Adaptive Capacity and Traps
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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). -
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. -
Incorporating Sociocultural Adaptive Capacity in Conservation Hotspot Assessments
UC Davis UC Davis Previously Published Works Title Incorporating Sociocultural Adaptive Capacity in Conservation Hotspot Assessments Permalink https://escholarship.org/uc/item/84q2m6x7 Journal Diversity and Distributions, 16(3) ISSN 13669516 14724642 Authors Sexton, Jason P Schwartz, Mark W Winterhalder, Bruce Publication Date 2010-04-13 DOI 10.1111/j.1472-4642.2010.00656.x Peer reviewed eScholarship.org Powered by the California Digital Library University of California Diversity and Distributions, (Diversity Distrib.) (2010) 16, 439–450 BIODIVERSITY Incorporating sociocultural adaptive VIEWPOINT capacity in conservation hotspot assessments Jason P. Sexton1*, Mark W. Schwartz2 and Bruce Winterhalder3 1Department of Plant Sciences, University of ABSTRACT California, 2Department of Environmental Aim To highlight the importance of combining the geographies of sociocultural Science and Policy, University of California, 3Department of Anthropology, University of adaptation and biodiversity risk for creating global change conservation strategies. California, Davis, CA 95616, USA Location Global. Methods We review global conservation adaptation strategies and the geographies that influence biological risk, as well as sociocultural capacity to set priorities for a conservation response. We then describe relationships among these geographies and discuss criteria for prioritizing areas that will have the greatest potential for effective adaptive action. Results Strategic conservation requires integrating biological geographies with A Journal of Conservation -
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. -
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. -
The Immune System As a Complex System Eric Leadbetter [email protected] CMSC 491H Dr
The Immune System as a Complex System Eric Leadbetter [email protected] CMSC 491H Dr. Marie desJardins Introduction The immune system is one of the most critical support systems in a living organism. The immune system serves to protect the body from harmful external agents. In order to accomplish its goal, the immune system requires both a detection mechanism that will discern harmful agents (pathogens) from self and neutral agents and an elimination mechanism to deal with discovered pathogens. Different levels of organismic complexity have increasingly complex detection and elimination mechanisms. The human immune system has a particular attribute that lends itself well to the study of complex systems: that of adaptation. The human immune system is able to “remember” which malicious agents have been encountered and subsequently respond more quickly when these agents are encountered again. In this way, the body can defend against infection much more efficiently. The problem with acquired immunity is that infectious agents are constantly evolving; thus, the immunological memory will lack a record for any newly evolved strains of disease. In addition to acquired immunity, humans have an innate immunity, which is the form of immune system that is also present in lower levels of organismic complexity such as simple eukaryotes, plants, and insects. The innate immune system serves as a primary response to infection by responding to pathogens in a non-specific manner. It promotes a rapid influx of healing agents to infected or injured tissues and serves to activate the adaptive immune system, among other things. A complex adaptive system is defined as such by the fact that the behavior of its agents change with experience as opposed to following the same rules ad infinitum. -
Complex Adaptive Systems: Exploring the Known, the Unknown and the Unknowable
BULLETIN (New Series) OF THE AMERICAN MATHEMATICAL SOCIETY Volume 40, Number 1, Pages 3{19 S 0273-0979(02)00965-5 Article electronically published on October 9, 2002 COMPLEX ADAPTIVE SYSTEMS: EXPLORING THE KNOWN, THE UNKNOWN AND THE UNKNOWABLE SIMON A. LEVIN Abstract. The study of complex adaptive systems, from cells to societies, is a study of the interplay among processes operating at diverse scales of space, time and organizational complexity. The key to such a study is an under- standing of the interrelationships between microscopic processes and macro- scopic patterns, and the evolutionary forces that shape systems. In particular, for ecosystems and socioeconomic systems, much interest is focused on broad scale features such as diversity and resiliency, while evolution operates most powerfully at the level of individual agents. Understanding the evolution and development of complex adaptive systems thus involves understanding how cooperation, coalitions and networks of interaction emerge from individual be- haviors and feed back to influence those behaviors. In this paper, some of the mathematical challenges are discussed. 