IV. Ecosmomics: An Independent, UniVersal, Source Code-Script of Generative Complex Network Systems
Mero, Laszlo. The Logic of Miracles. New Haven: Yale University Press, 2018. The Eötvös Loránd University, Budapest mathematician and psychologist provides a well reasoned rebuttal and alternative to Nassim Taleb’s 2010 The Black Swan (2010) about a chaotic unpredictability that besets complex natural and social societies. But if we refuse to accept this and press on for an inherent basis which underlies sufficiently regular events, one does actually appear. The approach involves a stronger perception of an infinite fractal self-similarity and scale-invariance across all natural to cultural realms. A further avail of ubiquitous scale-free networks braces the argument. Of course wild stuff happens, but not without some modicum of meaning and trace to a relatively reliable source.
We live in a much more turbulent world than we like to think, but the science we use to analyze economic, financial, and statistical events mostly disregards the world’s essentially chaotic nature. We need to get used to the idea that wildly improbable events are actually part of the natural order. The renowned Hungarian mathematician and psychologist László Mérő explains how the wild and mild worlds (which he names Wildovia and Mildovia) coexist, and that different laws apply to each. Even if we live in an ultimately wild universe, he argues, we’re better off pretending that it obeys Mildovian laws. Doing so may amount to a self fulfilling prophecy and create an island of predictability in a very rough sea. Perched on the ragged border between economics and complexity theory, Mérő proposes to extend the reach of science to subjects previously considered outside its grasp: the unpredictable, unrepeatable, highly improbable events we commonly call “miracles.”
Meyers, Robert, editor-in-chief. Encyclopedia of Complexity and Systems Science. Berlin: Springer, 2009. The 11 volume, 10,000 page compendium is now available, with a full listing of its 592 topical contents in 15 sections, and preface, posted on the Springer web citation. A broad range is covered, but constrained within narrowly defined sections such as Cellular Automata, Mathematical Basis of, which are muchly technical and pedantic. An author count averages 15 men to 1 woman, better than the Britannica. Some articles of note might be "Complex Gene Regulatory Networks' by Sui Huang and Stuart Kauffman, "Self-Organizing Systems" by Wolfgang Banzhaf, and Eric Chaisson's "Exobiology and complexity." We quote at length from its synopsis of this scientific frontier which languishes without a common terminology and vision so as to reveal a universally recurrent genesis cosmos.
The science and tools of complexity and systems science include theories of self-organization, complex systems, synergetics, dynamical systems, turbulence, catastrophes, instabilities, nonlinearity, stochastic processes, chaos, neural networks, cellular automata, adaptive systems, and genetic algorithms. Examples of near-term problems and major unknowns that can be approached through complexity and systems science include: The structure, history and future of the universe; the biological basis of consciousness; the integration of genomics, proteomics and bioinformatics as systems biology; human longevity limits; the limits of computing; sustainability of life on earth; predictability, dynamics and extent of earthquakes, hurricanes, tsunamis, and other natural disasters; the dynamics of turbulent flows; lasers or fluids in physics, microprocessor design; macromolecular assembly in chemistry and biophysics; brain functions in cognitive neuroscience; climate change; ecosystem management; traffic management; and business cycles. All these seemingly quite different kinds of structure formation have a number of important features and underlying structures in common. These deep structural similarities can be exploited to transfer analytical methods and understanding from one field to another.
Mikhailov, Alexander. From Cells to Societies: Models of Complex Coherent Action. Berlin: Springer, 2002. Using the approach to self-organizing systems known as synergetics, general principles are found to characterize the collective behavior of populations of interactive agents whether microbes or cultures.
Miller, James G. Living Systems. New York: McGraw-Hill, 1976. A classic treatise on the nested, hierarchical organization of biological and social life wherein 20 critical subsystems that process either matter-energy or information repeat at each subsequent level. These similar, isomorphic features “thread out” at each stage from the genetic to the global. The resultant field of Living Systems Theory has been elaborated in the journals Behavioral Science and its successor Systems Research and Behavioral Science.
Minkel, J. R. Hollow Universe. New Scientist. April 27, 2002. A report from the physics frontier of an encounter with an information based, fine-grained, holographic cosmos whereby the same “image,” “message” or “system” plays out everywhere in its emergent development.
