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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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VI. Earth Life Emergence: Development of Body, Brain, Selves and Societies

6. Cooperative Societies

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)

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. 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)

Cavagna, Andrea, et al. The Physics of Flocking. Physics Reports. Online December, 2017. Cavagna and Irene Giardina, CNR Institute of Complex Systems, Rome, and Tomas Grigera, National University of Plata, Argentina post an 80 page document which amounts to a ten year summary of this collegial project (search authors, W. Bialeck, T. Vicsek) to experimentally and theoretically explain the presence across creaturely groupings of beneficial dynamic topologies. The aerial display of starlings as they rapidly swirl, meld and flow make them an amenable species. The paper is thus another iconic example of later 2010s confirmations of nature’s communal viability by way of a universally recurrent entity and group complementarity. For a more historic and philosophical view, see The Essential Tension: Cooperation and Competition in Biological Evolution by neuroscientist Sonya Bahar (2017).

Collective behavior in biological systems spans scales in both space and time, involving taxonomically different organisms from bacteria and cell clusters, to insect swarms and up to vertebrate groups. It entails concepts as diverse as coordination, emergence, interaction, information, cooperation, decision-making, and synchronization. Amid this jumble, we cannot help noting many similarities between collective behavior in biological systems and collective behavior in statistical physics. Such similarities mostly regard the emergence of global dynamical patterns qualitatively different from individual behavior, and the development of system-level order from local interactions. It therefore serves to describe collective behavior in biology within the conceptual framework at least in part by the great predictive power of statistical physics. We illustrate here this affinity by way of collective behavior in bird flocks. Two key threads emerge within a single story: the presence of scale-free correlations and the dynamical mechanism of information transfer. In regard, starling flocks well reveal their scale-free nature, their explanation using maximum entropy, and a relation to their constant communication. (Abstract edited excerpts)

1.1 Biology, Physics and the Quest for Universality: A similar challenge is at the core of statistical physics, which studies systems where the large number of individual components allows the successful application of a probabilistic approach. In this way the daunting complexity of the system is tamed by focusing on a restricted set of properties by giving up the hope to track individual particles, one can understand and even predict the behavior of collective accessible variables, like pressure, temperature and magnetization. In critical phenomena, an even more striking simplification occurs: systems whose fluctuations are correlated over long ranges display universality, namely the fact that these large-scale properties are independent from the microscopic interactions, and only determined by the dimensions and symmetries of the system. In this way the details become irrelevant, and a wide variety of systems can be described by very simple models. (4)

Chase, Ivan, et al. Individual Differences Versus Social Dynamics in the Formation of Animal Dominance Hierarchies. Proceedings of the National Academy of Sciences. 99/5744, 2002. A discussion of the self-organizing dynamics which are found in evidence at every instance of natural development.

The importance of interaction among individuals for producing the patterns of organization in dominance hierarchies reveals these structures as self-organizing or self-structuring systems. These experiments are an empirical demonstration that dominance hierarchies are indeed self-organizing, and they confirm previous theoretical work. (5748)

Chen, Xiaowen, et al. Searching for Collective Behavior in a Small Brain. Physical Review. 99, 052418, 2019. Princeton University systems physicists including William Bialek travel to the minimum edge of life’s sensory cognizance and still find an inherent tendency form networks of beneficial coordinated action.

In large neuronal networks, it is believed that functions emerge through the collective behavior of many interconnected neurons. Recently, the development of techniques that allow recordings of calcium concentration from a large fraction of all neurons in Caenorhabditis elegans - a nematode with 302 neurons – leads us to ask if such emergence is universal, reaching down to even the smallest brains. Our various models exhibit signatures of collective behavior: the state of single cells can be predicted from the state of the rest of the network; the network, despite being sparse in a way similar to the structural connectome, distributes its response globally when locally perturbed; and the parameters that describe the real network are close to a critical surface in this family of models. (Abstract excerpt)

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