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

4. Universality Affirmations: A Critical Complementarity

Atay, Fatihcan, et al. Perspectives on Multi-Level Dynamics. arXiv:1606.05665. This complex paper is another example of realizations that a grand scientific synthesis from cosmic physics to social media can just now be gathered and achieved. A team of MPI Mathematics in the Sciences and University of Bielefeld researchers first cite an independent, generic model, and then illume its presence from information theory, Markov processes, agent-based models, mean-field methods in neuroscience, renormalization group theory, to quantum decoherence. While highly technical, the work conveys a broad conviction that as global collaborations build and converge, this historic goal is at last within reach.

As Physics did in previous centuries, there is currently a common dream of extracting generic laws of nature in economics, sociology, neuroscience, by focalising the description of phenomena to a minimal set of variables and parameters, linked together by causal equations of evolution whose structure may reveal hidden principles. (Abstract) It is generally agreed that complex systems are comprised of a large number of subcomponents and their interactions. Moreover, they often exhibit structures at various spatial and temporal levels. As a somewhat extreme example, spanning length and time scales of vastly different magnitudes, one can cite the hierarchy of molecules, neurons, brain areas, brains, individuals, social organizations, economies, etc., which can be viewed as manifestations of the same collective physical reality at different levels. (1).

Ayyad, Marouane and Saliya Coulibaly. The Cellular Automata Inside Optical Chimera States. Chaos, Solitons and Fractals. December, 2021. We note this entry by University of Lille, CNRS researchers as one example of how any natural phenomena seems to spontaneously seek and reside at optimum dynamic poise between more or less order.

Cellular automata are conceptual discrete dynamical systems useful in the theory of information. The spatiotemporal patterns that they produce are intimately related to computational mechanics in distributed complex systems. Here, we investigate their physical implementation in the framework of chimera states in which coherent and incoherent behavior coexist. Hence, chimera states were subject to quantitative and qualitative analyzes borrowing the same tools used to characterize cellular automata. Our results reveal the existence of cellular automata-type dynamics submerged in the dynamics exhibited by our optical chimera states. Thus, they share a panoply of attributes in terms of computational abilities.

Bachmann, Michael, et al. Recent Advances in Phase Transitions and Critical Phenomena. European Physical Journal Special Topics. 226/4, 2017. University of Georgia, USA, Coventry University, UK, Heidelberg University, and Leipzig University physicists, including Ralph Kenna, introduce a special issue on nature’s apparent propensity to move into and reside at a poised state betwixt chaos and order (chaorder?) everywhere from cosmos to culture. A typical paper might be From Dynamical Scaling to Local Scale-Invariance by Malte Henkel. For more, see also herein Criticality as It Could Be by Miguel Aguilera and Manuel Bedia.

Phase transitions and critical phenomena are of ubiquitous importance from the femtometre scale in quantum chromodynamics to galaxy formation in the universe, from the folding, adsorption or denaturation of bio-polymers to the magnetisation effects in storage media, from percolation in complex social networks to fragmentation transitions in atomic nuclei. The present issue discusses a cross section of the current research on phase transitions and critical phenomena in condensed-matter physics, with a focus on soft and hard matter systems as well as the most important methods used for studying such problems. (Abstract)

The study of phase transitions is by now a quite mature subject. Early notions akin to modern ideas of phase transitions are already present in ancient Greek philosophical texts, for instance in Aristotle’s theory of the elements. Still, it was only in the late 18th and early 19th century that the advent of the steam engine necessitated a profound theoretical description. (533) The character of this special point was only fully understood with the introduction of the renormalization group by (Leo) Kadanoff and (Kenneth) Wilson about 50 years ago, which explains scaling and universality and now serves as a complete fundamental theory of critical phenomena. (533-534)

