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

Bar Yam, Yaneer. Dynamics of Complex Systems. Reading, MA: Addison-Wesley, 1997. Arguably the best introduction to the subject. An 800-page formidable but accessible treatise on complex system dynamics from first principles to protein folding, neural networks, the origin and evolution of life and onto an emerging global civilization.

Baruchi, Itay, et al. Functional Holography of Complex Networks Activity – From Cultures to the Human Brain. Complexity. 10/3, 2005. In a similar way to holographic universe theories (see Quantum Cosmology) Baruchi, along with Vernon Towle and Eshel Ben-Jacob, find that biological and neural networks, in their algorithmic processes, take on the typical properties of a hologram. Here is still another approach which finds nature to be distinguished by the same pattern and process at each scale and instance.

In a similar way to holographic universe theories (see Quantum Cosmology) Baruchi, along with Vernon Towle and Eshel Ben-Jacob, find that biological and neural networks, in their algorithmic processes, take on the typical properties of a hologram. Here is still another approach which finds nature to be distinguished by the same pattern and process at each scale and instance.

Baum, Eric. What Is Thought? Cambridge: MIT Press, 2004. An important book because it purports to do for cognitive science what Ervin Schrodinger’s 1944 classic What Is Life? did for biology and genetics. As corporeal life is known to arise from a molecular information, so evolving brains and mental processes can similarly be attributed to a computational DNA. An original contribution to an algorithmic kind of universe which possesses both a genetic-like source and its manifest, animate, cognitive complexity. This programmatic realm is necessarily very compact so our language is deeply metaphorical because the same "story" repeats everywhere. The brain accomplishes this by a diverse array of subroutine modules, each engaged with the semantic meaning of an analogical world. By these theories, a cerebral and cognitive evolution of the ability to remember, think and learn is traced. A rich, dense work which begs for translation. The multifaceted book is cited elsewhere, check Search.

The book explains in some detail why computer scientists are confident that thought, and for that matter life, arises from the execution of a computer program. The execution of a computer program is always equivalent to pure syntax - the juggling of 1s and 0s according to simple rules. The key question, which has been posed primarily by philosophers, is how syntax comes to correspond to semantics, or real meaning in the world.
The answer this book suggests is that semantics arises from the principle, roughly speaking, that a sufficiently compact program explaining and exploiting a complex world essentially captures reality. The point is that the only way one can find an extremely short computer program that makes a huge number of decisions correctly in a vast and complex world is if the world actually has a compact underlying structure and the program essentially captures that structure. (3)

Like the computation of life, the computation of mind is rich, with modules connected to modules flowing in complex flow patterns. Like the computation of life, the computation of mind is the result of evolution. And it is coded by a short program, as the computation of life is, so that there is an underlying order. (65)

Bedau, Mark. Artificial Life: Organization, Adaptation and Complexity from the Bottom Up. Trends in Cognitive Sciences. 7/11, 2003. A good survey not only of ALife but of self-organizing, hierarchical, iterative complex systems.

Bellomo, Nicola, et al. Life and self-organization on the way to artificial intelligence for collective dynamic. Physics of Life Reviews.. Volume 51, December, 2024. NB, University of Granada, Marina Dolfin, King's College London and Jie Liao, Shanghai University biotheorists present their latest frontier studies with regard to a mutual integration of complex, self-organized system phenomena with AI neural network methods and procedures. See A Quest Towards a Mathematical Theory of Living Systems by Nicola Bellomo, et al (Springer, 2017) for an earlier edition.

This work is dedicated to the study, modeling, and simulation of the collective dynamics of interacting living entities. The first perspective is to develop a mathematical theory of swarm intelligence in this consideration. The second intent is to design conceptual tools for an artificial intelligence AI version whereby interacting entities learn from each other as well as the environment. Then, out of this collective learning process, a strategy can be formulated by which to pursue specific goals through a decision making process. Our contribution is to propose, scope out and foster an AI based collective dynamics.

