DOCSLIB.ORG

Self"Organization

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Self Guided Self!Organization:" from chaos to planning?! Francis Heylighen! Evolution, Complexity and Cognition Group! Vrije Universiteit Brussel! Newtonian Science! Making simple models, by means of:# •!Analysis! •!Reductionism! •!Determinism! •!Reversibility! Complexity! complexus = entwined# •!Many parts! •!that are connected so that they are di"cult to separate! In between Order and Disorder# •!the #edge of chaos$! From Newton to Complexity! Analysis → Connection# Reductionism → Emergence# •! The whole is more than the sum of its parts! Deterministic change → Uncertainty# •! We cannot predict exactly what will happen! Matter → Organization# •! Systems more important than elements or particles! Reversible movement → Evolution# •! Creativity and progress! Emergence! whole > sum of the parts# Whole has emergent properties # •! properties not reducible to properties of the parts! examples# •!car: maximum speed = emergent, weight = reducible! •!music: melody = emergent, volume= reducible! Open Systems! Inseparable from environment:# •!input = incoming %ows of matter, energy, information! •!output = outgoing %ows! SYSTEEM input throughput output grens OMGEVING Coupling! di$erent systems can be coupled via their input and output# three basic types# sequentiëel parallel circulair Networks! Many coupled systems form a network# Which can be seen as a system itself# Subsystems and Supersystems! subsystemen supersysteem Hierarchy! Linearity! Linearity = e$ect proportional to cause# •!using k times as much raw material → & produces k times as much output! •! f(k.a) = k.f(a) Therefore: Small causes ! small effects Large causes ! large effects Non'linearity! E$ect can be much larger or much smaller than cause# Usually caused by e$ect feeding back into cause# # positive feedback negative feedback! amplifying deviations suppressing deviations ! → unstable → stable # Chaos! sensitive dependence on initial conditions# •!Unobservably small perturbations can produce huge e(ects! the butter%y e$ect: %apping of wings may create a tornado! → intrinsic unpredictability and uncontrollability# Self'organization! Spontaneous emergence of order or organization# •!not imposed by outside or inside agent! Organization distributed over all the components# •!Collective, decentralized! •!Robust against disturbances! Mechanisms of self'organization! Global order emerges from local interactions# •!Components initially interact only with their neighbors! •!Until they #align$! •!i.e. )nd a stable, #&t$ arrangement! •!Alignments tend to propagate to further neighbors! •!until they spread over the system as a whole! Magnetization! Magnetization! B*nard convection! surface surface bottom (hot) bottom (hot) B*nard convection! Dynamics of Self'organization! Local variation ! Selective elimination of the #un)t$ arrangements! Growth of the )t arrangements! •!Positive feedback! Suppression of deviations from that arrangement! •!Negative feedback! Result: Initially rapid growth → stabilization → robustness and continuing adaptation# Attractor Dynamics! Non!linear systems typically have several attractors# Attractor=# •!!stable region in state space! •!that the system can enter& but not leave! •!surrounded by basin! •!System descends & spontaneously from & basin into attractor! •!and comes back to the & attractor if forced out of it! Order out of Chaos! Self!organization is stimulated by random variation 'noise, chaos(# •! more con)gurations are explored in the same time interval! •! higher probability of )nding a #deep$ attractor ! •! therefore, a stable outcome is reached more quickly and reliably! Example: shaking! Throw cubes randomly in a box# •!preference for position close to bottom! •!But cubes mutually obstruct → #friction$! Shaking reduces volume# •!Random variation explores many possible con)gurations! •!But preferentially retains the most stable ones at the bottom! Complex Adaptive Systems! Collection of interacting agents# Characterized by self"organization# •! global coordination and adaptation! •! emerging from local interactions! Examples# •! Societies, groups, communities! •! Cities, regions! •! Ecosystems, markets! •! World'Wide Web ! Agent! Goal!directed individual# •! tries to maximize #)tness$ ! Follows condition!action rules # •! if condition A sensed, then perform action B! •! A → B! •! where B in general increases )tness! Agents are diverse! Di$erent agents have in general di$erent abilities:# •! Knowledge of rules! •! Information about conditions! •! Capacity to perform actions! Bounded rationality! Agents have only # •! local information! •! Limited information processing ability! The environment is complex and unpredictable# •! non'linearity may give rise to chaos! Therefore, agents cannot predict the long!term e$ects of their actions# •! they must rely to some degree on trial'and'error! •! They must constantly adapt to the circumstances! Evolution of Synergy! Agents co!evolve# •!Variation and selection lead to