Maybe.…nature is storing the data about its most basic building blocks like a hologram. In a conventional hologram, a laser beam bouncing off an object is mixed with another laser beam and the resulting interference pattern is recorded on a flat surface. Shine new light onto the recording, and a three dimensional image leaps out. If nature works like this, then information somehow lives on the boundary of any region of spacetime. The material stuff within that boundary, the objects that we perceive and touch, is just the unpacked, higher-dimensional manifestation of that hologram. That is the holographic principle. (24)
Mitchell, Melanie. Complexity: A Guided Tour. Oxford: Oxford University Press, 2009. The Portland State University and Santa Fe Institute computer scientist, with John Holland and Douglas Hofstadter as doctoral mentors, draws on her two decades of experience and public lectures to offer an accessible entry to this multi-faceted endeavor. With an emphasis on computational simulations, nature’s propensities for scale-free networks, power laws, cellular automata, genetic algorithms, cross-communication, evolvability, and so on, along with their proponents, are clearly explained. But in a tacit response to our male scientific culture (every one else mentioned), which seems unable to cognitively perceive or admit intrinsic patterns, the search for or possibility of universal, independent abiding principles is mostly dismissed. Altogether a good introduction.
Now I can propose a definition of the term complex system: a system in which large networks of components with no central control and simple rules of operation give rise to complex collective behavior, sophisticated information processing, and adaptation via learning or evolution. (13) Systems in which organized behavior arises without an internal or external controller or leader are sometimes called self-organizing. Since simple rules produce complex behavior in hard-to-predict ways, the macroscopic behavior of such systems is sometimes called emergent. Here is an alternative definition of a complex system: a system that exhibits nontrivial emergent and self-organizing behaviors. (13)
Mobus, George and Michael Kalton.
Principles of Systems Science.
New York: Springer,
University of Washington interdisciplinary computer scientists provide a comprehensive, accessible textbook for this robust endeavor with both a theoretical cosmos to culture basis, and practical cases of its avail in business and medicine. A “systems universe” is the consequent paradigm, which is composed of myriad nested repetitive networks as “auto or self-organizing complex adaptive systems.” As one reads along, the impression is a novel, 21st century, natural creative reality, without yet its full realization. Instead of lumpen mechanism from nothing to nowhere, a vested Ptolemaic physics, an intrinsic vitality of systemness, patterns and hierarchies, networks of components and relations, dynamic complexities, an evolutionary emergence, information and knowledge gain, and so on, is explained, all due to nature’s own active self.
This new understanding of the process of mounting systemic organization reframes evolution. Darwinian natural selection remains critical in understanding the ongoing process of increasing complexity and diversity in the community of life, but the newer understanding of self-organization roots bio-evolution more deeply by exploring the rise of the physical and chemical complexity that takes a system to the threshold of life. In sum, a full system account should now be able to look at the junctures where chemistry emerges from physics, biology from chemistry, and sociology and ecology from biology. (39)
Moore, Douglas, et al. Cancer as a Disorder of Patterning Information: Computational and Biophysical Perspectives. Convergent Science Physical Oncology. 3/043001, 2017. DM and Michael Levin, Tufts University and Sara Walker, Arizona State University, who represent a new generation of complexity scientists, contribute a 30 page, 478 reference posting of the 2010s turn to factor in a mathematical presence of multiplex network phenomena. By this advance, previously identified cellular elements gain the missing dimension of their fluid interactive linkage. Their compass includes integrated information theory from neural studies as another way that agents form spatiotemporal patterns. See also in this journal The Physics of Life: Clinical, Biological and Physical Science Approaches for Cancer Research by Katharine Arney (4/040201, 2018), second quote. In the broader scheme of an ecosmos genesis, we might witness life’s long evolution as a self-healing, curing and now preventative process by virtue of such an emergent knowledge corpus which, in its genomic essence, can be fed back to heal, cure the beings it arose from.
The current paradigm views cancer as a clonally degradation of genetic information in single cells. A novel perspective is that cancer is due to a system disorder of algorithms that normally guide individual cell activities toward anatomical features and away from tumorigenesis. A view of cancer as a disease of geometry can focus on pathways that allow cells to cooperate, form and maintain large-scale patterning. Cancer may result when cells lose coherent structures and their computational selves reverts to a single-cell, self-serving stage. Here, we highlight two recent areas of theoretical advance. First, we review the roles that endogenous bioelectrical networks across many tissues in vivo foster information processing in tumor suppression, progression, and reprogramming. Second, we provide a primer to the development of computational methods for quantifying causal control structures in cancer and other complex biological systems. Finally, specific ways in which a synthesis of novel integrative biophysics and mathematical analysis may better understand and address cancer are stated. (Abstract edits)
Mora, Thierry and William Bialek. Are Biological Systems Poised at Criticality?. arXiv:1012.2242. Posted in November 2010, a Princeton University geneticist and a physicist contribute to the on-going, historic revision of the essential nature of informative “genetic” domains. Not only are discrete “genes” being subsumed into layers of relational webworks, these dynamic, creative dimensions ought to be seen as instantiations of a universal proclivity for self-organization. These findings of a natural presence of critically poised complex systems from galaxies to genomes to Gaia bode well for a profoundly new and different kind of genesis universe, and an obvious implication whereby such phenomena take on a “genetic” quality.