Bianconi, ginestra, et al. Complex Systems in the Spotlight: Next Steps after the 2021 Nobel Prize in Physics. Journal of Physics: Complexity. 4/010201, 2023. Since this Physics award to Gregory Parisi, a pioneer complexity theorist, recognized this major scientific endeavor and advance since the 1970s, the Queen Mary University of London network expert and this journal editor asked 18 researchers for their past and future opinions. For example, we note GB, Jacob Biamonte, Jurgen Kurths Adilson Motter, Matjaz Perc, Filippo Radicchi, Marta Sales-Pardo and Stefan Thurner. Topical items include a definition of complex systems, looking ahead 20 years, and interdisciplinary aspects. Their comments don’t lend to quotes, but the whole entry is available at this site. See also in this issue an interview by GB of G. Parisi. And with respect to this section and whole site, here is a good instance of the 21st century genesis universe revolution via our prodigious Earthuman progeny going forward to 2030 and beyond.

The 2021 Nobel Prize in Physics recognized the fundamental role of complex systems in the natural sciences. In order to celebrate this milestone, the editorial board of J. Phys. Complexity here reviews its achievements, challenges, and future prospects. To distinguish the voice and the opinion of each editor was asked about ther perspectives and reflections on selected themes. A comprehensive and multi-faceted view of complexity science emerges as a result.

Bizzarri, Mariano, et al. Complexity in Biological Organization: Key Concepts. Entropy. Online August 12, 2020. In a special Biological Statistical Mechanics issue, systems scientists from Italy, Russia and Cuba, surely a global online faculty, post a 21st century retrospective of advance, emphasis, clarification and convergence in this wide ranging study of nature’s nonlinear essence. The paper first reviews etymology origins of key concepts and terms within this organic revolution – complexity, systems, self-organization, emergence, hierarchy and so on. Renormalization theory, critical transitions and more also receive notice as a revolutionary organic universe to human genesis gains witness, articulation and credence.

The “magic” word complexity evokes a multitude of meanings that obscure its real sense. Here we try and generate a bottom-up reconstruction of the deep sense of complexity by looking at the convergence of different features shared by complex systems. We specifically focus on complexity in biology but stressing the similarities with analogous features encountered in inanimate and artifactual systems in order to track an integrative path toward a new “mainstream” of science overcoming the actual fragmentation of scientific culture. (Abstract)

This statement introduces some very relevant questions given that emergent phenomena share some common—universal traits that are largely insensitive to changes in their microphysical base, as pointed out by studies of the Renormalization Group. “Universality” refers to the fact that phase transitions arising in different systems often possess the same set of critical exponents, while the thermodynamic properties of a system near a phase transition depend only on a small number of features, such as dimensionality and symmetry, and are insensitive to the underlying microphysics. Conclusively, emergence is not an epistemic construct. Instead, it reflects a true ontological reality shared by complex systems of very different nature. (4)

Bonchev, Daniel and Dennis Rouvray, eds. Complexity in Chemistry, Biology, and Ecology. Berlin: Springer, 2005. An increasing number of works are seeking in diverse areas a common denominator and terminology for complex systems behavior. (see Chua below) Earlier on studies focused on a certain aspect such as network geometry or active agents. But all this goes on without examining what kind of universe such phenomena might spring from. So any organic organization remains couched in mechanistic verbiage. This text at once contributes new insights but is caught in this conflation.

The contemporary scientific method is built on reductionism. The surprising finding that this paradigm has limits gave rise to the concept of complexity. This book presents the new science of complexity by presenting diverse concepts from the analyses of a wide range of real world systems (chemical, biochemical, biological, and ecological). Based on a variety of approaches ranging from cellular automata and dynamic evolutionary networks to topology and information theory, the book contains methodologies of practical importance for assessing systems complexity and network analysis in medicine and biology. (Publisher’s Website)

Bornholdt, Stefan and Stuart Kauffman. Ensembles, Dynamics, and Cell Types: Revisiting the Statistical Mechanics Perspective on Cellular Regulation. arXiv:1902.00483. University of Bremen and Institute for Systems Biology, Seattle senior theorists look back 50 years to review Kauffman’s 1969 paper Metabolic Stability and Epigenesis in Randomly Constructed Genetic Nets (Journal of Theoretical Biology, 22/3, Abstract below). His 1993 work The Origins of Order played a major part in establishing the field of complex system studies. This posting continues its Self-Organization and Selection in Evolution subtitle by adding a statistical mechanics basis for biological regulation, along with selective effects. Into 2019 his prescient glimpses are well proven as we now know that gene regulatory networks do seek a self-organized criticality (search Bryan Daniels, Universality, Autocatalytic sections and elsewhere).

