Try Again. Fail Again. Fail Better: the Cybernetics in Design and the Design in Cybernetics
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Listen! (A Tribute to the Listen Inn, Shaftesbury, Dorset, UK) Ranulph Glanville
Listen! (A Tribute to the Listen Inn, Shaftesbury, Dorset, UK) Ranulph Glanville Independent Academic, CybernEthics Research, 52 Lawrence Road, Southsea, Hants PO5 1NY, UK: tel +44 (0) 23 9273 7779; fax +44 (0) 23 9276 7779; email [email protected] Text 1 “Beuys’ primary requirement for true communication was the existence of a reciprocal relationship between individuals. ‘For communication it’s necessary that there be someone who listens…There’s no sense in a transmitter if there’s noone who receives.’” Panel entitled “Communication” at the Joseph Beuys Exhibition, Royal Kilmainham Hospital, Dublin, read on June 2nd 1999. Abstract The argument is presented that, for participation and connectedness to be possible, we need to learn to listen, and to value listening, rather than being “given our voice”. Some benefits of listening are presented: as are side effects, which reflect qualities often thought of as being among the most desirable human qualities. Finally, examples of a demonstration and a workshop presented at the conference, concerned with developing listening skills, are introduced. Text 2 “Academics study precisely those areas in which they are least personally competent.” Academic Sod’s Law, Ranulph Glanville. Preamble The pianist enters the auditorium, and sits at the piano. The murmur in the audience subsides. He opens the keyboard and sits still for four and a half minutes. During this time, he makes no sound, plays no notes. At the end of this time, he gets up, takes his bow, and leaves. He has been performing John Cage’s piece, 4'32". (See Cage’s 1966 book, Silence.) You are in a community where no one will respond to anything you say, and no one will talk to you. -
An Information-Theoretic Metric of System Complexity with Application
An Information-Theoretic Metric Douglas Allaire1 of System Complexity With e-mail: [email protected] Application to Engineering Qinxian He John Deyst System Design Karen Willcox System complexity is considered a key driver of the inability of current system design practices to at times not recognize performance, cost, and schedule risks as they emerge. Aerospace Computational Design Laboratory, We present here a definition of system complexity and a quantitative metric for measuring Department of Aeronautics and Astronautics, that complexity based on information theory. We also derive sensitivity indices that indi- Massachusetts Institute of Technology, cate the fraction of complexity that can be reduced if more about certain factors of a sys- Cambridge, MA 02139 tem can become known. This information can be used as part of a resource allocation procedure aimed at reducing system complexity. Our methods incorporate Gaussian pro- cess emulators of expensive computer simulation models and account for both model inadequacy and code uncertainty. We demonstrate our methodology on a candidate design of an infantry fighting vehicle. [DOI: 10.1115/1.4007587] 1 Introduction methodology identifies the key contributors to system complexity and provides quantitative guidance for resource allocation deci- Over the years, engineering systems have become increasingly sions aimed at reducing system complexity. complex, with astronomical growth in the number of components We define system complexity as the potential for a system to and their interactions. With this rise in complexity comes a host exhibit unexpected behavior in the quantities of interest. A back- of new challenges, such as the adequacy of mathematical models ground discussion on complexity metrics, uncertainty sources in to predict system behavior, the expense and time to conduct complex systems, and related work presented in Sec. -
The BRAIN in Space a Teacher’S Guide with Activities for Neuroscience This Page Intentionally Left Blank
