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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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IV. Cosmome: A Universal Procreative Genome Code

As the prior chapter documented, if our worldwide mindkind is now finding, via a cosmic Copernican revolution, an organic genesis universe, then accordingly there ought to be an equivalent genetic code. And once and future biological nature, now a dynamic, evolutionary emergence, indeed reveals both phenotype and genotype. In such regard, this new 2010 introduction will present the nascent endeavor known as complex systems science. We begin with an extended Glossary of the many terminologies within its compass, and afterwards broach an historical timeline and natural philosophy orientation.

A scientific untanglement of nature’s intricacy that began incipiently in the 1960s and 1970s, grew in depth and breadth through the 1980s, 1990s, and especially the 2000s, is just coming together to imply and give credence to a grand genesis synthesis. But its occasion as a worldwide endeavor, across disparate fields from life’s origins and microbial colonies to linguistics and neighborhoods, has led to many versions, each with an abstract vernacular and certain emphasis. We next seek to gather, define, and reference the various schools, founders, applications, and often difficult nomenclature.

Agent-Based Modeling, Autopoiesis, Biosemiotics, Cellular Automata, Chaos Theory, Complex Adaptive Systems, Computational Information, Developmental Systems Theory , Dissipative Structures, Dynamical Systems Theory, Econophysics, Ecosystems, Emergence, Fractal Geometry, General Systems Theory, Hierarchy Theory, Multi-Agent Systems, Neural Networks, Non-equilibrium Thermodynamics, Nonlinear Phenomena, Renormalization Group Theory, Scale-Free Networks, Scale Invariance, Self-Organized Criticality, Self-Organization, Small-World Network, Statistical Physics, Swarm Intelligence, Symbiosis, Synergetics, Synergy, Universality

Nowadays all these subjects can be looked up via the Google cyberspace noosphere. A prime site is usually Wikipedia, the online public encyclopedia. Many of definitions are edited from there, or such as Biosemiotics which has its own site. A good book introduction is Complexity: A Guided Tour by Portland State University scientist Melanie Mitchell (Oxford, 2009). But there does not seem anywhere to be a Glossary essay like this to sort and synthesize. Before we begin, it might help to proffer a common, generic system, “methinks it is an elephant,” that each may have a piece and segment of.

As a start, the above verbiage can be loosely grouped with regard to which aspect, emphasis, or specialty they engage. As science goes on, work is necessarily done within a confined domain. While by their own admission they are beset by a jargonese, another problem is that several disciplines just deal with a partial facet such as the presence of networks, or a technical methodology.

Generic Complex System Agent-Based Modeling, Complex Adaptive System, Multi-Agent Systems

Complexity Science Names Nonlinear Phenomena, (Dynamical) Chaos Theory, Emergence

Computer-Based Techniques Cellular Automata, (Digital) Computation Information, Swarm Intelligence

Earlier & Other Versions General Systems Theory, Statistical Physics, Nonequilibrium Thermodynamics, Renormalization Group Theory

Topological Aspects Fractal Geometry, Self-Similarity, Hierarchy, Scale Invariance

CS Features Self-Organization, Autopoiesis, Scale-Free Networks, Self-Organized Criticality, Synergy, Small World, Universality

Field Specific Approaches Dynamical Systems Theory (Evolution), Developmental Systems Theory (Psychology), Neural Networks (Brain), Biosemiotics (Language), Econophysics, Symbiosis (Biology)

From its outset, this later 20th century confirmation that a natural intricacy is yet amenable to theoretical explanation held out an elusive promise of a universal, independent model that each take and method contributes to. This was an aspiration of the original 1960s General Systems view, later the Santa Fe Institute, since the 1980s the leading center of complexity studies. From a 2010 retrospect, a tentative synopsis might be attempted. As a seque, we offer this quote from Melanie Mitchell’s new book.

All the systems I described above consist of large networks of individual components (ants, B cells, neurons, stock-buyers, Web-site creators), each typically following relative simple rules, with no central control or leader. It is the collective actions of vast numbers of components that give rise to the complex, hard to predict, and changing patterns of behavior that fascinate us. (12)

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)

The most widely-used term is complex adaptive system, a dynamic pattern and process distinguished by two archetype-like components, modes or phases. One is a discrete, unitary element or entity such as a prokaryotic bacterium or financial investor. These multitudinous “agents” are involved in equally real “interactive” relations with each other, via cross-communication via agreed rules of behavior, say chemical quorum sensing for microbes, or no insider trading. Such an endemic complementarity of ‘me and we’ is an important, salutary finding.

Sans overall, top-down direction, such spontaneity proceeds to organize itself, often by a diversification of labor or tasks, into an increasing scalar complexification. A popular example is the evolution of eukaryotic, nucleated cells by the symbiotic, mutual assembly of specific microbes. Another is the webby nodes and links of neural, protein or animal networks. A further result or attribute of these self-organizing systems, as they spawn bounded, semantically meaningful, organic wholes, is to repeat the scale-invariant course all over again, which builds up and evolves nested hierarchies of viable sentience. After the glossary, a typical historical course the complexity sciences have taken will be scanned.

Agent-Based Modeling “An agent-based model (ABM) is a class of simulations for the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming.” (Wikipedia) A tendency carried over from reductive particle or object analysis has been to place more weight on the entity or element component of a CS. A basic, accessible text would be Agent-Based Models by Nigel Gilbert (Sage, 2008).

