V. Life's Corporeal Evolution Develops, Encodes and Organizes Itself: An Earthtwinian Genesis Synthesis

5. Cooperative Member/Group Societies

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)

Clutton-Brook, Timothy, et al. The Evolution of Society. Philosophical Transactions of the Royal Society. 364/3125, 2009. An Introduction to a Discussion Meeting on the subject, which could be seen as another sign, along with Gardner and Grafen below, of a new theoretical robustness by which social groupings can be understood as real entities in themselves, capable of their own genetic adaptations. Typical notable papers are “Culture and the Evolution of Human Cooperation” by Robert Boyd and Peter Richerson, “The Ecology of Social Transitions in Human Evolution” by Robert Foley and Clive Gamble, and especially David Queller and Joan Strassmann’s “Beyond Society: The Evolution of Organismality” (reviewed).

Conradt, Larissa and Christian List. Group Decisions in Humans and Animals. Philosophical Transactions of the Royal Society B. 364/719, 2009. A survey of this Theme Issue wherein leading researchers investigate these activities via quorum sensing self-organizations to influences of agreed global overviews in instances from ants to people. For example, a paper by Christian List, et al, bees seem to know best through a complementarity of independent and interdependent decisions.

Conradt, Larissa and Timothy Roper. Democracy in Animals: The Evolution of Shared Group Decisions. Proceedings of the Royal Society B. 274/2317, 2007. University of Sussex researchers find a common Metazoan propensity to behave advantageously as self-organizing, democratic systems.

A ‘consensus decision’ is when the members of a group choose, collectively, between mutually exclusive actions. In humans, consensus decisions are often made democratically or in an ‘equally shared’ manner, i.e. all group members contribute to the decision. Biologists are only now realizing that shared consensus decisions also occur in social animals (other than social insects).

Conradt, Larissa and Tomothy Roper. Group Decision-Making in Animals. Nature. 421/155, 2003. New findings that many social animals such as swans, deer and buffalo tend to behave in a democratic manner, rather than by despotic rule. For example, when and where a herd of buffalo moves depends on a majority consensus based on standing up or direction of gaze, often orchestrated by the matriarch.

Conradt, Larissa, et al. “Leading According to Need” in Self-Organizing Groups. American Naturalist. 173/3, 2009. Along with Jens Krause, Iain Couzin, and Timothy Roper, research ecologists find that the nonlinear coordination of large animal groupings is often enhanced and/or guided by members with an especial interest in reaching a destination or behavioral state.

Cooney, Daniel, et al. Evolutionary Dynamics Within and Among Competing Groups. arXiv:2209.02063. Across some 50 pages, and 125 references, University of Pennsylvania and Princeton University polyscholars including Simon Levin and Joshua Plotkin describe game theory models, broadly conceived, so to provide explanatory insights into how the oriented major transitions of whole entities within each other ascend from deep origins to animals onto we curious peoples. Into these 2020s, a convergent, proven sense is dawning of a common pattern and process which iterates as it emerges and exemplifies in procreative effect.

Biological and social systems are arrayed in multiple scales, whereof individuals in a group may act at odds to the whole collective incentive. Resolutions of this tension form the basis for major transitions as the origin of cellular life, multi-cellular organisms, and societies. Here we survey a growing literature that extends evolutionary game theory to describe such multilevel dynamics. In regard, we analyze how known modes that promote cooperation within a group like assortment, reciprocity, and population and affect competition between groups. We describe the broad applicability of multi-scale game models ranging from the production of diffusible metabolites in microbes to the management of common-pool resources in human societies. (Excerpt)

Couzin, Iain. Collective Animal Migration. Current Biology. 28/17, 2018. The MPI Animal Behavior pioneer systems behavior researcher provides a good summary to date of this 21st century discovery of common dynamic phenomena across all manner of active, mobile organisms and their groupings from embryonic forms to aquatic, avian, herding and onto people on the move. See his lab website for a stream of collegial papers.

Migratory movement is a strategy employed by a broad range of taxa as a response to temporally and spatially varying environments. Migrating animals can often be seen to move together, sometimes in vast numbers. Despite this, the social aspects of migration have, to date, received very limited attention. Synchronisation of migratory behaviour among organisms, itself, does not imply that migrants utilize social information. However, as will be outlined here, there is there is growing evidence that many migratory animals do utilize social cues, and that collective factors could shape migration in a variety of important ways. (Abstract excerpt)

Couzin, Iain and Jens Krause. Self-Organization and Collective Behavior in Vertebrates. Advances in the Study of Behavior. Volume 32, 2003. Animal ecologists now at the University of Leeds show that mathematical patterns due to simple interactions between group members, for example, in insects or microbes, will similarly appear in schooling fish, flocking birds, migrating ungulates, and vehicular traffic flow. See also Couzin’s note in Nature (445/715, 2007) on Collective Minds.

Couzin, Iain and Simon Levin, eds. Preface: Collective Behavior in Biological Systems. Journal of Statistical Physics. 158/3, 2015. A brief introduction to an issue on this novel confluence of traditional physics with complex systems science. Select papers are An Algorithmic Approach to Collective Behavior by Bernard Chazelle, Scale-Free Correlations, Influential Neighbours and Speed Control in Flocks of Birds, Charlotte Hemelrijk and Hanno Hildenbrandt, Copepod (Whales) Aggregations: Influences of Physics and Collective Behavior by Glenn Flierl and Nicholas Woods, and Saving Human Lives: What Complexity Science and Information Systems can Contribute by Dirk Helbing (search), et al. Here is a good example and promise of the coming grand synthesis via Anthropo and Cosmo Sapiens.

It is our intention to bring together, via the articles, scientists working in related disciplines involving animal swarms, flocking models, quorum sensing, etc. We believe that statistical mechanics can provide a unifying background theme through which to look at these varied cooperative phenomena in biology. (Preface)

The emergence of collective structure from the decentralized interaction of autonomous agents remains, with notable exceptions, a mystery. While powerful tools from dynamics and statistical mechanics have been brought to bear, sometimes with great success, an algorithmic perspective has been lacking. Viewing collective behavior through the lens of natural algorithms offers potential benefits. This article examines the merits and challenges of an algorithmic approach to the emergence of collective order. (Chazelle)

Previous   1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10  Next  [More Pages]