IV. Ecosmomics: Independent Complex Network Systems, Computational Programs, Genetic Ecode Scripts

Freeman, Walter. Foreword. Orsucci, Franco, ed. The Complex Matters of the Mind. Singapore: World Scientific, 1998. From the mid 1990s, a neuroscientist previews an imminent revolution in science.

Whereas the Newtonian dynamics that has dominated physics and biology for several centuries is rigid, deterministic, and precisely predictable, the new field of nonlinear dynamics opens a vast field of complexity to exploration and modeling. The key concept is self-organization. Given an adequate supply of energy and a sink for waste disposal, a collection of interacting elements such as molecules, neurons, organs or people can create new structure from within. (xiii)

Ganguly, Niloy, et al, eds. Dynamics On and Of Complex Networks: Applications to Biology, Computer Science, and the Social Sciences. Boston: Birkhauser, 2009. The proceedings of the Fourth European Conference on Complex Systems, Dresden, October 2007, with chapters by scientists from India and Germany. The meeting could well represent international collaborations entering upon a salutary genesis vista, out of the ruins of the 20th century. It is illuminating from the mid 2000s to see the project, as the quote notes, engage two distinct aspects – an initial distillation and discernment of independent, generic systems properties, and then their common, exemplary presence spreading to every area such as the book’s Biological, Social, and Informational Science sections.

The primary aim of this workshop was to systematically explore the statistical dynamics “on” and “of” complex networks that prevail across a large number of scientific disciplines. Dynamics on networks refers to the different types of processes, for instance, proliferation and diffusion, that take place on networks. The functionality/efficiency of these processes is strongly tied to the underlying topology as well as the dynamic behavior of the network. On the other hand, dynamics of networks mainly refers to the phenomena of self-organization, which in turn lead to the emergence of the complex structure of the network. Another important motivation of the workshop was to create a forum for researchers applying the theories of complex networks to various do mains as well as across several disciplines such as computer science, statistical physics, nonlinear dynamics, econometrics, biology, sociology and linguistics. (Preface)

Geard, Nicholas, et al. Developmental Motifs Reveal Complex Structure in Cell Lineages. Complexity. 16/4, 2010. As the quotes convey, University of Southampton, Houston, and Queensland biosystems researchers, including Seth Bullock and Janet Wiles, offer an explanation of how and why diverse dynamical phenomena across nature yet indeed display similar patterns and processes. The evidence thus grows stronger for an independent, universal source which becomes manifest in so many places and ways.

Many natural and technological systems are complex, with organizational structures that exhibit characteristic patterns but defy concise description. One effective approach to analyzing such systems is in terms of repeated topological motifs. Here we extend the motif concept to characterize the dynamic behavior of complex systems by introducing developmental motifs, which capture patterns of systems growth. (48)

As mentioned above, the evolutionary relationship among species and grammatical structure in linguistics are also commonly represented as trees. Furthermore, phylogenies and languages are also systems whose structure is likely to have been shaped both by intrinsic dynamics and external forces. It is intriguing to consider what types of regularity may be revealed by the application of developmental motifs to other complex systems. (55)

Gershenson, Carlos, et al. Self-Organization and Artificial Life. Artificial Life. 26/3, 2020. CG, National Autonomous University of Mexico, Vito Trianni, Italian National Research Council, Justin Werfel, Harvard, and Hiroki Sayama, SUNY Binghamton provide a tutorial upon this interface between complexity science and their advance via this computational frontier. An extensive list of 217 references bolsters the presentation.

Self-organization can be broadly defined as the ability of a system to display ordered spatiotemporal patterns solely due to interactions among its components. Placed at the frontiers between disciplines, artificial life has borrowed concepts and tools from the study of self-organization to interpret lifelike phenomena as well as constructivist approaches to artificial system design. In this review, we discuss aspects of self-organization and its usages within primary ALife domains of “soft” (mathematical computation), “hard” (physical robots), and “wet” (chemical/biological systems). (Abstract excerpt)

Gersherson, Carlos. Self-Organizing Systems: What, How, and Why?.. doi.org/10.20944/preprints202408.0549.v1. The SUNY Binghamton and Universidad Nacional Autónoma de México complexity theorist (bio below) has been a leading advocate and communicator of this 21st century organic revolution (search). This 2024 Preprint provides a latest progress review of its transitional scientific theories as theymay proceed to quantify, distill and express its spontaneous energies and vital formations.

I present a personal account of self-organizing systems which might help motivate useful discussions. The relevant contribution is to provide some steps towards framing better questions to understand self-organization, information, complexity, and emergence. With this aim, I start with a notion and examples of self-organizing systems (what?), continue with their properties and related concepts (how?), and close with applications (why?). (Abstract)

There are many examples of systems that we can usefully call self-organizing: flocks of birds, schools of fish, swarms of insects, herds of cattle, and crowds of people. For animal occasions, the collective behavior is a product of the interactions of individuals, not determined by a leader or an external signal. There are also several instances from non-living systems such as vortexes, crystallization, self-assembly, and pattern formation in general. In these cases, elements of a system also interact to achieve a global pattern. (1)

It is the function of science to discover the existence of a general reign of order in nature and to find the causes
governing this order. And this refers in equal measure to the relations of man — social and political — and to
the entire universe as a whole." (Dmitri Mendeleev, select quote)

Carlos Gershenson is a tenured professor at SUNY Binghamton and is affiliated with the Universidad Nacional Autónoma de México (UNAM) where he was a Research Professor (2008-2023). He is also the Editor-in-Chief of Complexity Digest (2007-), and member of the Board of Advisors for Scientific American (2018-).

Gisiger, T. Scale Invariance in Biology: Coincidence or Footprint of a Universal Mechanism? Biological Reviews. 76/2, 2001. After an introduction to dynamical systems in their physical embodiment, their power law self-similarity properties are shown to pervade biological and neurological realms so as to affirm a ‘universality’ throughout nature.

In the spirit of complex systems, we should try not to look at these examples as physical processes or reactions between chemical reactants, but instead as systems made of many particles, or 'agents,’ which interact with each other via certain rules. (163) These findings might therefore illustrate how an ecosystem self-organizes into a critical state as the web of interactions between species and individuals develops. (185) Scale invariance is very common in nature, but it is only since the early 1970s that the mathematical tools necessary to define it more clearly were introduced. (204)

Goldberg, Elkhonon. The Executive Brain. New York: Oxford University Press, 2001. A Russian-American neuroscientist recounts a lifetime of clinical experience from which arises a novel synthesis of brain evolution. A universal complex system is seen to drive this process of encephalization from early isolated thalamic modules to the mammalian neocortex with its gradiental, neural net integration. The same sequence is then observed to occur on a global scale as nation-states break up into autonomous microregions.

The search for such universal principles shared by superficially different systems is at the heart of the new field of ‘complexity’ emerging at the cutting edge of science and philosophy. Today a striking parallel is increasingly apparent between the changing world order and the evolution of the brain. (219)

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)

Gros, Claudius. Complex and Adaptive Dynamical Systems. Switzerland: Springer, 2024. This volume is the latest text edition by the Goethe University, Frankfurt systems physicist (search). It contains many updates and new sections across a wide range of pertinent subjects as the Contents next conveys.

Table of Contents Network Theory.- Bifurcations and Chaos in Dynamical Systems.- Dissipation, Noise and Adaptive Systems.- Self Organization.- Information Theory of Complex Systems.- Self-Organized Criticality.- Random Boolean Networks.- Darwinian Evolution, Hypercycles and Game Theory.- Synchronization Phenomena.- Complexity of Machine Learning.- Solutions.

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.

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