Managing Schools As Complex Adaptive Systems / Fidan & Balcı
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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). -
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. -
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. -
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. -
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. -
Higher Education As a Complex Adaptive System: Considerations for Leadership and Scale
Hartzler, R., & Blair, R. (Eds.) (2019). Emerging issues in mathematics pathways: Case studies, scans of the field, and recommendations. Austin, TX: Charles A. Dana Center at The University of Texas at Austin. Available at www.dcmathpathways.org/learn-about/emerging-issues-mathematics-pathways Copyright 2019 Chapter 6 Higher Education as a Complex Adaptive System: Considerations for Leadership and Scale Jeremy Martin The Charles A. Dana Center The University of Texas at Austin Abstract As state policies, economic pressures, and enrollment declines increase pressure on higher education systems to rapidly improve student outcomes in mathematics, many institutions have transformed the way they do business and have adopted mathematics pathways at scale. At the same time, many systems of higher education have struggled to move from piloting mathematics pathways to implementing reforms at a scale that supports every student’s success in postsecondary mathematics. Drawing on complexity science, this chapter presents a conceptual framework for leaders at all levels of higher education systems to design change strategies to adopt mathematics pathways principles at scale. How leaders think about the systems that they work in has material consequences for designing, implementing, and sustaining scaling strategies. The chapter offers a framework and recommendations for understanding higher education as a complex adaptive system and the role of strategic leadership in designing, implementing, and sustaining reforms at scale. Emerging Issues in Mathematics Pathways: Case Studies, Scans of the Field, and Recommendations 57 Introduction Higher Education as a Complex Adaptive System After decades of innovation and research on improving student success in postsecondary The term system refers to a set of connected parts mathematics, a convincing body of evidence that together form a complex whole. -
Complexity Management in the Semiconductor Supply Chain and Manufacturing Using PROS Analysis Can Sun, Thomas Rose, Hans Ehm, Stefan Heilmayer
Complexity Management in the Semiconductor Supply Chain and Manufacturing Using PROS Analysis Can Sun, Thomas Rose, Hans Ehm, Stefan Heilmayer To cite this version: Can Sun, Thomas Rose, Hans Ehm, Stefan Heilmayer. Complexity Management in the Semiconductor Supply Chain and Manufacturing Using PROS Analysis. 16th International Conference on Informatics and Semiotics in Organisations (ICISO), Mar 2015, Toulouse, France. pp.166-175, 10.1007/978-3-319- 16274-4_17. hal-01324975 HAL Id: hal-01324975 https://hal.inria.fr/hal-01324975 Submitted on 1 Jun 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License Complexity Management in the Semiconductor Supply Chain and Manufacturing Using PROS Analysis Can Sun1, 2, Thomas Rose1, 3, Hans Ehm2, Stefan Heilmayer2 1 RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany 2 Infineon Technologies AG, Am Campeon 1-12, 85579 Neubiberg, Germany 3 Fraunhofer FIT, Schloss Birlinghoven, 53754 Sankt Augustin, Germany {Can.Sun, Hans.Ehm, Stefan.Heilmayer}@infineon.com [email protected] Abstract. Supply chain complexity is a rising problem, especially in the semi- conductor industry. Many innovative activities occur in the daily supply chain and manufacturing, and these changes inevitably bring in the complexities to the organization. -
Complexity Management for Projects, Programmes, and Portfolios: an Engineering Systems Perspective
