statistics of driven systems, categorizing probabilistic complex systems into universality classes, by meaningful generalizations of statistical mechanics and information theory, and finally, by unifying approaches to evolution and co-evolution into a single mathematical framework. We comment on our view of the role of artificial intelligence and our opinion on the future of science of complex systems. (Future of Complex Systems Science excerpt)

Tsallis, Constantino, Murray Gell-Mann, and Yuzuru Sato. Special Scale-Invariant Occupancy of Phase Space Makes the Entropy Sq Additive. Santa Fe Institute Working Paper. 05-03-005, 2005. Available at www.santafe.edu where its authors present technical nonequilibrium thermodynamic reasons why nature exhibits a universal, nested self-similarity.

We conjecture that this mechanism is deeply related to the nearly ubiquitous emergence, in natural and artificial complex systems, of scale-free structures.

Turcotte, Donald and John Rundle. Self-Organized Complexity in the Physical, Biological, and Social Sciences. Proceedings of the National Academy of Sciences. 99/Supp. 1, 2002. This Supplement contains papers from a 2001 NAS meeting to glimpse precocious theories as they may express dynamic relations between the previously found separate parts and objects. Authorities such as Eugene Stanley, Luis Amaral, Geoffrey West, Per Bak, Mark Newman, Stephen Strogatz and Didier Sornette spoke on topics like Allometric Scaling of Metabolic Rate from Molecules and Mitochondria to Cells and Mammals, Fractal Dynamics in Physiology, Unified Scaling Law for Earthquakes, Self-organized Complexity in Economics and Finance, and Scaling Phenomena in the Internet. But today’s worldwide web where these papers can be readily accessed was not yet in place. Circa 2018, as we seek to document and convey, the project is now reaching a convergent universality phase and accomplishment. A decade and a half is all it takes for a our global brain via instant communications to achieve this on her/his own.

The National Academy of Sciences convened an Arthur M. Sackler Colloquium on “Self-organized complexity in the physical, biological, and social sciences” at the NAS Beckman Center, Irvine, CA, on March 23–24, 2001. The organizers were D.L.T. (Cornell), J.B.R. (Colorado), and Hans Frauenfelder (Los Alamos National Laboratory, Los Alamos, NM). The organizers had no difficulty in finding many examples of complexity in subjects ranging from fluid turbulence to social networks. However, an acceptable definition for self-organizing complexity is much more elusive. Symptoms of systems that exhibit self-organizing complexity include fractal statistics and chaotic behavior. Some examples of such systems are completely deterministic (i.e., fluid turbulence), whereas others have a large stochastic component (i.e., exchange rates). The governing equations (if they exist) are generally nonlinear and may also have a stochastic driver. Many of the concepts that have evolved in statistical physics are applicable (i.e., renormalization group theory and self-organized criticality). As a brief introduction, we consider a few of the symptoms that are associated with self-organizing complexity.

Turnbull, Laura, et al. Connectivity and Complex Systems: Learning from a Multi-Disciplinary Perspective. Applied Network Science. 3/11, 2018. As an example of our present integrative phase after years of special studies, eleven generalists from the UK, Germany, the Netherlands, Cyprus, and Sweden proceed to identify and describe a common feature, as the Abstract cites, which plays a formative role from geology to ecology. The paper first describes this fundamental associative property being found to join the prior pieces, as underway in genomic relations and neural networks. In this necessary turn isolate parts are brought into an actual unitary whole. Six subject areas as below are then reviewed to show how the same lineaments are in similar effect everywhere. With this in place, basic “toolbox” methods are specified to define connective features from the biosphere to cultures going forward. Several reference pages over the past decades document the research divergence and convergence.

In recent years, parallel developments in disparate disciplines have focused on what has come to be termed connectivity; a concept used in understanding and describing complex systems. Conceptualizations have evolved largely within their disciplinary boundaries, yet similarities in this concept and its application among disciplines are evident. This situation leads us to ask if there an approach to connectivity that might be applied to all disciplines. In this review we explore four ontological and epistemological challenges. These are: (i) defining the fundamental unit for the study of connectivity; (ii) separating structural connectivity from functional connectivity; (iii) understanding emergent behaviour; and (iv) measuring connectivity. We draw upon insights from Computational Neuroscience, Ecology, Geomorphology, Neuroscience, Social Network Science and Systems Biology to explore the use of connectivity among these disciplines. (Abstract excerpt)

Valverde, Sergi, et al. Emergent Behavior in Agent Networks. http://arxiv.org/abs/physics.0602003. A web posting of a paper accepted for a special issue of IEEE Intelligent Systems on Self-Management through Self-organization.

Villegas, Pablo, et al. Laplacian Renormalization Group for Heterogeneous Networks. arXiv:2203.07230. Into spring 2022, Four Enrico Fermi Research Center, Rome, IMT, Institute for Advanced Studies, Lucca, and University of Venice (Giudo Caldarelli) theorists put together still another way to quantify and discern nature’s common avail of complex, dynamic patterns and processes. The credible case for an intrinsic mathematical domain in universal keeps filling in as braced by these many approaches. By an attention to read the scientific literature as revealing such findings on its own, their content bodes well for a salutary mid 2020s discovery. See also Laplacian Paths in Complex Networks: Information Core Emerges from Entropic Transitions by this team at 2202.06669.

