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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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V. Life's Corporeal Evolution Develops, Encodes and Organizes Itself: An Earthtwinian Genesis Synthesis

5. Cooperative Member/Group Societies

Brask, Josefine, et al. Animal Social Networks: An Introduction for Complex Systems Scientists. Journal of Complex Networks. 9/2, 2021. University of Exeter animal behaviorists including Darren Croft show how equally real interactive relations between group members can reveal and achieve new insights and explanations. In regard, these topologies are not fixed or static in nature but provide a dynamic, beneficial matrix.

Many animals live in societies where individuals frequently interact socially with each other. Animal social network research, however, seems to not be well known by scientists outside of the animal behaviour field. Here we provide an introduction for complex systems researchers. In this paper, we describe what animal social networks are and how they are scientifically important; we give an overview of common methods; and highlight challenges where interaction between animal social network and general complex systems research could be valuable. We hope that this will help to facilitate future interdisciplinary collaborations and lead to better integration of these networks into the field of complex systems. (Abstract excerpt)

Brigatti, Edgardo and A. Hernandez. Exploring the Onset of Collective Intelligence in Self-Organized Trails of Social Organisms. arXiv:1705.04182. Federal University of Rio de Janeiro physicists finesse life’s insistent movement in active groupings toward this salutary state of common knowledge. See also Optimal Incentives for Collective Intelligence by Richard Mann and Dirk Helbing in PNAS (online May 2017) about ways that human groupings can facilitate this benefit.

We investigate the emergence of self-organised trails in collective motion of social organisms by means of an agent-based model. We present numerical evidences that an increase in the efficiency of navigation between the target areas, in dependence of the colony size, exists. Moreover, the shift, from the maladaptive to the adaptive behaviour, can be quantitative characterised, identifying and measuring a well defined crossover point. This point corresponds to the minimal number of individuals necessary for the onset of collective intelligence. Its scaling behaviour, as a function of the environment size, is clearly described. (Abstract)

Broom, Donald. The Evolution of Morality and Religion. Cambridge: Cambridge University Press, 2003. This advance occurred because instead of unrestrained competition, as in the old school, animal societies actually favor cooperation, friendship and sharing for group stability. These qualities then served in hominid and human communities as a biological basis for moral behavior and religious convictions.

Brush, Eleanor, et al. Conflicts of Interest Improve Collective Computation of Adaptive Social Structures. Science Advances. 4/1, 2018. Into the 2010s, computational biologists Brush, Princeton University, along with David Krakauer and Jessica Flack, Santa Fe Institute, present a collegial synthesis about how animal assemblies tend to a dynamic reciprocity of members and group. Search herein, Reunion of Biology and Physics, for similar entries by, for example, Tamas Vicsek, Andrea Cavagna, Nicholas Ouellette. To wit, computer program sciences are lately providing a novel way to substantiate these common fluid interactions of individual members within overall viable groupings. While primate behaviors are cited, a universal entity-troop, clan, flock complementarity (ubuntu universe) seems robustly in effect across creaturely life and evolution. In regard, as the Abstract cites, a constant reciprocity between evidential member opinions and their integrative assimilation results in a global viability. These cerebral archetypes occur in instant sequence for overall benefit. See also a concurrent paper, The Evolution of Distributed Sensing and Collective Computation in Animal Populations by Andrew Hein, et al, for a similar model.

In many biological systems, the functional behavior of a group is collectively computed by the system’s individual components. An example is the brain’s ability to make decisions via the activity of billions of neurons. A long-standing puzzle is how the components’ decisions combine to produce beneficial group-level outputs, despite conflicts of interest and imperfect information. We derive a theoretical model of collective computation from mechanistic first principles, using results from previous work on the computation of power structure in a primate model system. Collective computation has two phases: an information accumulation phase, in which (in this study) pairs of individuals gather information about their relationships, and an information aggregation phase, in which these decisions are combined to produce a collective computation. The successful application of a similar stochastic decision-making model in neural and social contexts suggests general principles of collective computation across substrates and scales. (Abstract excerpts)

Buck, Ross and Benson Ginsberg. Communicative Genes and the Evolution of Empathy. William Ickes, ed. Empathic Accuracy. New York: Guilford Press, 1997. An overview paper in a volume of research reports to quantify the importance of relational values in evolution.

