Use and Analysis of Complex Adaptive Systems in Ecosystem Science: Overview of Special Section
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). -
Dimensions of Ecosystem Complexity: Heterogeneity, Connectivity, and History
ecological complexity 3 (2006) 1–12 available at www.sciencedirect.com journal homepage: http://www.elsevier.com/locate/ecocom Viewpoint Dimensions of ecosystem complexity: Heterogeneity, connectivity, and history M.L. Cadenasso a,*, S.T.A. Pickett b, J.M. Grove c a Hixon Center for Urban Ecology, School of Forestry and Environmental Studies, Yale University, 205 Prospect Street, New Haven, CT 06511, United States b Institute of Ecosystem Studies, Box AB, Millbrook, NY 12545, United States c USDA Forest Service, Northeastern Research Station, 705 Spear Street, P.O. Box 968, Burlington, VT 05401, United States article info abstract Article history: Biocomplexity was introduced to most ecologists through the National Science Foundation’s Received 2 June 2005 grant program, and the literature intended to introduce that program. The generalities of that Received in revised form literature contrast with the abstract and mathematical sophistication of literature from 30 June 2005 physics, systems theory, and indeed even of pioneering ecologists who have translated the Accepted 2 July 2005 conceptintoecology. Thissituation leaves a middle ground, that isboth accessibletoecologists Published on line 23 January 2006 in general, and cognizant of the fundamentals of complexity, to be more completely explored. To help scope this middle ground, and to promote empirical explorations that may be located Keywords: there, we propose a non-exclusive framework for the conceptual territory. While recognizing Biocomplexity the deep foundations in the studies of complex behavior, we take ecological structure as the Framework entry point for framework development. This framework is based on a definition of biocom- Coupled systems plexity as the degree to which ecological systems comprising biological, social and physical Spatial heterogeneity components incorporate spatially explicit heterogeneity, organizational connectivity, and Legacies historical contingency through time. -
Database-Centric Programming for Wide-Area Sensor Systems
Database-Centric Programming for Wide-Area Sensor Systems 1 2 1 2 Shimin Chen , Phillip B. Gibbons , and Suman Nath ; 1 Carnegie Mellon University fchensm,[email protected] 2 Intel Research Pittsburgh [email protected] Abstract. A wide-area sensor system is a complex, dynamic, resource-rich col- lection of Internet-connected sensing devices. In this paper, we propose X-Tree Programming, a novel database-centric programming model for wide-area sen- sor systems designed to achieve the seemingly conflicting goals of expressive- ness, ease of programming, and efficient distributed execution. To demonstrate the effectiveness of X-Tree Programming in achieving these goals, we have in- corporated the model into IrisNet, a shared infrastructure for wide-area sensing, and developed several widely different applications, including a distributed in- frastructure monitor running on 473 machines worldwide. 1 Introduction A wide-area sensor system [2, 12, 15, 16] is a complex, dynamic, resource-rich collec- tion of Internet-connected sensing devices. These devices are capable of collecting high bit-rate data from powerful sensors such as cameras, microphones, infrared detectors, RFID readers, and vibration sensors, and performing collaborative computation on the data. A sensor system can be programmed to provide useful sensing services that com- bine traditional data sources with tens to millions of live sensor feeds. An example of such a service is a Person Finder, which uses cameras or smart badges to track people and supports queries for a person's current location. A desirable approach for develop- ing such a service is to program the collection of sensors as a whole, rather than writing software to drive individual devices. -
Existing Cybernetics Foundations - B
