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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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III. Ecosmos: A Revolutionary Fertile, Habitable, Solar-Bioplanet, Incubator Lifescape

2. Computational Systems Physics: Self-Organization

Lancichinetti, Andrea, et al. Detecting the Overlapping and Hierarchical Community Structure in Complex Networks. New Journal of Physics. 11/033015, 2009. From the Complex Networks Lagrange Laboratory, Institute for Scientific Interchange, Torino, and the Budapest University of Technology and Economics, an example of how physicists are lately articulating the natural universalities of nested nets composed of cellular-like entities.

Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this community structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. (033015)

Laughlin, Robert. A Different Universe. New York: Basic Books, 2005. When a Nobel Laureate in Physics announces a revolutionary new science and worldview, it is of significant notice. The 20th century phase of looking down into matter in search of fundamental particles and lawful certainty has run its course. Although a necessary step and not wrong, reducing the world to fragments misses its true character. Drawing upon novel conceptions of quantum physics, along with advances in nonlinear science, Laughlin takes the opposite viewpoint that nature is to be understood through an emergent, stratified complexity. In addition to things, innate principles of organization and relationship are at work. From many imperfect, inexact entities (molecules, organisms) yet arises a more predictable, collective order. It is just this nascent turn of perspective from mechanism to dynamic emergence that Natural Genesis is trying to express. (An endorsement by another physics laurate, Philip Anderson, can be found in Nature 434/701, 2005.)

Thus the tendency of nature to form a hierarchical society of physical laws is much more than an academic debating point. It is why the world is knowable. (8) In other words, superconducting behavior reveals to us through its exactness, that everyday reality is a collective organizational phenomenon. (32) What we are seeing is a transformation of worldview in which the objective of understanding nature by breaking it down into ever smaller parts is supplanted by the objective of understanding how nature organizes itself. (76) Emergence means complex organizational structure growing out of simple rules. (200) …I think a good case can be made that science has now moved from an Age of Reductionism to an Age of Emergence, a time when the search for ultimate causes of things shifts from the behavior of parts to the behavior of the collective. (208)

Licata, Ignazio. Almost-Anywhere Theories: Reduction and Universality of Emergence. Ecological Complexity. 15/6, 2010. The author is a physicist founder of the Institute for Scientific Methodology, Palermo, Italy, which is concerned with intersections of scientific worldviews and their cultural understanding. In such regard, this paper seeks to move beyond a “Theory of Everything” based on bottom level determinants to a complementary addition of dynamic interrelations at each and all risen, sequential realms of a creative evolution. A certain portal is said to be a proper appreciation of renormalization group theory, not easy to do, as a good way to express nature’s phenomenal self-similarity.

Here, we aim to show that reductionism and emergence play a complementary role in understanding natural processes and in the dynamics of science explanation. In particular, we will show that the renormalization group - one of the most refined tools of Theoretical Physics - allows (us) to understand the importance of emergent processes' role in Nature identifying them as universal organization processes, that is, they are scale independent. (Abstract, 11)

Thus, grasping the complexity of the world requires a theoretical scenario which reconciles the quantum veritas of reductionism with radical emergence processes. The key starting point to understand such scenario is that the world is not made of cellular automata, chess pieces or Newtonian particles, but it is fundamentally quantum-based, informationally open to the observer (entanglement and nonlocal information) and is affected by measurements. (14)

Strictly speaking, in QFT (quantum field theory), similarly and even more radically than in QM, a particle is not a nomological fundamental “object,” but an event fixed by a network of relations whose conditions of existence are set by the dynamics of the interacting fundamental fields. (15)

Mainzer, Klaus. Thinking in Complexity: The Computational Dynamics of Matter, Mind, and Mankind. Berlin: Springer, 2007. Although emphasizing informational aspects, a good entry to the nascent perception that all natural realms are graced by such a self-similar theory of every when and where.