mutual adaptation! •!Each acts so as )t in with its neighbors, and vice versa! Selected interaction characterized by# •!Minimal friction +con%ict, negative sum, ! •!Maximal synergy +#win'win$, positive sum,! Leads to stable )connection* or )alignment* # •!No more con%ict, but cooperation! Propagation of Alignment! More agents join alignment# •!Often ampli)ed by positive feedback! •!Larger aligned assembly exerts larger in%uence on non' aligned agents! •!Pressure to #join$ increases! Eventually, the whole system becomes aligned# All agents act synergetically# Emergence of & collective intelligenc"e Distributed cognition# •! di(erent agents contribute di(erent results at di(erent times and places! •! results are integrated! •! together, they solve the global problem:! •!co!ective inte!igence! Coordination mechanisms! Alignment of targets# minimizing con%ict or friction! Division of labor# Di(erentiation or specialization! Work%ow# Actions performed in right sequence! Aggregation of results# •!Into collective solution! Coordination! workflow division of labor aggregation Self'organization of coordination! Stigmergy# •!Result of action stimulates performance of subsequent action! •!Right agent incited to intervene at right moment! •!Aggregation into shared workspace! Reinforcement learning# •!Successful sequences of actions are reinforced! •!Unsuccessful ones are eliminated! Planning?! Planning is intrinsically di+cult for complex systems# •!Uncertainty, chaos, autonomy, openness….! Better: guided self!organization# •!Formulate desired outcomes! •!Let system itself evolve towards these targets! •!While adapting to any unforeseen complications! Don,t try to rigidly control the process!# Anticipating outcomes! Self!organizing systems evolve towards attractors# Attractors are complex and emergent# But can be foreseen to some degree# •!By using multi'agent simulations! •!Or by mapping out positive and negative feedback loops! •!Systems dynamics models! This allows us to target desired attractors# •!and avoid undesired ones! Controlling Emergent Outcomes! How can we make sure to get the desired result?# Initial situation# # problem to be dealt with by the process! Desired outcome# #situation that solves the problem! We need to stimulate moves in the right direction! But not impose a trajectory! Controlling direction! De&ne &tness criterion: # •!what is better or worse?! Reinforcement# • reward moves toward )tter situation! •!e.g. Subsidizing education or sustainable development! • inhibit moves that reduce )tness! •!e.g. Taxing pollution or waste ! Controlling boundary conditions! Set up architectural )sca$old*# Constraining structure# Make #right$ moves easy! Make #wrong$ moves di"cult! But leave enough space for exploration!! Examples# #Organs$ grown from cells! seeded on arti)cial frame! Stigmergy! Actions leaves a trace in a shared workspace# The trace stimulates agents to perform additional actions! Very robust way to collaborate# •!No need for planning or explicit coordination# •!Division of labor and work%ow emerge spontaneously# •!agents do not need to communicate! •!workspace stores and aggregates contributions! •!Successful contributions grow through positive feedback! Stigmergy: Wikipedia! Person A writes text on topic X# •!Action leaves trace on website! Person B independently reads text# Person B is stimulated to add to the text# Further contributions are aggregated on the same webpage! Positive feedback: # •!more edits → better text → more readers → more edits → ...! Stigmergy: termite hill construction! individual termite drops mud randomly! new termite attracted to add mud to already present mud! positive feedback: mud → more mud! the mud heap grows into a column! columns grow towards each other! → cathedral'like structure with arches# Stigmergy: Ant trails! Ants coming back from food source leave pheromone trace# Ants searching for food preferentially follow pheromone trace# •!deviate from trace occasionally! •!Allows them to )nd new food source! Strong trails get reinforced as more ants use them# Trails to exhausted food sources evaporate# Outcome: adaptive network of trails connecting food sources and nest in most e+cient way# Adaptation to disturbances! Sca(olding Stigmergy! A stigmergic medium can be pre!structured, e.g. :# •!Encyclopedia with templates +#stubs$, for missing articles! •!Plastic sca(old to grow an ear! •!City with some roads and landmarks, but no buildings yet! This can incite and facilitate desired self!organization# •!Attractive empty slots invite developments! •!Positive feedback: development → more development! •!Remains adaptive to unforeseen situations! Conclusion! Complexity precludes accurate prediction and control# •! → planning intrinsically unreliable! Self"organization produces robust, adaptive systems# •!Even in very complex, unpredictable environments! •!But outcomes may not be as desired! We need to investigate guided self"organization# •!Using reinforcement signals and sca(olds ! •!to steer stigmergic coordination in the desired direction!.