Artificial Life “While processes of self-organization, reproduction, learning, adaptation and evolution are in nature confined to the biological sphere, they can be duplicated in principle in computer simulations.” A scheme that researches via high speed computations how digital and analog equations are able to graphically simulate and model life from the bottom-up. Since organic forms do follow from genomic programs, it became rather easy to do. Founded in the late 1980s by Christopher Langton and others, it has grown worldwide through Artificial Life conferences, often published by MIT Press. A journal by the same name is edited by Mark Bedau, on its website can be downloaded the full text of the ALife XI proceedings.

Autopoiesis A neologism coined by Chilean biologists Humberto Maturana, and Francisco Varela in the 1970s to emphasize how living systems are “self-making” as they constantly strive to maintain a bounded viability by referring to and reapplying the internal descriptive processes that originally constituted them. In this view, all life from an original ur-protocell to multicellular organisms are most characterized by their propensity to form and maintain closed entities with their own included meaning. An accessible book by these scholars is Tree of Knowledge (1992). Principles of Biological Autonomy (1979) is an earlier technical work, while The Enactive Mind by Varela, Evan Thompson, and Eleanor Rosch takes the theory to cognitive domains. A prime expositor has been Thompson, especially in his luminous 2007 Mind and Life. The French scholar Nicholas Luhmann has employed it societal realms, and microbiologist Lynn Margulis advocates its use to understand microbial systems.

Biosemiotics “Biosemiotics is an interdisciplinary research agenda investigating the myriad forms of communication and signification found in and between living systems. It is thus the study of representation, meaning, sense, and the biological significance of codes and sign processes, from genetic code sequences to intercellular signaling processes to animal display behavior to human semiotic artifacts such as language and abstract symbolic thought.” (International Society for Biosemiotic Studies) Inspired by logician Charles Peirce (1839-1914), a founder was Thomas Sebeok (1920-2001), and principals now include Marcelo Barbieri, Jesper Hoffmeyer and Terrence Deacon. At the website for the above Society can be found much info, and news about its annual conferences, with paper abstracts. This persuasion falls within the broad information-computation turn that finds and places an increasing significance on a textual, storied, animate nature.

Cellular Automata Wikipedia has an extensive, but technical posting for this computation driven approach. The term simply means the use of graphic “cells” or boxes to which one of two values are typically ascribed. As various software programs run they spin out highly intricate forms, some of which look a lot like organisms. A famous initiator and theorist is Stephen Wolfram as per his 2002 tome A New Science of Life. CA has found utility, for example, to model urban neighborhoods and industrial production lines.

Chaos Theory “A field of study in mathematics, physics, economics and philosophy to observe the behavior of dynamical systems that are highly sensitive to initial conditions, often referred to as the butterfly effect. Small differences in initial conditions yield widely diverging outcomes for chaotic systems, rendering long-term prediction generally not possible.” This misnomer caught on and has stuck since the 1980s from early inklings that apparently chaotic behavior actually exhibits mathematical regularities. Also the Chaos title of James Gleick’s popular 1987 book that introduced the field, the likeness has tended to slant and give the endeavor a negative name.

Complex Adaptive Systems Introduced above, for more entry a video tutorial “The Discipline of Adaptive Complex Systems,” presented by SFI scientist Doyne Farmer on July 22, 2009, can be accessed at http://www.santafe.edu/research/videos/play/?id=f20f248f-6de0-4099-b0af-31c962afe6f0. After listing several versions as cited here, Farmer agrees that their awkward terminologies do get in the way of a common distillation. Again CAS are distinguished by many semi-autonomous parts or entities engaged in relational interaction, via communication by tacit rules or norms, from which emerges novel, feasible, organizations. The presence of this complementarity might even be taken to infer masculine and feminine principles. Farmer admits that getting the right definitions, and of SFI’s own mission going forward, is a major task, but the endeavor is still seen as more of a technical tool than a new window and portal upon a genesis nature.

Computational Information “In physics and cosmology, digital physics is a collection of theoretical perspectives based on the premise that the universe is describable by information, and therefore computable.” “Pancomputationalism is a view that the universe is a huge computational machine or rather a network of computational processes which following fundamental physical laws compute (dynamically develop) its own next state from the current one.” (Wikipedia)

All these areas involve hyperfast computers, so often slip into such as emphasis. A resultant philosophy, somewhat in between machine and organism, is a Digital Universe hypothesis per physicists Edward Fredkin and David Deutsch. A popular embellishment is Seth Lloyd’s Programming the Universe (Vintage, 2007). In its purview, software algorithms iteratively and recursively run so as to give rise to increasingly complex, material ‘hardware’ forms. A new work Information and Computation, edited by Gordana Dodig-Crnkovic and Mark Burgin, due January 2011 from World Scientific, carries this on by seeking historical roots and interfaces with self-organization theory. Albeit an advance beyond a mechanistic excess which can appreciate the role of information processing, to allow a brush with cognition and content, it remains a machine-like abstraction, and has been adopted by postmodernists to further confirm pluralism alone.

Developmental Systems Theory “Developmental systems theory (DST) is a collection of models of biological development and evolution that argue that the emphasis the modern evolutionary synthesis places on genes and natural selection as explanation of living structures and processes is inadequate. DST embraces a range of positions, from a need to include more explanatory reasons than genes and natural selection, to the view that evolutionary theory profoundly misconceives the nature of living processes.”

A main formulator is Susan Oyama, as per her Cycles of Contingency (MIT, 2001). Other phases in its inclusive scope would reach back to 1890s biologist James Baldwin and GSTs Ludwig von Bertalanffy, on to David Depew and Bruce Weber’s 1996 Darwinism Evolving, and lately the writings of Lenny Moss, Brian Goodwin, Eva Jablonka, many others, and to Stuart Kauffman’s corpus. In general, the approach is trying to get itself around the real presence of active epigenetic interrelations.