SYSTEMS SCIENCE AND CYBERNETICS – Vol. III - Existing Cybernetics Foundations - B. M. Vladimirski EXISTING CYBERNETICS FOUNDATIONS B. M. Vladimirski Rostov State University, Russia Keywords: Cybernetics, system, control, black box, entropy, information theory, mathematical modeling, feedback, homeostasis, hierarchy. Contents 1. Introduction 2. Organization 2.1 Systems and Complexity 2.2 Organizability 2.3 Black Box 3. Modeling 4. Information 4.1 Notion of Information 4.2 Generalized Communication System 4.3 Information Theory 4.4 Principle of Necessary Variety 5. Control 5.1 Essence of Control 5.2 Structure and Functions of a Control System 5.3 Feedback and Homeostasis 6. Conclusions Glossary Bibliography Biographical Sketch Summary Cybernetics is a science that studies systems of any nature that are capable of perceiving, storing, and processing information, as well as of using it for control and regulation. UNESCO – EOLSS The second title of the Norbert Wiener’s book “Cybernetics” reads “Control and Communication in the Animal and the Machine”. However, it is not recognition of the external similaritySAMPLE between the functions of animalsCHAPTERS and machines that Norbert Wiener is credited with. That had been done well before and can be traced back to La Mettrie and Descartes. Nor is it his contribution that he introduced the notion of feedback; that has been known since the times of the creation of the first irrigation systems in ancient Babylon. His distinctive contribution lies in demonstrating that both animals and machines can be combined into a new, wider class of objects which is characterized by the presence of control systems; furthermore, living organisms, including humans and machines, can be talked about in the same language that is suitable for a description of any teleological (goal-directed) systems. -
Environmental Influences: Family Systems Theory
Environmental Influences: Family Systems Theory Family Systems Theory provides a broad and comprehensive mechanism for understanding the core aspects of the Performance Competence Lifespan Framework — quality of life, member- ship, and a personal sense of competence. It also focuses on the most important component of environmental influences—home and family. From birth, a child’s Quality of Life is directly influ- enced by the kind of care, support, stimulation and education he or she receives from family mem- bers in the home. As infants begin to develop secure attachments with significant others, particu- larly family members, they begin to establish themselves as members of the first and most basic unit of society—the family, which forms the foundation for secure Membership in other groups throughout life. The infant begins to develop a Personal Sense of Competence when his mother responds consistently to his distress, when he takes his first step or says his first word, or when his father praises him for using the toilet. These early beginnings, then, are at the core of what each individual child will come to know and be able to do. As the PC Framework indicates, there are multiple environmental influences on performance and competence, but the family is the first and most important. The influence of family members on one another is not simple, but complex; it is not one-way, but reciprocal. The family, like a mechanical system, is made up of multiple parts that are interdependent. When one part does not function well, all other parts are impacted. Further, the family interacts with other systems, includ- ing those that provide direct services to the child—child care/preschools, schools and community agencies—and each system affects the other. -
Thermophilic Lithotrophy and Phototrophy in an Intertidal, Iron-Rich, Geothermal Spring 2 3 Lewis M
bioRxiv preprint doi: https://doi.org/10.1101/428698; this version posted September 27, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Thermophilic Lithotrophy and Phototrophy in an Intertidal, Iron-rich, Geothermal Spring 2 3 Lewis M. Ward1,2,3*, Airi Idei4, Mayuko Nakagawa2,5, Yuichiro Ueno2,5,6, Woodward W. 4 Fischer3, Shawn E. McGlynn2* 5 6 1. Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138 USA 7 2. Earth-Life Science Institute, Tokyo Institute of Technology, Meguro, Tokyo, 152-8550, Japan 8 3. Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 9 91125 USA 10 4. Department of Biological Sciences, Tokyo Metropolitan University, Hachioji, Tokyo 192-0397, 11 Japan 12 5. Department of Earth and Planetary Sciences, Tokyo Institute of Technology, Meguro, Tokyo, 13 152-8551, Japan 14 6. Department of Subsurface Geobiological Analysis and Research, Japan Agency for Marine-Earth 15 Science and Technology, Natsushima-cho, Yokosuka 237-0061, Japan 16 Correspondence: [email protected] or [email protected] 17 18 Abstract 19 Hydrothermal systems, including terrestrial hot springs, contain diverse and systematic 20 arrays of geochemical conditions that vary over short spatial scales due to progressive interaction 21 between the reducing hydrothermal fluids, the oxygenated atmosphere, and in some cases 22 seawater. At Jinata Onsen, on Shikinejima Island, Japan, an intertidal, anoxic, iron- and 23 hydrogen-rich hot spring mixes with the oxygenated atmosphere and sulfate-rich seawater over 24 short spatial scales, creating an enormous range of redox environments over a distance ~10 m. -
