The synchronized flashing of fireflies at night. The spiraling patterns of an aggregating slime mold. The anastomosing network of army-ant trails. The coordinated movements of a school of fish. Researchers are finding in such patterns—phenomena that have fascinated naturalists for centuries—a fertile new approach to understanding biological systems: the study of self-organization. This book, a primer on self-organization in biological systems for students and other enthusiasts, introduces readers to the basic concepts and tools for studying self-organization and then examines numerous examples of self-organization in the natural world. Self-organization refers to diverse pattern formation processes in the physical and biological world, from sand grains assembling into rippled dunes to cells combining to create highly structured tissues to individual insects working to create sophisticated societies. What these diverse systems hold in common is the proximate means by which they acquire order and structure. In self-organizing systems, pattern at the global level emerges solely from interactions among lower-level components. Remarkably, even very complex structures result from the iteration of surprisingly simple behaviors performed by individuals relying on only local information. This striking conclusion suggests important lines of inquiry: To what degree is environmental rather than individual complexity responsible for group complexity? To what extent have widely differing organisms adopted similar, convergent strategies of pattern formation? How, specifically, has natural selection determined the rules governing interactions within biological systems? Broad in scope, thorough yet accessible, this book is a self-contained introduction to self-organization and complexity in biology—a field of study at the forefront of life sciences research.
Self-organizing systems: what, how, and why?
I present a personal account of self-organizing systems, framing relevant questions to better understand self-organization, information, complexity, and emergence. With this aim, I start with a notion and examples of self-organizing systems (what?), continue with their properties and related concepts (how?), and close with applications (why?) in physics, chemistry, biology, collective behavior, ecology, communication networks, robotics, artificial intelligence, linguistics, social science, urbanism, philosophy, and engineering.
Swarm Intelligence: From Natural to Artificial Systems
Abstract. Social insects--ants, bees, termites, and wasps--can be viewed as powerful problem-solving systems with sophisticated collective intelligence. Co
Co-Creation: Systems Thinking Beyond the Machine
Complexity thinking is all the rage — in the arts and sciences. Yet, there are two different strands of thinking about complexity, which are often confounded. The first comes from cybernetics, focussing on control in complex situations. The second is ecological, aiming at sustainable participation in a reality beyond control. Their difference rests on a fundamental distinction: is complexity rooted in feedback regulation or collective co-creation? While feedback remains mechanistic, co-creation generates new spaces of possibilities. We need it to understand life and its evolution. And it empowers us to rewrite our future, to escape our mechanistic cage without abandoning scientific rigor. In this paper, we illustrate how we implement these powerful yet abstract principles through our artistic and philosophical practice.
EPISTEMIC STIGMERGY: NATURAL VS. ARTIFICIAL INTELLIGENCE
The article\(^{1}\) defends the thesis that intelligent behavior might require not internal complexity but complex interaction. This is demonstrated by the various forms of stigmergy that can be observed both in social insects and in humans. The exposition is structured as follows: (§0) explains how the term “intelligence” is interpreted in the following text; (§1) clarifies the relation between intelligence and complexity; (§2) shows that intelligent behavior does not require internal complexity; (§3) introduces the concept of stigmergy; (§4) presents the mechanisms that give rise to this phenomenon; (§5) distinguishes several types of stigmergic interaction; (§6) briefly discusses the evolutionary mechanisms that could have produced them; (§7) sketches the possible ways in which the concept of stigmergy is used outside biology; (§8) examines collaborative stigmergy in humans; (§9) points to its epistemic projections; (§10) outlines some conclusions concerning the role of artificial intelligence systems and their place in human society.
The emergence of consensus: a primer
The origin of population-scale coordination has puzzled philosophers and scientists for centuries. Recently, game theory, evolutionary approaches and complex systems science have provided quantitative insights on the mechanisms of social consensus. However, the literature is vast and widely scattered across fields, making it hard for the single researcher to navigate it. This short review aims to provide a compact overview of the main dimensions over which the debate has unfolded and to discuss some representative examples. It focuses on those situations in which consensus emerges ‘spontaneously’ in the absence of centralized institutions and covers topics that include the macroscopic consequences of the different microscopic rules of behavioural contagion, the role of social networks and the mechanisms that prevent the formation of a consensus or alter it after it has emerged. Special attention is devoted to the recent wave of experiments on the emergence of consensus in social systems.
