IV. Ecosmomics: Independent, UniVersal, Complex Network Systems and a Genetic Code-Script Source

Goldbeter, Albert. Dissipative Structures in Biological Systems: Bistability, Oscillations, Spatial Patterns and Waves. Philosophical Transactions A. 276/20170376, 2018. As a senior European authority, the Université Libre de Bruxelles theoretical biologist writes a strong endorsement to date of the advancing complexity revolution. In this case it is linked with the thermodynamic contributions of his Nobel chemist colleague Ilya Prigogine. (As noted, I had lunch in a small group with IP in 1987 at a conference).

This review article will discuss the conceptual relevance of dissipative structure for understanding the dynamical bases of non-equilibrium self-organization in biological systems, and to see where it has been applied in the five decades since it was initially proposed by Ilya Prigogine. Dissipative structures can be classified into four types: (i) multistability, in the form of bistability and tristability; (ii) temporal dissipative structures in the form of sustained oscillations; (iii) spatial dissipative structures known as Turing patterns; and (iv) spatio-temporal structures in the form of propagating waves. Rhythms occur with widely different periods at all levels of biological organization, from neural, cardiac and metabolic oscillations to circadian clocks and the cell cycle. (Excerpt)

Goldenfeld, Nigel. There’s Plenty of Room in the Middle: The Unsung Revolution of the Renormalization Group. arXiv:2306.06020. The veteran complexity physicist and author (search) has moved from many years at the University of Illinois to the UC San Diego. He was a main expositor in the 1990’s of from Kenneth Wilson’s 1970’s Nobel version, who collaborated with Fisher. As the quotes say, by the 2020s it has gained a wide acceptance and usage as a nonlinear to explain universe, life and we humans.

The technical contributions of Michael E. Fisher to statistical physics and the renormalization group are widely influential. But less well-known is his early appreciation of how this model advanced how physics -- in fact, all science -- is practiced. In this essay, I attempt to redress this imbalance, with examples from Fisher's writings and my own work. It is my hope that this tribute will help remove some of the confusion that surrounds the scientific usage of minimal models and renormalization group concepts, as well as their limitations, in the ongoing effort to understand emergence in complex systems. This paper will be published in 50 Years of the Renormalization Group, which is dedicated to M. E. Fisher, edited by Amnon Aharony, et al is in press at World Scientific.

Michael Ellis Fisher (1931 – 2021) was an English physicist, as well as chemist and mathematician, known for many contributions to statistical physics such as the theory of phase transitions and critical phenomena. He was a Professor of Chemistry, Physics, and Mathematics at Cornell University and later at the University of Maryland College of Computer, Mathematical, and Natural Sciences.

Renormalization group (RG) refers to a systematic investigation of the changes of a physical system as viewed at different scales. A change in scale is called a scale transformation. The renormalization group is intimately related to scale invariance and conformal invariance, symmetries in which a system appears the same at all scales, as a self-similarity .

Grauwin, Sebastian, et al. Complex Systems Science: Dreams of Universality, Reality of Interdisciplinary. Journal of the American Society for Information Science and Technology. 63/7, 2012. A French bioinformatics team including Eric Fleury and Sara Franceschelli review this fledgling field with regard to its quest for general, independent principles. Akin to my own 2009 survey that introduces this section, it involves a listing of diverse nomenclature by various theorists and schools, such as synergetics, econophysics, fractality, and so on. With many citations for “self-organized criticality, dynamical systems, and complex networks,” the whole endeavor can be mapped by way of network nodes, modules, communities. From this view, an “interdisciplinary” discourse proceeds as “trading zones,” e.g. computational systems biology, between groups and terms so as to distill better commonalities. The authors enlist a “transcriptomics data analysis” (see below) as it applies both to genetics and neuroscience. One is led to note similarities between genomics and maybe a “neuromics” for how collaborative science, as the paper depicts, also appears as a global learning activity. While “universality” here pertains more to its literature usage, its reality is said to remain promising.

