Verification of Adaptive Systems Federal Aviation Administration William J
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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. -
Before You Continue
NASA/CR–2015-218982 Application of SAE ARP4754A to Flight Critical Systems Eric M. Peterson Electron International II, Inc., Phoenix, Arizona November 2015 NASA STI Program . in Profile Since its founding, NASA has been dedicated to the CONFERENCE PUBLICATION. advancement of aeronautics and space science. The Collected papers from scientific and technical NASA scientific and technical information (STI) conferences, symposia, seminars, or other program plays a key part in helping NASA maintain meetings sponsored or this important role. co-sponsored by NASA. The NASA STI program operates under the auspices SPECIAL PUBLICATION. Scientific, of the Agency Chief Information Officer. It collects, technical, or historical information from NASA organizes, provides for archiving, and disseminates programs, projects, and missions, often NASA’s STI. The NASA STI program provides access concerned with subjects having substantial to the NTRS Registered and its public interface, the public interest. NASA Technical Reports Server, thus providing one of the largest collections of aeronautical and space TECHNICAL TRANSLATION. science STI in the world. Results are published in both English-language translations of foreign non-NASA channels and by NASA in the NASA STI scientific and technical material pertinent to Report Series, which includes the following report NASA’s mission. types: Specialized services also include organizing TECHNICAL PUBLICATION. Reports of and publishing research results, distributing completed research or a major significant phase of specialized research announcements and feeds, research that present the results of NASA providing information desk and personal search Programs and include extensive data or theoretical support, and enabling data exchange services. analysis. Includes compilations of significant scientific and technical data and information For more information about the NASA STI program, deemed to be of continuing reference value. -
Quality Assurance, Process Engineer
THOMMEN AIRCRAFT EQUIPMENT Renowned Swiss manufacturer of high precision Aviation Instruments, Air Data Computers, Digital Chronometers and Mission Equipment Established in 1853 under Revue Thommen AG, Thommen Aircraft Equipment Ltd is a renowned Swiss manufacturer of high precision aviation instruments, avionics and mission equipment. The company has celebrated its 100 years anniversary of supplying aviation products to its customers. Thommen Aircraft Equipment AG is currently in the phase of introducing several innovative and exciting products to the market and will gradually increase the general product offering in the course of 2018/2019. To sustain the new company plans, product development, we are looking to hire a skilled and experienced Quality Assurance / Process Engineer – Avionics 100% (m/f) The person will be responsible for leading activities involving Product Lifecycle Management processes. Focused on improving processes and tools, the position is ideal for a candidate seeking a broad technical and business process career. The position offers the opportunity to work as part of a global team which will require flexibility to support activities across multiple time zones for following process development activities. Our culture is to hire only the finest talent and to uphold our values of teamwork, accountability, humor, efficiency, candor and continual improvement. Responsibilities & Tasks • Develop DO-178C and DO-254 process compliance and quality plan, (QAP, SQAP HQAP) • Responsible for reporting assessment and evaluation -
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. -
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. -
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. -
Complex Adaptive Systems Theory Applied to Virtual Scientific Collaborations: the Case of Dataone
University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School 8-2011 Complex adaptive systems theory applied to virtual scientific collaborations: The case of DataONE Arsev Umur Aydinoglu [email protected] Follow this and additional works at: https://trace.tennessee.edu/utk_graddiss Part of the Communication Technology and New Media Commons, Library and Information Science Commons, and the Organizational Behavior and Theory Commons Recommended Citation Aydinoglu, Arsev Umur, "Complex adaptive systems theory applied to virtual scientific collaborations: The case of DataONE. " PhD diss., University of Tennessee, 2011. https://trace.tennessee.edu/utk_graddiss/1054 This Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council: I am submitting herewith a dissertation written by Arsev Umur Aydinoglu entitled "Complex adaptive systems theory applied to virtual scientific collaborations: The case of DataONE." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the equirr ements for the degree of Doctor of Philosophy, with a major in Communication and Information. Suzie Allard, Major Professor We