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

2. Computational Systems Physics: Self-Organization, Active Matter

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)

Buchanan, Mark. Birds of a Feather. Nature Physics. 9/7, 2013. In this month’s column, the physicist writer reports on the work of Cristina Marchetti, et al, and Andrea Cavagna, et al (search each) about how “scale-free collectives of interacting, self-propelling elements” from microbes and flocks to every animal assembly are becoming known as a natural form of “active matter.” This advance is reviewed more in Organic Universe, see the Marchetti paper, Sriram Ramaswamy, and others.

Castellano, Claudino, et al. Statistical Physics of Social Dynamics. Reviews of Modern Physics. 81/2, 2009. With co-authors Santo Fortunato and Vittorio Loreto, a significant tutorial, only just evident and possible, that joins the disparate domains of physical nature and human societies. In so doing a notable agreement arises. Statistical physics and nonlinear network systems, as they now morph into each other, are seen to convey one and the same phenomena. Each approach describes how the interactivity of many elements or entities results in the emergence of a self-organized critical order. (see also, e.g., C. Beck herein) Consequences may then work both ways. A new kind of animate universe is implied with an innate material propensity to progressively organize itself, and, much removed in time and space, from which our human world arises, as if genetically rooted in such a natural gestation.

In social phenomena the basic constituents are not particles but humans and every individual interacts with a limited number of peers, usually negligible compared to the total number of people in the system. In spite of that, human societies are characterized by stunning global regularities. There are transitions from disorder to order, like the spontaneous formation of a common language/culture or the emergence of consensus about a specific issue. There are examples of scaling and universality. These macroscopic phenomena naturally call for a statistical physics approach to social behavior, i.e., the attempt to understand regularities at large scale as collective effects of the interaction among single individuals, considered as relatively simple entities. (592)

Thus, the study of the self-organization and evolution of language and meaning has led to the idea that a community of language users can be seen as a complex dynamical system which collectively solves the problem of developing a shared communication system. In this perspective, which has been adopted by the novel field of semiotic dynamics, the theoretical tools developed in statistical physics and complex systems science acquire a central role for the study of the self-generated structures of language systems. (617)

Ceron, Steven, et al. Programmable Self-Organization of Heterogeneous Microrobot Collectives. PNAS. 120/24, 2023. We cite this June entry by Cornell, MIT, MPI Intelligent Systems, and ETH Zurich researchers as one more exemplary realization of mindful life’s common, innate propensity to compose itself (her/his) by applying and guiding these procreative energies as they gather and vivify, going forward.

At the microscale, coupled physical interactions between collectives of agents can be exploited to enable self-organization. Past systems typically consist of identical agents; however, heterogeneous agents can exhibit asymmetric pairwise interactions which can be used to generate more diverse patterns of self-organization. Here, we study the effect of size heterogeneity in microrobot collectives composed of circular, magnetic microdisks on a fluid–air interface. Our work furthers insights into self-organization in heterogeneous microrobot collectives and moves us closer to the goal of applying such collectives to programmable self-assembly and active matter. (Significance excerpt)

Cheng, Zhao, et al. Pattern Phase Transitions of Self-Propelled Particles. New Journal of Physics. 18/10, 2016. Huazhong University of Science and Technology, China, University of Newcastle, Australia, and Eotvos University, Hungary (Tamas Viczek) systems physicists advance our understanding of how internally motive entities interact together with a dynamic, formative consistence.

To understand the collective behaviors of biological swarms, flocks, and colonies, we investigated the non-equilibrium dynamic patterns of self-propelled particle systems using statistical mechanics methods and H-stability analysis of Hamiltonian systems. By varying the individual vision range, we observed phase transitions between four phases, i.e., gas, crystal, liquid, and mill-liquid coexistence patterns. In addition, by varying the inter-particle force, we detected three distinct milling sub-phases, i.e., ring, annulus, and disk. Based on the coherent analysis for collective motions, one may predict the stability and adjust the morphology of the phases of self-propelled particles, which has promising potential applications in natural self-propelled particles and artificial multi-agent systems. (Abstract)

Cichos, Frank, et al. Machine Learning for Active Matter. Nature Machine Intelligence. February, 2020. As many studies herein find that the movements of living systems from colloids and microbes to birds and people seem to be guided by and exhibit common self-organizing patterns, Leipzig University and University of Gothenburg add a further AI technique. An opening graphic cites molecular motors, turbulence, living crystals, growing tissues, chemotaxis, foraging, swimming, clustering and more, while a second shows convolutional neural nets, reservoir computing, genetic algorithms and other skill-sets. In regard, as brain-based deep AI gains wide, analytic utility, evinced by this application to an lively materiality, another window upon a natural genesis which avails and repeats the same iconic, bicameral, triality code everywhere is opened.

Machine learning techniques have already been successfully applied to active-matter data—for example, deep neural networks to analyse images and track objects, and recurrent nets and random forests to analyse time series. Yet machine learning can also help with the complexity of biological active matter, to establish a relation between the genetic code and bacterial behaviour, navigation strategies in complex environments, and to map physical cues onto animal behaviours. In this Review, we highlight the current state of the art and discuss opportunities and challenges. (Abstract excerpt)

Coleman, Piers. Frontier at Your Fingertips. Nature. 446/379, 2007. A note that physics and biology are converging as materiality becomes reinvented as a self-similar expression of universal collaborative principles. See also the Emergent Universe Project website noted in this section.