Using a large database (~ 215 000 records) of relevant articles, we empirically study the "complex systems" field and its claims to find universal principles applying to systems in general. The study of references shared by the papers allows us to obtain a global point of view on the structure of this highly interdisciplinary field. We show that its overall coherence does not arise from a universal theory but instead from computational techniques and fruitful adaptations of the idea of self-organization to specific systems. We also find that communication between different disciplines goes through specific "trading zones", i.e. sub-communities that create an interface around specific tools (a DNA microchip) or concepts (a network). (Abstract)

The transcriptome is the set of all RNA molecules, including mRNA, rRNA, tRNA, and other non-coding RNA produced in one or a population of cells. It differs from the exome in that it includes only those RNA molecules found in a specified cell population, and usually includes the amount or concentration of each RNA molecule in addition to the molecular identities. The term can be applied to the total set of transcripts in a given organism, or to the specific subset of transcripts present in a particular cell type. Unlike the genome, which is roughly fixed for a given cell line (excluding mutations), the transcriptome can vary with external environmental conditions. (Wikipedia)

Hadzikadic, Mirsad, et al. Complex Adaptive Systems and Game Theory. Complexity. 16/1, 2010. An effort by University of North Carolina and MIT system scientists, as the Abstract next notes, to join these two widely used approaches so as to better explain the universally active, self-organizing phenomena being found to occur across the wealth of social nature.

A Complex Adaptive System is a collection of autonomous, heterogeneous agents, whose behavior is defined with a limited number of rules. A Game Theory is a mathematical construct that assumes a small number of rational players who have a limited number of actions or strategies available to them. The CAS method has the potential to alleviate some of the shortcomings of GT. On the other hand, CAS researchers are always looking for a realistic way to define interactions among agents. GT offers an attractive option for defining the rules of such interactions in a way that is both potentially consistent with observed real-world behavior and subject to mathematical interpretation. This article reports on the results of an effort to build a CAS system that utilizes GT for determining the actions of individual agents.

Hazen, Robert. Emergence and the Origin of Life. Astrobiology. 1/3, 2001. An abstract from the NASA Astrobiology Institute General Meeting, April 2001. The study of life and intelligence on the scale of the universe gives credence to its self-organizing dynamics and their resultant planes of sentient complexity.

The geochemical origin of life may be modeled as a sequence of ‘emergent’ events, each of which adds to molecular complexity and order….Natural systems with many interacting components, such as atoms, molecules, cells or stars, often display complex, ‘emergent’ behavior not associated with their individual components….This observed emergent behavior of highly ordered systems, including galaxies, planets, and life, points to a universal organizing principle. (301)

Helbing, Dirk. Traffic and Related Self-driven Many-particle Systems. Reviews of Modern Physics. 73/4, 2001. A technical survey of how human behavioral patterns such as vehicular and pedestrian traffic have a mathematical basis in terms of nonlinear dynamical theories. This power-law criticality is then seen to similarly apply to biological, economic, and cognitive systems.

Heylighen, Francis, et al, eds. The Evolution of Complexity. Dordrecht: Kluwer Academic, 1999. One of eight volumes from the interdisciplinary Einstein Meets Magritte conference held in Brussels in 1996. Each book is a color of the rainbow plus white. An abstract from the “Violet” book summarizes its theme:

The basic idea is that evolution leads to the spontaneous emergence of systems of higher and higher complexity or ‘intelligence’ from elementary particles, via atoms, molecules, living cells, multicellular organisms, plants and animals to human beings, culture and society. The volume thus wishes to revive the trans-disciplinary tradition of general systems theory by integrating the recently developed insights of the ‘complex adaptive systems’ approach, pioneered among others by the Santa Fe Institute. (xiii)

Holland, John. Emergence. Reading, MA: Addison-Wesley, 1998. Further explorations by the University of Michigan systems theorist with an emphasis on nature's tendency to deploy ascendant scales of complexity.

We are everywhere confronted with emergence in complex adaptive systems - ant colonies, networks of neurons, the immune system, the Internet, and the global economy, to name a few - where the behavior of the whole is much more complex than the behavior of the parts. There are deep questions about the human condition that depend on understanding the emergent properties of such systems. (2)

Holland, John. Hidden Order. Reading, MA: Addison-Wesley, 1995. An introduction to complex adaptive systems by one of its founders. In the author’s technical terms, many autonomous agents engaged in networks of interaction, immersed in an environment, and guided by tacit rules, will give rise to emergent organization and behavior.

Holovatch, Yurij, et al. Complex Systems: Physics beyond Physics. European Journal of Physics. 38/023002, 2017. (arXiv:1610.01002) Physicists Holovatch, National Academy of Sciences of Ukraine, Ralph Kenna, Coventry University, and Stefan Thurner, Medical University of Vienna, report on how much nonlinear complexity phenomena seems in effect across every social, urban, cultural and economic domain.

