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

5. Common Code: A Further Report of Reliable, Invariant Occasions

Watson, Richard A., et al. Global Adaptation in Networks of Selfish Components: Emergent Associative Memory at the System Scale. Artificial Life. Early View, May, 2011. Watson and Rob Mills, Natural Systems, University of Southampton, and Chris Buckley, Informatics, Sussex University, post an advanced, technical analysis of the presence and activity of dynamic network phenomena across life’s nested evolution. By this work, and many akin, a growing quantification and admission accrues that something much more creative is going on to drive and direct life’s historic development. A universal complementarity of semi-autonomous agents or entities and communicative interrelations that serves both self and society is described as it repeats from biomolecules to ecosystems. For a further take see also next Watson, Richard A., et al. “Adaptation Without Natural Selection.” Both articles are available on Watson’s web publication page. We offer extended quotes to convey the technical verbatim, which may suggest, in translation, a cosmic genetic code at work.

In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organise into structures that enhance global adaptation, efficiency or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalisation and optimisation, are well-understood. (Abstract, 1)

Specifically, the key to understanding this result is that selfish agents necessarily modify connections in a manner consistent with Hebb’s rule – a simple learning rule familiar in computational neuroscience (Methods). This means that a system of selfish agents, each modifying its connections with other agents selfishly and in a completely distributed manner, will produce dynamical consequences for the system as a whole that are functionally identical to a learning neural network. (3)

Conclusions: We have shown that organisational principles familiar in organismic learning occur implicitly in distributed complex adaptive systems. System-level ‘learning’ or associative induction happens as a direct consequence of the fact that selfish modifications to relationships between components are equivalent to Hebb’s rule. Thus networks of selfish agents self-organise the connection structure of a network in a manner that creates an associative memory of state configurations that the system experiences. (22) These findings suggest that distributed complex adaptive systems of self-interested components, such as individuals in a social network or species in an ecosystem, may exhibit organisational principles in common with those familiar in organismic learning, developing an associative memory of their past behaviour that enhances system-level efficiency in future. This work thereby demonstrates a completely distributed adaptive process that we view as a natural extension to the “emergent collective computational abilities” that come ‘for free’ in physical systems [22].

West, Bruce, et al. Fractal structure of human and primate social networks optimizes information flow. Proceedings of the Royal Society A. May, 2023. As our literature review proceeded this year, it became evident that a new convergent phase of a revolutionary 21st century synthesis was well underway across the topical sections. For example, BW, Garland Culbreth and Paolo Grigolini, University of North Texas and Robin Dunbar, Oxford University draw upon a decade of prior studies, along with an extensive bibliography, to affirm just such a robust consolidation. The opening paragraphs cite social instances from local villages and primate groups to online media whence the same active pattern repeats in every case. Herein its exemplar is the Dunbar scale (search, see quotes) which gains verification as a result. The fact that this pattern seems to be so widespread in so many different social contexts suggests that it is underpinned by very general structural principles. (2) The paper goes on to develop mathematical methods so as to provide a common ground and verification. In regard, we might say that an ecosmic universality may just now have been found by our Earthuman cumulative intelligence.

Primate and human social groups exhibit a fractal structure over a definitive range of preferred layer sizes with groups of 5, 15, 50 and in humans to 150. In primates, this same fractal distribution is observed in the distribution of species mean group sizes and internal network structure. Here we demonstrate that this preferential sequence arises due to the critical nature of dynamic self-organization within complex social networks. We go on to study the scalar properties of animal assemblies to an extent that this aggregate behaviour can exhibit a form of collective intelligence.. This robust feature and finding then provides a theory-based rationale for the fractal layering of primate and human social groups. (Abstract)

In sum, it appears that Dunbar layering is a consequence of the nonlinear dynamics of the underlying complexity of their networks which set up a series of fractally patterned attractors for group size as a consequence of efficient information flow. It has been suggested that several physical and chemical properties due to the thermodynamics of finite-sized systems, including protein folding and the chain length dependence of the optical properties of Perovskites, may similarly be due to such collective behaviour. (10-11)

West, Geoffrey. The Surprising Math of Cities and Corporations. http://www.ted.com/talks/geoffrey_west_the_surprising_math_of_cities_and_corporations.html.. The physicist and philosopher, once president of Santa Fe Institute, describes in this July 2011, Edinburgh, TED video presentation, the remarkable findings of research teams he has mentored and contributed to over 15 years. In the later 1990s West joined with ecologists James Brown and Brian Enquist in an endeavor to quantify within creaturely anatomy and physiology from mice to elephants, and biota from leaves to a forest, a pervasive recurrence of the same pattern and process, such metabolic rate. This project met with much success (search names herein) so that West extended the effort in the 2000s with Luis Bettencourt, Deborah Strumsky, Jose Lobo, and other colleagues to human settlements and commercial institutions. The talk is mainly on this aspect, but covers the whole expanse over orders of magnitude from microbes to a metropolis.

