IV. Ecosmomics: An Independent, UniVersal, Source Code-Script of Generative Complex Network Systems
Fontana, Walter and Leo Buss. The Arrival of the Fittest. Bulletin of Mathematical Biology. 56/1, 1994. Its subtitle is “Toward a Theory of Biological Organization.” Since natural selection cannot explain how organisms occur in the first place, this oft-cited, important paper proposes that independent dynamic, autopoietic networks serve to organize a hierarchical scale of life.
Forrest, Stephanie and Melanie Mitchell. Adaptive Computation: The Multidisciplanary Legacy of John H. Holland. Communications of the ACM. August, 2016. University of New Mexico, and Portland State University complexity scientists write an insightful biography about this premier founder of the complexity revolution. Holland (1929-2015) was a pioneer professor of computer science at the University of Michigan, and is well known for his theory of genetic algorithms, which have gone on to many versions and applications. Holland also conceived the theory of complex adaptive systems (search JH) as a universal way to express such non-equilibrium evolutionary dynamics of statistical search and optimization.
Here, we consider this larger framework, sketching the recurring themes that were central to Holland’s theory of adaptive systems: discovery and dynamics in adaptive search; internal models and prediction; exploratory modeling; and universal properties of complex adaptive systems. (58) As the discussions about complex adaptive systems matured, a consensus developed about their basic properties. Such systems are composed of many components with nonlinear interactions; are characterized by complex emergent behavior; exhibit higher-order patterns; operate at multiple (and often nested) spatial and temporal scales, with some behavior conserved across all scales and other behaviors changing at different scales; and are adaptive, with behavioral rules continually adjusted through evolution and learning. (62)
Frame, Michael and Amelia Urry. Fractal Worlds: Grown, Built, and Imagined. New Haven: Yale University Press, 2016. A Yale mathematician and a journalist achieve a comprehensive, insightful survey of nature’s intrinsic self-similar topologies. To an integral degree not before covered, a nested self-similarity in kind is illuminated from galactic clusters, solar flares and planet formation to fitness landscapes, DNA globules, physiologies, broccoli florets, coastlines, clouds, onto literary narratives and human artifices. An Appendix lists 100 such instances, which are explained at length. Along with tutorials on how to calculate fractal dimensions, 50 reference pages make this a unique text. Michael Frame is most qualified for he was a junior colleague at Yale with Benoit Mandelbrot (1924-2010). Together they authored Fractals, Graphics, and Mathematics Education in 2002. MF with Nathan Cohn also wrote Benoit Mandelbrot: A Life in Many Dimensions, a 2014 biography. The volume is a grand survey from Mandelbrot’s 1970s and 1980s discovery to this witness of an invariant genesis from uniVerse to humanVerse.
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)
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)
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)
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)
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.
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