Analysis of Habitat Fragmentation and Ecosystem Connectivity Within the Castle Parks, Alberta, Canada by Breanna Beaver Submit
Analysis of Habitat Fragmentation and Ecosystem Connectivity within The Castle Parks, Alberta, Canada by Breanna Beaver Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in the Environmental Science Program YOUNGSTOWN STATE UNIVERSITY December, 2017 Analysis of Habitat Fragmentation and Ecosystem Connectivity within The Castle Parks, Alberta, Canada Breanna Beaver I hereby release this thesis to the public. I understand that this thesis will be made available from the OhioLINK ETD Center and the Maag Library Circulation Desk for public access. I also authorize the University or other individuals to make copies of this thesis as needed for scholarly research. Signature: Breanna Beaver, Student Date Approvals: Dawna Cerney, Thesis Advisor Date Peter Kimosop, Committee Member Date Felicia Armstrong, Committee Member Date Clayton Whitesides, Committee Member Date Dr. Salvatore A. Sanders, Dean of Graduate Studies Date Abstract Habitat fragmentation is an important subject of research needed by park management planners, particularly for conservation management. The Castle Parks, in southwest Alberta, Canada, exhibit extensive habitat fragmentation from recreational and resource use activities. Umbrella and keystone species within The Castle Parks include grizzly bears, wolverines, cougars, and elk which are important animals used for conservation agendas to help protect the matrix of the ecosystem. This study identified and analyzed the nature of habitat fragmentation within The Castle Parks for these species, and has identified geographic areas of habitat fragmentation concern. This was accomplished using remote sensing, ArcGIS, and statistical analyses, to develop models of fragmentation for ecosystem cover type and Digital Elevation Models of slope, which acted as proxies for species habitat suitability. -
Nudging Cooperation in Public Goods Provision
A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Barron, Kai; Nurminen, Tuomas Article — Accepted Manuscript (Postprint) Nudging cooperation in public goods provision Journal of Behavioral and Experimental Economics Provided in Cooperation with: WZB Berlin Social Science Center Suggested Citation: Barron, Kai; Nurminen, Tuomas (2020) : Nudging cooperation in public goods provision, Journal of Behavioral and Experimental Economics, ISSN 2214-8043, Elsevier, Amsterdam, Vol. 88, Iss. (Article No.:) 101542, http://dx.doi.org/10.1016/j.socec.2020.101542 This Version is available at: http://hdl.handle.net/10419/216878 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have been made available under an Open gelten abweichend von -
Competition and Cooperation on Predation: Bifurcation Theory of Mutualism Author: Srijana Ghimire Xiang-Sheng Wang University of Louisiana at Lafayette
Competition and Cooperation on Predation: Bifurcation Theory Of Mutualism Author: Srijana Ghimire Xiang-Sheng Wang University of Louisiana at Lafayette Introduction Existence and Stability of E1,E+ and E− Existence and property of Hopf bifurcation 3. R > 1 and R > 3R − 2R2. In this case, Q = Q , 1 2 1 1 c 1 points We investigate two predator-prey models which take into con- xc = 1/R1, E1 always exists, E1 is locally asymptotically H E + H E E sideration the cooperation between two different predators and stable if and only if Q < Q1, E− does not exist, and E+ exits + + R H within one predator species, respectively. Local and global dy- if and only if Q > Q1. 2.0 E E E - namics are studied for the model systems. By a detailed bi- - - Q Q Q Q Q Q furcation analysis, we investigate the dependence of predation + + h + h1 h2 1.5 no Hopf bifurcation Existence conditions of positive equilibria. (a) case 1(a) (b) case 1(b) (c) case 1(c) dynamics on mutualism (cooperative predation). H H E R + 2 E E two supercritical + + 1.0 R2 > 1 Q H E+ E - E Q1 - E y y - First Predator-Prey Model with Competition 1 E 1 E 1 1 y 1 E 1 0.5 one supercritical NA Q Q Q Q Q Q Q Q Q and Co-operation 1 + 1 + 1 h + h1 1 h2 R2 < one Q R1 NA if Q < Q1 (d) case 2(a) (e) case 2(b) (f) case 2(c) E± subcritical E+ if Q ≥ Q1 E+ d 0 H 0 2 4 6 8 E x = 1 − x − p xy − p xz − 2qxyz, (1) + 1 2 Q+ E Q1 + 0 Figure: Existence and property of Hopf bifurcation points in the (d, R) y = p xy + qxyz − d y, (2) 2 1 1 R2 = 3R1 − 2R NA 1 H parameter space. -
