Facilitating Emergence: Complex, Adaptive Systems Theory and the Shape of Change Peter Martin Dickens Antioch University - Phd Program in Leadership and Change
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What Is a Complex Adaptive System?
PROJECT GUTS What is a Complex Adaptive System? Introduction During the last three decades a leap has been made from the application of computing to help scientists ‘do’ science to the integration of computer science concepts, tools and theorems into the very fabric of science. The modeling of complex adaptive systems (CAS) is an example of such an integration of computer science into the very fabric of science; models of complex systems are used to understand, predict and prevent the most daunting problems we face today; issues such as climate change, loss of biodiversity, energy consumption and virulent disease affect us all. The study of complex adaptive systems, has come to be seen as a scientific frontier, and an increasing ability to interact systematically with highly complex systems that transcend separate disciplines will have a profound affect on future science, engineering and industry as well as in the management of our planet’s resources (Emmott et al., 2006). The name itself, “complex adaptive systems” conjures up images of complicated ideas that might be too difficult for a novice to understand. Instead, the study of CAS does exactly the opposite; it creates a unified method of studying disparate systems that elucidates the processes by which they operate. A complex system is simply a system in which many independent elements or agents interact, leading to emergent outcomes that are often difficult (or impossible) to predict simply by looking at the individual interactions. The “complex” part of CAS refers in fact to the vast interconnectedness of these systems. Using the principles of CAS to study these topics as related disciplines that can be better understood through the application of models, rather than a disparate collection of facts can strengthen learners’ understanding of these topics and prepare them to understand other systems by applying similar methods of analysis (Emmott et al., 2006). -
Organization, Self-Organization, Autonomy and Emergence: Status and Challenges
Organization, Self-Organization, Autonomy and Emergence: Status and Challenges Sven Brueckner 1 Hans Czap 2 1 New Vectors LLC 3520 Green Court, Suite 250, Ann Arbor, MI 48105-1579, USA Email: [email protected] http://www.altarum.net/~sbrueckner 2 University of Trier, FB IV Business Information Systems I D-54286 Trier, Germany Email: [email protected] http://www.wi.uni-trier.de/ Abstract: Development of IT-systems in application react and adapt autonomously to changing requirements. domains is facing an ever-growing complexity resulting from Therefore, approaches that rely on the fundamental principles a continuous increase in dynamics of processes, applications- of self-organization and autonomy are growing in acceptance. and run-time environments and scaling. The impact of this Following such approaches, software system functionality trend is amplified by the lack of central control structures. As is no longer explicitly designed into its component processes a consequence, controlling this complexity and dynamics is but emerges from lower-level interactions that are one of the most challenging requirements of today’s system purposefully unaware of the system-wide behavior. engineers. Furthermore, organization, the meaningful progression of The lack of a central control instance immediately raises local sensing, processing and action, is achieved by the the need for software systems which can react autonomously system components themselves at runtime and in response to to changing environmental requirements and conditions. the current state of the environment and the problem that is to Therefore, a new paradigm is necessary how to build be solved, rather than being enforced from the “outside” software systems changing radically the way one is used to through design or external control. -
Adaptive Capacity and Traps
Copyright © 2008 by the author(s). Published here under license by the Resilience Alliance. Carpenter, S. R., and W. A. Brock. 2008. Adaptive capacity and traps. Ecology and Society 13(2): 40. [online] URL: http://www.ecologyandsociety.org/vol13/iss2/art40/ Insight Adaptive Capacity and Traps Stephen R. Carpenter 1 and William A. Brock 1 ABSTRACT. Adaptive capacity is the ability of a living system, such as a social–ecological system, to adjust responses to changing internal demands and external drivers. Although adaptive capacity is a frequent topic of study in the resilience literature, there are few formal models. This paper introduces such a model and uses it to explore adaptive capacity by contrast with the opposite condition, or traps. In a social– ecological rigidity trap, strong self-reinforcing controls prevent the flexibility needed for adaptation. In the model, too much control erodes adaptive capacity and thereby increases the risk of catastrophic breakdown. In a social–ecological poverty trap, loose connections prevent the mobilization of ideas and resources to solve problems. In the model, too little control impedes the focus needed for adaptation. Fluctuations of internal demand or external shocks generate pulses of adaptive capacity, which may gain traction and pull the system out of the poverty trap. The model suggests some general properties of traps in social–ecological systems. It is general and flexible, so it can be used as a building block in more specific and detailed models of adaptive capacity for a particular region. Key Words: adaptation; allostasis; model; poverty trap; resilience; rigidity trap; transformation INTRODUCTION it seems empty of ideas, it will be abandoned or transformed into something else. -
