Modelling Stigmergy Evolutionary Framework for System Design Shanu Sharma, Department of Design SPA Bhopal
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How Collective Intelligence Fosters Incremental Innovation
Journal of Open Innovation: Technology, Market, and Complexity Article How Collective Intelligence Fosters Incremental Innovation Jung-Yong Lee 1 and Chang-Hyun Jin 2,* 1 Department of Business Administration, SungKyunKwan University, Seoul 03063, Korea 2 Department of Business Administration, Kyonggi University, Suwon 16227, Korea * Correspondence: [email protected] Received: 2 July 2019; Accepted: 7 August 2019; Published: 8 August 2019 Abstract: The study aims to identify motivational factors that lead to collective intelligence and understand how these factors relate to each other and to innovation in enterprises. The study used the convenience sampling of corporate employees who use collective intelligence from corporate panel members (n = 1500). Collective intelligence was found to affect work process, operations, and service innovation. When corporate employees work in an environment where collective intelligence (CI) is highly developed, work procedures or efficiency may differ depending on the onset of CI. This raises the importance of CI within an organization and implies the importance of finding means to vitalize CI. This study provides significant implications for corporations utilizing collective intelligence services, such as online communities. Firstly, such corporations vitalize their services by raising the quality of information and knowledge shared in their workplaces; and secondly, contribution motivations that consider the characteristics of knowledge and information contributors require further development. Keywords: collective intelligence; social contribution motivation; personal contribution motivation; work process; operation; service innovation; incremental innovation 1. Introduction The ubiquity of information communication technologies—increasingly brought on by recent breakthrough developments—is apparent as a means of inducing intrinsic yet innovative change across corporate management practices. Competition is becoming among corporations that fight for survival amidst a rapidly changing global economic environment. -
Collective Intelligence and the Blockchain
Collective intelligence and the blockchain: Technology, communities and social experiments Andrea Baronchelli1,2,3,* 1City University of London, Department of Mathematics, London EC1V 0HB, UK 2The Alan Turing Institute, British Library, 96 Euston Road, London NW12DB, UK 3UCL Centre for Blockchain Technologies, University College London, London, UK. *[email protected] Blockchains are still perceived chiefly as a new technology. But each blockchain is also a community and a social experiment, built around social consensus. Here I discuss three examples showing how collective intelligence can help, threat or capitalize on blockchain- based ecosystems. They concern the immutability of smart contracts, code transparency and new forms of property. The examples show that more research, new norms and, eventually, laws are needed to manage the interaction between collective behaviour and the blockchain technology. Insights from researchers in collective intelligence can help society rise up to the challenge. For most people, blockchain technology is about on-chain consensus. And it certainly is. A blockchain provides users with a method and incentives for validating and storing a value or transaction without the need to trust a central authority, or any other participants in the system. Once approved, the transaction is recorded in an open ledger that cannot be altered retroactively. Since the system is trustless, everyone can join the network and read, write, or participate within the blockchain (in this article, I am considering only public and permissionless blockchains).1 But a public blockchain is not a self-contained universe. As a socio-technical system, its life extents behind the user base. For example, a blockchain can provide competition to existent socio-economic players, and it is shaped by what users do with it and, eventually, by the law.2, 3 Continuous updates and strategic decisions are therefore required to keep up with such a rapidly evolving landscape, meaning that blockchains are not self-sufficient. -
Collective Intelligence: an Overview
