Remembering John Warfield*
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Capabilities in Systems Engineering: an Overview
Capabilities in Systems Engineering: An Overview Gon¸caloAntunes and Jos´eBorbinha INESC-ID, Rua Alves Redol, 9 1000-029 Lisboa Portugal {goncalo.antunes,jlb}@ist.utl.pt, WWW home page: http://web.ist.utl.pt/goncalo.antunes Abstract. The concept of capability has been deemed relevant over the years, which can be attested by its adoption in varied domains. It is an abstract concept, but simple to understand by business stakeholders and yet capable of making the bridge to technical aspects. Capabilities seem to bear similarities with services, namely their low coupling and high cohesion. However, the concepts are different since the concept of service seems to rest between that of capability and those directly related to the implementation. Nonetheless, the articulation of the concept of ca- pability with the concept of service can be used to promote business/IT alignment, since both concepts can be used to bridge different concep- tual layers of an enterprise architecture. This work offers an overview of the different uses of this concept, its usefulness, and its relation to the concept of service. Key words: capability, service, alignment, information systems, sys- tems engineering, strategic management, economics 1 Introduction The concept of capability can be defined as \the quality or state of being capable" [19] or \the power or ability to do something" [39]. Although simple, it is a powerful concept, as it can be used to provide an abstract, high-level view of a product, system, or even organizations, offering new ways of dealing with complexity. As such, it has been widely adopted in many areas. -
Moving Towards a New Urban Systems Science
Ecosystems DOI: 10.1007/s10021-016-0053-4 Ó 2016 Springer Science+Business Media New York 20TH ANNIVERSARY PAPER Moving Towards a New Urban Systems Science Peter M. Groffman,1* Mary L. Cadenasso,2 Jeannine Cavender-Bares,3 Daniel L. Childers,4 Nancy B. Grimm,5 J. Morgan Grove,6 Sarah E. Hobbie,3 Lucy R. Hutyra,7 G. Darrel Jenerette,8 Timon McPhearson,9 Diane E. Pataki,10 Steward T. A. Pickett,11 Richard V. Pouyat,12 Emma Rosi-Marshall,11 and Benjamin L. Ruddell13 1Department of Earth and Environmental Sciences, City University of New York Advanced Science Research Center and Brooklyn College, New York, New York 10031, USA; 2Department of Plant Sciences, University of California, Davis, California 95616, USA; 3Department of Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, Minnesota 55108, USA; 4School of Sustain- ability, Arizona State University, Tempe, Arizona 85287, USA; 5School of Life Sciences and Global Institute of Sustainability, Arizona State University, Tempe, Arizona 85287, USA; 6USDA Forest Service, Baltimore Field Station, Baltimore, Maryland 21228, USA; 7Department of Earth and Environment, Boston University, Boston, Massachusetts 02215, USA; 8Department of Botany and Plant Sciences, University of California Riverside, Riverside, California 92521, USA; 9Urban Ecology Lab, Environmental Studies Program, The New School, New York, New York 10003, USA; 10Department of Biology, University of Utah, Salt Lake City, Utah 84112, USA; 11Cary Institute of Ecosystem Studies, Millbrook, New York 12545, USA; 12USDA Forest Service, Research and Development, District of Columbia, Washington 20502, USA; 13School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, Arizona 86001, USA ABSTRACT Research on urban ecosystems rapidly expanded in linchpins in the further development of sustain- the 1990s and is now a central topic in ecosystem ability science and argue that there is a strong need science. -
Need Means Analysis
The Systems Engineering Tool Box Dr Stuart Burge “Give us the tools and we will finish the job” Winston Churchill Needs Means Analysis (NMA) What is it and what does it do? Needs Means Analysis (NMA) is a systems thinking tool aimed at exploring alternative system solutions at different levels in order to help define the boundary of the system of interest. It is based around identifying the “need” that a system satisfies and using this to investigate alternative solutions at levels higher, the same and lower than the system of interest. Why do it? At any point in time there is usually a “current” or “preferred” solution to a particular problem. In consequence organizations will develop their operations and infrastructure to support and optimise that solution. This preferred solution is known as the Meta-Solution. For example Toyota, Ford, General Motors, Volkswagen et al all have the same meta-solution when it comes to automobile – in simple terms two-boxes with wheel at each corner. All of the automotive companies have slowly evolved in to an industry aimed at optimising this meta-solution. Systems Thinking encourages us to “step back” from the solution to consider the underlying problem or purpose. In the case of an automobile, the purpose is to “transport passengers and their baggage from one point to another” This is also the purpose of a civil aircraft, passenger train and bicycle! In other words for a given purpose there are alternative meta-solutions. The idea of a pre-eminent meta-solution has also been discussed by Pugh (1991) who introduced the idea of dynamic and static design concepts. -
