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

Wolchover, Natalie. In Mysterious Pattern, Math and Nature Converge. www.simonsfoundation.org/category/features/science-news. A February 5, 2013 posting on this Simons Foundation: Advancing Research in Basic Science website, funded by mathematician and philanthropist James Harris Simons. In an article reprinted on Wired.com, a science writer conveys the growing recognition in widely disparate studies of a universally recurring natural and social phenomena from “the energy spectrum of the uranium nucleus to the Cuernavaca, Mexico, urban bus system.” The author interviewed mathematicians Terence Tao, UCLA, Van Vu, Yale, and Laszlo Erdos, University of Munich, who among many others, support and attest to a common “universality” that unites self-organizing complex network systems everywhere. Search Hohenegger for “Transition in the Fractal Geometry of Arctic Melt Ponds” that is noted as a further geological exemplar.

Wolfram, Stephen. A New Kind of Science. Champaign, IL: Wolfram Media, 2002. In this large opus, the physicist and computer scientist who conceived the theory of cellular automata and the popular Mathematica software program proposes a novel scientific method. By this CA approach, the universe is at base computational in kind and ought to be seen as characterized by a set of simple rules or algorithms which are recursively run over and over. In so doing, they generate the complex, variegated, emergent reality from which humans beings appear. Although brilliant work, it has been widely seen as only part of the story and flawed because Wolfram sets aside any other theory than his own, with no standard reference citations. But surely a good read.

Yao, Nan, et al. Self-Adaptation of Chimera States. arxiv:1812.06336. (Chimera states are self-organized spatiotemporal patterns of coexisting coherence and incoherence.) A global team based in China, Scotland, and the USA begin with the standard statement that such critical dynamics are now known to commonly occur across all chemical to cerebral domains. A further aspect of these orderly or chaotic conditions is that this active phenomena seems to exhibit a certain sensory ability by which to guide its responses. All these happen as if the chimera was “intelligent” (5).

(Chimera states are self-organized spatiotemporal patterns of coexisting coherence and incoherence.) A global team based in China, Scotland, and the USA begin with the standard statement that such critical dynamics are now known to commonly occur across all chemical to cerebral domains. A further aspect of these orderly or chaotic conditions is that this active phenomena seems to exhibit a certain sensory ability by which to guide its responses. All these happen as if the chimera was “intelligent” (5).

Yin, Gao and William Herfel. Constructing Post-Classical Ecosystems Ecology: The Emerging Dynamic Perspective from Self-Organizing Complex Adaptive Systems.. Hooker, Cliff, ed. Philosophy of Complex Systems. Amsterdam: Elsevier, 2011. In a chapter that could be an ecological version of Petter Portin’s genome shift (search), University of Newcastle, Australia social philosophers relate the historic turn from the 20th century traditional, mechanistic balance of nature to far-from-equilibrium, complex ecosystems viabilities. As this 2011 volume attests, biotas and indeed every strata of is distinguished by diverse entities whom locally interrelate, in constant communication, as they form webby “neighborhoods” good for welfare and survival.

A novel dynamic perspective is emerging in ecology as models from complex systems dynamics are employed in ecology. In particular, models embracing network dynamics, self-organisation and complex adaptation, which we will explore in this part of the article, provide new insight at a fundamental level into the workings of ecosystems. As this perspective is adopted a truly organic approach to ecosystems ecology comes into focus with the self-organising dissipative structure replacing the heat engine as its central metaphor. (397)

We start by identifying the constitutive properties of systems capable of self-organisation:
• The focus is on systems composed of discrete individual elements; each element is simple and capable of being in two or more discrete states. • The elements are interconnected; collections of elements form an interacting network. • The system’s dynamics is the result of local interactions; generally these interactions are governed by simple deterministic rules. • The system exhibits distributed global control, that is, the global dynamics of the system is not controlled by any single element, and any regulation. (402)

The greatest achievements applying complex dynamics to ecology will come from the application of complex adaptive systems concepts to concrete ecological problems. It is only by adopting, not only the models of complex systems, but most importantly its dynamic perspective that will enable us to have the insight to deal with the new ecological challenges awaiting us in the Twenty-first Century. (417)

Yukalov, Viacheslav and Didier Sornette. Self-Organization in Complex Systems as Decision Making. arXiv:1408.1529. Circa August 2014, one could notice increasing attempts to express life’s viability by way of dynamical interactions as an iterative selection and optimization process from a population of probable options. Evolution as computation (Mayfield), quantum bayesianism (Mermin), cellular automaton interpretation (‘t Hooft), evolutionary connectionism (R. Watson), universal Darwinism (J. Campbell) and recourse to J. A. Wheeler’s It from Bit are instances and variations. Here senior ETH Zurich physicists contend that nature’s propensity to organize itself into a nested, repetitive emergence is characterized by such various choices that entities make. In some sense a relative subject – microbe, neuron, person – has an autonomous say and effect upon the system one abides in, be it biofilm, brain, or biosphere. By extension, if to follow and avail, we peoples actually have a huge significance to the course and fate of the earth, and of the universe.