The theory of nonlinear, complex systems has become by now a proven problem-solving approach in the natural sciences. It is also recognized that many, if not most, of our social, ecological, economical and political problems are essentially of a global, complex and nonlinear nature. And it is now further accepted than any holistic perspective of the human mind and brain can hardly be achieved by any other approach. In this wide-ranging, scholarly but very concise treatment, Klaus Mainzer (physicist, computer scientist and philosopher) discusses, in essentially nontechnical language, the common framework behind these ideas and challenges. Emphasis is given to the evolution of new structures in natural and cultural systems, and we are lead to see clearly how the new integrative approach can give insights not available from traditional reductionistic methods. The fifth edition enlarges and revises almost all sections and include an entirely new chapter on the complexity of economic systems. (Publisher)

Masucci, Adolfo, et al. Extracting Directed Information Flow Networks. Physical Review E. 83/026103, 2011. Researchers from Spain and Greece identify a universally applicable, seemingly independent, feature of complex systems in repetitive evidence across widely separate domains of genomic webs and the worldwide web. See also Masucci, et al “Wikipedia Information Flow Analysis Reveals the Scale-Free Architecture of the Semantic Space” in PLoS One (6/2, 2011).

We introduce a general method to infer the directional information flow between populations whose elements are described by n-dimensional vectors of symbolic attributes. The method is based on the Jensen-Shannon divergence and on the Shannon entropy and has a wide range of application. We show here the results of two applications: first we extract the network of genetic flow between meadows of the seagrass Poseidonia oceanica, where the meadow elements are specified by sets of microsatellite markers, and then we extract the semantic flow network from a set of Wikipedia pages, showing the semantic channels between different areas of knowledge. (026103)

Matek, Christian. Searching for a Conceptual Language in Systems Biology: Hints from Statistical Mechanics? Progress in Biophysics and Molecular Biology. Online September, 2012. In a brief note, a Rudolf Peierls Centre for Theoretical Physics, Oxford, researcher draws a strong affinity between these seemingly disparate biological and physical domains. Altogether now might they infer an innate, expansive “systems cosmology,” a many-body, condensed matter cosmos from animating spontaneous organization to creaturely organisms and we peoples?

The search for an underlying conceptual framework in systems Biology inspired by the lessons from Statistical Mechanics may not only guide the intuition towards new experimental ideas. It could also provide a potentially cleared and simpler understanding of the rich structures of biology, telling relevant from irrelevant aspects of large systems and their function, and thus helping to recognize the simple behind the seemingly complex. (3)

Mukherjee, Siddhartha. The Song of the Cell: An Exploration of Medicine and the New Human. New York: Scribners, 2022. The author is a renowned cancer physician and awarded science writer. This volume proceeds from his The Gene (2016) to enter a history of how life’s actual cellular basis became known. The account runs from 17th century discoveries onto its many vital findings such as immunity. But it is not until page 360 that a song and dance begins as a dynamic network interconnectivity can now be factored in. As Dr. Mukherjee views their physiological effect, these many interrelations are of equal importance as the discrete cells. So the work winds up with a 2020s somatic version of nature’s particle/wave, me/We incarnate complementarity. As our societies become torn asunder by their polarity, such realizations might well salve the body politic

Many readers might read the word song as metaphorical. But in my view, it is far from a metaphor. What the young man laments is that he hasn’t learned the interconnectedness of the individual inhabitants of the rain forest – their ecology and interdependence- how the forest acts and lives as a whole. A “song” can be both an internal message and also an external one: a message sent out from one being to another rto signal connective cooperativity. We can name cell, and their contents but have yet to learn such songs of cell biology. (362)

But powerful as it might be, “atomism” is reaching its explanatory limits, We can learn much about the physical, chemical and biological worlds through evolutionary agglomerations of atomistic units but these methods are straining at their limits. Genes, by themselves, are quite incomplete explanations of the complexities and diversities of organisms; we need to add gene-gene and gene-environment to explain organismical physiology and fates. (364-365)

The laws that govern the Newtonian ball are as real and tangible as they were during the conception of the universe. By the same logic, a cell and a gene are real. It’s just that they aren’t real in isolation. They are fundamentally cooperative, integrating units and together they they build, maintain and repair organisms. (365)