We regard empathy, rapport, intuition, altruism and related concepts as emergent properties of a primordial biological capacity for communication that inheres in the genes. (19)

Buhl, J., et al. From Disorder to Order in Marching Insects. Science. 312/1402, 2006. Models from theoretical physics for emergent complexity and coordination, wherein individuals act as “self-propelled particles” in response to close neighbors, can reveal constant behavioral patterns across the animal kingdom. Such findings are then said to be useful for the control of locust hordes.

Recent models from theoretical physics have predicted that mass-migrating animal groups may share group-level properties, irrespective of the type of animals in the group. (1402) Despite the huge differences in the scales of animal aggregations and the cognitive abilities of group members, the similarities in the patterns that such groups produce have suggested that general principles may underlie collective motion. (1402-1403)

Burns, Alicia, et al. Self-Organization and Information Transfer in Antarctic Krill Swarms. Proceedings of the Royal Society B. February, 2022. We cite this entry by an eight person Australian and German team including Jens Krause and Joseph Lizier as an example of how nature’s mathematical agencies and forces, as if a pervasive genetic presence, can be found in universal formative effect everywhere. A rush of findings as this which similarly quantify every aquatic to astronomic realm build a case for an infinite familial universality.

Antarctic krill swarms are one of the largest animal aggregations, but despite being the keystone species of the Southern Ocean, little is known about how swarms are formed and maintained. We studied the trajectories of wild-caught krill in 3D to learn individual interaction rules and quantify patterns of information flow. Our results show that krill align with near neighbors which regulates their direction and speed in relation to groupmates. Thus social factors are vital to swarm composition abd a novel form of collective organization by way of information flow and individual adjustments (Abstract excerpt)

Cantor, Mauricio, et al. Multilevel Animal Societies can Emerge from Cultural Transmission. Nature Communications. 6/8091, 2015. With lead coauthor Hal Whitehead, six behavioral ecologists from Canada, the Philippines and USA contend that due to extensive, 21st century studies of whale groupings, a socially communicative milieu can be identified of much benefit. A broader observation and take away may be that this same networking proclivity can be seen to reappear in each and every creaturely assembly, implying its independent existence.

Multilevel societies, containing hierarchically nested social levels, are remarkable social structures whose origins are unclear. The social relationships of sperm whales are organized in a multilevel society with an upper level composed of clans of individuals communicating using similar patterns of clicks (codas). Using agent-based models informed by an 18-year empirical study, we show that clans are unlikely products of stochastic processes (genetic or cultural drift) but likely originate from cultural transmission via biased social learning of codas. Distinct clusters of individuals with similar acoustic repertoires, mirroring the empirical clans, emerge when whales learn preferentially the most common codas (conformism) from behaviourally similar individuals (homophily). Cultural transmission seems key in the partitioning of sperm whales into sympatric clans. These findings suggest that processes similar to those that generate complex human cultures could not only be at play in non-human societies but also create multilevel social structures in the wild. (Abstract)

Casti, John and Anders Karlqvist. Cooperation and Conflict in General Evolutionary Processes. New York: Wiley, 1995. A broad survey of the title theme. Here is a quote from the Introduction:

…we feel that the twelve chapters presented here, spanning as they do fields as diverse as philosophy, physics, biology, and economics, give an excellent overview of how successful evolutionary adaptations rely on a judicious combination of self-interest and altruism.

Cavagna, Andrea, et al. Dynamical Renormalization Group Approach to the Collective Behavior of Swarms. arXiv:1905.01227. This is the first of two postings by a six member team of Italian and Argentine systems theorists including Irene Giardina. The second is Renormalization Group Crossover in the Critical Dynamics of Field Theories with Mode Coupling Terms at arXiv:1905.01228 (see quote). As a general review, by since there can only be one extant nature, whether it is variously described by RG, network, complexity, fractal, computational or other methods. By 2019 each version in its way cites a dual node and link-like interactives reciprocity. This innate cosmic vitality is now seen to consistently seek and reside at an optimum critical poise such as brains, animal groupings, or protein webs. By this analysis, once again a deep rooting in condensed matter physics is achieved. As we log in along with Dante Chialvo 2019, since the 1980s when this complexity revolution began, and intimated much earlier, we may finally glimpse the epic achievement *magnum opus) of this universe to human source code.