Warren Mcculloch and the British Cyberneticians
Warren McCulloch and the British cyberneticians Article (Accepted Version) Husbands, Phil and Holland, Owen (2012) Warren McCulloch and the British cyberneticians. Interdisciplinary Science Reviews, 37 (3). pp. 237-253. ISSN 0308-0188 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/id/eprint/43089/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher’s version. Please see the URL above for details on accessing the published version. Copyright and reuse: Sussex Research Online is a digital repository of the research output of the University. Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available. Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. http://sro.sussex.ac.uk Warren McCulloch and the British Cyberneticians1 Phil Husbands and Owen Holland Dept. Informatics, University of Sussex Abstract Warren McCulloch was a significant influence on a number of British cyberneticians, as some British pioneers in this area were on him. -
Putting Systems to Work
i PUTTING SYSTEMS TO WORK Derek K. Hitchins Professor ii iii To my beloved wife, without whom ... very little. iv v Contents About the Book ........................................................................xi Part A—Foundation and Theory Chapter 1—Understanding Systems.......................................... 3 An Introduction to Systems.................................................... 3 Gestalt and Gestalten............................................................. 6 Hard and Soft, Open and Closed ............................................. 6 Emergence and Hierarchy ..................................................... 10 Cybernetics ........................................................................... 11 Machine Age versus Systems Age .......................................... 13 Present Limitations in Systems Engineering Methods............ 14 Enquiring Systems................................................................ 18 Chaos.................................................................................... 23 Chaos and Self-organized Criticality...................................... 24 Conclusion............................................................................ 26 Chapter 2—The Human Element in Systems ......................... 27 Human ‘Design’..................................................................... 27 Human Predictability ........................................................... 29 Personality ............................................................................ 31 Social -
1 Umpleby Stuart Reconsidering Cybernetics
UMPLEBY STUART RECONSIDERING CYBERNETICS The field of cybernetics attracted great attention in the 1950s and 1960s with its prediction of a Second Industrial Revolution due to computer technology. In recent years few people in the US have heard of cybernetics (Umpleby, 2015a, see Figures 1 and 2 at the end of Paper). But a wave of recent books suggests that interest in cybernetics is returning (Umpleby and Hughes, 2016, see Figure at the end of Paper). This white paper reviews some basic ideas in cybernetics. I recommend these and other ideas as a resource for better understanding and modeling of social systems, including threat and response dynamics. Some may claim that whatever was useful has already been incorporated in current work generally falling under the complexity label, but that is not the case. Work in cybernetics has continued with notable contributions in recent years. Systems science, complex systems, and cybernetics are three largely independent fields with their own associations, journals and conferences (Umpleby, 2017). Four types of descriptions used in social science After working in social science and systems science for many years I realized that different academic disciplines use different basic elements. Economists use measurable variables such as price, savings, GDP, imports and exports. Psychologists focus on ideas, concepts and attitudes. Sociologists and political scientists focus on groups, organizations, and coalitions. Historians and legal scholars emphasize events and procedures. People trained in different disciplines construct different narratives using these basic elements. One way to reveal more of the variety in a social system is to create at least four descriptions – one each using variables, ideas, groups, and events (See the figures and tables in Medvedeva & Umpleby, 2015). -
Fundamentals of Systems Engineering
