IV. Ecosmomics: Independent, UniVersal, Complex Network Systems and a Genetic Code-Script Source

Biocomplexity Institute. http://www.indiana.edu/~bioc/. Accessed June 2011, this is an interdisciplinary endeavor of Indiana University at the leading edge of these lively sciences. Biophysicist James Glazier is director, members include biophysicist John Beggs, information visionary Katy Borner, psychologist Robert Goldstone, Allessandro Flammini and Luis Rocha, bioinformatics, and neuroscientist Olaf Sporns. We quote the Institute’s main definition, along with a statement for Goldstone’s Percepts and Concepts Laboratory, as they convey this revolutionary engagement with and discovery of a radical genesis nature.

Biocomplexity is the study of the emergence of self-organized, complex behaviors from the interaction of many simple agents. Such emergent complexity is a hallmark of life, from the organization of molecules into cellular machinery, through the organization of cells into tissues, to the organization of individuals into communities. The other key element of biocomplexity is the unavoidable presence of multiple scales. Often, agents organize into much larger structures; those structures organize into much larger structures, etc. A classic example is the primary, secondary, tertiary, and quaternary folding of DNA into chromosomes that allows a strand of a length of several centimeters to fold, without tangling or losing function, into a chromosome about one micron long. Biocomplexity is a methodology and philosophy as well as a field of study. It focuses on networks of interactions and the general rules governing such networks.

Typically, complex adaptive system models are applied to natural phenomena, such as the pattern of stripes on zebras or seeds on sunflowers. Our research goal is to apply these models to understanding how individual people learn and perceive, and how groups of people organize themselves into emergent structures which none of the individuals in the group may understand or even perceive. Our laboratory is currently exploring interactions between perceptual and conceptual learning, methods for learning abstract concepts using computer simulations, the perception of similarity and analogy, and group behavior from a complex systems perspective. Our typical modus operandi is to simultaneously conduct psychological experiments on humans and develop computational models of the observed behavior. The results from the experiments help to constrain and inform our computational models, and the computational models serve to organize and explain our empirical results. (R. Goldstone)

Center for Models of Life. www.cmol.nbi.dk. A graphically informative website for this endeavor located at the Niels Bohr Institute, University of Copenhagen and directed by Kim Sneppen. Research is pursued such areas as: Physics of Gene Regulation, Models of Biological Circuits, Networks and Communication, and Evolution and Dynamical Systems, each with referenced publications. If our extant nature from cell to city is found to exhibit these pervasive innate and invariant phenomena, by what sufficient proof, might it prompt and allow us to realize a greater genesis?

Complexity Digest. www.comdig.org. Founded by the late Gottfried Mayer, the present Editor-in-Chief is Carlos Gershenson who with international collaborators and audience, suggest and post new citations apropos the wavefront of the complex systems revolution. The engaging site enters the latest advances in articles, books, presentations and conferences, along with university programs, and more. Complexity Digest represents a premier resource for learning about and keeping up with the frontiers of a self-organizing universe and sustainable future.

New England Complex Systems Institute. www.necsi.org. Founded and run by systems scholar Yaneer Bar-Yam and colleagues, this multifaceted site is a rich resource for general and specific content all about the nonlinear systems revolution from evolution to economies. The group has run a biannual International Conference on Complex Systems in the Boston area since the late 1990s. I went to four of these incredible events to which luminaries such as Edward Lorenz, Stephen Wolfram, Stuart Kauffman, Gene Stanley, and everyone else it seems presents or attends. The Proceedings for the 2011 gala are accessible from the home page, click ICCS under Events.

The New England Complex Systems Institute (NECSI) is an independent academic research and educational institution with students, postdoctoral fellows and faculty. In addition to the in-house research team, NECSI has co-faculty, students and affiliates from MIT, Harvard, Brandeis and other universities nationally and internationally.

NECSI has been instrumental in the development of complex systems science and its applications. We study how interactions within a system lead to its behavioral patterns, and how the system interacts with its environment. Our new tools overcome the limitations of classical approximations for the scientific study of complex systems, such as social organizations, biological organisms and ecological communities. NECSI's unified mathematically-based approach transcends the boundaries of physical, biological and social sciences, as well as engineering, management, and medicine (see Complex Systems Resources).

NECSI research advances fundamental science and its applications to real world problems, including social policy matters. NECSI researchers study networks, agent-based modeling, multiscale analysis and complexity, chaos and predictability, evolution, ecology, biodiversity, altruism, systems biology, cellular response, health care, systems engineering, negotiation, military conflict, ethnic violence, and international development. (see NECSI Research).

NECSI conducts classes, seminars and conferences to assist students, faculty and professionals in their understanding of complex systems. NECSI sponsors postdoctoral fellows, provides research resources online, and hosts the International Conference on Complex Systems. Through its education, NECSI strives to contribute to science and the betterment of society (see NECSI Education).

