IV. Ecosmomics: An Independent, UniVersal, Source Code-Script of Generative Complex Network Systems

Deacon, Terrence. The Hierarchic Logic of Emergence: Untangling the Interdependence of Evolution and Self-Organization. Weber, Bruce and David Depew, eds. Evolution and Learning. Cambridge: MIT Press, 2003. An entry into recent work in process of the University of California at Berkeley biological anthropologist and author.

The contention of this paper is that biological evolution and evolutionary processes in general are a subset of processes drawn from a much larger set of novelty-producing processes that also includes self-assembly and self-organizing processes. (273) Evolutionary emergent systems can further interact to form multilayer systems of exceeding complexity. Indeed, this is the nature of complex organisms that is exemplified in the ascending levels of “self” that proceed from gene to cell to organism to lineage to species, and so on, in the living world. (302)

Deutsch, Andreas amd Sabine Dormann. Cellular Automaton Modeling of Biological Pattern Formation. International: Springer, 2018. Technical University of Dresden complexity bioscientists provide a latest tutorial about nature’s essential propensity to iteratively organize her/his self into viable, universal scales of emergent genesis. Some chapter and section titles are On the Origin of Patterns, Ontogeny and Phylogeny, and Physical Analogues, Morphogenesis.

The book introduces pattern-forming principles in biology and the various mathematical modeling techniques used to analyze them. Cellular automaton models are discussed for different types of cellular processes and interactions, such as random movement, cell migration, adhesive cell interaction, alignment and cellular swarming, growth processes, pigment cell pattern formation, tumor growth, and Turing-type patterns. The final chapter discusses potentials and limits of the cellular automaton approach in modeling various biological applications, along with future research directions. (Publisher)

Dingle, Kamaludin, et al. Input-Output Maps are Strongly Biased Towards Simple Outputs. Nature Communications. 9/761, 2018. By way of algorithmic information theory and system cartographic methods, Oxford University mathematicians KD, Chico Camargo and Ard Louis perceive an inherent tendency in complex network behavior to simplify and generalize themselves. The work merited notice as A Natural Bias for Simplicity by Mark Buchanan in Nature Physics (December 2018). See also by this group Deep Learning Generalizes because the Parameter-Function Map is Biased Towards Simple Functions at arXiv: 1805.08522.

Many systems in nature can be described using discrete input–output maps. Without knowing details about a map, there may seem to be no a priori reason to expect that a randomly chosen input would be more likely to generate one output over another. Here, by extending fundamental results from algorithmic information theory, we show instead that for many real-world maps, the a priori probability P(x) that randomly sampled inputs generate a particular output x decays exponentially with the approximate Kolmogorov complexity K~(x) of that output. We explore this strong bias towards simple outputs in systems ranging from the folding of RNA secondary structures to systems of coupled ordinary differential equations to a stochastic financial trading model. (Abstract)

Duarte, Ana, et al. An Evolutionary Perspective on Self-Organized Division Of Labor in Social Insects. Annual Review of Ecology, Evolution, and Systematics. Volume 42, 2011. With coauthors, Franz Weissing, Ido Pen, and Laurent Keller, University of Groningen theoretical biologists join the growing witness of innate, consistent patterns and processes that evolve, reiterate, and go on to foster selves and societies. The edition is scheduled for November.

Division of labor is a complex phenomenon observed throughout nature. Theoretical studies have focused either on its emergence through self-organization mechanisms or on its adaptive consequences. We suggest that the interaction of self-organization, which undoubtedly characterizes division of labor in social insects, and evolution, should be further explored. We review the factors empirically shown to influence task choice. In the light of these factors, we review the most important self-organization and evolutionary models for division of labor, and outline their advantages and limitations. We describe possible ways to unify evolution and self-organization in the theoretical study of division of labor and recent results in this area. Finally we describe the benchmarks and main challenges of this approach. (Abstract)

Eiraku, Mototsugu, et al. Self-Organizing Optic-Cup Morphogenesis in Three-Dimensional Culture. Nature. 472/51, 2011. Bioresearchers from the Organogenesis and Neurogenesis Group, RIKEN Center for Developmental Biology, Kobe, Japan, Computational Cell Biomechanics Team, RIKEN, Four-Dimensional Tissue Analysis Unit, RIKEN, Institute for Frontier Medical Sciences, Kyoto University, and Laboratory of Extracellular Matrix Biochemistry, Osaka University are surprised by their discovery that optical tissue cultures tend to spontaneously organize themselves into characteristic functional anatomies. Such phenomenal behavior is seen to evince and exemplify what must be an independent generative source from which it emerges. In so doing, this micro portal well testifies to and bodes for a radically different kind of natural genesis uniVerse of genotype and phenotype.

Bioresearchers from the Organogenesis and Neurogenesis Group, RIKEN Center for Developmental Biology, Kobe, Japan, Computational Cell Biomechanics Team, RIKEN, Four-Dimensional Tissue Analysis Unit, RIKEN, Institute for Frontier Medical Sciences, Kyoto University, and Laboratory of Extracellular Matrix Biochemistry, Osaka University are surprised by their discovery that optical tissue cultures tend to spontaneously organize themselves into characteristic functional anatomies. Such phenomenal behavior is seen to evince and exemplify what must be an independent generative source from which it emerges. In so doing, this micro portal well testifies to and bodes for a radically different kind of natural genesis uniVerse of genotype and phenotype.

