(logo) Natural Genesis (logo text)
A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
Table of Contents
Genesis Vision
Learning Planet
Organic Universe
Earth Life Emerge
Genesis Future
Recent Additions

VII. Our Earthuman Ascent: A Major Evolutionary Transition in Twndividuality

2. Complex Local to Global Network Biosocieties

Yang, Zhaohui and Kshitji Jerath. Multi-scale Traffic Flow Modeling: A Renormalization Group Approach. arXiv: 2403.13779. UMass Lowell engineers achieve a unique advancement in the mathematical study of human mobilities from the viewpoint of a significant physical phenomena, as the title notes. At once the work identifies this double dimension and serves to trace and connect our Earthuman travels with universal principles.

Traffic flow modeling is typically performed at one of three (microscopic, mesoscopic, or macroscopic) scales. Recent works to merge models have had some success, but a need still exists for a single framework that can model traffic flow across spatiotemporal phases. Here we utilize a renormalization group (RG) theoretic approach, building upon our prior research on statistical mechanics-inspired traffic flow studies. We measure the coarse-grained traffic flow simulation using a pixel-based image metric and find good correlation in each case. (Excerpt)

In theoretical physics, the term renormalization group refers to the systematic investigation of changes of a physical system as viewed at different scales. The renormalization group is intimately related to scale invariance and conformal invariance, symmetries in which a system appears the same at all scales (so-called self-similarity). (Wikipedia)

My overarching research goal is to advance the understanding of complex dynamics observed in large-scale self-organizing systems, and to design bottom-up control algorithms that guide such systems to desired states via minimal intervention. (K. Jerath)

Youngman, Paul and Mirsad Hadzikadic, eds. Complexity and the Human Experience: Modeling Complexity in the Humanities and Social Sciences. Singapore: Pan Stanford Publishing, 2014. An initial volume which gathers much material that can illustrate how complex system revisions have spread to and reinvigorated every field and aspect. Thus another application of generic “complex adaptive systems” in this cultural realm is achieved. Typical chapters are Complexity Theory and Political Change: Talcott Parsons Occupies Wall Street by Martin Zwick, and Scientific Paradigms in US Policy: Is It Time for Complexity Science? By Michael Givel and Liz Johnson.

Complexity science is the study of how large numbers of relatively simple entities organize themselves into a collective whole that creates patterns, uses information, and, in some cases, evolves and learns. Those collective wholes that do not evolve and learn are complex systems; those that do are complex adaptive systems (CAS). Complexity and its various systems have been a topic of study in the natural sciences for decades already Physics, chemistry, biology, mathematics, meteorology, and engineering practitioners have used the concept of complex systems to explain phenomena as diverse as phase transitions in physical matter, immune system functions, and weather patterns. Our authors show how complexity ontology with its corresponding emphasis on modeling has already effectively spread to the social sciences and is at the very threshold of making a significant impact on the humanities has already effectively spread to the social sciences and is at the very threshold of making a significant impact on the humanities. (Introduction Abstract)

Zhou, Wei-Zing, et al. Discrete Hierarchical Organization of Social Group Sizes. Proceedings of the Royal Society B. 272/439, 2005. An international team of social theorists that includes Zhou, East China University of Science and Technology, Didier Sornette, UCLA and Universite´de Nice-Sophia Antipolis, Russell Hall, University of Durham, and Robin Dunbar, University of Liverpool, find a fascinating mathematical pattern and sequence to underlie and guide human sociability. As the quote conveys, a primate and hominid evolutionary past continues on to our propensity to aggregate into nested, sequentially larger, assemblies. Their iterative formation is further noticed to follow a fractal self-similarity, nature’s repetitive creativity arises apace. But these quantitative insights, if availed, may then reveal and teach a better way forward. Rather than each child as a lone learner, children might prosper more in small, mixed, supportive teams. Moving up the scale, as Sustainable Ecovillages reports, a nominal 100 folks, the archetypal tribe, band, or clan size, again serves these intentional, reciprocal communities.

The ‘social brain hypothesis’ for the evolution of large brains in primates has led to evidence for the coevolution of neocortical size and social group sizes, suggesting that there is a cognitive constraint on group size that depends, in some way, on the volume of neural material available for processing and synthesizing information on social relationships. More recently, work on both human and non-human primates has suggested that social groups are often hierarchically structured. We combine data on human grouping patterns in a comprehensive and systematic study. Using fractal analysis, we identify, with high statistical confidence, a discrete hierarchy of group sizes with a preferred scaling ratio close to three: rather than a single or a continuous spectrum of group sizes, humans spontaneously form groups of preferred sizes organized in a geometrical series approximating 3–5, 9–15, 30–45, etc. Such discrete scale invariance could be related to that identified in signatures of herding behaviour in financial markets and might reflect a hierarchical processing of social nearness by human brains. (Abstract, 439)

In this sequence, the core social grouping is the support clique, defined as the set of individuals from whom the respondent would seek personal advice or help in times of severe emotional and financial distress; its mean size is typically 3–5 individuals. Above this may be discerned a grouping of 12–20 individuals (often referred to as a sympathy group) that characteristically consists of all the individuals with whom one has special ties; these individuals are typically contacted at least once per month. The ethnographic data on hunter-gatherer societies point to a grouping of 30–50 individuals as the typical size of overnight camps (sometimes referred to as bands); these groupings are often unstable, but their membership is always drawn from the same set of individuals, who typically number ca. 150 individuals. This last grouping is often identified in small-scale traditional societies as the clan or regional group. Beyond these, at least two larger-scale groupings have been identified in the ethnographic literature: the megaband of ca. 500 individuals and the tribe (a linguistic unit, commonly of 1000–2000 individuals). (440)

Zingg, Christian, et al. What is the Entropy of a Social Organization? Entropy. 21/9, 2019. ETH Zurich, System Design researchers including Frank Schweitzer achieve a novel network characterization of behavioral activities by overtly viewing members as node points which are then linked by constant, informational interconnections. By this constructive application, still another archetypal manifestation of nature’s quantome to genome, neurome, and textome universality continues forth to grace our busy groupings.

We quantify a social organization’s potentiality to attain different network configurations in which nodes correspond to individuals and edges to their multiple interactions. Altogether these models are treated as a network ensemble. To have the ability to encode interaction preferences, we choose the generalized hypergeometric form of random graphs, as described by a closed-form probability distribution. From this distribution we calculate Shannon entropy as a measure of potentiality. This allows us to compare different organizations as well as different stages in their development. The feasibility of the approach is demonstrated using data from three empirical and two synthetic systems. (Abstract edits)

[Prev Pages]   Previous   | 9 | 10 | 11 | 12 | 13 | 14