A significant difference then arises. While flora and fauna are seen as “sublinear,” i.e., metabolisms slow down from voles to whales, for villages to megacities, energy usages, and all activities, become “superlinear” as they increase with urban size and density. But notably, as we know, business companies hold to the sublinear range. The bigger they get, the more bureaucracy sets in, and viability decreases. As a result of this grand scenario, “generic universal principles” of nested network geometries that repeat with fractal-like self-similarity become robustly evident. What accrues is an historic discovery of a constant, iterative recurrence, as if from a mathematical source, across nature and culture. Galileo would say tell me about it. Geoffrey West, of British birth, brings to mind William Blake: “To see a world in a grain of sand, And a heaven in a wild flower, Hold infinity in the palm of your hand, And eternity in an hour.”

“The same principles, the same dynamics, the same organization is at work in all of these, including us, and it can scale over a range of 100 million in size. And that is one of the main reasons life is so resilient and robust -- scalability.”

West, Geoffrey and James Brown. Life’s Universal Scaling Laws. Physics Today. September, 2004. A popular survey of the theory that common properties of biological networks can explain the structural organization and metabolic dynamics of living systems.

Among the many fundamental variables that obey such scaling laws….are metabolic rate, life span, growth rate, heart rate, lengths of aortas and genomes, tree height, mass of cerebral grey matter, density of mitochondria, and concentration of RNA. (36) The starting point was to recognize that highly complex, self-sustaining, reproducing, living structures require close integration of enormous numbers of localized microscopic units that need to be serviced in an approximately “democratic” and efficient fashion. (38) Thus, growth and life-history events are, in general, universal phenomena governed primarily be basic cellular properties and quarter-power scaling. (40)

West, Geoffrey and James Brown. The Origin of Allometric Scaling Laws in Biology from Genomes to Ecosystems. Journal of Experimental Biology. 208/9, 2005. Physicist West and ecologist Brown, with other colleagues over the past decade, have achieved a significant articulation via complexity science of a quantitative theory of biological organization. This latest report discusses hierarchical branching networks which exhibit a nested self-similarity distinguished by power-law behavior across the entire realm of living nature. The implication of such findings, along with much other work, is the (re)discovery that our reality is distinguished by a repetition of the same pattern and process everywhere.

For example, metabolic rate scales as the ¾ power of mass over 27 orders of magnitude, from molecular and intracellular levels up to the largest organisms. Similarly, time-scales (such as lifespans and growth rates) and sizes (such as bacterial genome lengths, tree heights and mitochondrial densities) scale with exponents that are typically simple powers of ¼. The universality and simplicity of these relationships suggest that fundamental universal principles underlie much of the coarse-grained generic structure and organization of living systems. (1575)

Wheeler, Jennifer and Kit Yu Jaren Chan.. The Whole is Greater Than the Sum of Its Parts: Large-scale Phenomena Arising from Small-scale Biophysical Processes.. Integrative and Comparative Biology. 63/6, December, 2023. Memorial University, Newfoundland and Swarthmore College biologists introduce the vast conten, from this six day meeting, as the abstract cites,. Its address is sicb.org/wp-content/uploads/2023/04/2023-SICB-Program-R5.pdf Select papers have yet to be published, keep checking this subject journal. As appropriate for 2023, a new pervasive sense of basic commonalities across every size and scale of lively systems is becoming increasingly apparent.

The symposium “Large-scale biological phenomena arising from small-scale biophysical processes” at the SICB (Society for Integrative and Comparative Biology) 2023 Annual Meeting focused on the cross-disciplinary exploration of emergent phenomena in biology. Interactions between cells or organisms at small scales within a system can govern patterns occurring at larger scales in space, time, or biological complexity. This theme recurs in many sub-disciplines of biology, including cell and developmental biology, evolution, and ecology. This special issue showcases a wide range of cross-disciplinary collaborations among biologists, physicists, and engineers. We hope to demonstrate that cross-disciplinary research linking small-scale biophysics to larger-scale emergent phenomena can help us understand problems ranging from single-cell behaviors to tissue formation and function, evolution of form, and the dynamics of communities. (Abstract)

Whitfield, John. Across the Curious Parallel of Language and Species Evolution. PLoS Biology. 6/7, 2008. The British science writer reports in this online journal on the dawning realization that the molecular DNA code is strongly isomorphic and isodynamic with linguistic structures. One might add that an implication of this has not yet registered that human knowledge is genetic in kind, and that both codes must spring from the same innate source. By its employ, much as if people are as “genes,” we might intentionally continue creation.

Languages are extraordinarily like genomes…there could be very general laws of lexical evolution to rival those of genetic evolution. (1370)

Wurthner, Laeschkir, et al. Bridging Scales in a Multiscale Pattern-Forming System. arXiv:2111.12043. Ludwig-Maximilians-Universitat, Munchen and Delft University of Technology theorists including Erwin Frey advance studies of self-organizing structural qualities of from proteins to organisms. Their purpose is thus to reveal and express the occurrence of general, ultimately physical, principles and forces which serve as an independent source-code for life’s evolutionary and metabolic occasion.