Collective Intelligence: An Overview What is Collective Intelligence? The Internet has given rise to some remarkable technologies that enable people to collaborate en masse. In barely a decade, the biggest encyclopaedia in human history has been written by millions of authors with no centralised control. In under a year, ordinary people classified more than 50 million photos of distant worlds to help astronomers understand how galaxies are formed. All over the world, organisations are starting to open up social networks and collaborative environments from which emanate an endless stream of data and insight. The potential that lies within interconnected networks of computers and humans is only just starting to be realised. We all possess intelligence. However, intelligence can be thought of not only as something that arises within human brains – it also arises in groups of people. This is Collective Intelligence – individuals acting together to combine their knowledge and insight. Collective Intelligence is an emergent property. It emerges from the group, it does not reside within any individual members. The Wisdom of Crowds is simply another term for Collective Intelligence, stemming from the work of British scientist Sir Francis Galton. At a livestock exhibition in 1906, Galton observed a competition where people had to guess the weight of an ox. At the end of the contest, Galton gathered all the guesses and calculated the average. The crowd’s estimate turned out to be near perfect. Galton referred to this as the collective wisdom of the crowd: the fact that groups of people can be more intelligent than an intelligent individual and that groups do not always require intelligent people to reach a smart decision or outcome. -
Interactive Robots in Experimental Biology 3 4 5 6 Jens Krause1,2, Alan F.T
1 2 Interactive Robots in Experimental Biology 3 4 5 6 Jens Krause1,2, Alan F.T. Winfield3 & Jean-Louis Deneubourg4 7 8 9 10 11 12 1Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Department of Biology and 13 Ecology of Fishes, 12587 Berlin, Germany; 14 2Humboldt-University of Berlin, Department for Crop and Animal Sciences, Philippstrasse 15 13, 10115 Berlin, Germany; 16 3Bristol Robotics Laboratory, University of the West of England, Coldharbour Lane, Bristol 17 BS16 1QY, UK; 18 4Unit of Social Ecology, Campus Plaine - CP 231, Université libre de Bruxelles, Bd du 19 Triomphe, B-1050 Brussels - Belgium 20 21 22 23 24 25 26 27 28 Corresponding author: Krause, J. ([email protected]), Leibniz Institute of Freshwater 29 Ecology and Inland Fisheries, Department of the Biology and Ecology of Fishes, 30 Müggelseedamm 310, 12587 Berlin, Germany. 31 32 33 1 33 Interactive robots have the potential to revolutionise the study of social behaviour because 34 they provide a number of methodological advances. In interactions with live animals the 35 behaviour of robots can be standardised, morphology and behaviour can be decoupled (so that 36 different morphologies and behavioural strategies can be combined), behaviour can be 37 manipulated in complex interaction sequences and models of behaviour can be embodied by 38 the robot and thereby be tested. Furthermore, robots can be used as demonstrators in 39 experiments on social learning. The opportunities that robots create for new experimental 40 approaches have far-reaching consequences for research in fields such as mate choice, 41 cooperation, social learning, personality studies and collective behaviour. -
Henry Jenkins Convergence Culture Where Old and New Media
Henry Jenkins Convergence Culture Where Old and New Media Collide n New York University Press • NewYork and London Skenovano pro studijni ucely NEW YORK UNIVERSITY PRESS New York and London www.nyupress. org © 2006 by New York University All rights reserved Library of Congress Cataloging-in-Publication Data Jenkins, Henry, 1958- Convergence culture : where old and new media collide / Henry Jenkins, p. cm. Includes bibliographical references and index. ISBN-13: 978-0-8147-4281-5 (cloth : alk. paper) ISBN-10: 0-8147-4281-5 (cloth : alk. paper) 1. Mass media and culture—United States. 