1 Emergence of Universal Grammar in Foreign Word Adaptations* Shigeko
1 Emergence of Universal Grammar in foreign word adaptations* Shigeko Shinohara, UPRESA 7018 University of Paris III/CNRS 1. Introduction There has been a renewal of interest in the study of loanword phonology since the recent development of constraint-based theories. Such theories readily express target structures and modifications that foreign inputs are subject to (e.g. Paradis and Lebel 1994, Itô and Mester 1995a,b). Depending on how the foreign sounds are modified, we may be able to make inferences about aspects of the speaker's grammar for which the study of the native vocabulary is either inconclusive or uninformative. At the very least we expect foreign words to be modified in accordance with productive phonological processes and constraints (Silverman 1992, Paradis and Lebel 1994). It therefore comes as some surprise when patterns of systematic modification arise for which the rules and constraints of the native system have nothing to say or even worse contradict. I report a number of such “emergent” patterns that appear in our study of the adaptations of French words by speakers of Japanese (Shinohara 1997a,b, 2000). I claim that they pose a learnability problem. My working hypothesis is that these emergent patterns are reflections of Universal Grammar (UG). This is suggested by the fact that the emergent patterns typically correspond to well-established crosslinguistic markedness preferences that are overtly and robustly attested in the synchronic phonologies of numerous other languages. It is therefore natural to suppose that these emergent patterns follow from the default parameter settings or constraint rankings inherited from the initial stages of language acquisition that remain latent in the mature grammar. -
Clunio Populations to Different Tidal Conditions
Genetic adaptation in emergence time of Clunio populations to different tidal conditions DIETRICH NEUMANN Zoologisches Institut der Universitiit Wiirzburg, Wiirzburg KURZFASSUNG: Genetische Adaptation der Schliipfzeiten yon Clunio-Populationen an verschiedene Gezeitenbedingungen. Die Schliipfzeiten der in der Gezeitenzone Iebenden Clunio-Arten (Diptera, Chironomidae) sind mit bestimmten Wasserstandsbedingungen syn- chronisiert, und zwar derart, dat~ die nnmittelbar anschlief~ende Fortpflanzung der kurz- lebigen Imagines auf dem trockengefallenen Habitat stattfinden kann. Wenn das Habitat einer Clunio-Art in der mittleren Gezeitenzone liegt und parallel zu dem halbt~igigen Gezeiten- zyklus (T = 12,4 h) zweimal t~iglich auftaucht, dann kann sich eine 12,4stiindige Schliipf- periodik einstellen (Beispiel: Clunio takahashii). Wenn das Habitat in der unteren Gezeiten- zone liegt und nur um die Zeit der Springtiden auftaucht, dann ist eine 15t~igige (semilunare) SchRipfperiodik zu erwarten (Beispiele: Clunio marinus und C. mecliterraneus). Diese 15t~igige Schliipfperiodik ist synchronisiert mit bestimmten Niedrigwasserbedingungen, die an einem Kiistenort alle 15 Tage jewqils um die gleiche Tageszeit auftreten. Sie wird daher dutch zwei Daten eindeutig gekennzeichnet: (1) die lunaren Schliipftage (wenige aufeinanderfolgende Tage um Voll- und Neumond) und (2) die t~igliche Schliipfzeit. Wie experimentelle Untersuchungen iiber die Steuerung der Schliipfperiodik zeigten, kiSnnen die Tiere beide Daten richtig voraus- bestimmen. Die einzelnen Kiistenpopulationen -
Control Theory
Control theory S. Simrock DESY, Hamburg, Germany Abstract In engineering and mathematics, control theory deals with the behaviour of dynamical systems. The desired output of a system is called the reference. When one or more output variables of a system need to follow a certain ref- erence over time, a controller manipulates the inputs to a system to obtain the desired effect on the output of the system. Rapid advances in digital system technology have radically altered the control design options. It has become routinely practicable to design very complicated digital controllers and to carry out the extensive calculations required for their design. These advances in im- plementation and design capability can be obtained at low cost because of the widespread availability of inexpensive and powerful digital processing plat- forms and high-speed analog IO devices. 1 Introduction The emphasis of this tutorial on control theory is on the design of digital controls to achieve good dy- namic response and small errors while using signals that are sampled in time and quantized in amplitude. Both transform (classical control) and state-space (modern control) methods are described and applied to illustrative examples. The transform methods emphasized are the root-locus method of Evans and fre- quency response. The state-space methods developed are the technique of pole assignment augmented by an estimator (observer) and optimal quadratic-loss control. The optimal control problems use the steady-state constant gain solution. Other topics covered are system identification and non-linear control. System identification is a general term to describe mathematical tools and algorithms that build dynamical models from measured data. -
Ophthalmology and the Emergence of Artificial Intelligence