Bala, Arun. Complementarity Beyond Physics. Basingstoke, UK: Palgrave Macmillan, 2017. The National University of Singapore senior research fellow is the author of significant works such as The Dialogue of Civilizations in the Birth of Modern Science (2006). This novel edition covers a cultural history of intuitions that this encompassing existence wherein we find ourselves is distinctly composed of opposite but reciprocal gender-like archetypes. Its premier modern view is by Niels Bohr in the 1920s, aka the Copenhagen interpretation, which he enhanced by referrals to yin/yang Asian wisdom. As the quote says, while subject to debate, a quantum essence of dual particle and wave states or modes has since grown in veracity and scope. The timely volume proceeds with further evidence from biological, anthropological, behavioral and philosophical domains. For a 2018 regard, see The Consciousness Instinct by Michael Gazzaniga which makes a strong claim via Bohr along with Howard Pattee (search) that this prime principle does reveal and exemplify a cosmic to cognitive complementarity.

In this study Arun Bala examines the implications that Niels Bohr’s principle of complementarity holds for fields beyond physics. Bohr, one of the founding figures of modern quantum physics, argued that the principle of complementarity he proposed for understanding atomic processes has parallels in psychology, biology, and social science, as well as in Buddhist and Taoist thought. But Bohr failed to offer any explanation for why complementarity might extend beyond physics, and his claims have been widely rejected by scientists as speculation. Arun Bala offers a detailed defense of Bohr’s claim that complementarity has far-reaching implications for the biological and social sciences, as well as for comparative philosophies of science, by explaining Bohr’s parallels as responses to the omnipresence of grown properties in nature.

(Robert) Oppenheimer suggest that not only does quantum theory seem to violate the rules of classical logic but also that there are traditions of Eastern thought which have come to recognize, long before that to comprehensively understand phenomena in the universe often requires the use of mutually exclusive but complementary notions. (3)

Bansal, Kanika, et al. Cognitive Chimera States in Human Brain Networks. Science Advances. 5/4, 2019. Six system neurophysicists with postings across the USA from Columbia University to UC Santa Barbara provide an illustrated overview to date about how much cerebral activities as a elf-organized criticality are manifestly distinguished by a dynamic interplay of relatively order and disorder. Since this chimeric system is then to reside in both modes at the same time, this overall effect is dubbed a Metastable condition.

The human brain is a complex dynamical system, and how cognition emerges from spatiotemporal patterns of activity remains an open question. As brain regions interact to perform cognitive tasks, patterns of partial synchrony can be observed forming chimera states. We propose that these dual modes present a cognitively informed, chimera-based framework to explore how large-scale brain architecture affects its function. Our results suggest a classification of cognitive systems into four groups with differing levels of subject and regional variability that reflect their different functional roles. (Abstract excerpt)

Using a novel, chimera-based framework, we explored the dynamical states that emerge across large-scale cognitive systems following the spread of a targeted regional stimulation. We identified three distinct dynamical states — coherent, chimera, and metastable — that arise as a function of the structural connectivity of the stimulated regions. A core result across all analyses is the variety in frequency and distribution of the observed dynamical states. Chimera states are the most pervasive state to emerge following regional stimulation. This likely reflects the foundational role that partial synchrony serves in large-scale brain function to enable the intricate balance between segregated and integrated neural processing. (8)

Bar-Yam, Yaneer. From Big Data to Important Information. arXiv:1604.00976. As the quotes cite, the physicist, founder and president of the New England Complex Systems Institute since the 1990s, after some pages of technical discussion, contends in 2016 that a universal systemic recurrence is indeed evident across widely disparate realms.

Advances in science are being sought in newly available opportunities to collect massive quantities of data about complex systems. While key advances are being made in detailed mapping of systems, how to relate this data to solving many of the challenges facing humanity is unclear. The questions we often wish to address require identifying the impact of interventions on the system and that impact is not apparent in the detailed data that is available. Here we review key concepts and motivate a general framework for building larger scale views of complex systems and for characterizing the importance of information in physical, biological and social systems. We provide examples of its application to evolutionary biology with relevance to ecology, biodiversity, pandemics, and human lifespan, and in the context of social systems with relevance to ethnic violence, global food prices, and stock market panic. Framing scientific inquiry as an effort to determine what is important and unimportant is a means for advancing our understanding and addressing many practical concerns, such as economic development or treating disease. (Abstract)