2. Popular culture—United States. I. Title. P94.65.U6J46 2006 302.230973—dc22 2006007358 New York University Press books are printed on acid-free paper, and their binding materials are chosen for strength and durability. Manufactured in the United States of America c 15 14 13 12 11 p 10 987654321 Skenovano pro studijni ucely Contents Acknowledgments vii Introduction: "Worship at the Altar of Convergence": A New Paradigm for Understanding Media Change 1 1 Spoiling Survivor: The Anatomy of a Knowledge Community 25 2 Buying into American Idol: How We are Being Sold on Reality TV 59 3 Searching for the Origami Unicorn: The Matrix and Transmedia Storytelling 93 4 Quentin Tarantino's Star Wars? Grassroots Creativity Meets the Media Industry 131 5 Why Heather Can Write: Media Literacy and the Harry Potter Wars 169 6 Photoshop for Democracy: The New Relationship between Politics and Popular Culture 206 Conclusion: Democratizing Television? The Politics of Participation 240 Notes 261 Glossary 279 Index 295 About the Author 308 V Skenovano pro studijni ucely Acknowledgments Writing this book has been an epic journey, helped along by many hands. -
Malte Andersson 30.10.2019
Social evolution, and levels of selection Malte Andersson 30.10.2019 1 Social evolution, Malte Andersson, 30.10. 2019 Fitness: an individual’s expected genetic contribution to the next generation Direct fitness: numbers of surviving own offspring A behaviour leading to higher fitness is favored by selection and will spread over the generations 2 1 Mobbing a raptor is risky for a crow 3 A honey bee dies after stinging an enemy 4 2 Costly helping: how can it evolve and persist? Does it benefit the donor of help via: 1) direct fitness (individual selection) ? 2) delayed direct fitness, by reciprocity ? 3) donor’s relatives: kin selection ? 4) a larger community: group selection ? These questions are debated. They were first asked by Darwin (1859), about sterile workers in eusocial insects. Some individuals can not reproduce, but offer their lives for the colony (for instance honey bee workers). Can natural selection lead to such behaviour? 5 Altruism (meaning in ecology): Helpful behavior that raises the recipient’s but lowers the donor’s direct fitness 6 3 Alarm signals - selfish or altruistic? Black-throated Shrike-Tanager, Springbok stotting, Kalahari Belize Stotting tells a predator it is detected Warning flock members, also using by a gazelle in good condition. Better the signal for own feeding advantage to hunt another prey (Caro 1986) 7 Is sentinel behavior in meerkats altruistic? Probably not. Sentinels are safer and have a direct benefit from their behavior. (Clutton-Brock et al. 1999) 8 4 0.5 0.4 0.3 Females 0.2 Males 0.1 0 Without close With With offspring genetic relatives nondescendant relatives Black-tailed prairie dogs Individuals give alarm calls mainly with relatives (offspring and others). -
Animating Predator and Prey Fish Interactions
Georgia State University ScholarWorks @ Georgia State University Computer Science Dissertations Department of Computer Science 5-6-2019 Animating Predator and Prey Fish Interactions Sahithi Podila Follow this and additional works at: https://scholarworks.gsu.edu/cs_diss Recommended Citation Podila, Sahithi, "Animating Predator and Prey Fish Interactions." Dissertation, Georgia State University, 2019. https://scholarworks.gsu.edu/cs_diss/149 This Dissertation is brought to you for free and open access by the Department of Computer Science at ScholarWorks @ Georgia State University. It has been accepted for inclusion in Computer Science Dissertations by an authorized administrator of ScholarWorks @ Georgia State University. For more information, please contact [email protected]. ANIMATING PREDATOR AND PREY FISH INTERACTIONS by SAHITHI PODILA Under the Direction of Ying Zhu, PhD ABSTRACT Schooling behavior is one of the most salient social and group activities among fish. They form schools for social reasons like foraging, mating and escaping from predators. Animating a school of fish is difficult because they are large in number, often swim in distinctive patterns that is they take the shape of long thin lines, squares, ovals or amoeboid and exhibit complex coordinated patterns especially when they are attacked by a predator. Previous work in computer graphics has not provided satisfactory models to simulate the many distinctive interactions between a school of prey fish and their predator, how does a predator pick its target? and how does a school of fish react to such attacks? This dissertation presents a method to simulate interactions between prey fish and predator fish in the 3D world based on the biological research findings. -
Expert Assessment of Stigmergy: a Report for the Department of National Defence
