III. Ecosmos: A Revolutionary Fertile, Habitable, Solar-Bioplanet, Incubator Lifescape

2. Computational Systems Physics: Self-Organization

Many scientific fields are in the process of a major shift from reduction to emergence such as Systems Biology, Genetics, Neuroscience, and Chemistry, as this site reports. By finding and cataloging the particles and linear laws of cosmic and material realms, this phase has well plumbed materiality and spacetime. But a Google search for “Systems Physics” does not get any results. A curious inversion seems to occur whence this fundament atomic pursuit since Greece and Rome to Newton’s day may be the last to reconceive itself. A developmental self-organization from galaxies to genomes to Gaia does not appear in large colliders. Physicist advocates such as Philip Anderson, Brian Josephson, Robert Laughlin, Lee Smolin, Nigel Goldenfeld, William Bialek and many others are now trying to move beyond a benthic theory of everything to a recurrent vitality everywhere. A Systems Physics to reunite and reinvent quantum and cosmos from which life, intelligence and personhood in community are meant to emerge is an overdue imperative. Other sections herein such as Active Matter, Fractal Cosmology, Common Principles contain additional references.

2020: While systems biology and systems chemistry have become distinct research endeavors (see sections herein), this title phrase, which should be equally obvious, has not come into wider use (no hits on Google). The olden divide between life sciences and a mechanical physics is deeply rooted. This section went online in the mid 2000s for contributions that began to scope out the endemic presence of dynamically interconnected environs from universe to human. The current scientific revolution has now well advanced toward a natural unified, animate ecosmos, as Active Matter attests. Other sections such as A Survey of Common Principles contain more entries about complex network self-organizations everywhere.

Corbetta, Alessandro and Federico Toschi. Physics of Human Crowds. Annual Review of Condensed Physics. 14/311, 2023.
Drossel, Barbara. Strong Emergence in Condensed Matter Physics. arXiv:1909.01134.
Green, Sara and Robert Batterman. Making Sense of Top-Down Causation: Universality and Functional Equivalence in Physics and Biology. Voosholz, J and M. Gabriel, eds.. Top_Down Causation and Emergence. Cambridge: MIT Press, 2021.
Hidalgo, Jorge, et al. Cooperation, Competition and the Emergence of Criticality in Communities of Adaptive Systems. Journal of Statistical Mechanics. March, 2016.
Koorehdavoudi, Hana and Paul Bogdan. A Statistical Physics Characterization of the Complex Systems Dynamics. Nature Scientific Reports. 6/27602, 2016.

Kresic, Ivor, et al. Generating Multiparticle Entangled States by Self-Organization of Driven Ultracold Atoms. arXiv:2208.10111.
Nakamura, Eita and Kunihiko Kaneko. Statistical Evolutionary Laws in Music Styles. Nature Scientific Reports. 9/15993, 2019.
Perc, Matjaz. Beauty in Artistic Expressions through the Eyes of Networks and Physics. Journal of the Royal Society Interface. March 11, 2020.
Rodriguez, Quentin. Idealizations and Analogies. arXiv:2110.12712.
Rovelli, Carlo. The Relational Interpretation of Quantum Physics. arXiv:2109.09170.

2023:

Emergent Universe Project. www.emergentuniverse.org/emp. A new site by a diverse consortium of the Institute for Complex Adaptive Matter, a multi-campus research program of the University of California, the San Francisco Exploratorium, and the science museums of Chicago and Minnesota. Members include David Pines, veteran complexity theorist and founding co-director of ICAM (earlier of the Santa Fe institute), Piers Coleman, professor of physics at Rutgers, and Linda Feferman, a producer of educational films to convey emergent principles. Search the site for goodies, watch for coming attractions. A significant sign in the air of a revolution to a genesis cosmos which innately gives rise to personal life and mind.

The breathtaking quality of emergence lies in its broad applicability, from ants to people, and from electrons to galaxies. We assume that we can sing and dance together because we are intelligent and coordinate our behavior, and so it is surprising to see the coordinated chirping of crickets, and shocking to discover that the same principles apply to mindless things such as water molecules arranging themselves in a crystalline structure to form ice. When you get enough things together, and they interact in just the right way, they suddenly shift to coherent behavior. Emergent principles may govern the smallest units of matter, as in electrons humming together within a superconductor, to the largest, as when entire galaxies clump into regular patterns. Scientists across multiple fields have found that such systems don't require a central ringleader directing the way – their self-organization is inevitable, due to the local interactions of nearest neighbors.