The idea is advanced that self-organization in complex systems can be treated as decision making (as it is performed by humans) and, vice versa, decision making is nothing but a kind of self-organization in the decision maker nervous systems. A mathematical formulation is suggested based on the definition of probabilities of system states, whose particular cases characterize the probabilities of structures, patterns, scenarios, or prospects. In this general framework, it is shown that the mathematical structures of self-organization and of decision making are identical. This makes it clear how self-organization can be seen as an endogenous decision making process and, reciprocally, decision making occurs via an endogenous self-organization. The approach is illustrated by phase transitions in large statistical systems, crossovers in small statistical systems, evolutions and revolutions in social and biological systems, structural self-organization in dynamical systems, and by the probabilistic formulation of classical and behavioral decision theories. Behavioral biases of decision makers can be characterized in the same way as analogous to quantum fluctuations in natural systems. (Abstract)

In all cases, the procedure of self-organization is analogous to that of decision making, both being characterized by the same mathematical scheme. It is only the language that is slightly different. But there is a direct translation of one language onto another, which is exemplified in the following dictionary. Complex system= Decision maker, System states = Decision prospects, System fluctuations = Decision-maker deliberations, State probability = Prospect probability, System stability = Prospect preferability, Most stable state = Most preferable prospect, Quantum fluctuations = Behavioral biases, Self-organization = Decision making. (23) Our main aim here has been to show that the processes of self-organization in complex systems and of decision making by alive beings can be represented in the same mathematical language of the search for the highest probability corresponding to the most stable state or to the most preferable prospect. (23)

Our main aim here has been to show that the processes of self-organization in complex systems and of decision making by alive beings can be represented in the same mathematical language of the search for the highest probability corresponding to the most stable state or to the most preferable prospect. Several examples of complex systems that we have mentioned are already well-known, and we have used them to illustrate that all of them can be described in the same probabilistic framework. The principal novelty of the present article is the development of a general probabilistic approach allowing us to describe self-organization and decision making in the same mathematical terms, thus demonstrating that these two processes can be interpreted as being identical. (23)

Zafeiris, Anna and Tamas Vicsek. Why We Live in Hierarchies: A Qualitative Treatise. arXiv:1707.01744. A preprint by Eotvos University, Budapest, systems biophysicists for a SpringerBriefs in Physics edition. Akin to Geoffrey West’s Scale and other works, into the 2010s it is possible to cast overviews whence the same patterns and processes, as shown in a graphic display from neural networks to animal groupings, are found to repeat in kind at every spatial and temporal stage. In this instance, the “hierarchy” word is meant to represent this recurrent, nested emergence. Self-organizing collective behaviors, which Vicsek (search) has studied for two decades, are emphasized through their appearance in prehistoric and urban human societies. From Robin Dunbar, Marcus Hamilton (search each) and colleagues, integral wholes within wholes are cited from a “support clique” of 3-5 to a sympathy group of nom. 16, camp or band of nom. 45, onto clan or troop size of some 90-130. One then wonders how all these complex theory and anthropological findings might ever be realized as a natural, palliative guidance for a better world.

Since hierarchy is abundant in nature and society, but many of its quantitative aspects are still unexplored, the main goal we intend to achieve is the systematic interpretation and documentation of new unifying principles and basic laws describing the most relevant aspects of hierarchy (being perhaps the most widespread organizing principle in the Universe). To do so we shall discuss recent experiments and models that are both simple and realistic enough to reproduce the observations and develop concepts for a better understanding of the complexity of systems consisting of many organisms. We shall cover systems ranging from flocks of birds to groups of people. The related research goes beyond being interdisciplinary and can be rather described as multidisciplinary, since it involves many kinds of systems (both living and non-living), various techniques and technologies typically used in different branches of science and engineering. (1)

Zelinka, Ivan, et al, eds. How Nature Works: Complexity in Interdisciplinary Research and Applications. Berlin: Springer, 2014. A volume in the Emergence, Complexity and Computation series. With coeditors Ali Sanayei, Hector Zenil, and Otto Rossler, a good entry to current broadly European systems thinking with regard to both theoretical advances, and practical applications. A lead chapter by pioneer coeditor Otto Rossler is “Complexity Decomplexified: A List of 200+ Results Encountered Over 55 Years.” Glenda Eoyang (third quote) is founder and director of the Human Systems Dynamics Institute, Minnesota.

This book is based on the outcome of the “2012 Interdisciplinary Symposium on Complex Systems” held at the island of Kos. The book consists of 12 selected papers of the symposium starting with a comprehensive overview and classification of complexity problems, continuing by chapters about complexity, its observation, modeling and its applications to solving various problems including real-life applications. More exactly, readers will have an encounter with the structural complexity of vortex flows, the use of chaotic dynamics within evolutionary algorithms, complexity in synthetic biology, types of complexity hidden inside evolutionary dynamics and possible controlling methods, complexity of rugged landscapes, and more. All selected papers represent innovative ideas, philosophical overviews and state-of-the-art discussions on aspects of complexity.