Expert Assessment of Stigmergy: A Report for the Department of National Defence Contract No. W7714-040899/003/SV File No. 011 sv.W7714-040899 Client Reference No.: W7714-4-0899 Requisition No. W7714-040899 Contact Info. Tony White Associate Professor School of Computer Science Room 5302 Herzberg Building Carleton University 1125 Colonel By Drive Ottawa, Ontario K1S 5B6 (Office) 613-520-2600 x2208 (Cell) 613-612-2708 [email protected] http://www.scs.carleton.ca/~arpwhite Expert Assessment of Stigmergy Abstract This report describes the current state of research in the area known as Swarm Intelligence. Swarm Intelligence relies upon stigmergic principles in order to solve complex problems using only simple agents. Swarm Intelligence has been receiving increasing attention over the last 10 years as a result of the acknowledgement of the success of social insect systems in solving complex problems without the need for central control or global information. In swarm- based problem solving, a solution emerges as a result of the collective action of the members of the swarm, often using principles of communication known as stigmergy. The individual behaviours of swarm members do not indicate the nature of the emergent collective behaviour and the solution process is generally very robust to the loss of individual swarm members. This report describes the general principles for swarm-based problem solving, the way in which stigmergy is employed, and presents a number of high level algorithms that have proven utility in solving hard optimization and control problems. Useful tools for the modelling and investigation of swarm-based systems are then briefly described. -
The Ecology of Mutualism
Annual Reviews www.annualreviews.org/aronline AngRev. Ecol. Syst. 1982.13:315--47 Copyright©1982 by Annual Reviews lnc. All rightsreserved THE ECOLOGY OF MUTUALISM Douglas 1t. Boucher Departementdes sciences biologiques, Universit~ du Quebec~ Montreal, C. P. 8888, Suet. A, Montreal, Quebec, CanadaH3C 3P8 Sam James Departmentof Ecologyand Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, USA48109 Kathleen H. Keeler School of Life Sciences, University of Nebraska,Lincoln, Nebraska,USA 68588 INTRODUCTION Elementaryecology texts tell us that organismsinteract in three fundamen- tal ways, generally given the namescompetition, predation, and mutualism. The third memberhas gotten short shrift (264), and even its nameis not generally agreed on. Terms that may be considered synonyms,in whole or part, are symbiosis, commensalism,cooperation, protocooperation, mutual aid, facilitation, reciprocal altruism, and entraide. Weuse the term mutual- by University of Kanas-Lawrence & Edwards on 09/26/05. For personal use only. ism, defined as "an interaction betweenspecies that is beneficial to both," Annu. Rev. Ecol. Syst. 1982.13:315-347. Downloaded from arjournals.annualreviews.org since it has both historical priority (311) and general currency. Symbiosis is "the living together of two organismsin close association," and modifiers are used to specify dependenceon the interaction (facultative or obligate) and the range of species that can take part (oligophilic or polyphilic). We make the normal apologies concerning forcing continuous variation and diverse interactions into simple dichotomousclassifications, for these and all subsequentdefinitions. Thus mutualism can be defined, in brief, as a -b/q- interaction, while competition, predation, and eommensalismare respectively -/-, -/q-, and -t-/0. There remains, however,the question of howto define "benefit to the 315 0066-4162/82/1120-0315 $02.00 Annual Reviews www.annualreviews.org/aronline 316 BOUCHER, JAMES & KEELER species" without evoking group selection. -
Basic Hunting Booklet
Basic Hunting Passing on a time-honored tradition Kentucky Department of Fish and Wildlife Resources From the Commissioner. The North American Model of Wildlife Management depends on hunters. This model incorporates principles of conservation from the past 100 years and was formally adopted in 2002. Over the years the number of hunters has decreased. Fish and Wildlife agencies from across the nation view this as a major problem. Many factors contrib- ute to this decline, including habitat loss, a growing urban population, and increased competition for leisure time. We do have one key ele- ment upon which we can rely: the people who enjoy the opportunity to step outside and enjoy shooting and hunting. Kentucky’s Department of Fish and Wildlife Resources has worked for several decades to give the shooter and hunter every pos- sible opportunity. We have