Many of life's most fascinating phenomena emerge from interactions among many elements – many amino acids determine the structure of a single protein, many genes determine the fate of a cell, many neurons are involved in shaping our thoughts and memories. Physicists have long hoped that these collective behaviors could be described using the ideas and methods of statistical mechanics. In the past few years, new, larger scale experiments have made it possible to construct statistical mechanics models of biological systems directly from real data. We review the surprising successes of this “inverse" approach, using examples form families of proteins, networks of neurons, and flocks of birds. Remarkably, in all these cases the models that emerge from the data are poised at a very special point in their parameter space - a critical point. This suggests there may be some deeper theoretical principle behind the behavior of these diverse systems. (Abstract, 1)
Moreno, Alvaro, et al. The Impact of the Paradigm of Complexity on the Foundational Frameworks of Biology and Cognitive Science. Hooker, Cliff, ed. Philosophy of Complex Systems. Amsterdam: Elsevier, 2011. Alvaro Moreno and Kepa Ruiz-Mirazo, University of the Basque Country, and Xabier Barandiaran, University of Sussex, biophilosophers voice the theme of the volume by once more contrasting, as per the quotes, an older emphasis on physics and chemistry, whereof cosmos and creatures are determined, insensate mechanisms, and in actuality, a dynamic, quickening vitality, and vista just adawning. By this project, prior, reduced particles can be equitably joined in ubiquitous scale-free networks to illume a lively, processional, awakening emergence. Thus to properly study and understand body and brain, organic form and cerebral acumen, in the 21st century, it is essential to move from detailed analysis to an integral systems survey across these temporal and spatial expanses.
The study of networks with strongly and recurrently interacting components allowed scientists to deal with holistic systems, showing that, despite their variety, they share certain generic properties. For many, these advances seemed to reflect some common, universal architecture of complex systems (or, at least, the fact that they all belonged to a critical region ‘at the edge of chaos.’ So the blossoming of the sciences of complexity, among other important reasons, induced –and is still inducing– a profound change in biology and neuroscience toward less analytic and more synthetic-holistic views. (313)
Morin, Edgar. RE: From Prefix to Paradigm. World Futures. 61/4, 2005. This essay by the French polymath philosopher, (1921- ), was first published in La Methode II: La Vie de la Vie (Editions du Seuil, 1980), here translated by Frank Poletti and Sean Kelly. Please view Morin’s bio on Wikipedia for his activist life from the Spanish civil war to WW II resistance movements, Marxism and its rejection, and on to these nonlinear engagements. A crucial theme is how to convey the deep essence of our reality as a manifest repetition of a generative, complexifying agency, algorithm, or process. In its day, the theories seem as an independent version of Maturana and Varela’s autopoietic, self-referencing living systems. Morin goes on to advise that anything new “Meta” need be couched in familiar “Retro” terms, a good guide. And although cited as abstractions, could we appreciate one more valiant attempt to express the actual, constant presence and operation of a cosmos to child genetic code, as a grand revelatory secret?
This article is a translated chapter from a large study of the philosophy of ecology and biology. It looks at the vast array of reiterative processes in nature and culture and argues that continuous recursion is the core activity that sustains living processes at all levels. Therefore, the prefix “re,” which is central to the concepts of repetition, renewal, reinforcement, regeneration, reorganization, recursion, and religion, is a radical concept that should be considered at the paradigmatic level. The author shows that by working “revolutions into its revolutions” the process of RE complexly unifies and intermixes the past and future in order to generate the creative pulse of evolution. (Abstract, 254)
Morowitz, Harold and Jerome Singer, eds. The Mind, the Brain, and Complex Adaptive Systems. Reading, MA: Addison-Wesley, 1995. Explorations of the welling realization that dynamical theories offer an effective new way to theoretically understand what an evolving nature is about.
In the 1980s and 1990s we are witnesses to a new paradigmatic shift in science. Theorists in many fields are moving away from linear, reductionist, simple cause-effect models toward confronting the challenges of complex adaptive systems. Such systems are found in fields as diverse as astrophysics and economics, cerebral neurobiochemistry and cognitive psychology. (1)