The mapping of water to vapor transitions onto magnetic transitions illustrates how one type of behavior can describe many possible systems. As renormalization group was more widely applied, many instances were found of systems that have the same behavior even though they differ in detail, a concept that became referred to as universality. Still, while many systems have the same behavior, there are systems that have distinct behaviors. Together this means that systems fall into classes of behaviors, leading to the term `universality class.' Power laws often arise in the context of behaviors that exist across scales, and the value of the exponent became used as a signature of the universality class. (6)

The study of universality enables us to identify classes of systems whose behaviors can be described the same way and can be captured by a common mathematical model. This is the principle of universality that is formalized by the analysis of renormalization group and generalized by the application of multiscale information theory to the scientific study of complex systems. A good way to think about this is that the mathematical model describes one member of the class. (7)

Bashan, Amir, et al. Universality of Human Microbial Dynamics. Nature. 534/259, 2016. In the currency of the Human Microbiome Project to quantify our myriad bacterial species that suffuse, help or harm us, Harvard Medical School, MIT Physics of Living Systems, and Dana-Farber Cancer Institute researchers apply the latest algorithmic, computational methods to reveal common patterns and processes in anatomical forms and physiological functions. A commentary, Rules of the Game for Microbiota, in the same issue (534/182) lauds the findings as proof that for everyone, bacteria behave in a common, repeatable fashion. As a result, this makes medical treatments more amenable.

Human-associated microbial communities have a crucial role in determining our health and well-being, and this has led to the continuing development of microbiome-based therapies such as faecal microbiota transplantation. These microbial communities are very complex, dynamic and highly personalized ecosystems, exhibiting a high degree of inter-individual variability in both species assemblages and abundance profiles. It is not known whether the underlying ecological dynamics of these communities, which can be parameterized by growth rates, and intra- and inter-species interactions in population dynamics models, are largely host-independent (that is, universal) or host-specific. If the inter-individual variability reflects host-specific dynamics due to differences in host lifestyle, physiology or genetics, then generic microbiome manipulations may have unintended consequences. Alternatively, microbial ecosystems of different subjects may exhibit universal dynamics, with the inter-individual variability mainly originating from differences in the sets of colonizing species.

Here we develop a new computational method to characterize human microbial dynamics. By applying this method to cross-sectional data from two large-scale metagenomic studies—the Human Microbiome Project and the Student Microbiome Project—we show that gut and mouth microbiomes display pronounced universal dynamics, whereas communities associated with certain skin sites are probably shaped by differences in the host environment. Notably, the universality of gut microbial dynamics is not observed in subjects with recurrent Clostridium difficile infection but is observed in the same set of subjects after faecal microbiota transplantation. These results fundamentally improve our understanding of the processes that shape human microbial ecosystems, and pave the way to designing general microbiome-based therapies. (Abstract)

Bastidas, Victor, et al. Chimera States in Quantum Mechanics. arXiv:1807.08056. Technical University of Berlin physicists post another take on how everyday complex self-organizing networks can be found in this once opaque domain. The paper opens by noting how such phenomena are apply in kind across every natural and neural realm. In addition, these semi-synchronized conditions are seen to exhibit a dynamic critical balance, which can carry and process information. See also Chaos in Dirac Electron Optics: Emergence of a Relativistic Quantum Chimera in Physical Review Letters (120/124101, 2018) for a concurrent contribution and Quantum Signatures of Chimera States (1505.02639) for an earlier view.

Classical chimera states are paradigmatic examples of partial synchronization patterns emerging in nonlinear dynamics. These states are characterized by the spatial coexistence of two dramatically different dynamical behaviors, i.e., synchronized and desynchronized dynamics. Our aim in this contribution is to discuss signatures of chimera states in quantum mechanics. We study a network with a ring topology consisting of N coupled quantum Van der Pol oscillators. We describe the emergence of chimera-like quantum correlations in the covariance matrix. Further, we establish the connection of chimera states to quantum information theory by describing the quantum mutual information for a bipartite state of the network. (Abstract)

Behler, Jorg. Neural Network Potential-Energy Surfaces in Chemistry. Physical Chemistry Chemical Physics. 13/17930, 2011. Reviewed more in systems Chemistry, a Ruhr University Bochum theoretical chemist finds these iconic cerebral dynamic topologies to be readily adaptable to chemical phenomena.