Some believe that emergence implies an abandonment of reductionism in favour of a more hierarchical structure of science, with disconnected principles developing at each level. Perhaps. But in almost every branch of physics, from string theory to condensed-matter physics, we find examples of collective, emergent behavior that share common principles. (379) To me, this suggests that emergence does not spell the end for reductionism, but rather indicates that it be realigned to embrace collective behavior as in integral part of our Universe. (379)

Costa, Luciano da Fontoura, et al. Analyzing and Modeling Real-World Phenomena with Complex Networks. Advances in Physics. 60/3, 2011. Drawing upon a departmental focus on this field, eight University of Sao Paulo physicists provide a 108 page survey, with 565 references, of this real dynamic materiality across nature and society. After noting Basic Concepts, topical areas are Social, Communications, Economy, Finance, Computers, Internet, World Wide Web, Citations, Transportation Power Grids, Biomolecular, Medicine, Ecology, Neuroscience, Linguistics, Earthquakes, Physics, Chemistry, Mathematics, Climate, and Epidemics – that is everywhere. From these many exemplars can be distilled a common, independent, complex system topology. Circa 2012, how could it dawn upon international collaborative science that this ubiquitous discovery is actually revealing a procreative genesis universe? In such regard, other such citations lately weigh in, e.g., Li and Peng in Complex Human Societies, Dorogovtsev, the Nature Physics Insight review, all herein, boding a critical credence. We append extended quotes.

The many achievements of physics over the last few centuries have been based on reductionist approaches, whereby the system of interest is reduced to a small, isolated portion of the world, with full control of the parameters involved (e.g., temperature, pressure, electric field). An interesting instance of reductionism, which is seldom realized, is the modeling of non-linear phenomena with linear models by restricting the parameters and variables in terms of a linear approximation. In establishing the structure of matter with the quantum theory in the first few decades of the 20th century, for example, reductionism was key to reaching quantitative treatment of the properties of atoms, molecules and then sophisticated structures such as crystalline solids. Indeed, deciphering the structure of matter was decisive for many developments – not only in physics but also in chemistry, materials science and more recently in biology. Nevertheless, with reductionist approaches only limited classes of real-world systems may be treated, for the complexity inherent in naturally-occurring phenomena cannot be embedded in the theoretical analysis. (4)

The success of complex networks is therefore to a large extent a consequence of their natural suitability to represent virtually any discrete system. Moreover, the organization and evolution of such networks, as well as dynamical processes on them, involve non-linear models and effects. The connectivity of networks is ultimately decisive in constraining and defining many aspects of systems dynamics. For instance, the behavior of biological neuronal networks, one of the greatest remaining scientific challenges, is largely defined by connectivity. Because of its virtually unlimited generality for representing connectivity in the most diverse real systems in an integrative way, complex networks are promising for integration and unification of several aspects of modern science, including the inter-relationships between structure and dynamics. Such a potential has been confirmed with a diversity of applications for complex networks, encompassing areas such as ecology, genetics, epidemiology, physics, the Internet and WWW, computing, etc. In fact, applications of complex networks are redefining the scientific method through incorporation of dynamic and multidisciplinary aspects of statistical physics and computer science. (4-5)

Frequently, the success of new areas of physics are judged not only from their theoretical contributions, but also from their potential for applications to real-world data and problems. Despite its relatively young age, the area of complex network research has already established itself, especially through its close relationship with formal theoretical fields such as statistical mechanics and graph theory, as a general and powerful theoretical framework for representing and modeling complex systems. It has been capable of taking into account not only the connectivity structure of those systems, but also intricate dynamics. Judging by the large number of areas and articles reviewed in the present survey, complex networks have performed equally well (if not better) with respect to their application potential. Indeed, the generality and flexibility of complex networks extends to virtually every real-world problems, from neuroscience to earthquakes, encompassing at least the 22 areas considered in the present work. (71)

Crosato, Emanuele, at al. Thermodynamics of Emergent Structure in Active Matter. Physical Review E. Online October, 2019. Nine years after this title phrase came about, University of Sydney, Complex Systems Research Group theorists EC, Mikhail Prokopenko, and Richard Spinney quantify energetic properties that further distinguish this spontaneously animate materiality. As the quotes say, as 2020 near, our website survey is well able to report a truly organic, fertile ecosmos from which phenomenal persons arise, awaken and discover.

Active matter is rapidly becoming a key paradigm of out-of-equilibrium soft matter exhibiting complex collective phenomena, yet the thermodynamics of such systems remain poorly understood. In this letter we study the nonequilbrium thermodynamics of large scale active systems capable of mobility-induced phase separation and polar alignment, using a fully under-damped model which exhibits hidden entropy productions not previously reported in the literature. We quantify steady state entropy production at each point in the phase diagram, revealing characteristic dissipation rates associated with the distinct phases and configurational structure. This reveals sharp discontinuities in the entropy production at phase transitions and facilitates identification of the thermodynamics of micro-features, such as defects in the emergent structure. (Abstract)

In this letter we have explored the thermodynamic character that emerges from the rich collective dynamics exhibited by active matter and highlighted a hidden entropy production where rotational timescales impact dissipation in the translational degrees of freedom. Our results suggest that the richness, commonly associated with the phase structure of active matter, is mirrored in its thermodynamics, opening up a new tool to study collective phenomena on both a micro and macroscopic scale. Important questions remain, including the delicate issue of TRS which we have shown to dramatically influence any thermodynamic interpretation. We hope that the work will contribute to a deeper understanding of the thermodynamics of active systems and, more broadly, the dynamics that can lead to emergent structures. (5)

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