Perhaps one manifesto for the future of cell biology is to integrate “atomism” and “holism.” Multicellularity evolved again and again, because cells while retaining their boundaries could find multiple benefits in citizenship. That, more than any other, is the advantage of understanding cellular system, and beyond to cellular ecosystems. (365-366)

Siddhartha Mukherjee is a professor of medicine at the Irving Cancer Research Institute, Columbia University. A Rhodes scholar, he graduated from Stanford University, University of Oxford, and Harvard Medical School. He is the author of The Gene: An Intimate History, and The Emperor of All Maladies: A Biography of Cancer, a 2011 Pulitzer Prize winner..


Siddhartha Mukherjee is a professor of medicine at the Irving Cancer Research Institute, Columbia University. A Rhodes scholar, he graduated from Stanford University, University of Oxford, and Harvard Medical School. He is the author of The Gene: An Intimate History, and The Emperor of All Maladies: A Biography of Cancer, a 2011 Pulitzer Prize winner..

Nakamura, Eita and Kunihiko Kaneko. Statistical Evolutionary Laws in Music Styles. Nature Scientific Reports.. 9/15993, 2019. In late 2019, Kyoto University and University of Tokyo, Universal Biology Institute offer a good example of our 21st century worldwise project reaching a systemic synthesis across these widest ecosmos to cultural occasions, and every other natural and social phase in between. In significant regard, a reciprocal presence even in musical compositions of dual phases of conserved tradition, and a creative originality is recorded. So once again an iconic reciprocity akin to physical energy and our bicameral brains is found to grace score and song. See also Cultural Evolution of Music by Patrick Savage in Nature Communications (5/16, 2019).

If a cultural feature is transmitted over generations and exposed to stochastic selection, its evolution may be governed by statistical laws. Music exhibits steady changes of styles over time, with new characteristics developing from traditions. Here we analyze Western classical music data and find statistical evolutionary laws. We then study an evolutionary model where creators learn from past data so to generate new data to be socially selected according to the content dissimilarity (novelty) and style conformity (typicality). The model reproduces the observed statistical laws and can make predictions for independent musical features. In addition, the same model with different parameters can predict the evolution of Japanese enka music. Our results suggest that the evolution of musical styles can partly be explained and predicted by the evolutionary model incorporating statistical learning. (Abstract excerpts)

In the evolutionary process studied here, the balance between novelty and typicality (i.e. content dissimilarity and style conformity) plays an essential role. As we saw in the classical music data and enka music data, relative values can influence the direction and speed of evolution. The novelty and typicality biases can then be important for other types of culture. Evolutionary dynamics of language, other genres of music, scientific topics, and sociological phenomena are among topics under investigation. Another relevant topic is the evolution of bird songs, where selection-based learning is important. Bird song dynamics have been studied to describe the interaction between generators (singing birds) and imitators, which is similar to the novelty-typicality dyad in this study. (7, edits)

Naldi, Giovanni, et al, eds. Mathematical Modeling of Collective Behavior in Socio-Economic and Life Sciences. Boston: Birkhauser. 2010. University of Milan (Naldi), Ferrera (Lorenzo Pareschi), and Pavia (Guiseppe Toscani) systems physicists provide, per the Preface quotes next, a cogent update on the worldwide discovery of a universality of complex dynamical systems of agents and affairs. As an emergent convergence, statistical mechanics and complexity science now flow together to reveal a new genesis nature from molecule to metropolis.

The description of emerging collective behaviors and self–organization in a group of interacting individuals has gained increasing interest from various research communities in biology, engineering, physics, as well as sociology and economics. In the biological context, swarming behavior of bird flocks, fish schools, insects, bacteria, and people is a major research topic in behavioral ecology with applications to artificial intelligence. Likewise, emergent economic behaviors, such as distribution of wealth in a modern society and price formation dynamics, or challenging social phenomena such as the formation of choices and opinions are also problems in which the emergence of collective behaviors and universal equilibria has been shown. (v)