Berezutskii, Aleksandr, et al. Probing Criticality in Quantum Spin Chains with Neural Networks. Journal of Physics: Complexity. 1/3, 2020. We cite entry this by an international team based in Canada, Russia, and Hungary including Jacob Biamonte as an example of how even quantum phenomena can and does exhibit nature’s independent preference for this widely prevalent state of dynamic balance.

The numerical emulation of quantum systems often requires an exponential number of degrees of freedom which translates to a computational bottleneck. Methods of machine learning have been used in adjacent fields for effective feature extraction and high-dimensional datasets. Recent studies have revealed that neural networks are suitable for the determination of macroscopic phases of matter as well as efficient quantum state representation. In this work, we address phase transitions in quantum spin chains and show that even neural networks with no hidden layers can be effectively trained to distinguish between magnetically ordered and disordered phases. Our results extend to a wide class of interacting quantum many-body systems and illustrate the wide applicability of neural networks to many-body quantum physics. (Abstract excerpt)

Betzel, Richard, et al. Diversity of Meso-Scale Architecture in Human and Non-Human Connectomes. arXiv:1702,02807. We place this entry in Mid 2010s Universalities since University of Pennsylvania neuroscientists Betzel, John Medaglia and Danielle Bassett report common cerebral geometries across Metazoan creaturely evolution from insects and mice to primates and homo sapiens.

The brain's functional diversity is reflected in the meso-scale architecture of its connectome, i.e. its division into clusters and communities of topologically-related brain regions. The dominant view, and one that is reinforced by current analysis techniques, is that communities are strictly assortative and segregated from one another, purportedly for the purpose of carrying out specialized information processing. Such a view, however, precludes the possibility of non-assortative communities that could engender a richer functional repertoire by allowing for a more complex set of inter-community interactions. Here, we use weighted stochastic blockmodels to uncover the meso-scale architecture of Drosophila, mouse, rat, macaque, and human connectomes.

We confirm that while many communities are assortative, others form core-periphery and disassortative structures, which in the human better recapitulate observed patterns of functional connectivity and in the mouse better recapitulate observed patterns of gene co-expression than other community detection techniques. We define a set of network measures for quantifying the diversity of community types in which brain regions participate. Finally, we show that diversity is peaked in control and subcortical systems in humans. In summary, our report paints a more diverse portrait of connectome meso-scale structure and demonstrates its relevance for cognitive performance. (Abstract)

Bialek, William and Jousha Shaevitz.. Long time scales, individual differences, and scale invariance in animal behavior.. arXiv:2304.09608. Princeton University and Rockefeller University system theorists contribute still another animate phase which is characterized and sustained by beneficial self-similarities.

The explosion of data on animal behavior in natural contexts reveals that these behaviors exhibit correlations across many time scales. But there are major challenges in analyzing these data: records of behavior in single animals have fewer independent samples; individual differences can mimic long-ranged temporal patterns; and so on. We suggest an analysis scheme that addresses these items, an application to data on the spontaneous behavior of walking flies, which will help evince an invariant scale over nearly three decades in time, from seconds to one hour. (Excerpt).

Animals, including humans, exhibit behaviors with structure on many time scales. In one view, the wide range of time scales emerges from interactions among many underlying degrees of freedom, perhaps approaching a nearly scale invariant continuum [3]. Scale invariance is especially tantalizing because of possible links to
the renormalization group and critical phenomena. This is an opportune moment to revisit the experiments because we have seen an explosion of quantitative data on animal behavior under more naturalistic conditions. Examples include the variability of eye movement trajectories in primates [7]; the postural dynamics of freely moving C elegans, walking flies, and mice; behavioral bouts in zebrafish larvae; birdsong and other acoustic sequences. Although we focus here on the behavior of individual organisms, there is strong evidence for scale invariance in collective behaviors of flocks and swarms. (1)

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