Educational Product National Aeronautics and Teachers Grades 5–12 Space Administration The BRAIN in Space A Teacher’s Guide With Activities for Neuroscience This page intentionally left blank. The Brain in Space A Teacher’s Guide With Activities for Neuroscience National Aeronautics and Space Administration Life Sciences Division Washington, DC This publication is in the Public Domain and is not protected by copyright. Permission is not required for duplication. EG-1998-03-118-HQ This page intentionally left blank. This publication was made possible by the National Acknowledgments Aeronautics and Space Administration, Cooperative Agreement No. NCC 2-936. Principal Investigator: Walter W.Sullivan, Jr., Ph.D. Neurolab Education Program Office of Operations and Planning Morehouse School of Medicine Atlanta, GA Writers: Marlene Y. MacLeish, Ed.D. Director, Neurolab Education Program Morehouse School of Medicine Atlanta, GA Bernice R. McLean, M.Ed. Deputy Director, Neurolab Education Program Morehouse School of Medicine Atlanta, GA Graphic Designer and Illustrator: Denise M.Trahan, B.A. Atlanta, GA Technical Director: Perry D. Riggins Neurolab Education Program Morehouse School of Medicine Atlanta, GA This page intentionally left blank. Contributors This publication was developed for the National Aeronautics and Space Administration (NASA) under a Cooperative Agreement with the Morehouse School of Medicine (MSM). Many individu- als and organizations contributed to the production of this curriculum. We acknowledge their support and contributions. Organizations: Joseph Whittaker, Ph.D. Atlanta Public School System Neurolab Education Program Advisory Board: Society for Neuroscience Gene Brandt The Dana Alliance for Brain Initiatives Milton C. Clipper, Jr. Mary Anne Frey, Ph.D. NASA Headquarters: Charles A. -
Introduction to Cyber-Physical System Security: a Cross-Layer Perspective
IEEE TRANSACTIONS ON MULTI-SCALE COMPUTING SYSTEMS, VOL. 3, NO. 3, JULY-SEPTEMBER 2017 215 Introduction to Cyber-Physical System Security: A Cross-Layer Perspective Jacob Wurm, Yier Jin, Member, IEEE, Yang Liu, Shiyan Hu, Senior Member, IEEE, Kenneth Heffner, Fahim Rahman, and Mark Tehranipoor, Senior Member, IEEE Abstract—Cyber-physical systems (CPS) comprise the backbone of national critical infrastructures such as power grids, transportation systems, home automation systems, etc. Because cyber-physical systems are widely used in these applications, the security considerations of these systems should be of very high importance. Compromise of these systems in critical infrastructure will cause catastrophic consequences. In this paper, we will investigate the security vulnerabilities of currently deployed/implemented cyber-physical systems. Our analysis will be from a cross-layer perspective, ranging from full cyber-physical systems to the underlying hardware platforms. In addition, security solutions are introduced to aid the implementation of security countermeasures into cyber-physical systems by manufacturers. Through these solutions, we hope to alter the mindset of considering security as an afterthought in CPS development procedures. Index Terms—Cyber-physical system, hardware security, vulnerability Ç 1INTRODUCTION ESEARCH relating to cyber-physical systems (CPS) has In addition to security concerns, CPS privacy is another Rrecently drawn the attention of those in academia, serious issue. Cyber-physical systems are often distributed industry, and the government because of the wide impact across wide geographic areas and typically collect huge CPS have on society, the economy, and the environment [1]. amounts of information used for data analysis and decision Though still lacking a formal definition, cyber-physical making. -
Cyber-Physical Systems, a New Formal Paradigm to Model Redundancy and Resiliency Mario Lezoche, Hervé Panetto
Cyber-Physical Systems, a new formal paradigm to model redundancy and resiliency Mario Lezoche, Hervé Panetto To cite this version: Mario Lezoche, Hervé Panetto. Cyber-Physical Systems, a new formal paradigm to model redun- dancy and resiliency. Enterprise Information Systems, Taylor & Francis, 2020, 14 (8), pp.1150-1171. 10.1080/17517575.2018.1536807. hal-01895093 HAL Id: hal-01895093 https://hal.archives-ouvertes.fr/hal-01895093 Submitted on 13 Oct 2018 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. Cyber-Physical Systems, a new formal paradigm to model redundancy and resiliency Mario Lezoche, Hervé Panetto Research Centre for Automatic Control of Nancy (CRAN), CNRS, Université de Lorraine, UMR 7039, Boulevard des Aiguillettes B.P.70239, 54506 Vandoeuvre-lès-Nancy, France. e-mail: {mario.lezoche, herve.panetto}@univ-lorraine.fr Cyber-Physical Systems (CPS) are systems composed by a physical component that is controlled or monitored by a cyber-component, a computer-based algorithm. Advances in CPS technologies and science are enabling capability, adaptability, scalability, resiliency, safety, security, and usability that will far exceed the simple embedded systems of today. CPS technologies are transforming the way people interact with engineered systems. -
What Is a Complex System?