Load more

Recommended publications
  • Toward a New Science of Information
    Data Science Journal, Volume 6, Supplement, 7 April 2007 TOWARD A NEW SCIENCE OF INFORMATION D Doucette1*, R Bichler 2, W Hofkirchner2, and C Raffl2 *1 The Science of Information Institute, 1737 Q Street, N.W. Washington, D.C. 20009, USA Email: [email protected] 2 ICT&S Center, University of Salzburg - Center for Advanced Studies and Research in Information and Communication Technologies & Society, Sigmund-Haffner-Gasse 18, 5020 Salzburg, Austria Email: [email protected], [email protected], [email protected] ABSTRACT The concept of information has become a crucial topic in several emerging scientific disciplines, as well as in organizations, in companies and in everyday life. Hence it is legitimate to speak of the so-called information society; but a scientific understanding of the Information Age has not had time to develop. Following this evolution we face the need of a new transdisciplinary understanding of information, encompassing many academic disciplines and new fields of interest. Therefore a Science of Information is required. The goal of this paper is to discuss the aims, the scope, and the tools of a Science of Information. Furthermore we describe the new Science of Information Institute (SOII), which will be established as an international and transdisciplinary organization that takes into consideration a larger perspective of information. Keywords: Information, Science of Information, Information Society, Transdisciplinarity, Science of Information Institute (SOII), Foundations of Information Science (FIS) 1 INTRODUCTION Information is emerging as a new and large prospective area of study. The notion of information has become a crucial topic in several emerging scientific disciplines such as Philosophy of Information, Quantum Information, Bioinformatics and Biosemiotics, Theory of Mind, Systems Theory, Internet Research, and many more.
    [Show full text]
  • A New Transdisciplinary Paradigm for the Study of Complex Systems?", In: Self-Steering and Cognition in Complex Systems, Heylighen F., Rosseel E
    Heylighen F. (1990): "A New Transdisciplinary Paradigm for the Study of Complex Systems?", in: Self-Steering and Cognition in Complex Systems, Heylighen F., Rosseel E. & Demeyere F. (ed.), (Gordon and Breach, New York), p. 1-16. A NEW TRANSDISCIPLINARY PARADIGM FOR THE STUDY OF COMPLEX SYSTEMS? Francis Heylighen Transdisciplinary Research Group Free University of Brussels ABSTRACT: two paradigms for studying the relation between autonomy and cognition are reviewed and contrasted: the "artificial" paradigm, which sees autonomous systems as linear, information-processing organizations, and the "autopoietic" paradigm, which sees them as circular, self-producing organizations. It is argued that these two paradigms are not inconsistent but complementary, and that they can be synthesized in an encompassing paradigm based on the self-organization of complex systems through variation-and-selective retention, leading to the emergence of relatively autonomous subsystems. Some implications of such an encompassing paradigm on the level of science, technology, individual persons and society are outlined, with reference to the papers in this collection. It is argued that the further development of such a transdisciplinary approach will lead to a new "science of complexity". 1. Introduction In the preface to this book it was argued that some recent trends in different disciplines seem to converge around the concepts of self-organization, autonomy and cognition. Whether these developments really announce the emergence of a new paradigm remains to be proven. Only time can tell. In this position paper, I will assume that they do, and investigate where this new paradigm would be coming from, and where it might be heading to.