Perspectives Ophthalmology and the emergence of artificial intelligence Rapid advances in AI in ophthalmology are a harbinger of things to come for other fields of medicine he autonomous detection and triage of eye for retinopathy using retinal photography. This disease, or even accurate estimations of gender, vast demand for diabetic eye screening services Tage, and blood pressure from a simple retinal has stimulated the development of AI algorithms to photo, may sound like the realms of science fiction, identify sight-threatening disease. Several algorithms but advances in artificial intelligence (AI) have already have achieved performance that meets or exceeds that made this a reality.1 Ophthalmology is at the vanguard of human experts.4,5 Accordingly, in 2018, the United of the development and clinical application of AI. States Food and Drug Administration approved an AI Advances in the field may provide useful insights into system to detect referable diabetic retinopathy from the application of this technology in health care more retinal photographs, the first autonomous diagnostic broadly. system to be approved in any field of medicine.6 Advances in deep learning have extended to other Artificial intelligence imaging modalities that are commonly used in ophthalmology. Ocular coherence tomography is an Once described as the capacity of intelligent machines imaging technology that produces highly detailed, to imitate human intelligence and behaviour, AI now depth-resolved images of the retina. A recent describes many theories and practices used to achieve collaboration between researchers and clinicians computer intelligence (Box 1).2 Machine learning is at Google DeepMind, Moorfields Eye Hospital an application of AI that uses algorithms or statistical and University College London culminated in the models to make decisions or predictions. -
Systems That Adapt to Their Users
Systems That Adapt to Their Users Anthony Jameson and Krzysztof Z. Gajos DFKI, German Research Center for Artificial Intelligence Harvard School of Engineering and Applied Sciences Introduction This chapter covers a broad range of interactive systems which have one idea in common: that it can be worthwhile for a system to learn something about individual users and adapt its behavior to them in some nontrivial way. A representative example is shown in Figure 20.1: the COMMUNITYCOMMANDS recommender plug-in for AUTO- CAD (introduced by Matejka, Li, Grossman, & Fitzmau- rice, 2009, and discussed more extensively by Li, Mate- jka, Grossman, Konstan, & Fitzmaurice, 2011). To help users deal with the hundreds of commands that AUTO- CAD offers—of which most users know only a few dozen— COMMUNITYCOMMANDS (a) gives the user easy access to several recently used commands, which the user may want to invoke again soon; and (b) more proactively suggests com- mands that this user has not yet used but may find useful, Figure 20.1: Screenshot showing how COMMUNITY- given the type of work they have been doing recently. COMMANDS recommends commands to a user. (Image supplied by Justin Matejka. The length of the darker bar for a com- Concepts mand reflects its estimated relevance to the user’s activities. When the user A key idea embodied in COMMUNITYCOMMANDS and the hovers over a command in this interface, a tooltip appears that explains the command and might show usage hints provided by colleagues. See also other systems discussed in this chapter is that of adapta- http://www.autodesk.com/research.) tion to the individual user. -
The Emergence of Artificial Intelligence in the Home: Products, Services, and Broader
Western Oregon University Digital Commons@WOU Student Theses, Papers and Projects (Computer Department of Computer Science Science) 3-8-2017 The meE rgence of Artificial Intelligence in the Home: Products, Services, and Broader Developments of Consumer Oriented AI Bingqing Tang [email protected] Follow this and additional works at: https://digitalcommons.wou.edu/ computerscience_studentpubs Part of the Management Information Systems Commons Recommended Citation Tang, Bingqing, "The meE rgence of Artificial Intelligence in the Home: Products, Services, and Broader Developments of Consumer Oriented AI" (2017). Student Theses, Papers and Projects (Computer Science). 6. https://digitalcommons.wou.edu/computerscience_studentpubs/6 This Professional Project is brought to you for free and open access by the Department of Computer Science at Digital Commons@WOU. It has been accepted for inclusion in Student Theses, Papers and Projects (Computer Science) by an authorized administrator of Digital Commons@WOU. For more information, please contact [email protected]. The Emergence of Artificial Intelligence in the Home: Products, Services, and Broader Developments of Consumer Oriented AI Bingqing Tang IS642 MIS Graduate Project Dr. David Olson, Dr. John Leadley & Dr. Scot Morse March 15, 2017 Tang 1 Abstract Current home automation system merges a family's lifestyle with the latest technology & energy management tools to simplify people's lives. It allows users to easily manipulate a variety of home systems, including appliances, security systems, and environmental systems. Setting up a home automation system confuses many consumers. Multiple product lines and platforms make choosing the best system difficult. Basic requirements of setting up a home automations system and the comparison between different platforms are explained. -
Systems Theory