This work is going to present the cause of complexity in nature from an analytical and computational point of view. The cause of complex pattern formation is explained by the local activity of cells in complex systems which are analytically modeled by nonlinear reaction-diffusion equations in physics, chemistry, biology, and brain research. There are not only rigorous analytical criteria of local activity and the edge of chaos, but also constructive procedures to visualize them by computer simulations. In technology, the question arises whether these criteria and procedures can be used to construct artificial life and artificial minds. (The Cause of Complexity in Nature, Klaus Mainzer)

A useful computational model of complex human systems dynamics could support advancements in theory and practice for social systems from intrapersonal experience to global politics and economics. Models of human interactions have evolved from Newtonian assumptions, which served a variety of needs in the past. Another class of models has been informed by nonlinear dynamics. None of the existing models, however, sufficiently represents the open, high dimension, and nonlinear self-organizing dynamics of social systems. A conceptual model, CDE Conditions for Self-organizing in Human Systems, is explored as an alternative. While the CDE overcomes the limitations of previous models, it also provides an explanatory base for prospective analysis to inform meaning making and action taking in response to complex conditions. An invitation is extended to engage in developing a computational model that incorporates the assumptions, meta-variables, and relationships of this conceptual model of the complex dynamics of human systems. (Toward a Computational Model of Complex Human Dynamics, Glenda Eoyang)

Zhang, Mengsen, et al. Topological Portraits of Multiscale Coordination Dynamics. Journal of Neuroscience Methods. Vol. 339, 2020. Florida Atlantic University and Stanford University researchers including Scott Kelso and Emmanuelle Tognoli (search) continue to finesse the presence and importance of nature’s scored choreography as it graces, informs and empowers our responsive cerebral performance. We note the mathematic approaches in bold as they find application to an increasing number of disparate many areas, which altogether imply a mindful ecosmic coordination.

Living systems exhibit complex yet organized behavior on multiple spatiotemporal scales. To investigate this phenomena, one needs a meaningful way to quantify the complex dynamics. This work shows how computational algebraic topology and dynamical systems theory can help meet this challenge. We propose, for example, a method to study dynamic topological information using persistent homology, which allows us to effectively construct a multiscale topological portrait of rhythmic coordination. The present work demonstrates how the analysis of multiscale coordination dynamics can benefit from topological methods, thereby paving the way for further systematic quantification of complex, high-dimensional dynamics in living systems. (Abstract excerpt)

Zschaler, Gerd. Adaptive-Network Models of Collective Dynamics. European Physical Journal Special Topics. 211/1, 2012. A Max Planck Institute for the Physics of Complex Systems theorist publishes his doctoral dissertation which offers another example this year of novel abilities to robustly verify a natural reality as distinguished by nonlinear emergent complexities. Similar to Jaroslaw Kwapien and Stanislaw Drozdz’s “Physical Approach to Complex Systems” (Physics Reports 2012), their essential presence can be discerned both in local instances everywhere (third quote) such as animal groups and social networks, and an inferred universality of the same independent self-organized complex adaptive systems from which they arise and exemplify. Along with many citations in Systems Physics, this section, Universal Principles, and throughout, these sophisticated theories augur for a revolutionary new kind of procreative greater creation. These achievements beg a palliative, visionary 21st century natural philosophy to a genesis universe.

Collective phenomena are bound to appear in systems built from a large number of interacting units, such as the cars during rush hour, football fans in a stadium performing the Mexican wave, or investment bankers whose collective behaviour determines the rise and fall of stock prices. In fact, one may be tempted to say that life in a collective phenomenon, driven by the collective dynamics of molecules, genes, cells, organisms, groups, societies, and so on. (1) Collective phenomena appear as a macroscopic effect resulting from the microscopic interactions of many units. While these microscopic interactions are in many cases well-understood, it is often unclear how they can give rise to unexpected, large-scale behaviour which is very different from the microscopic dynamics. (1)

One of the central notions in statistical physics is the concept of emergence. Collective phenomena are emergent in a system of interacting units, which means that they cannot be anticipated from the microscopic properties of the units alone, but arise from the interactions among many of them. One possible definition characterizes a complex system as: “… a system in which large networks of components with no central control and simple rules of operation give rise to complex collective behaviour, sophisticated information processing, and adaptation via learn or evolution.” (Mitchell 2009). (2) In this definition, an important concept for the understanding of complex systems is introduced. Although the nature of the interacting entities may differ fundamentally in different systems, they generally can be viewed as an abstract or physical network. In this network, the interacting parts of the systems are represented by abstract nodes and the interactions among them by links, which may undergo complex changes over time. Many different systems can be considered from this point of view, so that the common feature of an underlying network structure may serve as a starting point for a unifying description of collective phenomena in complex systems. (2)

In the main part of this work, which is essentially my PhD thesis, I propose three different adaptive-network models for collective phenomena in three different research fields, namely collective motion in animal groups, opinion formation in social networks, and the evolution of cooperation in evolutionary game theory. Considering the different systems studied in these fields as adaptive networks makes it possible to investigate their dynamics within the same framework and reveal their similarities. (3)

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