The novelty here is that important phenomena in seemingly different areas such as sociology, economy and biology can be described by closely related mathematical models. In this book we present selected research topics that can be regarded as new and challenging frontiers of applied mathematics. These topics have been chosen to elucidate the common methodological background underlining the main idea of this book: to identify similar modeling approaches, similar analytical and numerical techniques, for systems made out of a large number of “individuals” that show a “collective behavior,” and obtain from them “average” information. The expertise obtained from dealing with physical situations is considered as the basis for the modeling and simulation of problems for applications in the socio-economic and life sciences, as a newly emerging research field. In most of the selected contributions, the main idea is that the collective behaviors of a group composed of a sufficiently large number of individuals (agents) could be described using the laws of statistical mechanics as it happens in a physical system composed of many interacting particles. This opens a bridge between classical statistical physics and the socio-economic and life sciences. (v-vi)

Nemenman, Ilya. Information Theory and Adaptation. Wall, Michael, ed. Quantitative Biology: From Molecular to Cellular Systems. Boca Raton: CRC Press, 2012. In this chapter, the Emory University, Departments of Physics, Biology, Computational and Life Sciences Strategic Initiative, theorist embarks on this mission to understand the many ways that communicated content plays a major role in biological viability.


In this Chapter, we ask questions (1) What is the right way to measure the quality of information processing in a biological system? and (2) What can real-life organisms do in order to improve their performance in information-processing tasks? We then review the body of work that investigates these questions experimentally, computationally, and theoretically in biological domains as diverse as cell biology, population biology, and computational neuroscience. (Abstract, arXiv:1011.5466)

“What is physics? ... -- The idea ... that the world is understandable.” John J. Hopfield. I am a physicist working to understand how biological systems, such as cells, organisms, and populations, learn from their surrounding environment and respond to it (we call this "biological information processing"). I am interested in physical problems in this biological domain. That is “Are there phenomenological, coarse-grained, and yet functionally accurate representations of biological processes, or are we forever doomed to every detail mattering?” I hope to achieve some quantitative understanding of such complex phenomena as evolution, sensory processes, animal behavior, human cognition, and, who knows, maybe one day even human consciousness. (Ilya Nemenman website)

Newman, Mark. Communities, Modules and Large-scale Structure in Networks. Nature Physics. 8/1, 2012. The University of Michigan, Center for the Study of Complex Systems, mathematician is a leading theorist, advocate and explainer of this grand perception of a whole integral dimension of cosmos, life, persons, and culture beyond reduced, isolate things alone. In an Insight – Complexity section, a recent emphasis arising from these studies is the common tendency to form semi-autonomous modular communities, indeed an array of nested networks that much increases robust survival. His latest book Networks: An Introduction (Oxford, 2010) is a standard source.

Nieves, Veronica, et al. Maximum Entropy Distributions of Scale-Invariant Processes. Physical Review Letters. 105/118701, 2010. Postdoc physicist Nieves, grad student Jingfeng Wang and Rafael Bras, Dean of Engineering (newly Georgia Tech Provost), University of California, Irvine, and grad student Elizabeth Wood, MIT, find that circa 2010 nature’s grand self-similarity, as many other recent papers attest, is robustly evident from cosmos to civilization, and can indeed be modeled in its universal mode by such physical principles.

Organizations of many variables in nature such as soil moisture and topography exhibit patterns with no dominant scales. The maximum entropy (ME) principle is proposed to show how these variables can be statistically described using their scale-invariant properties and geometric mean. The ME principle predicts with great simplicity the probability distribution of a scale-invariant process in terms of macroscopic observables. The ME principle offers a universal and unified framework for characterizing such multiscaling processes. (Abstract, 118701)

Such scale-invariant behavior resulting from self-organization emerges as the most probable and macroscopically reproducible state. It turns out that the geometric mean provides essential information for shaping river networks. (118701-4) The geometric mean is also identified as an important parameter, in addition to the moments, in characterizing multiscaling incremental processes of soil moisture and topography. ….this analysis supports the assertion that the ME theory is a universal and unified framework to characterize those processes governed by scale-invariant laws. (118701-4)

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