    [Show full text]
  • Artificial Intelligence
    Intelligence, Artificial Intelligence and Wisdom in the Global Sustainable Information Society Fourth International Conference on Philosophy of Information, IS4SI Summit Berkeley 2019, UC Berkeley, 2-6 June 2019 Wolfgang Hofkirchner Director, GSIS – The Institute for a Global Sustainable Information Society, Vienna Contents 1 A complex systems view 1.1 The Great Bifurcation 1.2 The Transformation into a Global Sustainable Information Society 2 Conditions for thriving and surviving 2.1 Globality 2.2 Sustainability 2.3 Informationality 3 Intelligence, AI, and wisdom 1 A complex systems view Seen from a complex systems view, the evolution of mankind faces a Great Bifurcation. Global challenges might cause the extermination of mankind. At the same time, global challenges can be mastered through a transformation into a global sustainable information society. 1.1 The Great Bifurcation Civilisation at the breakthrough to a higher level crossroads (rise of complexity): integration of differentiated, interdependent social systems into a single meta-/suprasystem – Global Sustainable Information global space of possible Society challenges trajectories (multicrisis in all techno-, eco-, social breakdown (decline of complexity): subsystems) impossible trajectories disintegration and tipping point* falling apart of civilisation * Ervin László 1.2 Transformation Metasystem transition* agency system n interacting agency (proto-element) (network) * Francis Heylighen et al. system n+1 (proto-element) 1.2 Transformation Metasystem …organisational transition
    [Show full text]
  • Towards a Cybernetic Foundation for Natural Resource Governance
    Towards a Cybernetic Foundation for Natural Resource Governance A Thesis Presented to the Academic Faculty by Talha Manzoor In Partial Fullfilment of the Requirements for the Degree of Doctor of Philosophy in Electrical Engineering Supervisor: Abubakr Muhammad (LUMS) Co-supervisor: Elena Rovenskaya (IIASA) arXiv:1803.09369v1 [cs.SY] 20 Mar 2018 Syed Babar Ali School of Science and Engineering Lahore University of Management Sciences December 2017 © 2017 by Talha Manzoor To Marwa and her never-ending quest for adventure. Abstract This study explores the potential of the cybernetic method of inquiry for the problem of natural resource governance. The systems way of thinking has already enabled scientists to gain considerable headway in framing global environmental challenges. On the other hand, technical solutions to environmental problems have begun to show significant promise, driven by the advent of technology and its increased proliferation in coupled human and natural systems. Such settings lie on the interface of engineering, social and environmental sciences, and as such, require a common language in order for natural resources to be studied, managed and ultimately sustained. In this dissertation, we argue that the systems theoretic tradition of cybernetics may provide the necessary common ground for examining such systems. After discussing the relevance of the cybernetic approach to natural resource governance, we present a mathematical model of resource consumption, grounded in social psychological research on consumer behavior. We also provide interpretations of the model at various levels of abstraction in the social network of the consuming population. We demonstrate the potential of the model by examining it in various theoretic frameworks which include dynamical systems, optimal control theory, game theory and the theory of learning in games.