Dissipative Structures A term associated with nonequilibrium thermodynamics with regard to open systems that receive, process to maintain and foster viability, and throw off or dissipate energy, matter and information. A closed system, by contrast, has no such outside inputs, and is a inappropriate standard carried over from studies of inorganic, equilibrium phenomena. Next is an edited example with quote among many from the A Thermodynamics of Life section.

Attard, Phil. “The Second Law of Nonequilibrium Thermodynamics.” Advances in Chemical Physics. (140/1, 2008). A lengthy treatise by the University of Sydney physicist which avers that dynamic living systems which are by definition open to energy and information flows are so special that they merit an equivalent “second law” to counter Boltzman’s that can aptly convey their progressive increase of complex, sentient order.

“The philosophical and conceptual ramifications of the non-equilibrium Second Law are very deep. Whereas the equilibrium Second Law of Thermodynamics implies that order decreases over time, the non-equilibrium Second Law of Thermodynamics explains how it is possible that order can be induced and how it can increase over time. The question is of course of some relevance to the creation and evolution of life, society, and the environment.” (83)

Dynamical Systems Theory Another DST, this time an employ of complexity phenomena tailored over two decades by Indiana University psychologists Esther Thelen, Linda Smith, and others, for the study of all phases of the kinetic, behavioral, linguistic and cognitive maturation of infants and children. The approach has caught on and is now used by many practitioners such as Scott Kelso, Alan Fogel, Marc Lewis, Kathleen Nelson, and in the Netherlands, Paul van Geert. For a large literature that goes with it, please review Somatic and Behavioral Development.

Econophysics An interdisciplinary research field that combines statistical, many-body physics with nonlinear dynamics to provide novel solutions for complicated economics issues and policies. Its use for the study of stock markets has been termed ‘statistical finance.’ A main founder is Boston University systems theorist Eugene Stanley. From tentative beginnings in the 1990s, the field has grown and taken hold worldwide, with much utility and variations. The technical journal Physica A: Statistical Physics, and others, now contain many such papers as another aspect of the welling meld of physics phenomena and complex systems science.

Ecosystems “An ecosystem consists of all the organisms living in a particular area, as well as all the nonliving, physical components of the environment with which the organisms interact, such as air, soil, water, and sunlight. The entire array of organisms inhabiting a particular ecosystem is called a community. An ecosystem is a functional unit consisting of living things in a given area, non-living chemical and physical factors of their environment, linked together through nutrient cycle and energy flow., often in the form of food webs.”

We include this familiar field to note that before other domains, because of its “tangled bank” intricacy, multi-faceted nature from soil and microbe to ungulates and condors was tabbed a “system.” Accordingly a major shift has occurred from an early equilibrium “balance of nature” to now view environments as icons of dynamic complexity, to an extent their health can be measured by how well they are poised far-from-equilibrium. A main advocate is Princeton ecologist Simon Levin who closes his article “The Evolution of Ecology” in The Chronicle Review for August 13, 2010 with these lines:

“Ecology views biological systems as wholes, not as independent parts, while seeking to elucidate how the wholes emerge from and affect the parts. Increasingly, such a holistic perspective, rechristened at places like the Santa Fe Institute as "the theory of complex adaptive systems," has informed understanding and improved management of economic and financial systems, social systems, complex materials, and even physiology and medicine. Essentially, that means little more than taking an ecological approach to such systems.” (13)

Emergence “In philosophy, systems theory, science, and art, emergence is the way complex systems and patterns arise out of a multiplicity of relatively simple interactions. Emergence is central to the theories of integrative levels and of complex systems.” On Wikipedia, Emergence is paired with “Spontaneous Order” to wit: “The formation of order out of seeming chaos; of various kinds of social order from a combination of self-interested individuals who are not intentionally trying to create order. The evolution of life on Earth, language, and a free market economy are all examples of spontaneous emergence.” This abstract term for regnant, cognizant life is often posed as a positive alternative to a mechanical, reductive disassembly of nature. A prime expositor is theologian Philip Clayton, as conveyed in his Mind and Emergence: From Quantum to Consciousness (Oxford, 2004).

Fractal Geometry A fractal is "…a rough or fragmented geometric shape that can be split into parts, each of which is (at least approximately) a reduced-size copy of the whole.” The term was coined by Benoît Mandelbrot from the Latin fractus meaning "broken" or "fractured." A mathematical fractal, often displayed in spectacular graphic array, results from a software equation or algorithm that is recursively run again and again. A fractal shape is too irregular to be described by planar Euclidean geometry, and has a ‘self-similar’ affinity across its myriad nested dimensions. (I once asked Mandelbrot whether the word ‘hierarchy’ would apply and he said it would not.) Natural objects approximated by fractals are clouds, mountain ranges, lightning bolts, coastlines, snow flakes, flora like cauliflower and broccoli, and animal coloration patterns, that is about everything. Capable of infinitely variegated and stunning appearances, fractals have become an iconic image for creative complexity.

General Systems Theory “A trans-disciplinary approach that abstracts and considers a system as a set of independent and interacting parts. The main goal is to study general principles of system functioning to be applied to all types of systems in all fields of research. As a technical and general academic area of study it predominantly refers to the science of systems that resulted from (Ludwig) Bertalanffy's General System Theory (GST), among others, in initiating what became a project of systems research and practice.”