Plamen Ch. Ivanov website. physics.bu.edu/people/show/plamen. We cite this home page of the Bulgarian-American, Boston University research professor as an example of the creative, worldwide frontiers of nonlinear, self-organizing complex network theories. From this site, the Keck Laboratory for Network Physiology which Ivanov directs, can be accessed with its rich array of projects, people, and publications. A recent contribution is the discovery of non-equilibrium critical dynamics in bursts of cortical dynamics in sleep/wake cycles (search for 2019 paper). His collegial research across a wide range from condensed matter to cardiac, neural, somatic onto societies well attests to nature’s universally recurrent manifestation of the same mathematical dynamics everywhere.

My research group has introduced several innovative approaches to analyze physiologic data by adapting concepts from modern statistical physics, nonlinear dynamics, and applied mathematics. These methods have been successfully applied to cardiac, respiratory, locomotion, and brain systems, along with sleep-stage transitions and circadian rhythms. Those data-driven approaches enabled us to discover basic laws of physiologic regulation of individual systems whose results were published in leading journals such as Nature, PNAS and Physical Review Letters. Our overall research objective is to develop a new interdisciplinary field, Network Physiology, integrating efforts across statistical and computational physics, biomedical engineering, human physiology, and medicine.

PLoS Complex Systems.. plos.org/complex-systems-research-journal. We note this new publication website as a way to record its occasion, along with two other complexity science, broadly conceived, online appearances. PRX Life newly joins the Physical Review series of the American Physical Society, and npj Complexity as a 2024 Nature Partner Journal issue. Into the 21st century every natural and societal phase has totally reconceived itself by way this theoretic and exemplary actuality, so it is appropriate that mainline venues provide dedicated sources. As a starter we enter a brief intro from their sites.

PLOS Complex Systems will bring together researchers working to understand complex systems. We will partner with the community to drive Open Science practices to enable rapid dissemination of results, cross-fertilization of knowledge, and collaboration to address the fundamental issues that affect individuals and societies. Research content will cover subjects such as network theory, nonlinear relationships, and the use of data, and computational analysis to model and understand natural and chaotic systems.

PRX Life: Where Physics and Life Sciences Converge. With Serena Bradde and Margaret Gardel as editors, it will give this vibrant frontier a voice, enhance the report of salient results across many topical features, promote the exchange of ideas with a personalized review process, and inspire new generations of with Perspectives and Reviews. By publishing with PRX Life, you can help us foster a more inclusive research landscape in the physics of living systems and push the boundaries of knowledge in this exciting interdisciplinary field.

Complexity science studies how large numbers of components can combine to produce rich emergent behaviours at multiple scales. Complex systems require a collective approach across scales and disciplinary domains. The mission of npj Complexity is to be a home for a publication interface of multiple fields. It is an online open-access venue dedicated to high quality peer-reviewed research in all subject aspects across domains and expertises across the globe.

Santa Fe Institute. www.santafe.edu. The original, innovative center since 1984 for the theoretical and practical study of complex, dynamical system insights into natural and social worlds. Typical subject areas include the Physics of Complex Systems, Emergence and Innovation in Evolutionary Systems, Information Processing and Computation in Nature and Society, and Emergence, Organization and Dynamics of Living Systems. For publications, the SFI Bulletin, (e.g., Volume 24, 2009), a long list of Working Papers each year, and under Research, a Bibliography of papers by SFI members cited on the website convey the leading edge of nonlinear studies.

Mission The Santa Fe Institute is a transdisciplinary research community that expands the boundaries of scientific understanding. Its aim is to discover, comprehend, and communicate the common fundamental principles in complex physical, computational, biological, and social systems that underlie many of the most profound problems facing science and society today.

Vision Many of society’s most pressing problems fall far from the confines of disciplinary research. Complex problems require novel ideas that result from thinking about non-equilibrium and highly connected complex adaptive systems. We are dedicated to developing advanced concepts and methods for these problems, and pursuing solutions at the interfaces between fields through wide-ranging collaborations, conversations, and educational programs. SFI combines expertise in quantitative theory and model building with a community and infrastructure able to support cutting-edge, distributed and team-based science. At the Santa Fe Institute, we are asking big questions that matter to science and society.

One of the grand challenges of 21st century science is the search for fundamental principles beyond the genetic code and Darwinian evolutionary process that govern how the complexity of life emerges from its underlying simplicity.

Abreu, Carlos, et al. Extreme fractal dimension at periodicity cascades in parameter spaces. . We cite this journal article by five physicists based in Sao Paulo, Brazil and Oldenburg, Germany as a current observance of nature’s inherent self-similar universality across every atom, cosmos and human infinity phase.