1 Systems Theory BRUCE D. FRIEDMAN AND KAREN NEUMAN ALLEN iopsychosocial assessment and the develop - nature of the clinical enterprise, others have chal - Bment of appropriate intervention strategies for lenged the suitability of systems theory as an orga - a particular client require consideration of the indi - nizing framework for clinical practice (Fook, Ryan, vidual in relation to a larger social context. To & Hawkins, 1997; Wakefield, 1996a, 1996b). accomplish this, we use principles and concepts The term system emerged from Émile Durkheim’s derived from systems theory. Systems theory is a early study of social systems (Robbins, Chatterjee, way of elaborating increasingly complex systems & Canda, 2006), as well as from the work of across a continuum that encompasses the person-in- Talcott Parsons. However, within social work, sys - environment (Anderson, Carter, & Lowe, 1999). tems thinking has been more heavily influenced by Systems theory also enables us to understand the the work of the biologist Ludwig von Bertalanffy components and dynamics of client systems in order and later adaptations by the social psychologist Uri to interpret problems and develop balanced inter - Bronfenbrenner, who examined human biological vention strategies, with the goal of enhancing the systems within an ecological environment. With “goodness of fit” between individuals and their its roots in von Bertalanffy’s systems theory and environments. Systems theory does not specify par - Bronfenbrenner’s ecological environment, the ticular theoretical frameworks for understanding ecosys tems perspective provides a framework that problems, and it does not direct the social worker to permits users to draw on theories from different dis - specific intervention strategies. -
Information Systems Foundations Theory, Representation and Reality
Information Systems Foundations Theory, Representation and Reality Information Systems Foundations Theory, Representation and Reality Dennis N. Hart and Shirley D. Gregor (Editors) Workshop Chair Shirley D. Gregor ANU Program Chairs Dennis N. Hart ANU Shirley D. Gregor ANU Program Committee Bob Colomb University of Queensland Walter Fernandez ANU Steven Fraser ANU Sigi Goode ANU Peter Green University of Queensland Robert Johnston University of Melbourne Sumit Lodhia ANU Mike Metcalfe University of South Australia Graham Pervan Curtin University of Technology Michael Rosemann Queensland University of Technology Graeme Shanks University of Melbourne Tim Turner Australian Defence Force Academy Leoni Warne Defence Science and Technology Organisation David Wilson University of Technology, Sydney Published by ANU E Press The Australian National University Canberra ACT 0200, Australia Email: [email protected] This title is also available online at: http://epress.anu.edu.au/info_systems02_citation.html National Library of Australia Cataloguing-in-Publication entry Information systems foundations : theory, representation and reality Bibliography. ISBN 9781921313134 (pbk.) ISBN 9781921313141 (online) 1. Management information systems–Congresses. 2. Information resources management–Congresses. 658.4038 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying or otherwise, without the prior permission of the publisher. Cover design by Brendon McKinley with logo by Michael Gregor Authors’ photographs on back cover: ANU Photography Printed by University Printing Services, ANU This edition © 2007 ANU E Press Table of Contents Preface vii The Papers ix Theory Designing for Mutability in Information Systems Artifacts, Shirley Gregor and Juhani Iivari 3 The Eect of the Application Domain in IS Problem Solving: A Theoretical Analysis, Iris Vessey 25 Towards a Unied Theory of Fit: Task, Technology and Individual, Michael J. -
Emergent Behavior in Systems of Systems John S
Emergent Behavior in Systems of Systems John S. Osmundson Thomas V. Huynh Department of Information Sciences Department of Systems Engineering Naval Postgraduate School Naval Postgraduate School 1 University Circle 1 University Circle Monterey, CA 93943, USA Monterey, CA 93943, USA [email protected] [email protected] Gary O. Langford Department of Systems Engineering Naval Postgraduate School 1 University Circle Monterey, CA 93943 [email protected] Copyright © 2008 by John Osmundson, Thomas Huynh and Gary Langford. Published and used by INCOSE with permission. Abstract. As the development of systems of systems becomes more important in global ventures, so does the issues of emergent behavior. The challenge for systems engineers is to predict and analyze emergent behavior, especially undesirable behavior, in systems of systems. In this paper we briefly discuss definitions of emergent behavior, describe a large-scale system of system that exhibits emergent behavior, review previous approaches to analyzing emergent behavior, and then present a system modeling and simulation approach that has promise of allowing systems engineers to analyze potential emergent behavior in large scale systems of systems. Introduction Recently there has been increased interest in what has been become known as systems of systems (SoS) and SoS engineering. Examples of SoS are military command, control, computer, communications and information (C4I) systems (Pei 2000), intelligence, surveillance and reconnaissance (ISR) systems (Manthrope 1996), intelligence collection management systems (Osmundson et al 2006a), electrical power distribution systems (Casazza and Delea 2000) and food chains (Neutel 2002.), among others. Much of this current interest in SoS is focused on the desire to integrate existing systems to achieve new capabilities that are unavailable by operation of any of the existing single individual systems.