From the 1950s into the 1980s, along with its cousin cybernetics, this was the main school in prescient search for holistic integrations for an obviously organismic nature. Von Bertalanffy wrote a 1968 book by this title, while a magisterial opus is Living Systems by James G. Miller (McGraw Hill, 1976). An International Society for the Systems Sciences is online at http://isss.org/world, where paper abstracts for annual conferences can be found.

Hierarchy Theory As a sample, this text is by systems biologist Stan Salthe, from the SEE: Semiosis, Evolution Energy website. “Hierarchies are ordinations, as from smaller to larger, or from simpler to more complex. In science-related discourses there have been two forms of hierarchy theory, one based on scale (extension) and another on descriptive complexity (intension). This delivers two models of the material world; the scalar hierarchy represents extensional complexity (defined as the situation where dynamics of different scale contextualize each other at one locale, resulting in non- linear dynamics), while the specification hierarchy represents intensional complexity (defined as the situation where more than a single discourse can be brought to bear upon a phenomenon).

The scalar hierarchy represents subsystems (or components) nested within supersystems (or wholes). The specification hierarchy too has a synchronic interpretation, as when we overlay a physical description of an organism on a chemical description. Each of these represents a different integrative level. Higher levels here transitively integrate dynamics and phenomena at lower levels, as when biology selects or harnesses a few chemical pathways, or a few physical processes, from the many possible to interact at the biological level. The specification hierarchy is the core of the philosophy of nature as received from the Nineteenth Century.”

Multi-Agent Systems “A multi-agent system is composed of multiple interacting intelligent agents. MAS can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve.” Another phrasing of a generic CAS, akin to ABS above, that may be variously adopted by a discipline, e.g. as Applied Anthropology used to model the spread of crack use in Baltimore. The phrase gives the impression of still another technique, which it is not. Search “multi-agent” on Natural Genesis for some sources and examples.

Neural Networks Brain development and cognitive function has been a major area of CS application for both cerebral phases have been found to be premier exemplars of self-organization. In 1982 CalTech’s John Hopfield first proposed the theory to explain how neurons are entrained by way of synapses and axons into myriad, active webworks. As we think, neural nets ever change their ‘weights,’ emphasis, or presence based on the content they receive and convey. As documented in Systems Neuroscience, aided by computer neuro-imaging, the field is being transformed by such discoveries, with a promise to reveal an archetypal microcosm in our heads. A computational version called artificial neural networks or ANN has found use in simulating ecosystems and many other areas.

Nonequilibrium Thermodynamics In this case we use an edited introduction to A Thermodynamics of Life on this site. “The sterile, mechanical universe of 19th century theories was conceived as a closed, isolated system tending to equilibrium. As predicted by the second law of thermodynamics, it inexorably expires as available energy is spent and converted to entropy. But a recent revision is underway by which life has become known as an open system infused and organized by a flow of energy and information. The gloomy fate has been superseded by far-from-equilibrium version which can describe and qualify the florescent rise of life and its human phase. An effort to articulate a “fourth law of thermodynamics” counter to the second is now much in progress. Altogether these efforts presage a real source and explanation for and impetus to biological and cultural evolution, which can reunite organic beings with a conducive cosmos.”

This scientific genre of the past decades is a critical contribution to explaining a genesis universe spontaneously made to develop, quicken, and become increasingly alive. Whereas closed systems constitute a determinism, open organizations are said to epitomize a non-deterministic reality. The Russian- American physical chemist Ilya Prigogine (1917-2003) received the 1975 Nobel prize in chemistry for the achievement. Today a worldwide field of study, it remains one of the “non-abstractions” because the shift to a cosmic embryogeny has not yet occurred. (See also Dissipative Structures above)

Nonlinear Phenomena “Nonlinear” has been a generic banner for complexity studies, in contrast to earlier, mainly physical sciences that understandably dealt with simpler “linear” cause and effect. But complex self-organization is not straight-forward, is affected by small influences at its outset, (e.g., the so-called butterfly effect where the flapping of insect wings can impact the weather), so that resultant forms and activities can be locally unpredictable. However, as Statistical Physics avers below, large-scale patterns tend to average out and are often quite reliable in outcome.

For a sample brush with its esoteric technicalities, one might visit websites at the Center for Nonlinear Dynamics at the University of Texas at Austin at http://chaos.utexas.edu, or the Center for Nonlinear Studies at Los Alamos National Laboratory, http://cnls.lanl.gov/External.

Renormalization Group Theory “In theoretical physics, the renormalization group (RG) refers to a mathematical apparatus that allows one to investigate the changes of a physical system as one views it at different distance scales. A change in scale is called a "scale transformation" or "conformal transformation." Renormalization group is formally related to "scale invariance," a symmetry that appears the same at all scales (so-called self-similarity).” We cite this term, first used by the 1975 Nobel physics laureate Kenneth Wilson, because it occurs in some CS papers, but also since it is said to be so awkward as to get in the way of its own understanding and use. So a task of translation across the sciences would quite avail and facilitate.

Scale-Free Networks Simpler object interconnections such as telephones are equal point to point, or ‘random graphs,’ with no characteristic geometry. In the late 1990s it was realized by Notre Dame physicist Albert-Laszlo Barabasi and colleagues that nonlinear nature and society was actually graced by a distinctive type of arrangement whereof certain nodes or hubs had many links, while most other had only a few. Barabasi said he came to this by looking at a map of urban airports and routes on a flight. This insight has since grown into a major field of complex system research, and is seen as a salient attribute. In many cases a node is itself a network, such nested cascades are said to exhibit a “power law” feature. As a result, the same scale or form is repeated in self-similar fashion over and over, e.g., in food webs, brains or social media. The Internet is a prime example, which might imply cerebral propensities. A popular introduction by this title appeared in the May 2003 issue of Scientific American by Barabasi and Eric Bonabeau, a founder of ‘Swarm Intelligence.’

