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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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IV. Ecosmomics: Independent, UniVersal, Complex Network Systems and a Genetic Code-Script Source

4. Universality Affirmations: A Critical Complementarity

Tsallis, Constantino. Inter-Occurrence Times and Universal laws in Finance, Earthquakes and Genomes. arXiv:1601.03688. The Greek-Brazilian scientist (search) is the founder of a well-received 21st century thermodynamic theory known as non-extensive statistics. If one types in his last name on this site you get: Your query resulted in too many hits, only 1,000 are displayed. We note in this new section because once again a universality is claimed by how the same phenomena is present in the three title realms. See also A New Entropy Based on a Group-Theoretical Structure by Tsallis and coauthors in the Annals of Physics (366/21, 2016).

vandermeer, John, et al. New Forms of Structure in Ecosystems Revealed with the Kuramoto Model. arXiv:2006.16006. Reviewed more in Dynamic Ecosystems, we make note here as an example of how chimeric effects can even be apparent in these natural environs.

Villani, Marco, et al. Dynamic Criticality in Gene Regulatory Networks. Complexity. October, 2018. University of Modena theorists, along with coauthor Stuart Kauffman, show how his original prescience (search Bornholdt) that living systems reside at an dynamic edge between order and chaos is currently being robustly verified, as this section reports. While other studies in neuroscience (see VI. G. 2) also confirm, here this optimum state is found to hold for genomes. Search Villani for much more about this long foreseen, often elusive, historic discovery of one uniVerse to human epitome creative code.

Wang, Zhen, et al. Evolutionary Games on Multilayer Networks. arXiv:1504.04359. An introduction to a special issue of the European Physical Journal B, by an international team, including Matjaz Perc, with postings in China, Hungary, Slovenia, and Saudi Arabia. In regard, the paper surveys the progress of complexity science from the late 1980s to today. As the quote advises, nature’s creative course by which many discrete agents arrange into viable collectives is seen as most distinguished by interlinking network topologies. A novel reality is thus revealed and quantified of organically nested systems which repeat the same patterns and dynamics at every strata and species. It is then stated that keen insights can be gained if this developmental phenomena is seen as a strategic, decision-making game activity.

The hallmark property of a complex system is that a large number of simple units give rise to fascinating collective phenomena that could not be anticipated from an individual unit. Social order, biological complexity, brain power, ant colonies, and economic interconnectedness are all prime examples of topics one might attempt to study with a complex system at the heart of the research endeavor. But what is behind the emergent complexity? What turns people to societies and simple cells like neurons to a brain? The answer is, primarily, the network. Although phenomena such as self-organization and pattern formation might play a pivotal role too, it is mainly the way the simple units that form the complex system are connected with each other that makes them so much more than just the sum of their parts. (1)

Williams, Steven and Larry Yaeger. Evolution of Neural Dynamics in an Ecological Model. Geosciences. 7/3, 2017. In this MDPI online journal, Indiana University informaticians draw widely removed parallels between brains and ecosystems. By so doing, one more portal is opened as nature’s phenomenal genesis avails the same iconic, archetypal bicameral source at each and every instance and scale. A further notice is here made of a preferred bicameral criticality between control and freedom.

What is the optimal level of chaos in a computational system? If a system is too chaotic, it cannot reliably store information. If it is too ordered, it cannot transmit information. A variety of computational systems exhibit dynamics at the “edge of chaos”, the transition between the ordered and chaotic regimes. In this work, we examine the evolved neural networks of Polyworld, an artificial life model consisting of a simulated ecology populated with biologically inspired agents. As these agents adapt to their environment, their initially simple neural networks become increasingly capable of exhibiting rich dynamics. Dynamical systems analysis reveals that natural selection drives these networks toward the edge of chaos until the agent population is able to sustain itself. After this point, the evolutionary trend stabilizes, with neural dynamics remaining on average significantly far from the transition to chaos. (Abstract)

Wilting, Jens and Viola Priesemann. 25 Years of Criticality in Neuroscience. arXiv:1903.05129. MPI Dynamics and Self-Organization researchers begin with 1990s inklings that cerebral activity spontaneously seem to take on “dynamic reverberations” and ”power-law distributed avalanches” between reciprocal tighter or looser, more or less controlled, open or closed states. The survey is braced by some 90 references over the time span. See also Criticality Signatures in a Self-Organizing Recurrent Neural Network by Bruno Del Papa, et al in PLoS One (May 26, 2017) with Viola P. as a coauthor. We also note 25 Years of Self-Organized Criticality in Astrophysics in (Aschwanden, 2015) as this common propensity becomes known from universe to human.