    [Show full text]
  • Envisaging a Comprehensible Global Brain As a Playful Organ
    Alternative view of segmented documents via Kairos 2 December 2019 | Draft Envisaging a Comprehensible Global Brain -- as a Playful Organ Patterns connecting the dots between hemispheres, epicycles and quavers - / - Introduction Patterning and framing a global brain? Systemic feedback cycles of global brain interrelationships in 2D Various representations of cyclic dynamics with implications for a global brain Implication of 3D representation of a global brain Dynamic patterns of play engendered by Homo ludens and Homo undulans? Requisite helical cognitive engagement within a global brain Brainwaves and feedback loops in a global brain? Pathology of the global brain? Global brain as an organ: playable, playful or neither? References Introduction Collectively distributed cognition: This exploration is partly inspired by the work of Stafford Beer, as a management cybernetician, into the design of an appropriate collective brain (Brain of the Firm: the managerial cybernetics of organization, 1981), especially given the requisite complexity for a "viable system" (Giuliana Galli Carminati, The Planetary Brain: From the Web to the Grid and Beyond, 2011). Use of the metaphor followed from the influential study by an earlier cybernetician, W. Ross Ashby (Design for a Brain: the origin of adaptive behavior, 1952). The design preoccupation was the primary feature of a presentation by Shann Turnbull (Design Criteria for a Global Brain, 2001) as presented to the First Global Brain Workshop (Brussels, 2001). The argument follows from the controversial assertion recently made by President Macron of France with respect to the "brain death" of NATO and the potential implications for any "global brain" (Are the UN and the International Community both Brain Dead -- given criteria recognizing that NATO is brain dead? 2019).
    [Show full text]
  • The Emergence of Distributed Cognition: a Conceptual Framework
    submitted for Proceedings of Collective Intentionality IV, Siena (Italy), to be published as a special issue of Cognitive Systems Research The Emergence of Distributed Cognition: a conceptual framework Francis HEYLIGHEN, Margeret HEATH and Frank VAN OVERWALLE Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium {fheyligh, mheath, fjvoverw}@vub.ac.be ABSTRACT: We propose a first step in the development of an integrated theory of the emergence of distributed cognition/extended mind. Distributed cognition is seen as the confluence of collective intelligence and “situatedness”, or the extension of cognitive processes into the physical environment. The framework is based on five fundamental assumptions: 1) groups of agents self-organize to form a differentiated, coordinated system, adapted to its environment, 2) the system co-opts external media for internal propagation of information, 3) the resulting distributed cognitive system can be modelled as a learning, connectionist network, 4) information in the network is transmitted selectively, 5) novel knowledge emerges through non-linear, recurrent interactions. The implication for collective intentionality is that such a self-organizing agent collective can develop “mental content” that is not reducible to individual cognitions. Extended Mind: collective intelligence and distributed cognition From a cybernetic perspective [Heylighen & Joslyn, 2001], a cognitive system cannot be understood as a discrete collection of beliefs, procedures, and/or modules. Cognition is a continuously evolving process which relates present perceptions via internal states to potential further perceptions and actions. It thus allows an agent to anticipate what may happen, adapt to changes in its environment, and moreover effect changes upon its environment [Kirsch & Maglio, 1994].
    [Show full text]
  • Rafael Capurro's Full Text [.V2]
    In Search of Ariadne's Thread in Digital Labyrinths Rafael Capurro International Center for Information Ethics (ICIE) Karlsruhe, Germany "Times, they are 'a changin" Bob Dylan Abstract The aim of the following presentation is to provide a brief personal account of the results of some of the panels and sessions that took place at the Vienna Summit 2014 "The Information Society at the Crossroads" (June 3-7, 2015) in which I was involved as a participant and speaker. I will first summarize what I learned regarding some of the challenges in the fields of information ethics, dealing particularly with issues of social responsibility, critical theory, robotics, global brain, and philosophy of information. Secondly, I will relay the results of an email exchange that took place following the Vienna Summit conference between a number of colleagues and myself in which we explored our perceptions of the issues at hand and the stakes involved and whether or not we were able to trace to any length the myth of Ariadne's thread in digital labyrinths. I will clarify how even though such labyrinths and digital threads are a part of today's societies they are often confused with society and with our being-in-the- world itself. To signify my argument, and to show how such confusion can result in mortal consequences, I will conclude by outlining the case of the Chinese poet and migrant worker Xu Lizhi (aged 24) who committed suicide after three years' working for Foxconn. Introduction In 1994 a group of scholars and scientists started an initiative called "Foundations of Information Science" (FIS).