developed shooting ranges, assisted land- owners in improving habitat, and enhanced the quality of the hunt for numerous species. In addition, we strive to provide Wildlife Manage- ment Areas for public use. In fact, the amount of land the department manages has more than doubled in the last 10 years. You and your family and friends are essential to the future of hunting. Each new hunter must have consistent support to continue to grow and expand his or her interest. I think you will find this expe- rience extremely rewarding for everyone involved. In a tribute to his father, Alan Jackson stated in a song about boating that it was “…a piece of my childhood that will never be forgotten.” I feel your involve- ment in hunting with your friends and family will fall in that “special memory” category. -
Interaction Ruling Animal Collective Behavior Depends on Topological Rather Than Metric Distance: Evidence from a Field Study
Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study M. Ballerini*†, N. Cabibbo‡§, R. Candelier‡¶, A. Cavagna*ʈ**, E. Cisbani†, I. Giardina*ʈ, V. Lecomte††‡‡, A. Orlandi*, G. Parisi*‡§**, A. Procaccini*‡, and M. Viale‡§§, and V. Zdravkovic* *Centre for Statistical Mechanics and Complexity (SMC), Consiglio Nazionale delle Ricerche-Istituto Nazionale per la Fisica della Materia, ‡Dipartimento di Fisica, and §Sezione Instituto Nazionale di Fisica Nucleare, Universita’ di Roma ‘‘La Sapienza,’’ Piazzale Aldo Moro 2, 00185 Roma, Italy; †Istituto Superiore di Sanita’, viale Regina Elena 299, 00161 Roma, Italy; ʈIstituto dei Sistemi Complessi (ISC), Consiglio Nazionale delle Ricerche, via dei Taurini 19, 00185 Roma, Italy; and ††Laboratoire Matie`re et Syste`mes Complexes, (Centre National de la Recherche Scientifique Unite Mixte de Recherche 7057), Universite´Paris VII, 10 rue Alice Domon et Le´onie Duquet, 75205 Paris Cedex 13, France Contributed by G. Parisi, December 4, 2007 (sent for review September 25, 2007) Numerical models indicate that collective animal behavior may no bird remains isolated, and soon the flock reforms as whole. emerge from simple local rules of interaction among the individ- The question we want to answer is ‘‘what kind of interaction uals. However, very little is known about the nature of such maintains cohesion in such a robust way?’’ interaction, so that models and theories mostly rely on aprioristic To grant cohesion, models make the sound assumption that assumptions. By reconstructing the three-dimensional positions of individuals align and attract each other, and that such interaction individual birds in airborne flocks of a few thousand members, we decays with increasing distance between individuals. -
Social Media and Collective Intelligence: Ongoing and Future Research Streams
Social Media and Collective Intelligence: Ongoing and Future Research Streams Detlef Schoder, Peter A. Gloor, Panagiotis Takis Metaxas The tremendous growth in the use of Social Media has led to radical paradigm shifts in the ways we communicate, collaborate, consume, and create information. Our focus in this special issue is on the reciprocal interplay of Social Media and Collective Intelligence. We therefore discuss constituting attributes of Social Media and Collective Intelligence, and we structure the rapidly growing body of literature including adjacent research streams such as Social Network Analysis, Web Science, and Computational Social Science. We conclude by making propositions for future research where in particular the disciplines of artificial intelligence, computer science, and information systems can substantially contribute to the interdisciplinary academic discourse. Introduction Over the last few years, the use of Social Media has increased tremendously all over the world. For example, Facebook has increased the number of its subscribers worldwide from approx. 660 million in March 2011 to approx. 840 million in March 2012 (http://www.internetworldstats.com/facebook.htm). At the time of this writing, according to Twitter announcements, the number of ‘tweets’ people send is more than one billion every 3 days or more than 4,000 tweets/sec each consisting of a text message of 140 characters or less, and the number of Wikipedia articles has increased from 3.5 million in January 2011 to 3.8 million in January 2012 (http://en.wikipedia.org/wiki/Wikipedia:Size_of_Wikipedia). The Chinese microblogging service Sina Weibo, claims to have registered more than 300 million users in just 3 years.