Heinz von Foerster 100 Self-Organization and Emergence Congress. www.univie.ac.at/hvf11/congress/EmerQuM.html. Heinz von Foerster (1911-2002) was an Austrian American physicist, philosopher and a pioneer of cybernetics and systems theory. This centenary conference was held in November 2011 in Vienna with a dual focus on Self-Organization and Emergence in Nature and Society, and Emergent Quantum Mechanics. Keynoters for the first topic are Albert-Laszlo Barabasi, John Holland and Didier Sornette, and for the other Stephen Adler, Gerard ‘t Hooft, and Lee Smolin. Abstract are available for these talks, and some fifty others such as Emergence, Gravity, and Thermodynamics by Bei-Lok Hu, reviewed in A Thermodynamics of Life.

I review the proposal made in my 2004 book, that quantum theory is an emergent theory arising from a deeper level of dynamics. The dynamics at this deeper level is taken to be an extension of classical dynamics to non-commuting matrix variables, with cyclic permutation inside a trace used as the basic calculational tool. With plausible assumptions, quantum theory is shown to emerge as the statistical thermodynamics of this underlying theory, with the canonical commutation-anticommutation relations derived from a generalized equipartition theorem. Brownian motion corrections to this thermodynamics are argued to lead to state vector reduction and to the probabilistic interpretation of quantum theory, making contact with phenomenological proposals for stochastic modifications to Schroedinger dynamics. (Adler)

Highly interconnected networks with amazingly complex structure de-scribe systems as diverse as the World Wide Web, our cells, social systems or the economy. In the past decade we learned that most of these networks are the result of self-organizing processes governed by simple but generic laws, resulting in architectural features that makes them much more similar to each other than one would have expected by chance. I will discuss the recurring patterns of our interconnected world and its implications to network robustness and spreading processes. (Barabasi)

Various classical systems are discussed that can be approached with standard statistical methods. It is shown how quantum mechanical procedures can be applied to such systems to study features such as large-distance behavior. As a result, one finds that the time evolution of its large distance correlations can be written in terms of rigorously quantum mechanical Schroedinger equations. One concludes that even though the dynamical laws are classical, the probability distributions are described by quantum states, showing quantum entanglement. These quantum states violate Bell's inequalities. The suspicion that our universe is also described by such a classical, deterministic underlying theory leads to a natural interpretation of quantum mechanics. (Gerard ‘t Hooft)

Quantum Systems In and Out of Equilibrium. ergodic.ugr.es/cp. A site for a June 2017 seminar at the University of Granada, Spain, which we note as an example of current frontiers in this fundamental realm in the later 2010s. Some 20 theorists spoke such as Sandu Popescu, Beatriz Olmos, and Hans Briegel, with available Abstracts. It is sponsored by the UG Statistical Physics Group, linked to this site.

The aim of this meeting is to bring together scientists interested in Quantum aspects of Thermalization, Quantum Transport, Quantum Effects in Macroscopic Systems (condensed matter, biology, etc.), Quantum Computation, Open Quantum Systems, Quantum Fluctuations and Large Deviations, and Quantum Thermodynamics.

STATPHYS25. http://www.statphys25.org/index.htm. A site for this 25th International Conference on Statistical Physics of the International Union of Pure and Applied Physics, held July 2013 in Seoul, Korea. We cite two presentations as examples of the field’s turn to and melding with nonlinear complexity and its application to living systems. The first Abstract is for “The Complexity, Modularity and Evolution of Self-Assembling Structures in Biology” by Sebastian Ahnert, a University of Cambridge biophysicist, and the second for “Statistical Physics of Driven DNA” by Sanjay Kumar, a Banaras Hindu University physicist. Click on Abstract Book Download on this page for the full 700 page volume. See also Advani, Madhu, et al. “Statistical Mechanics of Complex Neural Systems and High Dimensional Data” by Madhu Advant, et al in Journal of Statistical Mechanics (P03015, 2013) for a similar convergence of many agent physics and many body biology.