Fundamentals of Systems Engineering Prof. Olivier L. de Weck Session 12 Future of Systems Engineering 1 2 Status quo approach for managing complexity in SE MIL-STD-499A (1969) systems engineering SWaP used as a proxy metric for cost, and dis- System decomposed process: as employed today Conventional V&V techniques incentivizes abstraction based on arbitrary do not scale to highly complex in design cleavage lines . or adaptable systems–with large or infinite numbers of possible states/configurations Re-Design Cost System Functional System Verification Optimization Specification Layout & Validation SWaP Subsystem Subsystem . Resulting Optimization Design Testing architectures are fragile point designs SWaP Component Component Optimization Power Data & Control Thermal Mgmt . Design Testing . and detailed design Unmodeled and undesired occurs within these interactions lead to emergent functional stovepipes behaviors during integration SWaP = Size, Weight, and Power Desirable interactions (data, power, forces & torques) V&V = Verification & Validation Undesirable interactions (thermal, vibrations, EMI) 3 Change Request Generation Patterns Change Requests Written per Month Discovered new change “ ” 1500 pattern: late ripple system integration and test 1200 bug [Eckert, Clarkson 2004] fixes 900 subsystem Number Written Number Written design 600 major milestones or management changes component design 300 0 1 5 9 77 81 85 89 93 73 17 21 25 29 33 37 41 45 49 53 57 61 65 69 13 Month © Olivier de Weck, December 2015, 4 Historical schedule trends -
ESD.00 Introduction to Systems Engineering, Lecture 3 Notes
Introduction to Engineering Systems, ESD.00 System Dynamics Lecture 3 Dr. Afreen Siddiqi From Last Time: Systems Thinking • “we can’t do just one thing” – things are interconnected and our actions have Decisions numerous effects that we often do not anticipate or realize. Goals • Many times our policies and efforts aimed towards some objective fail to produce the desired outcomes, rather we often make Environment matters worse Image by MIT OpenCourseWare. Ref: Figure 1-4, J. Sterman, Business Dynamics: Systems • Systems Thinking involves holistic Thinking and Modeling for a complex world, McGraw Hill, 2000 consideration of our actions Dynamic Complexity • Dynamic (changing over time) • Governed by feedback (actions feedback on themselves) • Nonlinear (effect is rarely proportional to cause, and what happens locally often doesn’t apply in distant regions) • History‐dependent (taking one road often precludes taking others and determines your destination, you can’t unscramble an egg) • Adaptive (the capabilities and decision rules of agents in complex systems change over time) • Counterintuitive (cause and effect are distant in time and space) • Policy resistant (many seemingly obvious solutions to problems fail or actually worsen the situation) • Char acterized by trade‐offs (h(the l ong run is often differ ent f rom the short‐run response, due to time delays. High leverage policies often cause worse‐before‐better behavior while low leverage policies often generate transitory improvement before the problem grows worse. Modes of Behavior Exponential Growth Goal Seeking S-shaped Growth Time Time Time Oscillation Growth with Overshoot Overshoot and Collapse Time Time Time Image by MIT OpenCourseWare. Ref: Figure 4-1, J. -
Lecture 9 – Modeling, Simulation, and Systems Engineering
Lecture 9 – Modeling, Simulation, and Systems Engineering • Development steps • Model-based control engineering • Modeling and simulation • Systems platform: hardware, systems software. EE392m - Spring 2005 Control Engineering 9-1 Gorinevsky Control Engineering Technology • Science – abstraction – concepts – simplified models • Engineering – building new things – constrained resources: time, money, • Technology – repeatable processes • Control platform technology • Control engineering technology EE392m - Spring 2005 Control Engineering 9-2 Gorinevsky Controls development cycle • Analysis and modeling – Control algorithm design using a simplified model – System trade study - defines overall system design • Simulation – Detailed model: physics, or empirical, or data driven – Design validation using detailed performance model • System development – Control application software – Real-time software platform – Hardware platform • Validation and verification – Performance against initial specs – Software verification – Certification/commissioning EE392m - Spring 2005 Control Engineering 9-3 Gorinevsky Algorithms/Analysis Much more than real-time control feedback computations • modeling • identification • tuning • optimization • feedforward • feedback • estimation and navigation • user interface • diagnostics and system self-test • system level logic, mode change EE392m - Spring 2005 Control Engineering 9-4 Gorinevsky Model-based Control Development Conceptual Control design model: Conceptual control sis algorithm: y Analysis x(t+1) = x(t) + -
The Applications of Systems Science