In the parameter spaces of nonlinear dynamical systems, we investigate the boundaries between periodicity and chaos so to discern the existence of fractal sets with a singular dimension that deviates from other fractals in their vicinity. We show that such singular sets dwell along parameter curves that intersect periodicity cascades at their centers of stability across all scales and spaces. The results reported here are exemplified by the class of one-dimensional maps with at least two control parameters. (Excerpt)

Ahmad, Mohammad, et al.. Defining Complex Adaptive Systems: An Algorithmic Approach.. Systems. 12/2, 2024. We cite this entry by University of Huddersfield, UK computer scientists for their novel consideration of better ways to understand nature’s nonlinear, dynamic complexity phenomena.

Despite a profuse literature on complex adaptive systems (CAS), it still remains to definitely answer whether a given system is of this kind. In this work, we propose a novel description for CASs in the form of a concise, scientific algorithmic framework. Our model first asks whether it meets complexity-related attributes and then considers attributes related to adaptivity, including autonomy, memory, self-organisation, and emergence. We demonstrate by case studies in medical and supply chain domains. Our novel approach is meant as an efficient auditing tool by which to provide insights for the relevant users to optimise their processes and organisational structures. (Excerpt)

The proposed algorithmic framework represents a synthesis of CAS theories described in the literature. The main intention is to facilitate the process of determining whether a system is a CS, a CAS, or neither. Based on our definition, a system is a CS if and only if it involves many agents who exhibit nonlinear behaviour. The complexity of a system, with autonomous, pro-active, and reactive agents, along with their learning and adaptation leading to an evolutionary phenomenon, form the preconditions to achieve self-organisation and emergence and fulfil all the requirements for a CAS. (16)

Altan-Bonnet, Gregoire, et al. Quantitative Immunology for Physicists. Physics Reports. Online January, 2020. Veteran complexity theorists G A-B, National Cancer Institute, USA, with Thierry Mora Aleksandra Walczak, CNRS Sorbonne University, Paris post a 70 page tutorial which reviews the latest perceptions of this important biological process. It then shows how much the immune system has become understood as another vital manifestation of nature’s universal complexities. Some sections are Ligand-Receptor Interaction, Antigen Diiscrimination, Cel to Cell Communication, and Populations Dynamics of Pathogens and Hosts.

The adaptive immune system is a dynamical, self-organized multiscale system that protects vertebrates from both pathogens and internal irregularities, such as tumours. For these reason it fascinates physicists, yet the multitude of different cells, molecules and sub-systems is often also petrifying. Despite this complexity, as experiments on different scales of the adaptive immune system become more quantitative, many physicists have made both theoretical and experimental contributions that help predict the behaviour of ensembles of cells and molecules that participate in an immune response. Here we review some recent contributions with an emphasis on quantitative questions and methodologies. We also provide a more general methods section that presents some of the wide array of theoretical tools used in the field. (Abstract)

Altmann, Eduardo. Statistical Laws in Complex Systems. arXiv:2407.19874.. A University of Sydney mathematical physicist draws on extensive studies (search) to provide a latest text for this field to be published by Springer in December. In this year, its contribution is a further grounding of these nonlinear features in the deep theories of statistical physics. After an introduction in this regard, the next chapter offers manifest exemplars from earthquakes and cities to metabolisms and literary texts. Followed by a long session on ways to gain samples, analyze data, identify scales and so on, the work closes with views of machine learning and artificial intelligence. As the third quote cites, an overarching theme is a natural universality as the same patterns and processes are found to repeat in kind everywhere.

Statistical laws describe regular patterns observed in diverse scientific domains such as the magnitude of earthquakes (Gutenberg-Richter law) and metabolic rates in organisms (Kleiber's law), the frequency distribution of words in texts (Zipf's laws), and productivity metrics of cities (urban scales). This monograph provides an unifying approach to the study of these statistical phenomena in the theoretical understanding of complex systems and the different data-analysis methods to evaluate them. Starting with simple examples and progressing to more advanced time-series methods, the text will provide comprehensive material for researchers interested in analyzing data, testing and comparing different laws, and interpreting datasets. (Abstract excerpt)

From a complex-systems perspective, statistical laws are emergent properties with inherent characteristics which are universally observed across different scenarios. Their explanation considers microscopic models that lead to the manifest observations at macroscopic scales. Numerous scientific disciplines have adopted this paradigm to understand system processes by the identification of emergent patterns. Today the influx of data inundating science and technology in the 21st century has brought not only opportunities for applications of statistical laws but also their reevaluation of their relevance and validity. (6)

An unified view on statistical laws The main motivation and crucial point of this monograph is to argue for an unified treatment of statistical laws in complex-system research. The justification for this unified approach is not that the same functional forms or generative models apply for different laws, as has been the motivation for the unified treatment of power-law distributions (e.g., underlying rich-get richer mechanisms) and scaling laws (e.g., connections to fractal geometry and critical phenomena). Instead, the more abstract commonality we explore in this monograph is based on the conceptual use of statistical laws in different settings and by various research communities. (106)

Altmann, G. and Walter Koch, eds. Systems: New Paradigms for the Human Sciences. Berlin: de Gruyter, 1998. A European compendium which situates and contemplates the human phase within a self-developing universe.

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