One of the most rigorous quantitative definitions of complexity is the notion of algorithmic complexity, discovered independently by Kolmogorov and Chaitin. It is based on the idea that the length of the shortest algorithmic description of a set of data can tell us about the complexity of the data. Here we will employ this principle to measure the physical complexity of a structure, with a particular focus on self-assembling biological structures. Self-assembly is a widespread process in biology, and is essential in the formation of structures such as DNA, protein complexes, and viruses. By minimising the information required to specify the building blocks and interactions that give rise to a structure, we obtain a quantitative measure of the structure’s complexity. Using a genetic algorithm with the building blocks as a genotype and the assembled structure as a phenotype we can investigate a number of questions, including how modularity and symmetry arise in biological evolution. We then apply this approach to study the evolution, assembly and classification of protein complexes and discover new fundamental organising principles, which result in a periodic table of protein quaternary structures. (Anhert Abstract)

The separation of a double stranded DNA to two single stranded DNA below its melting point is a prerequisite for processes like transcription and replication. To execute such processes, various proteins work far away from equilibrium in a staggered way. In this talk, we shall discuss some aspects of unzipping of DNA under a drive in non-equilibrium conditions. We propose the dynamic transition, where without changing the physiological condition, it is possible to bring DNA from the zipped/unzipped state to a new dynamic (hysteretic) state by varying the frequency of the applied force. Our studies revealed that the area of the hysteresis loop grows with the same exponents as of the spin systems. We shall propose a steady state phase diagram of driven DNA, which along with scaling exponents are amenable to verification in force spectroscopic experiments. (Kumar Abstract)

Ambjorn, Jan, et al. The Self-Organizing Quantum Universe. Scientific American. July, 2008. Noted more in the above section, an exemplary case of an integrative cosmology.

Allard, Antoine, et al. The Geometric Nature of Weights in Real Complex Networks. Nature Communications. 8/14103, 2017. We note this entry by University of Barcelona, Institute of Complex Systems, theorists including Marian Boguna as a 2017 fulfillment of a constant invariance from cosmos to civilization. As many such papers do, generic network topologies and dynamics are first described, which are then be seen to be instantiated everywhere. This particular works notes their presence from cellular functions to global commerce.

The topology of many real complex networks has been conjectured to be embedded in hidden metric spaces, where distances between nodes encode their likelihood of being connected. Besides of providing a natural geometrical interpretation of their complex topologies, this hypothesis yields the recipe for sustainable Internet’s routing protocols, sheds light on the hierarchical organization of biochemical pathways in cells, and allows for a rich characterization of the evolution of international trade. Here we present empirical evidence that this geometric interpretation also applies to the weighted organization of real complex networks. (Abstract)

Anderson, Philip. More Is Different - One More Time. N. Phuan Ong and Ravin Bhatt, eds. More Is Different. Princeton: Princeton University Press, 2001. The Nobel laureate physicist revisits his landmark 1967 paper which helped turn science from a fixation on subatomic domains to the complexity revolution.

The actual universe is the consequence of layer upon layer of emergence, and the concepts and laws necessary to understand it are as complicated, subtle and, in some cases, as universal as anything the particle folks are likely to come up with. (7)

Ansari, Mohammad and Lee Smolin. Self-Organized Criticality in Quantum Gravity. Classical and Quantum Gravity. 25/095016, 2008. Perimeter Institute theorists advance an early glimpse of nature’s actual innate tendency to seek and maintain itself in an active balance between two coincidental, often complementary opposite states.

We study a simple model of spin network evolution motivated by the hypothesis that the emergence of classical spacetime from a discrete microscopic dynamics may be a self-organized critical process. Self-organized critical systems are statistical systems that naturally evolve without fine tuning to critical states in which correlation functions are scale invariant. We study several rules for evolution of frozen spin networks in which the spins labeling the edges evolve on a fixed graph. We find evidence for a set of rules which behaves analogously to sand pile models in which a critical state emerges without fine tuning, in which some correlation functions become scale invariant. (Abstract)

Barabasi, Albert-Laszlo. Taming Complexity. Nature Physics. 1/2, 2005. The main discoverer of ‘complex networks’ composed of weighted nodes and links, rather than random or Poisson nets of equal rank, surveys these past theories as a way to sight future directions. In the ten years of their realization, scale-free networks are so widely prevalent as to infer a ‘universality’ which springs from an independent source. Both a common web geometry, and a tendency to form modular communities can now be established. A salient article in this new journal that could presage a quite different organic cosmic genesis from the mechanical multiverse paradigm.