A Complex Adaptive Systems Perspective to Appreciative Inquiry: a Theoretical Analysis Payam Saadat George Fox University
http://journals.sfu.ca/abr ADVANCES IN BUSINESS RESEARCH 2015, Volume 6, pages 1-13 A Complex Adaptive Systems Perspective to Appreciative Inquiry: A Theoretical Analysis Payam Saadat George Fox University Appreciate inquiry is utilized to facilitate organizational change by encouraging stakeholders to explore positives and generative capacities within their organization. In the literature, analysis of the effectiveness of AI is confined to psychological and managerial explanations such as highlighting the promotion of positive mindset and collective organizational planning. This paper will discuss a complex adaptive systems (CAS) perspective and present a new model for understanding the functionality of AI. The emphasis of this paper is placed on exploring the effects of AI on the behavior and interactions of agents/employees related to how they cope with change. An analysis of AI’s functionality through the lens of CAS reveals two critical insights: a) AI enhances adaptability to change by strengthening communication among agents, which in turn fosters the emergence of effective team arrangements and a more rapid collective response to change and b) AI possesses the potential to generate a collective memory for social systems within an organization. Furthermore, a systematic analysis of AI indicates a close connection between this method and CAS-based styles of management. This paper concludes by suggesting that AI might represent a potential method with the capacity to place organizational teams at the edge of chaos. Keywords: appreciative inquiry, organizational change, management, complex adaptive systems, edge of chaos Introduction In today’s fast-paced world where the fluctuating preferences of consumers, the growth in the global web of interdependence, and technological advancements guide the co-evolving relationship between the dominant business environment and the social systems within its domain, an organization’s capacity to cope with change in a timely manner determines its survival (Hesselbein & Goldsmith, 2006; Macready & Meyer, 1999; Senge, 2006). -
Ecological Systems of the United States a Working Classification of U.S
ECOLOGICAL SYSTEMS OF THE UNITED STATES A WORKING CLASSIFICATION OF U.S. TERRESTRIAL SYSTEMS NatureServe is a non-profit organization dedicated to providing the scientific knowledge that forms the basis for effective conservation action. Citation: Comer, P., D. Faber-Langendoen, R. Evans, S. Gawler, C. Josse, G. Kittel, S. Menard, M. Pyne, M. Reid, K. Schulz, K. Snow, and J. Teague. 2003. Ecological Systems of the United States: A Working Classification of U.S. Terrestrial Systems. NatureServe, Arlington, Virginia. © NatureServe 2003 Ecological Systems of the United States is a component of NatureServe’s International Terrestrial Ecological Systems Classification. Á Funding for this report was provided by a grant from The Nature Conservancy. Front cover: Maroon Bells Wilderness, Colorado. Photo © Patrick Comer NatureServe 1101 Wilson Boulevard, 15th Floor Arlington, VA 22209 (703) 908-1800 www.natureserve.org ECOLOGICAL SYSTEMS OF THE UNITED STATES A WORKING CLASSIFICATION OF U.S. TERRESTRIAL SYSTEMS Á Á Á Á Á Patrick Comer Don Faber-Langendoen Rob Evans Sue Gawler Carmen Josse Gwen Kittel Shannon Menard Milo Pyne Marion Reid Keith Schulz Kristin Snow Judy Teague June 2003 Acknowledgements We wish to acknowledge the generous support provided by The Nature Conservancy for this effort to classify and characterize the ecological systems of the United States. We are particularly grateful to the late John Sawhill, past President of The Nature Conservancy, who was an early supporter of this concept, and who made this funding possible through an allocation from the President’s Discretionary Fund. Many of the concepts and approaches for defining and applying ecological systems have greatly benefited from collaborations with Conservancy staff, and the classification has been refined during its application in Conservancy-sponsored conservation assessments. -
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.