The renormalization group is the cornerstone of the modern theory of universality and phase transitions, a powerful tool to scrutinize symmetries and organizational scales in dynamical systems. However, its network counterpart is particularly challenging due to correlations between intertwined scales. To date, the explorations are based on hidden geometries hypotheses. Here, we propose a Laplacian RG diffusion-based picture in complex networks, defining both the Kadanoff supernodes' concept, the momentum space procedure, and applying this RG scheme to real networks in a natural and parsimonious way. (Abstract)

An open question is how to perform network reduction to generate replicas that connect internal scales above a microscopic phase. This scenario invites a powerful method of in modern physics, the Renormalization Group (RG). RG provides an elegant and precise theory of criticality and allows for connections across the varied spatiotemporal scales, so as to demonstrate a ubiquitous invariance. (1)

Waldrop, Mitchell. Complexity. New York: Simon & Schuster, 1993. A science writer spins a vivid, insightful narrative of the Santa Fe Institute and its mission, which has been a center for innovative work in nonlinear science since 1984. In a typical vignette John Holland explains the complex adaptive systems he sees everywhere from cells to ecosystems. And here is Stuart Kauffman’s epiphany as he formed his self-organization vision:

I had a holy sense of a knowing universe, a universe unfolding, a universe of which we are privileged to be a part. I felt that God would reveal how the world works to anyone who cared to listen. (133)

Ward, Mark. Universality: The Underlying Theory Behind Life, the Universe and Everything. London: Macmillan, 2001. A readable account of the scientific discovery that the same dynamical processes are present from quanta and galaxies to human biology and society.

The processes that produce you and me are ubiquitous, from the smallest scales on earth to the spread of stars across the heavens. We lie somewhere in the middle….We are part of a self-similar pattern reflected throughout the universe. (297)

Watson, Richard A., et al. Adaptation without Natural Selection. Fellermann, Harold, et al, eds. Artificial Life XII. Cambridge: MIT Press, 2011. From the same team, another entry toward a theoretical explanation for whatever is obviously propelling life’s convergent, emergent florescence prior to and in addition to subsequent selection. As noted elsewhere, this entire volume in online at MIT Press.

How can a system become better adapted over time without natural selection? Although some argue for ‘organismic’ properties such as robustness and self-sustaining regulation in non-evolved systems, others insist that natural selection is the only source of true adaptation [3]. We suggest that understanding how adaptation can occur without natural selection remains a fundamental open question for the Artificial Life community. For example, the origin of life, the origin of evolution, and the origin of new units of selection in the major evolutionary transitions/biological dynamical hierarchies, all seem to imply an adaptive process, or at least a non-arbitrary organisational process, that precedes the onset of natural selection proper (at each level of organisation). (80)

We present an abstract model and simulation of this process and discuss how it relates to a number of different domains: the evolution of evolvability in gene regulation networks [12], the evolution of new units of selection [10] via symbiosis [15] and 'social niche construction' [8,9], games on adaptive networks [2], distributed optimisation in multi-agent complex adaptive systems [13,14] and multi-scale optimisation algorithms [6,7]. (80)

West, Bruce. Fractal Physiology, Complexity, and the Fractional Calculus. Coffey, William and Yuri Kalmykov, eds. Fractals, Diffusion, and Relaxation in Disordered Complex Systems. Hoboken, NJ: Wiley-Interscience, 2006. Volume 133 in the Advances in Chemical Physics series. An extensive paper by the veteran complexity theorist which can be summarized by this quote.

There are two different ways in which neurons can be fractal. The first way is through their geometrical structure. The shape of nerve cells may be fractal in space just as observed for the cardiac conduction system and the architecture of the lung. The fractal dimension has been used to classify the different shapes of neurons and to suggest mechanisms of growth responsible for these shapes. The second way neurons can be fractal is through the time intervals between the action potentials recorded from nerves, again as was observed for the interbeat interval distribution in cardiac time series, the interbreath interval distribution for breathing, and the interstride interval for walking. (23)

Wieczorek, Michal, et al. Neural Network Powered COVID-19 Spread Forecasting Model. Chaos, Solitons and Fractals. Online August, 2020. We cite this entry by Silesian University of Technology, Poland mathematicians for itself and as an example amidst an intense worldwide effort to quantify the actual network dynamics that underlie viral epidemics. It is part of a journal collection Modeling and Forecasting of Epidemic Spreading: The Case of COVID-19 and Beyond edited by Stefano Boccalletti, et al. In regard, it needs to be emphasized that an independent, universal topology does exist which constrains its course, and if it is well defined, this common knowledge can guide palliative management and future preventions.

Virus spread prediction is very important to effectively plan actions. Invasive viruses not easy to control, since their speed and reach depends on many factors from environmental to social ones. Here, we present research results on the development of a Neural Network model for COVID-19 spread prediction. It is based on a classic deep architecture which learns by using the NAdam training model. Results of prediction are done for countries but also regions to provide a wide spectrum of values about predicted COVID-19 spread. (Abstract)

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