Twenty-five years ago, Dunkelmann and Radons (1994) proposed that neural networks should self-organize to a critical state. In models, criticality offers a number of computational advantages. Thus this hypothesis, and in particular the experimental work by Beggs and Plenz (2003), has triggered an avalanche of research, with thousands of studies referring to it. Nonetheless, experimental results are still contradictory. How is it possible, that a hypothesis has attracted active research for decades, but nonetheless remains controversial? We discuss the experimental and conceptual controversy, and then present a parsimonious solution that (i) unifies the contradictory experimental results, (ii) avoids disadvantages of a critical state, and (iii) enables rapid, adaptive tuning of network properties to task requirements. (Abstract)

Wolchover, Natalie. The Universal Law that Aims Time’s Arrow. Quanta. August 1, 2019. A new look at a ubiquitous phenomenon has uncovered unexpected fractal behavior that could give us clues about the early universe and the arrow of time. The science journalist reports on a confluence of findings which seem to quantify and affirm an intrinsic cosmic self-similarity. By way of a natural philosophia view, if of a mind to perceive, a worldwide human quest may at last be closing on a phenomenal discovery. As long intimated, an infinite recurrence of the same pattern and process in kind really does exist and emerge on its own. As a nascent sapiensphere can prove and realize this, organic nature’s genome-like source code can reach, as planned, our intentional, procreative furtherance.

Notable papers are Prescaling and Far from Equilibrium Hydrodynamics in the Quark-Gluon Plasma by Alekson Mazeliauskas and Jurgen Berges in Physical Review Letters (122/122301, 2019), Universal Dynamics Far from Equilibrium by C. M. Schmied, et al at arXiv:1810.08143, Observation of Universal Dynamics in a Spinor Gas by Max Prufer, et al in Nature (563/217, 2018) and Prescaling in a Far from Equilibrium Bose Gas by C. M. Schmied, et al in Physical Review Letters (122/170404, 2019). See also Bubble Experiment finds Universal Laws by Charlie Wood in Quanta for July 31, 2019.

In the new work, researchers see far-from-equilibrium systems undergoing fractal-like universal scaling across both time and space. Take the birth of the universe. After cosmic inflation, the hypothetical oscillating, space-filling condensate would have quickly transformed into a dense field of quantum particles all moving with the same characteristic speed. (Jurgen) Berges and his colleagues conjecture that these non-equilibrium particles then exhibited fractal scaling governed by universal scaling exponents as they began the thermal evolution of the universe.

Wu, J. H. and Q. Jia. A Universal Mechanism of Extreme Events and Critical Phenomena. Nature Scientific Reports. 6/21612, 2016. Nanjing University of Posts and Telecommunications of China, and Hohai University, Nanjing, researchers propose a “general probability density distribution” by which a seemingly chaotic situation can become mathematically predictable. While self-organized criticalities are rife from finance markets and climate change to neural activity and seismicity, in this way a reliable anticipation can be possible.

The occurrence of extreme events and critical phenomena is of importance because they can have inquisitive scientific impact and profound socio-economic consequences. Here we show a universal mechanism describing extreme events along with critical phenomena and derive a general expression of the probability distribution without concerning the physical details of individual events or critical properties. The general probability distribution unifies most important distributions in the field and demonstrates improved performance. The shape and symmetry of the general distribution is determined by the parameters of the fluctuations. Our work sheds judicious insights into the dynamical processes of complex systems with practical significance and provides a general approach of studying extreme and critical episodes in a combined and multidisciplinary scheme. (Abstract)

Wu, Ling-Na. et al.. Indication of critical scaling in time during the relaxation of an open quantum system.. . Nine physicists at University Kaiserslautern-Landau and Technische Universität Berlin achieve another mathematical quantification recognition of nature’s persistent tendency to seek and reside at an optimum middle balance. See also Macrostates vs. Microstates in the Classical Simulation of Critical Phenomena in Quench Dynamics of 1D Ising Models by Anupam Mitra, et al. at arXiv:2310.08567 for similar findings.

In open quantum systems, near phase transitions, universal power-law scaling, characterized by critical exponents, emerges. This behavior reflects the singular responses of physical phenomena to control parameters like temperature or external fields. Here we experimentally realize the spin of individual Cesium atoms coupled to a bath of ultracold Rubidium atoms. Our research unveils critical scaling also in time during the relaxation dynamics along with a critical point in the thermodynamic limit. (Excerpt)

yang, Ruochen and Paul Bogdan. Controlling the Multifractal Generating Measures of Complex Networks. Nature Scientific Reports. 10/5541, 2020. In this special year, University of Southern California computer scientists (search PB) add to confirmations of a common presence of self-similar forms and functions across nature’s array from geologic to genomic, cerebral and onto our behavioral activities. See also in regard Quantifying Emergence and Self-Organization of Microbial Communities by V. Balaban, et al (USC) in NSR (8/12416, 2018).