Complex systems are characterized by specific time-dependent interactions among their many constituents. As a consequence they often manifest rich, non-trivial and unexpected behavior. Examples arise both in the physical and non-physical world. The study of complex systems forms a new interdisciplinary research area that cuts across physics, biology, ecology, economics, sociology, and the humanities. In this paper we review the essence of complex systems from a physicist's point of view, and try to clarify what makes them conceptually different from systems that are traditionally studied in physics. Our goal is to demonstrate how the dynamics of such systems may be conceptualized in quantitative and predictive terms by extending notions from statistical physics and how they can often be captured in a framework of co-evolving multiplex network structures. We mention three areas of complex-systems science that are currently studied extensively, the science of cities, dynamics of societies, and the representation of texts as evolutionary objects. (Abstract)

Hooker, Cliff, ed. Philosophy of Complex Systems. Amsterdam: Elsevier, 2011. Volume 10 in Elsevier’s Handbook of the Philosophy of Science series. As the table of contents excerpt attests, the 1,000 page tome is a significant recognition of nature’s nonlinear creative energies across the span of material, evolutionary, genomic, biological, cognitive, ecological, social, economic, and artifactual domains. Salient chapters by Moreno, et al, Newman, Snyder, et al, Gao and Herfel, Rickles, are reviewed separately. But the corpus, by 34 men and 3 women, remains by habit unable to realize that not only a novel theoretical approach is found, a profound spontaneous, iterative emergence from cosmos to child is being discovered. We should note, for further example, Adam Sheya and Linda B. Smith’s chapter Dynamics of the Process of Development whence the same fluid patterns that grace galaxies are at work in our early years. And several papers, it ought to be noted, allude in closing that if such an insightful natural knowledge is gained, we are invited to imagine a new future “synthetic” creation.

Part I. General Foundations Introduction to Philosophy of Complex Systems. (Cliff Hooker), Systems and Process Metaphysics (Mark H. Bickhard), Computing and Complexity: Networks, Nature and Virtual Worlds (David G. Green and Tania Leishman), Evolutionary Games and the Modelling of Complex Systems (William Harms) General System Theory (Wolfgang Hofkirchner and Matthias Schafranek),
Conceptualising Reduction, Emergence and Self-organisation in Complex Dynamical Systems (Cliff Hooker).

Part II. Biology Complex Biological Mechanisms: Cyclic, Oscillatory, and Autonomous (William Bechtel and Adele Abrahamsen), The Impact of the Paradigm of Complexity on the Foundational Frameworks of Biology and Cognitive Science (Alvaro Moreno, Kepa Ruiz-Mirazo, and Xabier Barandiaran), Complexity in Organismal Evolution (Stuart A. Newman), Part III. Ecology Constructing Post-classical Ecosystems Ecology: The Emerging Dynamic Perspective from Self-organising Complex Adaptive Systems (Yin Gao and William Herfel), Complex Ecological Systems (Jay Odenbaugh)

Part V. Climatology The Complex Dynamics of the Climate System: Constraints on our Knowledge, Policy Implications and the Necessity of Systems Thinking (Carolyn W. Snyder, Michael D. Mastrandrea, and Stephen H. Schneider). Part VII. Anthropology Complexity and Anthropology (J. Stephen Lansing and Sean S. Downey). Part VIII. Psychology Dynamics of the Process of Development (Adam Sheya and Linda B. Smith), Living in the Pink: Intentionality, Wellbeing, and Complexity (Guy C. Van Orden, Heidi Kloos and Sebastian Wallot.

Ilachinski, Andrew. Cellular Automata. Singapore: World Scientific, 2001. A treatise on the computer based physics and mathematics of nonlinear systems. By this approach, a discrete, information-rich universe is revealed in a process of organizing and discovering itself by way of the same generative dynamics everywhere. Theoretical support is then achieved for a radically creative, increasingly personified reality. Ilachinski, a Russian-American systems scientist, comes to a quite different reading by way of these theories than does Stephen Wolfram.

Cellular automata (CA) are fundamentally the simplest mathematical representations of a much broader class of complex systems (where, for the moment, ‘complex system’ means any dynamical system that consists of more than a few - typically nonlinearly - interacting parts). As such, CA have proven to be extremely useful idealization of the dynamical behavior of many real complex systems…. (1)

Do the complex machinations of the world commodities market, the intricate swirls of the Belousov-Zhabotinski chemical reaction, the firings of the neurons in my brain and the interconnected co-evolving patterns of the earth’s global ecosystem all obey the same core set of underlying universal principles? While no-one yet knows the “deep” answer, evidence strongly suggests that the qualitative behavior of systems that emerge on the macro-scale is often very similar for systems that share the same basic structural organization, the same basic set of interactions among the components out of which they are constructed, the same basic patterns of information flow, etc. (612)

Previous   1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10  Next  [More Pages]