Genetic regulatory networks control ontogeny. For fifty years Boolean networks have served as models of such systems, ranging from ensembles of random Boolean networks as models for generic properties of gene regulation to working dynamical models of a growing number of sub-networks of real cells. At the same time, their statistical mechanics has been thoroughly studied. Here we recapitulate their original motivation in the context of current theoretical and empirical research. We discuss ensembles of random Boolean networks whose dynamical attractors model cell types. There is now strong evidence that genetic regulatory networks are dynamically critical, and that evolution is exploring the critical sub-ensemble. The generic properties of this sub-ensemble predict essential features of cell differentiation. Thus, the theory correctly predicts a power law relationship between the number of cell types and the DNA contents per cell, and a comparable slope. (2019 Abstract excerpt)

Proto-organisms probably were randomly aggregated nets of chemical reactions. The hypothesis that contemporary organisms are also randomly constructed molecular automata is examined by modeling the gene as a binary (on-off) device and studying the behavior of large, randomly constructed nets of these binary “genes”. The results suggest that, if each “gene” is directly affected by two or three other “genes”, then such random nets behave with great order and stability; undergo behavior cycles whose length predicts cell replication time as a function of the number of genes per cell; and under the stimulus of noise are capable of differentiating directly from any mode of behavior to at most a few other modes of behavior. The possibility of a general theory of metabolic behavior is suggested. (1969 SK Abstract excerpt)

Bountis, Tassos, et al. The Science of Complexity and the Role of Mathematics. European Physical Journal Special Topics. 225/883, 2016. Greek and British systems theorists introduce a special issue on this title subject. As the quotes say, and this site documents, as these endeavors reach a broad veracity, we ought to avail their wider natural, social and global benefit. And how appropriate that some two millennia later, such a scientific and philosophical advance comes from mainly Greece. If this robust 21st century natural knowledge can be translated, understood, and put to practical service, we might be able to resolve a local and global free-fall into economic, political, and internecine chaos. Sadly their own country is a prime example. Some papers are Regular and Chaotic Orbits in the Dynamics of Exoplanets, Hypernetworks, and Controlled Aggregation in Complex Systems. See also Bridging the Gaps at the Physics-Chemistry-Biology Interface in Philosophical Transactions of the Royal Society A (374/2080, 2016) for a similar edition.

In the middle of the second decade of the 21st century, Complexity Science has reached a turning point. Its rapid advancement over the last 30 years has led to remarkable new concepts, methods and techniques, whose applications to complex systems of the physical, biological and social sciences has produced a great number of exciting results. The approach has so far depended almost exclusively on the solution of a wide variety of mathematical models by sophisticated numerical techniques and extensive simulations that have inspired a new generation of researchers interested in complex systems. Still, the impact of Complexity beyond the natural sciences, its applications to Medicine, Technology, Economics, Society and Policy are only now beginning to be explored. Furthermore, its basic principles and methods have so far remained within the realm of high level research institutions, out of reach of society’s urgent need for practical applications. (Abstract excerpt)

“Complexity” is the Latin version of the Greek word, which refers to a multitude of twisting and folding structures similar to what one finds in the braids of a lady’s hair, the foliages of a tree or the flocking behavior of birds. At first sight, an object, or natural phenomenon characterized as complex (or “polyplokon”) evokes feelings of confusion and perplexity. When expressed in mathematical terms, however, it often reveals deep geometrical, dynamical and statistical properties and global unifying features that allow us to associate it with some particular universality class. (884) In this regard, we are no longer interested in the trajectories of individual particles, but wish to analyze the statistical behavior of the particular ensemble. We thus discover that the most interesting systems of natural, biological and social sciences are far from equilibrium, and exhibit self organization and emergence of patterns and coherent structures that cannot be explained by the behaviour of individual components. These are known as complex systems. (884)