Self-organized pattern formation is vital for many biological processes. Mathematical modeling using reaction-diffusion models has advanced our understanding of how biological systems develop spatial structures. However, biological processes inherently involve multiple spatial and temporal scales and transition from one pattern to another over time. Here, we describe mass-conserving reaction-diffusion systems that can reconstruct information about patterns across the large-scale dynamics. We illustrate by the Min protein system which produces multiscale patterns in a spatially heterogeneous geometry. Since conservation laws are inherent in many different physical environs, our general approach can uncover underlying principles in pattern-forming systems. (Abstract excerpt)

Yang, Ang and Yin Shan, eds. Intelligent Complex Adaptive Systems. Hershey, PA: IGI Publishing, 2008. This technical volume illustrates a revolution in conceptual modeling as applied across a wide range from computational processes to international conflicts. All such phenomena can now be expressed as universally manifest multi-agent, interactive, modular, information-driven dynamic networks.

Yang, Haiqian, et al. Configural Fingerprints of Multicellular Living Systems. Proceedings of the National Academy of Sciences. 118/44, 2021. Seven bioengineers from MIT, University of Ottawa, and Northeastern University quantify deeper rootings of life’s evolutionary sequential course into invariant physical phenomena such as dynamic state transitions. As substantial matter is found to possess an active spontaneity, it becomes a fertile soil for regnant flora and fauna.

Cells cooperate as groups to achieve structure and function at the tissue level, during which specific material characteristics emerge. Analogous to phase transitions in classical physics, transformations in multicellular assemblies are essential for a variety of vital processes including morphogenesis, wound healing, and cancer. In this work, we develop configurational fingerprints of particulate and multicellular assemblies and extract volumetric and shear order parameters to quantify the system disorder. These two parameters form a complete and unique pair of signatures for the structural disorder of a multicellular system. (Abstract excerpt)

Tissues are composed of many cells that coordinate in space, through which structural formations emerge. While recent progress has shown that many biological processes are analogous to material phase transitions, a systematic framework to describe the spatial order of complex living systems has not yet occurred. We develop a unified method to quantify the evolution of spatial order across different types of disordered systems, including jammed thermal systems, 2D cell monolayers, 3D epithelial spheroids, and Drosophila embryos. Using scaling analysis, we show successful differentiation of gas-like, liquid-like, and solid-like phases in various living systems. (Significance)

Yeung, Chi Ho, et al. From the Physics of Interacting Polymers to Optimizing Routes on the London Underground. Proceedings of the National Academy of Sciences. 110/13717, 2013. As the Abstract and Synopsis detail, Aston University, Birmingham, UK, and Hong Kong University of Science and Technology, systems physicists quantify nature’s universal preference for metabolic movement from biochemicals to biocities, so as carry it forth for a better organic design of our human scale dynamic systems.

Optimizing paths on networks is crucial for many applications, ranging from subway traffic to Internet communication. Because global path optimization that takes account of all path choices simultaneously is computationally hard, most existing routing algorithms optimize paths individually, thus providing suboptimal solutions. We use the physics of interacting polymers and disordered systems to analyze macroscopic properties of generic path optimization problems and derive a simple, principled, generic, and distributed routing algorithm capable of considering all individual path choices simultaneously. We demonstrate the efficacy of the algorithm by applying it to: (i) random graphs resembling Internet overlay networks, (ii) travel on the London Underground network based on Oyster card data, and (iii) the global airport network. (Abstract)

From Polymer Physics to Quicker Commuter Travel. Whether planning water distribution routes, military convoy movements, internet traffic, or simply the best way to the airport, path optimization algorithms are essential for everyday logistics. Global optimization techniques that consider all path choices simultaneously are computationally difficult. As a result, most existing routing algorithms choose paths individually, but these methods tend to favor the shortest path regardless of the choice’s impact on other routes. Chi Ho Yeung et al. have borrowed from the physics of polymers to create a simple, generic, and distributed global path optimizing algorithm. The researchers tested their statistical physics-based technique on large real-world data sets, including the London Underground subway system and global air traffic. Compared to current methods, the algorithm decreased overall congestion at the cost of a slightly longer path length. (PNAS Summary)

Yip, Jacky, et al. Cosmology with persistent homology. Journal of Cosmology and Astroparticle Physics.. September, 2024. We cite this paper to record and exemplify how the same mathematical methods such as the title case, algebraic topology, renormalization theories and so on are being readily applied across nature’s infinities. Another instance is cerebral architectures and cognition (D. Bassett). See also Cosmological Parameter Estimation from the Large-scale Structure by this collegial group at arXiv:2308.02636, which includes a neural net version.

Persistent homology naturally addresses the multi-scale topological characteristics of the large-scale structure as a distribution of clusters, loops, and voids. We apply this tool to dark matter halo catalogs to build a summary statistic for comparison with the joint power spectrum regarding their information content on cosmological parameters. We run a series of consistency checks to consolidate our results, and conclude that our findings motivate incorporating persistent homology into inference pipelines for cosmological survey data. (Excerpt)

Persistent homology is a method for computing topological features of a space at different spatial resolutions. More persistent features are detected over a wide range of spatial scales and are deemed more likely to represent true features of the underlying space.

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