We are surrounded by complex systems – from a biological cell, made of thousands of different molecules that seamlessly work together, to our society, a collection of six billion mostly cooperating individuals – which display endless signatures of order and self-organization. (68) The ubiquitous scale-free property in real networks indicates that drastically different networks follow common organizing principles. (69)

The true intellectual thrill for a physicist studying complex networks comes from the recognition that despite this microscopic randomness, a few fundamental laws and organizing principles can explain the topological features of such divers systems as the cell, the Internet or society. (70) At that point we will have a chance to understand the key to nature’s secret code for multitasking – the one that orchestrates the actions of uncountable domponents into a magic dance of order and ultimate elegance. (70)

Barabasi, Albert-Laszlo, Organizer. Predictability: From Physical to Data Sciences. http://aaas.confex.com/aaas/2013/webprogram/Session5856.html. A Symposia in the Physical Sciences tract at the February 2013 AAAS annual meeting in Boston, organized by the Northeastern University physicist and director of its Center for Complex Network Research. Speakers include Dirk Helbing on Towards Simulating the Foundations of Society, Marta Gonzalez's Understanding Road Usage Patterns in Urban Areas, and Alessandro Vespignani on From Human Mobility to Real Time Numerical Forecasts of Global Epidemic Spreading. As the session Abstract notes, these papers, and many others (e.g., the Barabasi Lab site), augur for a discovery of the universal presence and creativity of such complex system principles across all scales and an increasingly dynamic, vital natural cosmos.

There is a newfound convergence between physical and data sciences. The large amount of raw data that society and technology is generating and collecting, combined with the predictive tools of physical sciences, offers unparalleled predictive understanding of social phenomena, affecting domains of inquiry that could not be quantified in the past. The availability of data has lead to the emergence of several new research fields as the boundary of physical and other sciences, resulting in revolutionary advances in understanding complex networks, human mobility, and human dynamics. The tools generated by these are fueling the emergence of network science, computational social science, and digital humanities. This symposium will present how the tools of physical sciences aid our understanding of complex socioeconomic and technical systems. In the spirit of Wigner, we will explore the unreasonable effectiveness of the quantitative tools of natural sciences in social and engineering domains, bringing experts that apply these in various fields outside of physics. In contrast to data mining approaches, which are prevalent in the big data domain, here we focus on uncovering the mechanism and explaining collective phenomena using the predictive tools of natural sciences. (Abstract)

Baran, Nicole, et al. Applying Gene Regulatory Network Logic to the Evolution of Social Behavior. Proceedings of the National Academy of Sciences. 114/5886, 2917. With Patrick McGrath and Todd Streelman, Georgia Tech biologists (no longer just rambling wrecks) discern an innate affinity between genomic and neural (neuromic) network complexities, which can then be traced to and tracked by creaturely activities. As a surmise, a generic, independent source of node and link topologies and dynamics is quite implied.

Animal behavior is ultimately the product of gene regulatory networks (GRNs) for brain development and neural networks for brain function. The GRN approach has advanced the fields of genomics and development, and we identify organizational similarities between networks of genes that build the brain and networks of neurons that encode brain function. In this perspective, we engage the analogy between developmental networks and neural networks, exploring the advantages of using GRN logic to study behavior. Applying the GRN approach to the brain and behavior provides a quantitative and manipulative framework for discovery. We illustrate features of this framework using the example of social behavior and the neural circuitry of aggression. (Abstract)

Briegel, Hans. On Creative Machines and the Physical Origins of Freedom. Nature Scientific Reports. 2/522, 2012. The University of Innsbruck physicist affirms from the latest integration of statistical mechanics with nonlinear dynamics (article keywords) that “higher biological entities” like us do indeed possess a valid free will. This does not quite accord with his “creative machines” term, so natural philosophy clarifications are still in order. We also refer to Briegel’s companion Projective Simulation for Artificial Intelligence in this journal (2/400, 2012) which is the basis for the Giuseppe Paparo, et al, paper on Quantum Learning Systems (search).

We discuss the possibility of free behavior in embodied systems that are, with no exception and at all scales of their body, subject to physical law. We relate the discussion to a model of an artificial agent that exhibits a primitive notion of creativity and freedom in dealing with its environment, which is part of a recently introduced scheme of information processing called projective simulation. This provides an explicit proposal on how we can reconcile our understanding of universal physical law with the idea that higher biological entities can acquire a notion of freedom that allows them to increasingly detach themselves from a strict dependence on the surrounding world. (2/522 Abstract)

We can show, on the basis of physical laws as we understand them today, that entities with a certain degree of physical or biological organization, capable of evolving a specific type of memory, can indeed develop an original notion of creativity and freedom in their dealing with the environment. Our argument will be based on the concept of projective simulation which is a physical model of information processing for artificial agents. (1) This demonstrates, first, that a notion of freedom can indeed exist for entities that operate, without exception and at all scales, under the laws of physics. It also shows that free behavior can be understood as an emergent property of biological systems of sufficient complexity that has evolved a specific form of memory. (2)

We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation. (2/400 Abstract)

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