Scale Invariance Another common phrase to denote the ubiquitous CS property of repeating themselves again and again, often in a layered emergence of the same animate pattern and process. “Self-similarity” is also used to describe this recurrence. Its iconic representation would be geometric fractals, whose infinite repetition arises from iterations of mathematical, software-like equations. Such a constant recapitulation across every natural and social realm from galaxies to Gaia could be taken to infer the presence of an innate, implicate, cosmic genetic-like source.

“In mathematics, a self-similar object is exactly or approximately similar to a part of itself (i.e. the whole has the same shape as one or more of the parts). Many objects in the real world, such as coastlines, are statistically self-similar: parts of them show the same statistical properties at many scales. Self-similarity is a typical property of fractals. Scale invariance is an exact form of self-similarity where at any magnification there is a smaller piece of the object that is similar to the whole.” (Wikipedia)

Self-Organized Criticality A concept mainly from the late Danish scientist Per Bak, well told in his How Nature Works (Springer, 1996), to explain how so much natural phenomena resides in a dynamic, and creative, state or zone poised between order and chaos. The phrase and theory is now widely used from origin of life studies to brain cerebration. As a comment, we include this quote from Complexity and Criticality by Kim Christensen and Nicholas Moloney, which again notes a need to get clear, common definitions.

“The word `complexity' takes on a variety of meanings depending on the context, and its official definition is continuously being revised. This is because complexity is a rapidly developing field at the forefront of mathematics, physics, geophysics, economics and biology, to name just a few sciences. And yet, nobody agrees on a clear and concise theoretical formalism with which to study complexity. The danger is therefore that complexity research may become unstructured or even misleading. For our purposes, complexity refers to the repeated application of simple rules in systems with many degrees of freedom that gives rise to emergent behaviour not encoded in the rules. Self-organized criticality is a cooperative feature emerging from the repeated application of the microscopic laws of a system of interacting parts.”

Self-Organization “The process where a structure or pattern appears in a system without a central authority or external direction imposing it. This globally coherent pattern appears from the local interaction of the elements that makes up the system, thus the organization is achieved in a way that is parallel (all elements act at the same time) and distributed (no element is a coordinator). The most robust and unambiguous examples of self-organizing systems are from the physics of non-equilibrium processes. Self-organization is also relevant in chemistry, where it has often been taken as being synonymous with self-assembly. The concept of self-organization is central to the description of biological systems, from the subcellular to the ecosystem level.” (Wikipedia)

“Self-organization is the spontaneous often seemingly purposeful formation of spatial, temporal, spatio-temporal structures or functions in systems composed of few or many components. In physics, chemistry and biology self-organization occurs in open systems driven away from thermal equilibrium. The process of self-organization can be found in many other fields also, such as economy, sociology, medicine, technology.” (Scholarpedia)

As opposed to any mechanical or architectural artifact, which are made by outside, centralized control, per preset plans or instructions, an iconic property of complex systems is their ability to arrange into increasingly multifaceted forms and viability. A profound articulator has been Stuart Kauffman, in his brilliant writings over some 40 years.

Small-World Networks “In mathematics, physics and sociology a small-world network is a type of mathematical graph in which most nodes are not neighbors of one another, but most nodes can be reached from every other by a small number of hops or steps. A small world network, where nodes represent people and edges connect people that know each other, captures the small world phenomenon of strangers being linked by a mutual acquaintance. Social networks, the connectivity of the Internet, and gene networks all exhibit small-world network characteristics.”

Circa 1998 Duncan Watts and Stephen Strogatz came up with this mathematical model and name as another perspective on and attribute of scale-free networks, based on how close or remote “nodes” are to each other. The name is from the “six degrees of freedom” cultural claim that every person over the whole earth is hardly more removed from each other than by six acquaintances.

Statistical Physics “A condensed matter or many-body physics that endeavors to explain and predict the macroscopic properties and behavior of a system on the basis of known characteristics and interactions of its microscopic constituents, usually when the number of such constituents is very large. Variously akin to “statistical mechanics” and “statistical thermodynamics.” In the past few years, the field has branched into CS realms because scientists realized that both studied the spontaneous activity of many agents in relational behaviors. An entry example of this merger might be the Centre for Statistical Mechanics and Complexity, University of Rome, website (Google).

Swarm Intelligence An artificial intelligence scheme that deals with natural and societal systems composed of many interactive ‘individuals’ that coordinate using distributed control, which engenders self-organization. In particular, the discipline focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. Examples are colonies of ants and termites, schools of fish, flocks of birds, herds of land animals, along with some artifacts as robot motions. Several books with this title give more technical detail, which stands as another excursion and apply of the generic CAS.

Symbiosis “From the Greek: σύν syn "with"; and βίωσις biosis "living," a broadly used description of close and often long-term interactions between different biological species. The term was first used in 1879 by the German mycologist Heinrich Anton de Bary, who defined it as "the living together of unlike organisms." The symbiotic relationship may be mutualistic, commensal, or parasitic in nature. Some are obligate, meaning that both symbionts entirely depend on each other for survival, such as lichens which consist of a fungal and plant symbiont that cannot live on their own. Symbiotic relationships include those associations in which one organism lives on another (ectosymbiosis, such as mistletoe), or where one partner lives inside the other (endosymbiosis, such as lactobacilli and other bacteria in humans or zooxanthelles in corals).”