The Applications of Systems Science Understanding How the World Works and How You Work In It Motivating Question • What does it mean to UNDERSTAND? – Feeling of understanding – listening to a lecture or reading a book – Using understanding – solving a problem or creating an artifact – Understanding processes – predicting an outcome Understanding • Various categories of what we call knowledge – Knowing What*: facts, explicit, episodic knowledge • Conscious remembering – Knowing How: skills, tacit knowledge • Performance of tasks, reasoning – Knowing That: concepts, relational knowledge • Connecting different concepts – Knowing Why: contexts, understanding • Modeling and explaining • Thinking is the dynamics of the interactions between these various categories • Competencies based on strength of cognitive capabilities * Includes knowing when and where The System of Knowledge and Knowing An example of a “Concept Map” Knowing Why contexts modeling understanding explaining Thinking remembering connecting performing Knowing That concepts relational knowledge Knowing What Knowing How facts skills explicit knowledge tacit knowledge Perceiving, Learning, & Memory recognizing encoding linking recalling Cognitive Capabilities Thinking • Thinking capabilities depend on general cognition models – Critical thinking • Skepticism, recognizing biases, evidence-based, curiosity, reasoning – Scientific thinking • Critical thinking + testing hypotheses, analyzing evidence, formal modeling – Systems thinking • Scientific thinking + holistic conceptualization, -
Biological and Environmental Systems Science
Biological and Environmental Systems Science Oak Ridge National Laboratory’s (ORNL’s) Biological and Environmental Systems Science Directorate (BESSD) leads convergence research in biology, ecology, 145 engineering, data discovery, physical sciences, and computing to advance Scientists and engineers US competitiveness in the global bioeconomy and Earth system sustainability. Our researchers collaborate with experts across the Laboratory and in industry 20 Research and academia to advance renewable energy solutions, improve Earth system groups models, and push the frontiers of systems and synthetic biology. Focus areas include understanding how genes influence ecosystem-level processes; learning more about how biodiversity shapes the world 1,689 around us; developing novel, secure biodesign tools and testbeds for Journal publications in enzyme engineering; applying the world’s fastest supercomputers to the past 5 years transform biological and environmental data into knowledge; advancing signature technologies for dynamic characterization of complex biological and environmental systems; and applying emerging capabilities that 38 promise to transform how science is done through automated, data-rich, and Patents in the past 5 years interconnected systems. Through our R&D, we aim to strengthen the nation’s economic competitiveness, 4 enable resilient and sustainable economies, and facilitate stewardship of managed Governor’s and natural resources. Chairs “We harness next-generation tools in biology, ecology, data analytics, neutron, and computational sciences to translate fundamental understanding into models that advance scientific discovery.” Stan Wullschleger, Associate Laboratory Director Our Research Environmental Sciences—Our researchers expand scientific knowledge and develop innovative strategies and technologies that help sustain Earth’s natural resources. Biosciences—Our scientists advance knowledge discovery and develop technology to characterize and engineer complex biological systems that benefit the environment and our bioeconomy. -
What IS Systems Science?
What is Systems Science? Systems Science constitutes a somewhat fuzzily defined academic domain, that touches virtually all traditional disciplines, from mathematics, technology and biology to philosophy and the social sciences. It is more specifically related to the recently developing "sciences of complexity", including AI, neural networks, dynamical systems, chaos, and complex adaptive systems. Systems science argues that however complex or diverse the world that we experience, we will always find different types of organization in it, and such organization can be described by concepts and principles which are independent from the specific domain at which we are looking. Hence, if we would uncover those general laws, we would be able to analyze and solve problems in any domain, pertaining to any type of system. The systems approach distinguishes itself from the more traditional analytic approach by emphasizing the interactions and connectedness of the different components of a system. Although the systems approach in principle considers all types of systems, it in practices focuses on the more complex, adaptive, self-regulating systems which we might call "cybernetic". Many of the concepts used by system scientists come from the closely related approach of cybernetics: information, control, feedback, communication... In its present incarnation of "second- order cybernetics", its emphasis is on how observers construct models of the systems with which they interact*. Systems theory studies organization independent of the substrate in which it is embodied and is focused on the structure of systems and their models. ... from Principia Cybernetica Web http://pespmc1.vub.ac.be/ * This interaction demands the study of uncertainty, complexity, knowledge discovery, informatioin, decision making, formal concept analysis, dynamics and evolution.