Are biological systems poised at criticality?
Many of life's most fascinating phenomena emerge from interactions among many elements--many amino acids determine the structure of a single protein, many genes determine the fate of a cell, many neurons are involved in shaping our thoughts and memories. Physicists have long hoped that these collective behaviors could be described using the ideas and methods of statistical mechanics. In the past few years, new, larger scale experiments have made it possible to construct statistical mechanics models of biological systems directly from real data. We review the surprising successes of this "inverse" approach, using examples form families of proteins, networks of neurons, and flocks of birds. Remarkably, in all these cases the models that emerge from the data are poised at a very special point in their parameter space--a critical point. This suggests there may be some deeper theoretical principle behind the behavior of these diverse systems.
Systems of Global Governance in the Era of Human-Machine Convergence
Technology is increasingly shaping our social structures and is becoming a driving force in altering human biology. Besides, human activities already proved to have a significant impact on the Earth system which in turn generates complex feedback loops between social and ecological systems. Furthermore, since our species evolved relatively fast from small groups of hunter-gatherers to large and technology-intensive urban agglomerations, it is not a surprise that the major institutions of human society are no longer fit to cope with the present complexity. In this note we draw foundational parallelisms between neurophysiological systems and ICT-enabled social systems, discussing how frameworks rooted in biology and physics could provide heuristic value in the design of evolutionary systems relevant to politics and economics. In this regard we highlight how the governance of emerging technology (i.e. nanotechnology, biotechnology, information technology, and cognitive science), and the one of climate change both presently confront us with a number of connected challenges. In particular: historically high level of inequality; the co-existence of growing multipolar cultural systems in an unprecedentedly connected world; the unlikely reaching of the institutional agreements required to deviate abnormal trajectories of development. We argue that wise general solutions to such interrelated issues should embed the deep understanding of how to elicit mutual incentives in the socio-economic subsystems of Earth system in order to jointly concur to a global utility function (e.g. avoiding the reach of planetary boundaries and widespread social unrest). We leave some open questions on how techno-social systems can effectively learn and adapt with respect to our understanding of geopolitical complexity.
Software in the natural world: A computational approach to hierarchical emergence
Understanding the functional architecture of complex systems is crucial to illuminate their inner workings and enable effective methods for their prediction and control. Recent advances have introduced tools to characterise emergent macroscopic levels; however, while these approaches are successful in identifying when emergence takes place, they are limited in the extent they can determine how it does. Here we address this limitation by developing a computational approach to emergence, which characterises macroscopic processes in terms of their computational capabilities. Concretely, we articulate a view on emergence based on how software works, which is rooted on a mathematical formalism that articulates how macroscopic processes can express self-contained informational, interventional, and computational properties. This framework establishes a hierarchy of nested self-contained processes that determines what computations take place at what level, which in turn delineates the functional architecture of a complex system. This approach is illustrated on paradigmatic models from the statistical physics and computational neuroscience literature, which are shown to exhibit macroscopic processes that are akin to software in human-engineered systems. Overall, this framework enables a deeper understanding of the multi-level structure of complex systems, revealing specific ways in which they can be efficiently simulated, predicted, and controlled.
Making Sense of Chaos
From a pioneer in the field of complexity science and chaos theory, a plan for solving the world’s most pressing problems “Farmer convincingly argues t...