Downloaded from orbit.dtu.dk on: Sep 26, 2021 Complexity Management for Projects, Programmes, and Portfolios: An Engineering Systems Perspective Oehmen, Josef; Thuesen, Christian; Parraguez, Pedro ; Geraldi, Joana Publication date: 2015 Document Version Publisher's PDF, also known as Version of record Link back to DTU Orbit Citation (APA): Oehmen, J., Thuesen, C., Parraguez, P., & Geraldi, J. (2015). Complexity Management for Projects, Programmes, and Portfolios: An Engineering Systems Perspective. Project Management Institute, PMI. PMI White Paper General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. WHITE PAPER Complexity Management for Projects, Programmes, and Portfolios: An Engineering Systems Perspective By Josef Oehmen, Christian Thuesen, Pedro Parraguez Ruiz, and Joana Geraldi Complexity Management for Projects, Programmes, -
Transformations)
TRANSFORMACJE (TRANSFORMATIONS) Transformacje (Transformations) is an interdisciplinary refereed, reviewed journal, published since 1992. The journal is devoted to i.a.: civilizational and cultural transformations, information (knowledge) societies, global problematique, sustainable development, political philosophy and values, future studies. The journal's quasi-paradigm is TRANSFORMATION - as a present stage and form of development of technology, society, culture, civilization, values, mindsets etc. Impacts and potentialities of change and transition need new methodological tools, new visions and innovation for theoretical and practical capacity-building. The journal aims to promote inter-, multi- and transdisci- plinary approach, future orientation and strategic and global thinking. Transformacje (Transformations) are internationally available – since 2012 we have a licence agrement with the global database: EBSCO Publishing (Ipswich, MA, USA) We are listed by INDEX COPERNICUS since 2013 I TRANSFORMACJE(TRANSFORMATIONS) 3-4 (78-79) 2013 ISSN 1230-0292 Reviewed journal Published twice a year (double issues) in Polish and English (separate papers) Editorial Staff: Prof. Lech W. ZACHER, Center of Impact Assessment Studies and Forecasting, Kozminski University, Warsaw, Poland ([email protected]) – Editor-in-Chief Prof. Dora MARINOVA, Sustainability Policy Institute, Curtin University, Perth, Australia ([email protected]) – Deputy Editor-in-Chief Prof. Tadeusz MICZKA, Institute of Cultural and Interdisciplinary Studies, University of Silesia, Katowice, Poland ([email protected]) – Deputy Editor-in-Chief Dr Małgorzata SKÓRZEWSKA-AMBERG, School of Law, Kozminski University, Warsaw, Poland ([email protected]) – Coordinator Dr Alina BETLEJ, Institute of Sociology, John Paul II Catholic University of Lublin, Poland Dr Mirosław GEISE, Institute of Political Sciences, Kazimierz Wielki University, Bydgoszcz, Poland (also statistical editor) Prof. -
Language Is a Complex Adaptive System: Position Paper
Language Learning ISSN 0023-8333 Language Is a Complex Adaptive System: Position Paper The “Five Graces Group” Clay Beckner Nick C. Ellis University of New Mexico University of Michigan Richard Blythe John Holland University of Edinburgh Santa Fe Institute; University of Michigan Joan Bybee Jinyun Ke University of New Mexico University of Michigan Morten H. Christiansen Diane Larsen-Freeman Cornell University University of Michigan William Croft Tom Schoenemann University of New Mexico Indiana University Language has a fundamentally social function. Processes of human interaction along with domain-general cognitive processes shape the structure and knowledge of lan- guage. Recent research in the cognitive sciences has demonstrated that patterns of use strongly affect how language is acquired, is used, and changes. These processes are not independent of one another but are facets of the same complex adaptive system (CAS). Language as a CAS involves the following key features: The system consists of multiple agents (the speakers in the speech community) interacting with one another. This paper, our agreed position statement, was circulated to invited participants before a conference celebrating the 60th Anniversary of Language Learning, held at the University of Michigan, on the theme Language is a Complex Adaptive System. Presenters were asked to focus on the issues presented here when considering their particular areas of language in the conference and in their articles in this special issue of Language Learning. The evolution of this piece was made possible by the Sante Fe Institute (SFI) though its sponsorship of the “Continued Study of Language Acquisition and Evolution” workgroup meeting, Santa Fe Institute, 1–3 March 2007.