1. Introduction The notion of complex adaptive systems has found expression in everything from cells to societies, in general with reference to the self-organization of complex enti- ties, across scales of space, time and organizational complexity. Much of our under- standing of complex adaptive systems comes from observations of Nature, or from simulations, and a daunting challenge is to summarize these observations mathe- matically. In essence, we need a statistical mechanics of heterogeneous populations, in which new types are continuously appearing through a variety of mechanisms, mostly unpredictable in their details. -
Complex Adaptive Systems
Evidence scan: Complex adaptive systems August 2010 Identify Innovate Demonstrate Encourage Contents Key messages 3 1. Scope 4 2. Concepts 6 3. Sectors outside of healthcare 10 4. Healthcare 13 5. Practical examples 18 6. Usefulness and lessons learnt 24 References 28 Health Foundation evidence scans provide information to help those involved in improving the quality of healthcare understand what research is available on particular topics. Evidence scans provide a rapid collation of empirical research about a topic relevant to the Health Foundation's work. Although all of the evidence is sourced and compiled systematically, they are not systematic reviews. They do not seek to summarise theoretical literature or to explore in any depth the concepts covered by the scan or those arising from it. This evidence scan was prepared by The Evidence Centre on behalf of the Health Foundation. © 2010 The Health Foundation Previously published as Research scan: Complex adaptive systems Key messages Complex adaptive systems thinking is an approach that challenges simple cause and effect assumptions, and instead sees healthcare and other systems as a dynamic process. One where the interactions and relationships of different components simultaneously affect and are shaped by the system. This research scan collates more than 100 articles The scan suggests that a complex adaptive systems about complex adaptive systems thinking in approach has something to offer when thinking healthcare and other sectors. The purpose is to about leadership and organisational development provide a synopsis of evidence to help inform in healthcare, not least of which because it may discussions and to help identify if there is need for challenge taken for granted assumptions and further research or development in this area. -
Social Vulnerability & Adaptive Capacity
United States Department of Agriculture Key Concepts and Methods in Social Vulnerability and Adaptive Capacity Daniel J. Murphy Carina Wyborn Laurie Yung Daniel R. Williams Forest Service Rocky Mountain Research Station General Technical Report RMRS-GTR-328 July 2015 Murphy, Daniel J.; Wyborn, Carina; Yung, Laurie; Williams, Daniel R. 2015. Key concepts and methods in social vulnerability and adaptive capacity. Gen. Tech. Rep. RMRS-GTR-328. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 24 p. Abstract National forests have been asked to assess how climate change will impact nearby human com- munities. To assist their thinking on this topic, we examine the concepts of social vulnerability and adaptive capacity with an emphasis on a range of theoretical and methodological approaches. This analysis is designed to help researchers and decision-makers select appropriate research approaches suited to particular planning and management needs. We first explore key conceptual frameworks and theoretical divisions, including different definitions of vulnerability and adaptive capacity. We then focus on the different methods that have been used to assess vulnerability and adaptive capacity and their respective pros and cons. Finally, we present and discuss three case examples and their respective research approaches. Keywords: climate change, adaptation, vulnerability, adaptive capacity. Authors Daniel J. Murphy is an Assistant Professor of Anthropology at University of Cincinnati. Carina Wyborn is a Postdoctoral Research Fellow in the Department of Society and Conservation at the University of Montana. Laurie Yung is an Associate Professor of Natural Resource Social Science in the Department of Society and Conservation at the University of Montana. -
The Impact of Adaptive Leadership Capacity on Complex Organizational Health Systems