Erdi, Peter. Complexity Defined. Berlin: Springer, 2007. A textbook due in June. As per the quote, insights into a new kind of genesis cosmos whose universal creative source instantiates at each emergent stage. But this epochal revolution and discovery has not yet registered, which is an aim of this website.

It is shown that very different complex phenomenon of nature and society can be analyzed and understood by nonlinear dynamics since many of the systems of very different fields, such as physics, chemistry, biology, economics, psychology and sociology, etc. have similar architecture. (Publisher’s website)

Feistel, Rainer and Werner Ebeling. Physics of Self-Organization and Evolution. Weinheim: Wiley-VCH, 2011. A thoroughly revised edition of a pioneer 1982 volume by the authors, who are German system scientists. At the center of European complexity thinking for over three decades, often in dialogue with Ilya Prigogine, Manfred Eigen, Herman Haken and many others, within the noted heritage of Gregor Hegel, the work is a substantial review of non-equilibrium thermodynamics, nonlinear dynamics, origins of life, evolutionary theories, and much more. A 2011 added emphasis is an appreciation of nature’s informational essence, which is seen to ascend with evolving generative structures and processes.

Fellermann, Harold, et al, eds. Artificial Life XII: Proceedings of the Twelfth International Conference on the Synthesis and Simulation of Living Systems. Cambridge: MIT Press, 2010. To everyone’s credit, the full text of this August 2010 meeting in Denmark was posted on the MIT Press site shortly afterwards. A sense of its cosmic scope can be gained by the seventeen sections listed below. Although many papers, Jerzy Maselko and Hiroki Sayama cited herein, may infer a greater organic genesis, with still no women among 27 organizers and section leaders, and few presenters, this imminent, vitalizing discovery, the very idea, yet awaits.

Chemical Self-Assembly and Complexity, Origin of Life, Bottom-up Synthetic Cells, Systems Biology, Biological and Chemical Information Processing and Production, Artificial Chemistries Minimal Cognition and Physical Intelligence, Evolutionary Dynamics, Theoretical and Computational Frameworks, Complex Networks, Ecology, Collective Intelligence, Emergent Engineering, Intelligence and Learning, Socio-Technical Systems, Philosophy.

Feudel, Ulrike, et al. Multistability and Tipping: From Mathematics and Physics to Climate and Brain: A Minireview. Chaos. 28/3, 2018. Physical chemists U. Feudel, University of Oldenburg, Alexander Pisarchik, Technical University of Madrid, and Kenneth Showalter, West Virginia University introduce an issue section for studies about these ubiquitous dynamic tendencies. They are distinguished by nonlinear attractor states and critical transitions which seem to instantiate in kind everywhere from quantum realms to stormy weather. Some papers herein are Emergence, Evolution, and Control of Multistability in a Hybrid Topological Quantum/Classical System; Detecting, Anticipating, Implications of Tristability in Pattern-Forming Ecosystems; and Predicting Critical Transitions in Spatially Extended Systems.

Multistability refers to the coexistence of different stable states in nonlinear dynamical systems. In this introduction, we introduce the classes of dynamical systems in which this phenomenon has been found and the concept of critical transitions. We then present some specific applications in physics, neuroscience, biology, ecology, and climate science. Many dynamical systems in nature possess several coexisting stable states (attractors) for a given set of parameters and/or external forcings — a phenomenon called multistability. Whenever such a coexistence of a multitude of states is found, the system can switch from one stable state to another either randomly by perturbations or in a desired way employing a control strategy or whenever parameters are varied in a specific way. These switchings are called critical transitions; in physics, they are also termed phase transitions. (Abstract edited excerpts)

Finlay, Bland, et al. Self-Similar Patterns of Nature. Proceedings of the Royal Society B. 273/1935, 2006. An extensive study from Britain and Denmark of insect diversities from meadow to bioregion reveals a constant nested series of size-frequency distributions across geometrical dimensions. So a grand new nature appears, it could be noted, no longer intractably tangled but comprehensible due to a universally repetitive archetype.

We have shown that the global diversity of insects is supported by a framework of self-similar patterns that emerge with some force, are relevant in Both Northern Southern Hemispheres, and across spatial scales from a few hectares to global. (1939) Finally, a further challenge will be to determine whether self-similar patterns lie hidden within other species-rich animal taxa. We suspect they do, and when they are revealed, they too will provide useful tools for characterizing and monitoring biodiversity across spatial scales. (1940)

Fitch, W. Tecumseh. Glossogeny and Phylogeny. Trends in Genetics. 24/8, 2008. Along with a rush of 2010 citations in A Cultural Code, one more realization that genomes and languages are an emergent manifestation of what might be seen as an independent, indeed parental, genome source.

Evolutionary theorists since Darwin have been interested in the parallels and interactions between biological and cultural evolution. Recent applications of empirical techniques originally developed to analyze molecular genetic data to linguistic data offer new insights into the historical evolution of language, revealing fascinating parallels between language change and biological evolution. This work offers considerable potential toward unified theories of genetic and cultural change. (373)

Flake, Gary. The Computational Beauty of Nature. Cambridge: MIT Press, 1998. A technical appreciation through nonlinear science of the recurrent harmony of the natural kingdoms. The website for the book with an extensive glossary is: www.mitpress.mit.edu/books/FLAOH/cbnhtml/home

….in order for the universe to move coherently from one state to the next, the universe must ‘remember’ previous states. (428) Looking at the organization of nature, we find that most interesting things are composed of smaller interesting things. Each level is nearly a universe in itself, since all of them use and support types of structural and functional self-similarity, multiplicity and parallelism, recursion and feedback, and self-reference. (429)

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