The success of the theory of critical phenomena is based upon a simple observation: systems with very different microscopic details behave in strikingly similar ways when correlations are sufficiently strong. This experimental fact eventually crossed over into theory with the formulation of the phenomenological scaling laws, whose key idea is that the only relevant scale ruling the spatio-temporal behaviour of a system near its critical point is the correlation length. Eventually, the great conceptual edifice of the Renormalization Group (RG) tied everything together, explaining why microscopically different systems shared so much at the macroscopic level, giving a demonstration of universality through the concept of attractive fixed points, and providing a method to calculate experimentally accessible quantities, most conspicuously the critical exponents. (1905.01228, 1)

Cavagna, Andrea, et al. Natural Swarms in 3.99 Dimensions. arXiv:2107.04432. Eight physicists from Italy and Argentina including Irene Giardina provide a further quantitative basis for the universal presence of optimum member-group integral behaviors which can be rooted in statistical physics. A definitive explanation can they be derived from Renormalization Group theories about relations between nested active scales. As the authors note, their advance is the first time that such a specific connection has been made.

The dynamical critical exponent z of natural swarms is calculated using the renormalization group. To order \epsilon = 4-d, a novel fixed point emerges, where both off-equilibrium activity and mode-coupling inertial interactions are relevant. In three dimensions the critical exponent at the new fixed point is z=1.3, in fair agreement with experiments. (Abstract)

Collective behaviour is found in a wide variety of biological systems from clusters of bacteria and colonies of cells, up to insects, bird flocks, and vertebrate groups. A unifying ingredient, which can be based in statistical physics, is the presence of strong, consistent correlations. Studies of avian flight, fish schools, mammal herds, swarms, microbes and proteins have found that the correlation length is much larger than the microscopic scales. Another key hallmark of statistical physics is dynamic scaling, which has been verified in many of these occasions. the case of natural swarms of insects. (1)

Within statistical physics, strong correlations and scaling laws are the two stepping stones leading to the Renormalization Group (RG): when we coarse-grain short-wavelength fluctuations, the parameters of different models flow towards one common fixed point ruling their large-scale behaviour. RG fixed points therefore organize the macroscopic behavior of strongly correlated systems into few universality classes. Biology is vastly more complex than physics, but the widespread presence of strong correlations and the validity of scaling laws cannot be considered a coincidence. They rather call for an exploration of the correlation-scaling-RG path also in collective biological systems. (1)

Cavagna, Andrea, et al. Scale-free Correlations in Bird Flocks. arXiv:0911.4393v1. A remarkable degree of experimental sophistication and theoretical insight allows these Universita’ di Roma ‘La Sapienza’ complex system scientists, Irene Giardina and Giorgio Parisi amongst, to mathematically quantify of this common phenomenon that graces animal groups across Metazoa from microbes to cetaceans. A day after accessing this on December 13, 2009, I watched a CNN clip of a vast flock of starlings perform an aerial ballet as if a single sensuous wave. We quote the whole abstract to convey its essence.

From bird flocks to fish schools, animal groups often seem to react to environmental perturbations as if of one mind. Most studies in collective animal behaviour have aimed to understand how a globally ordered state may emerge from simple behavioural rules. Less effort has been devoted to understanding the origin of collective response, namely the way the group as a whole reacts to its environment. Yet collective response is the adaptive key to survivor, especially when strong predatory pressure is present. Here we argue that collective response in animal groups is achieved through scale-free behavioural correlations. By reconstructing the three-dimensional position and velocity of individual birds in large flocks of starlings, we measured to what extent the velocity fluctuations of different birds are correlated to each other. We found that the range of such spatial correlation does not have a constant value, but it scales with the linear size of the flock. This result indicates that behavioural correlations are scale-free: the change in the behavioural state of one animal affects and is affected by that of all other animals in the group, no matter how large the group is. Scale-free correlations extend maximally the effective perception range of the individuals, thus compensating for the short-range nature of the direct inter-individual interaction and enhancing global response to perturbations. Our results suggest that flocks behave as critical systems, poised to respond maximally to environmental perturbations. (Abstract)

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