    [Show full text]
  • Comments on Donald T. Campbell's
    Contents W. Callebaut/R. Riedl 2 Preface Donald T. Campbell 5 From Evolutionary Epistemology Via Selection Theory to a Sociology of Scientific Validity Linnda R. Caporael 39 Vehicles of Knowledge: Artifacts and Social Groups W. D. Christensen/C. A. Hooker 44 Selection Theory, Organization and the Development of Knowledge Andy J. Clark 49 Evolutionary Epistemology and the Scientific Method Ed Constant 55 Comments on Donald T. Campbell’s “From Evolutionary Epistemology Via Selection Theory to a Sociology of Scientific Validity” Steve Fuller 58 Campbell’s Failed Cultural Materialism Francis Heylighen 63 Objective, Subjective, and Intersubjective Selectors of Knowledge Aharon Kantorovich 68 The Rationality of Innovation and the Scientific Community as a Carrier of Knowledge Elias L. Khalil 71 Can Artificial Selection Remedy the Failing of Natural Section with Regard to Scientific Validation? Kyung-Man Kim 75 D. T. Campbell’s Social Epistemology of Science Marc De Mey 81 Vision as Paradigm: From VTE to Cognitive Science Erhard Oeser 85 The Two-stage Model of Evolutionary Epistemology Henry Plotkin 89 Knowledge and Adapted Biological Structures Massimo Stanzione 92 Campbell, Hayek and Kautsky on Societal Evolution Franz M. Wuketits 98 Four (or Five?) Types of Evolutionary Epistemology Evolution and Cognition: ISSN: 0938-2623 Published by: Konrad Lorenz Institut für Evolutions- und Kognitionsforschung, Adolf-Lorenz-Gasse 2, A-3422 Alten- Impressum berg/Donau. Tel.: 0043-2242-32390; Fax: 0043-2242-323904; e-mail: [email protected]; World Wide Web: http://www.kla.univie.ac.at/ Chairman: Ru- pert Riedl Managing Editor: Manfred Wimmer Layout: Alexander Riegler Aim and Scope: “Evolution and Cognition” is an interdisciplinary forum devoted to all aspects of research on cognition in animals and humans.
    [Show full text]
  • Conceptualizing Complex Meaning Systems: the Case of Management Fads
    Conceptualizing complex meaning systems: The case of management fads A thesis presented to the Queensland University of Technology In fulfilment of the requirements for the degree of DOCTOR OF PHILOSOPHY By Wendy Corfield B.A., Grad. Dip Ed. Student #: 00482129 December 2006 Creative Industries Research and Applications Centre Creative Industries Faculty QUEENSLAND UNIVERSITY OF TECHNOLOGY DOCTOR OF PHILOSOPHY THESIS EXAMINATION CANDIDATE NAME: Wendy Corfield CENTRE: CIRAC RESEARCH CONCENTRATION: CREATIVE INDUSTRIES PRINCIPAL SUPERVISOR Prof. Greg Hearn ASSOCIATE SUPERVISOR: Dr Caroline Hatcher THESIS TITLE: Conceptualizing complex meaning systems: The case of management fads Under the requirements of PhD regulation 9.2, the above candidate is to be examined orally by the Faculty. The members of the panel set up for this examination will recommend that the thesis be accepted by the University and forwarded to the appointed Committee for examination. Name: Prof. Greg Hearn Signature…………............................................... Panel Chairperson (Principal Supervisor) Name: Prof. Stephen Towers Signature…………............................................... Panel Member (Associate Supervisor) Keywords: meaning, philosophy, complex systems, management fads, management gurus, decision making, emergence, epistemology, ontology i Table of Contents Keywords: ............................................................................................................i Table of Contents ................................................................................................