Bourgine, Paul, et al, eds. The CSS Roadmap for Complex Systems Science and its Applications 2012 – 2020. http://unitwin-cs.org/documents.html. A mission guide for the European based UniTwin UNESCO Complex Systems Digital Campus, a network of research and teaching institutions. CSS is Complex Systems Society, Director Bourgine is a CREA-Ecole Polytechnique, Paris, senior researcher. Publications on the webpage appear in four languages – French, English, Spanish, and Portuguese. Scroll down and click on this title, other Brochures are also available, along with an “African Roadmap” in French. The text, and burgeoning project, is another sign of the broad historical shift to better understand and guide human societies by way of these palliative organic vitalities.

The new science of complex systems is providing radical new ways of understanding, modeling, predicting, managing the physical, biological, ecological, and social universe. Complex systems are characterised by emergent structures that occur in many domains and questions that apply across the domains in the modern world. Radical new strategies of research and teaching are necessary for all the previous transversal questions through all kind of complex systems, from atoms to complex matter, from the molecules to organisms, from organisms to the ecosphere, from neurotransmitters to the individual and social cognition, from individuals to human society. This huge effort is necessary for reconstructing the observed multi-scale dynamics relevant for the “human scales” in between the physics of the two infinites, the nuclear physics in one side and the cosmology in the other side.

The Science of Complex Systems will develop in the same way that physics has developed during the three last centuries through a constant process of reconstructing models from constantly improving data. The reconstruction of the multi-level dynamics of complex systems, i.e. integrated models, presents a major challenge to modern science but it is becoming more and more accessible through ubiquitous cloud computing. (3) The science of complex system is therefore different to any other particular science because it focuses on the methods of reconstructing the dynamics of systems of heterogeneous systems across the traditional domains. This methodological perspective and the trans-disciplinary nature of complex systems science make it unique in that it is an integrative science that strives to combine the methods, knowledge and theory of other domain-based science. (5)

As an example for a social system consider the people evacuating a building in an emergency. The motion of people in crowds is observed and a phenomenological model is created of the ways people move with respect to each other. Using this phenomenological model, an agent-based computer simulation can be used to create an augmented phenomenology for this system. A theoretical model of pedestrian flows can be proposed and permit spatio-temporal simulations to create another augmented phenomenology. In fact this new science can assist the authorities to redesign Mecca for the Hajj pilgrimage which hitherto was subject to fatal accidents with large numbers of people being trampled as the dynamics of the crowd changed. This is one of the major success stories of complex systems science. (5-6)

Brauns, Fridtjof, et al. Phase-Space Geometry of Reaction-Diffusion Dynamics. arXiv:1812.08684. In a densely technical, tightly composed 55 page paper, Ludwig-Maximilians University system physicists FB, Jacob Halatek and Erwin Frey (search) continue their decadal project to explain by way of nonequilibrium thermodynamics, structural formations, Turing-like morphogenesis, self-organized critical complexities, computational biology, and more how life proceeds to develop and maintain its physiological vitality. With 158 references, in these later 2010s a collaborative sense of a realistic model is evident. It is proposed in closing that such cellular coherence is an generalization which could apply to other natural systems. See also Rethinking Pattern Formation in Reaction-Diffusion Systems by Halatek and Frey in Nature Physics (14/5, 2018) and for example Guiding Self-Organized Pattern Formation in Cell Polarity Establishment by Peter Gross, et al (NP December 2018)

Experimental studies of protein pattern formation have stimulated new interest in the dynamics of reaction--diffusion systems. However, a comprehensive theoretical understanding of the dynamics of such highly nonlinear, spatially extended systems is still missing. Here we show how a description in phase space, which has proven invaluable in shaping our intuition about the dynamics of nonlinear ordinary differential equations, can be generalized to mass-conserving reaction--diffusion (McRD) systems. We present a comprehensive theory for two-component McRD systems, which serve as paradigmatic minimal systems. The fundamental elements of the theory presented suggest ways of experimentally characterizing pattern-forming systems on a mesoscopic level and are generalizable to a broad class of spatially extended non-equilibrium systems, and thereby pave the way toward an overarching theoretical framework. (Abstract excerpt)

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