This word has gained significance from the tireless work of biologist Lynn Margulis who discovered and proved that eukaryotic, nucleated cells evolved from and are distinguished by a many-faceted, mutually beneficial symbiosis of simpler, prokaryotic bacteria. Each mitochondria, spirochete, undulipodia, and so on bring a quality such as digestion or mobility. Search Natural Genesis for some writings. She is currently helping to publish the work of Russian biologists from earlier in the 20th century who presciently championed such a “symbiogenesis.”

Synergetics Still another interdisciplinary approach to explain the formation and self-organization of patterns and structures in open systems far from thermodynamic equilibrium. It was founded and fostered from the 1970s by Hermann Haken, a University of Stuttgart laser physicist. In its idiom, self-organization requires a 'macroscopic' system, consisting of many nonlinearly interacting subsystems. A Springer series in Synergetics has published dozens of technical volumes since 1977. A prime advocate and advancer is J. A. Scott Kelso of Florida Atlantic University who has effectively applied it to dynamic systems in neuroscience.

Synergy “Synergy, in general, may be defined as two or more agents working together to produce a result not obtainable by any of the agents independently.” We mention as another abstraction in the mix, most used by systems guru Peter Corning, in accessible works such as Nature’s Magic: Synergy in Evolution and the Fate of Humankind (2003) and Holistic Darwinism (2005) that convey innate propensities for cooperation. Symbiosis would be another take. A cousin is Synchrony per mathematician Stephen Strogatz in his Sync: The Emerging Science of Spontaneous Order (2003).

Universality “In statistical mechanics, universality is the observation that there are properties for a large class of systems that are independent of the dynamical details of the system.” A word used in different ways across the sciences and humanities, which for complexity science stands for the same dynamical system in evidence from galaxies to genomes to Gaia. Its archetypal attributes would be many interactive agents, scale-free networks, and nested self-organization. This is the grand goal and prize over the decades. If such a ubiquitous repetition can be finally and succinctly articulated, it infers both an independent, creative source, and illumes, as wisdom teaches, nature’s manifest, illustrative repetition.

This tower of fabel, whereof each vernacular phrase may express an organic essence and development, begs translation unto a familiar, meaningful human and earth account. Historically, this “new dialogue with nature” commenced in the 1960s with the general systems theory school. Ilya Prigogine and colleagues’ non-equilibrium thermodynamics went on from the 1970s to counter the second law entropic fate and reveal a creative universe somehow actually winding itself up. A prescient 1980 work, The Self-Organizing Universe by Erich Jantsch, sought to convey its luminous promise. Another milestone was Mitchell Feigenbaum’s 1982 finding that apparent chaos, such as turbulent fluid flow, in fact exhibited mathematical regularities. A consolidation occurred in 1987 with James Gleick’s Chaos: Making a New Science, which brought the endeavor to popular awareness

As the usual course, the first blush of commonalities and easy answers soon wore off. The effort diversified and morphed into technical and personal preferences and specialized detail, as listed above, facilitated by vast computer capacities not available earlier. Through the 1990s and growing in intensity from 2000, its worldwide scientific engagement can be seen to emphasize two main aspects.

One by one, the scientific study of natural and societal realms from cosmos to civilization, from astrophysics and genomics to psychology and economics, found these new approaches to bring heretofore elusive theoretical explanations, and went on to reinterpret their fields by such complexity concepts. Each domain from galactic clusters to life’s origin, animal behaviors, disease epidemics, conversational speech, and so on could be seen to exemplify a complex adaptive system of many interactive agents or interlinked nodes. Whether prairie ecology or financial market, protein web or neural architecture, each exhibited the dynamic, biological structure of a nested, repetitive, emergent self-organization.

As a consequence of these discoveries, into the 2000s a second area arose in the pages of Physical Review Letters, European Physical Journal B, and Physica A: Statistical Physics and other journals. Surely such overt, invariant evidence of a profoundly intelligible nature after all, often as an “explicate” order per David Bohm, would seem to imply a deep “implicate” source from which they must spring. In regard, this origin would be innately independent and eternal in kind, so as to generate a “universality” of the same form and fluidity everywhere. Here is newly broached, some four centuries later, a sense of Galileo’s numinous mathematics of nature’s testament.

Circa 2006, writings on nonlinear systems began to cite and include both modes of a dynamical, recurrent complexity so obvious that it begged an inferred cause from which to arise. Examples are Self-Organization in Complex Ecosystems by Jordi Bascompte and Richard Sole (Princeton, 2006), and Marten Scheffer’s Critical Transition in Nature and Society (Princeton, 2009). For one instance, a 2008 paper Collective Behavior in Animal Groups: Theoretical Models and Empirical Studies by Irene Giardina and associates at the University of Rome (search) contends that since starling flocks or tuna schools, and all animal communities, are so graced by a reciprocity of autonomous members in beneficial group formations, it clearly infers a generic physical origin.

In the same period, scientists began to realize that the separate fields of complexity studies and statistical physics were actually were studying the same phenomena albeit with different approaches and terminologies. A chapter “Complexity, Collective Effects, and Modeling of Ecosystems” in Ecological Complexity and Sustainability (Annals of the New York of Sciences, Vol. 1195) by Imperial College London mathematicians Henrik Jensen and Elsa Arcaute calls for an integrative cross-fertilization of statistical mechanics and complexity science. But what is not yet seen and this site tries to document, more than a novel methodology is involved, rather a new kind of self-creative universe with dual domains of emergent phenotype development and immanent genetic-like source, regnant organic complexity and informed, stirring consciousness, is just now being discovered.