    [Show full text]
  • Ethics and Complexity: Why Standard Ethical Frameworks Cannot Cope with Socio-­‐Technological Change
    Ethics and Complexity: Why standard ethical frameworks cannot cope with socio-­‐technological change Clément Vidal & Francis Heylighen Center Leo Apostel Vrije Universiteit Brussel [email protected] & [email protected] Abstract: Standard ethical frameworks struggle to deal with transhumanism, ecological issues and the rising technodiversity because they are focused on guiding and evaluating human behavior. Ethics needs its Copernican revolution to be able to deal with all moral agents, including not only humans, but also artificial intelligent agents, robots or organizations of all sizes. We argue that embracing the complexity worldview is the first step towards this revolution, and that standard ethical frameworks are still entrenched in the Newtonian worldview. We first spell out the foundational assumptions of the Newtonian worldview, where all change is reduced to material particles following predetermined trajectories governed by the laws of nature. However, modern physical theories such as relativity, quantum mechanics, chaos theory and thermodynamics have drawn a much more confusing and uncertain picture, and inspired indecisive, subjectivist, relativist, nihilist or postmodern worldviews. Based on cybernetics, systems theory and the new sciences of complexity, we introduce the complexity worldview that sees the world as interactions and their emergent organizations. We use this complexity worldview to show the limitations of standard ethical frameworks such as deontology, theology, consequentialism, virtue ethics, evolutionary
    [Show full text]
  • Modeling the Emergence of Complexity: Complex Systems, The
    A A ARTISTS’ ARTICLE - R L T I F Modeling the Emergence E of Complexity: Complex Systems, the Origin of Life and Interactive A B S T R A C T On-Line Art The origin of this paper lies in the fundamental question of how complexity arose in the course of evolution and how one might construct an artistic Christa Sommerer and Laurent Mignonneau interactive system to model and simulate this emergence of complexity. Relying on the idea that interaction and communica- tion between entities of a system drive the emergence of structures that are more com- he Internet seems especially suited to inter- connected web of people who can plex than the mere parts of that T system, the authors propose to actions and transformations of data. Internet users can be con- transmit visual and written infor- apply principles of complex sidered entities or particles who transmit information (e.g. mation over the Internet. As the in- system theory to the creation of written texts or images). As these data or entities are transferred formation is transported from VERBARIUM, an interactive, from location to location they could, in principle, change in location to location, it would be computer-generated and audience-participatory artwork status and value. One could imagine a system that could in- transformed, creating an intercon- on the Internet, and to test crease its internal complexity as more and more users interact nected, open-ended system featur- whether complexity can emerge with its information. Just like a genetic string or “meme” as de- ing phase transitions toward more within this system.
    [Show full text]
  • Internet-Based Collective Intelligence, Creative Labour, and Social Production
    Article Thesis Eleven 1–19 Noosphere rising: ª The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav Internet-based collective DOI: 10.1177/0725513615575932 intelligence, creative the.sagepub.com labour, and social production Michael A Peters University of Waikato, New Zealand James Reveley University of Wollongong, Australia Abstract Our article relocates the debate about creative labour to the terrain of peer-to-peer interneting as the paradigmatic form of nonmarket – social – production. From Yann Moulier Boutang we take the point that creative labour is immaterial; it is expressed through people connected by the internet. Drawing on two social systems thinkers, Francis Heylighen and Wolfgang Hofkirchner, we transpose this connectedness up to a conception of creative labour as a supra-individual collective intelligence. This intelli- gence, we argue, is one of the internet’s emergent properties. We then present a model of internet development that flags the potential of digitally-evoked collective intelligence to facilitate what the Marxist philosopher George Caffentzis calls ‘postcapitalist com- moning’. Yoking together systems theorizing about the internet and socialist envisioning of social transformation, we identify two sets of internet tools for coordination that can assist with the convivial reconstruction of society along the lines of peer-based production. Keywords Collective intelligence, creative labour, internet, peer production, systems thinking Corresponding author: Michael A Peters, Faculty of Education, University of Waikato, Hamilton, New Zealand. Email: [email protected] Downloaded from the.sagepub.com at University of Waikato Library on September 19, 2016 2 Thesis Eleven Introduction One of the new economic sociology’s greatest insights is that economic activity under capitalism is embedded within dense networks of social relations (Granovetter, 1985; Swedberg, 2003).
    [Show full text]