Walden University ScholarWorks Walden Dissertations and Doctoral Studies Walden Dissertations and Doctoral Studies Collection 2017 The mpI act of Adaptive Leadership Capacity on Complex Organizational Health Systems Outcomes Laura Lentenbrink Walden University Follow this and additional works at: https://scholarworks.waldenu.edu/dissertations Part of the Medicine and Health Sciences Commons This Dissertation is brought to you for free and open access by the Walden Dissertations and Doctoral Studies Collection at ScholarWorks. It has been accepted for inclusion in Walden Dissertations and Doctoral Studies by an authorized administrator of ScholarWorks. For more information, please contact [email protected]. Walden University College of Health Sciences This is to certify that the doctoral dissertation by Laura Lentenbrink has been found to be complete and satisfactory in all respects, and that any and all revisions required by the review committee have been made. Review Committee Dr. Jeff Snodgrass, Committee Chairperson, Health Services Faculty Dr. Vasileios Margaritis, Committee Member, Health Services Faculty Dr. Mehdi Agha, University Reviewer, Health Services Faculty Chief Academic Officer Eric Riedel, Ph.D. Walden University 2017 Abstract The Impact of Adaptive Leadership Capacity on Complex Organizational Health Systems Outcomes by Laura M. Lentenbrink JD, Cooley Law School, 1991 MS-N, Andrews University 1984 BSN, Nazareth College, 1973 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Health Services Walden University May 2017 Abstract Nonlinear and chaotic environmental changes characterize health services organizations as complex adaptive systems in which leaders must exercise non-traditional leadership practices to succeed. Health services leaders who have learned and implemented traditional linear management approaches are ill prepared to lead in complex environments. -
Adaptation to Climate Change: Key Terms
ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT INTERNATIONAL ENERGY AGENCY ADAPTATION TO CLIMATE C HANGE: KEY TERMS www.oecd.org/env/cc Ellina Levina and Dennis Tirpak, OECD www.iea.org May 2006 Unclassified COM/ENV/EPOC/IEA/SLT(2006)1 Organisation de Coopération et de Développement Economiques Organisation for Economic Co-operation and Development 09-May-2006 ___________________________________________________________________________________________ _____________ English - Or. English ENVIRONMENT DIRECTORATE INTERNATIONAL ENERGY AGENCY Unclassified COM/ENV/EPOC/IEA/SLT(2006)1 ADAPTATION TO CLIMATE CHANGE: KEY TERMS Ellina Levina and Dennis Tirpak, OECD The ideas expressed in this paper are those of the author and do not necessarily represent the views of the OECD, the IEA, or their member countries, or the endorsement of any approach described herein. English - Or. English JT03208563 Document complet disponible sur OLIS dans son format d'origine Complete document available on OLIS in its original format COM/ENV/EPOC/IEA/SLT(2006)1 Copyright OECD/IEA, 2006 Applications for permission to reproduce or translate all or part of this material should be addressed to: Head of Publications Service, OECD/IEA 2 rue André Pascal, 75775 Paris Cedex 16, France or 9 rue de la Fédération, 75739 Paris Cedex 15, France. 2 COM/ENV/EPOC/IEA/SLT(2006)1 FOREWORD This document was prepared by the OECD and IEA Secretariats in March-May 2006 in response to the Annex I Expert Group on the United Nations Framework Convention on Climate Change (UNFCCC). The Annex I Expert Group oversees development of analytical papers for the purpose of providing useful and timely input to the climate change negotiations. -
Adaptive Systems: History, Techniques, Problems, and Perspectives
Systems 2014, 2, 606-660; doi:10.3390/systems2040606 OPEN ACCESS systems ISSN 2079-8954 www.mdpi.com/journal/systems Article Adaptive Systems: History, Techniques, Problems, and Perspectives William S. Black 1;?, Poorya Haghi 2 and Kartik B. Ariyur 1 1 School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, IN 47907, USA; E-Mail: [email protected] 2 Cymer LLC, 17075 Thornmint Court, San Diego, CA 92127, USA; E-Mail: [email protected] ? Author to whom correspondence should be addressed; E-Mail: [email protected]. External Editors: Gianfranco Minati and Eliano Pessa Received: 6 August 2014; in revised form: 15 September 2014 / Accepted: 17 October 2014 / Published: 11 November 2014 Abstract: We survey some of the rich history of control over the past century with a focus on the major milestones in adaptive systems. We review classic methods and examples in adaptive linear systems for both control and observation/identification. The focus is on linear plants to facilitate understanding, but we also provide the tools necessary for many classes of nonlinear systems. We discuss practical issues encountered in making these systems stable and robust with respect to additive and multiplicative uncertainties. We discuss various perspectives on adaptive systems and their role in various fields. Finally, we present some of the ongoing research and expose problems in the field of adaptive control. Keywords: systems; adaptation; control; identification; nonlinear 1. Introduction Any system—engineering, natural, biological, or social—is considered adaptive if it can maintain its performance, or survive in spite of large changes in its environment or in its own components.