This next section is the original 2002 introduction.

In the 1980’s and 1990’s 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. Harold Morowitz, Jerome Singer

As noted in Part II, An Anthropocosmic Code, at the heart of religious wisdom is a structural scale by which to relate earth and heaven, human and Divine. The same image recurs at each stage because everything arises from and epitomizes a singular, gender complementary source. The great project, then as now, is to realize that the world is distinguished and revealed by this innate property, that it comes with a code. A familiar icon is Yin and Yang, whose feminine and masculine principles animate a numinous creation.

This portal became lost and forgotten to the mechanical phase of science, which reduced nature to a bottom level arbiter. But within a humankind compass, the recognition of a multi-level, iterative progression is being achieved once again, this time with the temporal expanse of an evolutionary genesis. What can move our current multiverse from unfathomable mystery to salutary understanding is to rediscover it is made to be knowable by this feature. Its latest version is facilitated via Rosetta-like translations, by the sciences of nonlinear, self-organizing complexity. These various theoretical and empirical advances are tabulated next, whose arcane terms are also in the Glossary. When gathered altogether, a “universality” can be distilled whereby an independent activity known as a “complex adaptive system” appears in manifest evidence from quarks to genes, persons, civilizations and galaxies.

Nonequilibrium Thermodynamics. A theory of energy and information flow, usage, bifurcation and dissipation for open living systems. (Ilya Prigogine, et al)

Fractal Geometry. Nature is characterized by the same shapes and topologies with fractional dimensions at every scale. (Benoit Mandelbrot)

Complex Adaptive Systems. Many agents (neurons, people) in constant local interaction, guided by a few algorithmic rules or agreed norms give rise to an emergent fluid order. (John Holland, Murray Gell-Mann)

Self-Organization. As these systems proceed without centralized direction or set program, they arrange themselves into a nested scale of whole entities. (Stuart Kauffman, et al)

Universality. The same self-organized complex adaptive dynamics and network structures are found throughout nature from cosmos to civilization. (Eugene Stanley, Mark Buchanan)

Modularity. The tendency of complex systems in evolution and development to form modular, symbiotic components and processes from genes to societies. (Herbert Simon, Gunter Wagner)

Autopoiesis. A property of these bounded systems is that maintain themselves by referring to their own internal description. (Humberto Maturana, Francisco Varela)

Self-Organized Criticality. Whereby complex dynamic systems tend to become poised at the edge of order and chaos. (Per Bak)

Scale-Free Networks. Elemental nodes or hubs interconnected in similar ways across hierarchical levels from cellular metabolism to ecosystems and the Internet. (Albert-Laszlo Barabasi, Duncan Watts)

Synergetics. A more physically based theory of a universal self-organization. (Herman Haken, Scott Kelso)

Artificial Life. Digital computer simulation of molecular, genetic, organic, social and economic societies and their evolution. (Chris Langton, Chris Adami, et al)

Cellular Automata. A computational process based on simple, algorithmic rules which generates a repetitive self-assembly and complex emergent order. (Stephen Wolfram, Andrew Ilachinski)

Hierarchy Theory. Evolving organisms and ecosystems consistently deploy into a scalar sequence and arrangement. (Stan Salthe, Niles Eldredge)

Neural Networks. The brain is distinguished by multi-connected networks of neurons, synapses and axons in constant flux due to weighted inputs and experience. Also applied as Artificial Neural Networks (ANN) to many other areas. (John Hopfield, Stephen Grossberg, et al)

Connectionism. A cognitive science theory of how neurons compute and handle cerebral information, variously known as parallel distributed processing or ANN. (David Rumelhart, et al)

Synchronicity. Phenomena from electrons and fireflies to planetary orbits synchronize in unison, which gives rise to a spontaneous order. (Steven Strogatz)

Synergy. Cooperative combinations bring selective advantages for organisms, which goes on to spawn an increasing complexity. (Peter Corning)

Living Systems Theory. An earlier insight whence some twenty metabolic, anatomical, cognitive and social features repeat in threaded-out layers from cells to global civilization. (James G. Miller)

General Systems Theory. The pioneer witness of a dynamic natural and social realms most characterized by holistic interconnections. (Ludwig von Bertalanffy, Ervin Laszlo)

As a preview, a good illustration is science itself. For example, research on the origin of life defines an exploratory niche or landscape to be inhabited and adapted to. At the outset, separate investigators study aspects such as early atmospheres or biomolecules. As these efforts increase in number, individual argument and dispute eventually proceeds toward the advantages of relational collaboration. Experiment, theory and published papers are guided by standard protocol. The subject field is filled as researchers check what colleagues have done or are doing when embarking on projects. Regional and international meetings expand the interchange. Finally an integral working explanation is stated. But the whole accomplishment occurs and organizes itself without a managed direction.

In the old insensate, material universe, life is a contingent tangent and natural selection the only formative influence. But as these many contributions attest, something far more is going on. Prior to selective forces, the independent dynamic, complementary system noted above is in persistent, creative effect. As explored next in Part V, A Quickening Evolution, a central progression or axis is defined by its sequential nested scale of molecules, bacteria, cells, organisms and societies, which is the typical structure of an emergent, self-organized, cognitive system. An organic universe thus seems to come with a genetic-like program.

