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

2. Complex Local to Global Network Biosocieties

Wilson, Edward, O. The Social Conquest of Earth. New York: Norton, 2012. The octogenarian entomologist and philosopher proceeds with another erudite, concerned volume. Drawing upon lifelong studies of colonial insects, and on recent collaborations with Martin Nowak and David Sloan Wilson, the work goes on to emphasize the active, evolutionary role of “group selection” in propelling our Homo Sapiens Sapiens take over. The message is that if properly understood, admitted and availed, the wisdom might help temper our species’ obsession for internecine conflicts.

Wolpert, David and Kyle Harper. The computational power of a human society: a new model of social evolution.. arXiv:2408.08861.. Sante Fe Institute and University of Oklahoma theorists consider a new mid 2020s version of traditional sociology by updating with current complex system and computational attributes. The intense, 67 page article then courses from Erwin Schroedinger’s 1943 view of “living matter,” code-scripts, onto major evolutionary transitions, thermodynamics and much more. Altogether an innovative fertile excursion.

Social evolutionary theory seeks to explain increases in the scale and complexity of human history. We propose that advances in complex systems and computer science could further reveal how societies co-evolve with their biotic environments. We construe a set of interacting occupations and technologies, and a bioregion with distinct ecological and climatic processes. This view then engages the requisite energetic costs and the ways it can extract resources from the environment to cover those costs.

Wu, Bin, et al. Evolution of Cooperation on Stochastic Dynamical Networks. PLoS One. 5/6, 2010. Peking University, Max Planck Institute, and Harvard University systems scientists propose to solve the Darwinian dichotomy between our human penchant for community and natural selection alone which should be in opposition to this. If the involvement of complex network processes can newly be factored in, they serve to explain how individual gains can accrue from ones appropriate behavior to foster a supportive social viability.

Cooperation is ubiquitous in the real world ranging from genes to multicellular organisms. Most importantly, human society is based upon cooperation. However this cooperative behavior apparently contradicts natural selection. Selfish behavior will be rewarded during competition between individuals, because selfish individuals enjoy the benefits from the cooperation of others, but avoid the associated costs. Therefore, the puzzle how natural selection can lead to cooperation has fascinated evolutionary biologists since Darwin. (1)

Cooperative behavior that increases the fitness of others at a cost to oneself can be promoted by natural selection only in the presence of an additional mechanism. One such mechanism is based on population structure, which can lead to clustering of cooperating agents. Recently, the focus has turned to complex dynamical population structures such as social networks, where the nodes represent individuals and links represent social relationships. (1)

Yang, Wu, et al. Nonlinear Effects of Group Size on Collective Action. Proceedings of the National Academy of Sciences. 110/10916, 2013. Center for Systems Integration and Sustainability, Michigan State University, researchers update prior work with complex systems science, as the Abstract explains, to achieve better guidance for a human abide and benefit for individual, community, and environment. The working unit is a “household,” but it is not said how many members. In any event, once again an “intermediate group size” that reciprocates “free-riders” and communal values, i.e., a balance of chaos and order, appears best. While this is not seen as an independent principle, one could cite a “me + we,” ubuntu, competitive coherence, or “creative union” exemplar. And what collective faculty (the seven authors are Chinese and American) has now appeared out of human millennias able to so quantify and reflect?

For decades, scholars have been trying to determine whether small or large groups are more likely to cooperate for collective action and successfully manage common-pool resources. Using data gathered from the Wolong Nature Reserve since 1995, we examined the effects of group size (i.e., number of households monitoring a single forest parcel) on both collective action (forest monitoring) and resource outcomes (changes in forest cover) while controlling for potential confounding factors. Our results demonstrate that group size has nonlinear effects on both collective action and resource outcomes, with intermediate group size contributing the most monitoring effort and leading to the biggest forest cover gain. We also show how opposing effects of group size directly and indirectly affect collective action and resource outcomes, leading to the overall nonlinear relationship. The findings also suggest that it should be possible to improve collective action and resource outcomes by altering factors that lead to the nonlinear group-size effect, including punishing free riding, enhancing overall and within-group enforcement, improving social capital across groups and among group members, and allowing self-selection during the group formation process so members with good social relationships can form groups autonomously. (Abstract)

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)

Zhang, Yi, et al. Emergence of social phases in human movement. Physical Review E. 110/044303, 2024. Eight University of Miami physicists including Chaoming Song post a latest example of many studies that re proceeding to explain our pedestrian walkabouts as an emergent manifestation, albeit far removed, of substantial phenomena. While such wide groundings gain theoretic credibility the import of these findings has not dawned on us. To wit, they imply that, unbeknownst until now, our daily lives are influenced by deep, immaterial, mathematic forces. We dance to their tune but can’t hear the music. (A concurrent case is how epidemics are found to exhibit similar patterns.)

Recent empirical studies have found different thermodynamic phases for collective motion in animals. Here, we used radio frequency technology UWB-RFID to collect spatiotemporal data on children’s movements in four classroom and playground settings. We observed two unique social phases: a gaslike mode of free activity and a liquid-vapor coexistence by small social groups. We then developed a statistical physics model that can reproduce these empirically observed phases. Our UWB-RFID technology can also be used to study active matter systems, animal behavior, robotic swarms, and human interactions within complex systems in social physics. (Excerpt)

By observing the motion of preschool children, researchers have developed a thermodynamic basis of human movement that shows collective phases emerging when interactions are strong. Based on their empirical data, Song and his colleagues developed a statistical-physics model that reproduced the two identified phases. They say that their radio-tracking technology could be used to produce analogous phase diagrams for the dynamics of other active-matter systems, such as swarms of microrobots. (Editor)

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)

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