Social Insects : Ecology and Behavioural Biology
1 online resource; Here is a guide to the ecology of social insects. It is intended for general ecologists and entomologists as well as for undergraduates and those about to start research on social insects; even the experienced investigator may find the comparison between different groups of social insects illuminating. Most technical terms are translated into common language as far as can be done without loss of accuracy but scientific names are unavoidable. Readers will become familiar with the name even though they cannot visualize the animal and could reflect that only a very few of the total species have been studied so far! References too are essential and with these it should be possible to travel more deeply into the vast research literature, still increasing monthly. When I have cited an author in another author's paper, this implies that I have not read the original and the second author must take responsi bility for accuracy! Many hands and heads have helped to make this book. I thank all my colleagues past and present for their enduring though critical support, and I thank with special pleasure: E.]. M. Evesham who fashioned the diagrams;]. Free, D.J. Stradling and]. P.E.C. Darlington who supplied photographs; D.Y. Brian and R.A. Weller who were meticulous on the linguistic side; and G. Frith and R.M. Jones who collated the references. List of plates 1. Fungus combs of Acromyrmex octospinosus and Macrotermes michaelseni. 13 2. Mouthparts of larval Myrmica; 1 Introduction -- 2 Food -- 2.1 Termites as decomposers -- 2.2 Wasps and ants as predators -- 2.3 Sugars as fuel save prey -- 2.4 Seed eaters -- 2.5 Leaf eaters -- 2.6 Pollen eaters -- 3 Foraging by individuals -- 3.1 Foraging strategy -- 3.2 Worker variability -- 4 Foraging in groups -- 4.1 Communication about food -- 4.2 Group slave-raiding -- 4.3 Tunnels and tracks -- 4.4 Nomadic foraging -- 5 Cavity nests and soil mounds -- 5.1 Cavities and burrows -- 5.2 Soil mounds -- 6 Nests of fibre, silk and wax -- 6.1 Mounds of vegetation and tree nests -- 6.2 Combs of cells -- 7 Microclimate -- 7.1 Environmental regulation -- 7.2 Metabolic regulation -- 8 Defence -- 8.1 Painful and paralysing injections -- 8.2 Toxic smears and repellants -- 9 Food processing -- 9.1 Mastication, extraction and regurgitation -- 9.2 Yolk food supplements -- 9.3 Head food glands -- 10 Early population growth -- 10.1 Food distribution -- 10.2 Colony foundation -- 10.3 The growth spurt -- 11 Maturation -- 11.1 Simple models of reproduction -- 11.2 Social control over caste -- 11.3 Males in social Hymenoptera -- 11.4 Maturation in general -- 12 Reproduction -- 12.1 Caste morphogenesis -- 12.2 Copulation and dispersal -- 12.3 Production -- 12.4 Summary -- 13 Evolution of insect societies -- 13.1 Theories of individual selection -- 13.2 Models of these theories -- 13.3 Group selection -- 13.4 Conclusions -- 14 Colonies -- 14.1 The colony barrier -- 14.2 Queen number and species ecology -- 14.3 Queen interaction and queen relatedness -- 15 Comparative ecology of congeneric species -- 15.1 Ant and termite races -- 15.2 Desert ants and termites -- 15.3 Ants and termites in grassland -- 15.4 Forest ants and termites -- 15.5 Wasps and bumblebees -- 15.6 Advanced bees -- 16 Communities -- 16.1 Temperate zone communities in grass and woodland -- 16.2 Desert communities -- 16.3 Tropical rain forest -- 16.4 Conclusions -- 17 Two themes -- 17.1 Plant mutualism -- 17.2 Social organization -- References -- Author index
Niklas Luhmann: What is Autopoiesis?
The term autopoiesis (self-creation) is a neologism coined in 1972 by Varela and Maturana, Chilean cellular biologists and systems theorists, to describe
Tom Seeley: Honeybee Democracy
The Care of the Self within a Biopolitical Paradigm: Integrating Cognitive Psychology to resist Subjectification
Contemporary theories of resistance to biopolitical subjectification often reify unfreedom by lacking a plausible model of agency. This thesis resolves this by establishing an ontological foundation for the agent as fundamentally autopoietic and semiotic, drawing on contemporary cognitive science. It then proposes a new foundation for resistance by synthesizing Michel Foucault’s later work on the care of the self with the 4P/5E model of embodied cognition. I show how this interdisciplinary approach establishes Foucault’s ethical techniques as a systematic ecology of practices for cultivating a free, self-determining agent and by reframing resistance as a practical, embodied ethics of self-formation, it inherently fosters two vital skills: the gain of self-knowledge and self-mastery.
Lectures on Perception: An Ecological Perspective
Lectures on Perception: An Ecological Perspective addresses the generic principles by which each and every kind of life form—from single celled organisms (e.g., difflugia) to multi-celled organisms (e.g., primates)—perceives the circumstances of their living so that they can behave adaptively. It focuses on the fundamental ability that relates each and every organism to its surroundings, namely, the ability to perceive things in the sense of how to get about among them and what to do, or not