A pertinent observation is that few generic components or attributes distinguish the ubiquitous system. Whether bacterial assemblies, financial investors or a scientific endeavor as we just saw, many autonomous entities interact through constant communication with and in response to their neighbors and the environment, guided by common rules and norms, from which arises a ‘higher’ whole of organization. A reciprocal interplay of free agents and local interactions, nodes and links in network terms, can be indentified in each case. In this guise they can be seen to form complements of particulate and holistic, discrete and systematic, dot and connect phases. And all this proceeds unbeknownst to those members who are creating it.

As this activity goes through its cycle or spiral of self-emergence, a standard sequence is also traced. In its initial stages, units or elements (microbes, neurons, organisms, researchers) propagate and compete. As densities increase, a modular specialization and division of labor occurs. Constant dialogue occurs whether by chemicals, electric potentials, behaviors or language. Over time cooperation succeeds over competition. Specific modules aid their survival by merging into bounded ‘cellular’ units. At a threshold of viable coherence, a new level or sphere of relative individuality is achieved. A good example is the formation of nucleated eukaryotic cells by the symbiotic union of diverse prokaryotic microbes.

An attempt or leap might now be made to align this phenomena with human qualities. Its mode of many independent actors seems to exemplify a masculine principle, while relational interaction is more feminine in kind. Surely this is exploratory, please refer to sections such as Part VI, The Bicameral Brain and Gender Complementarity for additional references. The realm of tacit information or canons can take on a genetic aspect. Most notably, a consequence of such a finding would be to identify a cosmic feminine principle equivalent and complementary to the masculine, a repair we so desparately need.

To continue a translation into human terms, it is suggested that the generative, self-organizing round matches the archetypal trajectory of psychic individuation, a theme engaged in Part VI, Integral Persons and Macrohistory as Psychic Individuation. Disparate components, such as facets of personality, initially vie and compete as they struggle toward a mutually beneficial accord. Gender phases of anima and animus separate, conflict and ultimately integrate. At a sufficient coherency, a unique person achieves their own self-recognition and acceptance. In medieval times, an adult man stood as exemplar, today by our humankind vista, ones entire life course may serve as its dynamic image, an important difference and expansion.

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A. Natural Algorithms

As Biosemiotics in the next chapter, our hope is to notice trends, shifting paradigms, new approaches, so as to gather and document. This Cosmic Code reports an array of scientific encounters with the presence of an intrinsic material spontaneity as a dynamic self-organization of complexity and cognition. From our humankind vista, one can lately perceive in evolutionary research and papers a growing admittance that some such mathematical, program-like agency seems in effect. As Systems Evolution next conveys, there are increasing recognitions that selection alone is inadequate, there must be a prior source at generative work. These citations record various interpretations by way of a general computational and algorithmic method.

As Christos Papadimitriou (2014) proposes, nature has two broad categories of resultant animate complexity, and a mathematical milieu from which it arises. Another leading advocate is Bernard Chazelle who goes on to draw connections between an algorithmic realm and animate self-organizations such as aerial bird flocks. Here is a case where different schools can be joined because they express the same phenomenal finding. A book length study Evolution as Computation (2013) by John Mayfield joins a chorus who view life’s sequential procession due to operational programs and resultant iterative optimizations. The work remains to translate all these intimations into a common literacy, witness and discovery.

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B. Mid 2010s Universality Affirmations

This is a new 2015 section to report scientific confirmations of a natural evolutionary developmental genesis from universe to us that in fact repeats and iterates the same self-organizing, complex adaptive network system patterns and processes at every exemplary scale and instance. As we track the prolific literature, one finds an increased use and claim of such a “universality,” which then strongly implies an independent, complementary mathematical source code.

By late 2016, another aspect and sign is the interchangeable employ of (artificial) neural networks, aka deep learning, along with genetic sequencing techniques, to study genomes and brains (neurome). Further afield, deep learning algorithms, self-organizing maps, astroinformatics, statistical physics, phylogenetic trees and more, are being availed to better quantify areas from cosmology, chemistry to ecosystems and cultures.

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C. Network Physics: A Cosmic Connectome

The first title phrase can serve to distinguish this 21st century scientific endeavor from a long, prior, necessary particle emphasis. It reports the elucidation of nature’s scale-free mode of dynamic interconnections everywhere, originally much due, circa 2000, to Albert-Laszlo Barabasi and Reka Albert, then University of Notre Dame physicists, search each name. In contrast to random graph, Poisson, or Erdos-Renyi networks, such as telephones, natural phenomena from galaxies to genomes, organisms, societies and languages, as these references attest, are connected by “preferential attachments and growth” of nodes and linkages. As such, they contain larger and smaller components or hubs with more or less connections and influence, for example neural nets, animal and human groupings, airports, Internet websites.

The initial model applied so well that it has, with much updating and expansion, been adopted by every scientific field and realm, as ALB notes in a 2012 Nature Physics article, and this section documents. Network Science textbooks by Mark Newman, Shlomo Havlin, Barabasi, and many others show how robust this realization of equally real interactions between parts, objects, elements, entities has become. A ubiquitous tendency is further found to form internal modules, communities, multiplex layers, and so on. In April 2016, one can find on the arXiv e-print site disparate postings such as The Network Behind the Cosmic Web (1604.03236) and Mapping Out Narrative Structures and Dynamics Using Networks (1604.03029), wherein both papers employ these same principles.

In just 15 worldwise years, a general, salutary surmise is lately possible. As a common structural topology and dynamic growth becomes evident at each phase and instance, it well implies an independent, universally exemplified, mathematical source. Moreover, if we might wonder what is being perceived, a novel genesis universe gains a vital anatomy and physiology, whence the archetypal networks from cosmos to brains to culture then become similar to genomes. Thus we add A Cosmic Connectomics as a subtitle.

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