Self-repeating patterns and multifractality exist in many real-world complex systems such as brain, genetic, geoscience, and social networks. To better comprehend the multifractal behavior in the real networks, we propose the weighted multifractal graph to model the spatiotemporal complexity and heterogeneity encoded in interaction weights. We apply this approach to two specific complex systems, namely (i) the chromosome interactions of yeast cells in quiescence and in exponential growth, and (ii) the brain networks of healthy people and patients exhibiting mild cognitive impairment leading to Alzheimer disease. We find that our method provides a novel way to understand the self-similar structure of complex networks and to discriminate network structures. (Abstract excerpt)

From a geometrical perspective, many large-scale complex networks from sociology and biology exhibit self-similar and multifractal characteristics. Multifractal geometric analysis makes it possible to capture the heterogeneous and multiscale interaction rules of large networks . It efficiently characterizes large-scale complex systems and can be employed to measure nodes similarity and detect community structures. For instance, the multifractality of geochemistry mapping explains the element concentration values distribution and spatial covariance structure in rock samples. (1)

Youn, Hyejin, et al. Scaling and Universality in Urban Economic Diversification. Journal of the Royal Society Interface. Vol.13/Iss.114, 2016. A premier team of Youn and Geoffrey West, Oxford University, Luis Bettencourt, Santa Fe Institute, Jose Lobo and Deborah Strumsky, Arizona State University, and Horacio Samaniego, Universidad Austral de Chile, attest to the presence of a common, complex adaptive, self-similar system that distinguishes in kind every aspect, topology, degree, function, dynamic of citified human habitation from local neighborhoods to a megametropolis. See also Invention as a Combinatorial Process: Evidence from US Patents by team members in this journal (Vol.12, Iss.106). Circa 2015, some 50 years after general systems theory and 30 years since SFI began, here is a robust achievement of their quest to find a constant repetition of the same archetypal pattern and process at each and every scale and instance. As a result, an implied independent, universal mathematic source code becomes strongly evident.

Understanding cities is central to addressing major global challenges from climate change to economic resilience. Although increasingly perceived as fundamental socio-economic units, the detailed fabric of urban economic activities is only recently accessible to comprehensive analyses with the availability of large datasets. Here, we study abundances of business categories across US metropolitan statistical areas, and provide a framework for measuring the intrinsic diversity of economic activities that transcends scales of the classification scheme. A universal structure common to all cities is revealed, manifesting self-similarity in internal economic structure as well as aggregated metrics (GDP, patents, crime). We present a simple mathematical derivation of the universality, and provide a model, together with its economic implications of open-ended diversity created by urbanization, for understanding the observed empirical distribution. (Abstract)

Zakharaova, Anna. Chimera Patterns in Networks. International: Springer, 2020. In a chimera state, a network spontaneously splits into two parts with different dynamics separated in space: one demonstrating coherent behavior and the other exhibiting incoherent behavior. (2) After some years of collegial papers (Google), a Technical University of Berlin theoretical physicist has written the first book length treatment of this newly recognized natural systemic propensity to reside at a dynamic poise of more or less relative order or stability. Typical subjects are Coherence-Incoherence Patterns with Topologies, Power-Law Coupling, Fractal Connectivities, and Multiplex Networks. Such synchronization phenomena is lately being detected in kind across quantum, chemical, and biologic areas onto neural and socio-economic domains, in a way as akin to self-organizing criticalities.

For main prior references the author recommends, Scholl, Eckehard. Synchronization Patterns and Chimera States in Complex Networks by Eckehard Scholl in European Physical Journal Special Topics (225/891, 2016) and Chimera States: Coexistence of Coherence and Incoherence in Networks by Mark Panaggio and Daniel Abrams in Nonlinearity (28/R67, 2015).

This is the first book devoted to chimera states - peculiar partial synchronization patterns in networks. Providing an overview of the state of the art in research on this topic, it explores how these hybrid states, which are composed of spatially separated domains of synchronized and desynchronized behavior, arise surprisingly in networks of identical units and symmetric coupling topologies. The book not only describes various types of chimeras, but also discusses the role of time delay, stochasticity, and network topology for these synchronization-desynchronization patterns. Moreover, it addresses the question of robustness and control of chimera states, which have various applications in physics, biology, chemistry, and engineering.

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