What is a Complex System? James Ladyman, James Lambert Department of Philosophy, University of Bristol, U.K. Karoline Wiesner Department of Mathematics and Centre for Complexity Sciences, University of Bristol, U.K. (Dated: March 8, 2012) Complex systems research is becoming ever more important in both the natural and social sciences. It is commonly implied that there is such a thing as a complex system, different examples of which are studied across many disciplines. However, there is no concise definition of a complex system, let alone a definition on which all scientists agree. We review various attempts to characterize a complex system, and consider a core set of features that are widely associated with complex systems in the literature and by those in the field. We argue that some of these features are neither necessary nor sufficient for complexity, and that some of them are too vague or confused to be of any analytical use. In order to bring mathematical rigour to the issue we then review some standard measures of complexity from the scientific literature, and offer a taxonomy for them, before arguing that the one that best captures the qualitative notion of the order produced by complex systems is that of the Statistical Complexity. Finally, we offer our own list of necessary conditions as a characterization of complexity. These conditions are qualitative and may not be jointly sufficient for complexity. We close with some suggestions for future work. I. INTRODUCTION The idea of complexity is sometimes said to be part of a new unifying framework for science, and a revolution in our understanding of systems the behaviour of which has proved difficult to predict and control thus far, such as the human brain and the world economy. -
Modeling, Analysis & Control of Dynamic Systems
Dynamic System Investigation Process • Dynamic System Investigation Overview • Stages of a Dynamic System Investigation – Balance: The Key to Success! • Dynamic System Examples • Dynamic System Investigation Case Study Mechatronics K. Craig Dynamic System Investigation 1 Dynamic System Investigation Physical Physical Mathematical System Model Model Experimental Mathematical Comparison Analysis Analysis Design Changes Mechatronics K. Craig Dynamic System Investigation 2 Measurements, Calculations, Which Parameters to Identify? Model Manufacturer's Specifications What Tests to Perform? Parameter ID Physical Physical Math System Model Model Assumptions Physical Laws Equation Solution: Experimental and Analytical Analysis Engineering Judgement Model Inadequate: and Numerical Modify Solution Actual Predicted Dynamic Compare Dynamic Behavior Behavior Make Design Design Modify Model Adequate, Model Adequate, Complete or Decisions Performance Inadequate Performance Adequate Augment Dynamic System Investigation Mechatronics K. Craig Dynamic System Investigation 3 Dynamic System Investigation Overview • Apply the steps in the process when: – An actual physical system exists and one desires to understand and predict its behavior. – The physical system is a concept in the design process that needs to be analyzed and evaluated. • After recognizing a need for a new product or service, one generates design concepts by using: – Past experience (personal and vicarious) – Awareness of existing hardware – Understanding of physical laws – Creativity Mechatronics K. Craig Dynamic System Investigation 4 • The importance of modeling and analysis in the design process has never been more important. • Design concepts can no longer be evaluated by the build-and-test approach because it is too costly and time consuming. • Validating the predicted dynamic behavior in this case, when no actual physical system exists, then becomes even more dependent on one's past hardware and experimental experience. -
Physical Information Systems †
Abstract Physical Information Systems † John Collier School of Religion, Philosophy and Classics, University of KwaZulu-Natal, Durban 4041, South Africa; [email protected]; Tel.: +27-31-260-3248 † Presented at the IS4SI 2017 Summit DIGITALISATION FOR A SUSTAINABLE SOCIETY, Gothenburg, Sweden, 12–16 June 2017. Published: 9 June 2017 Abstract: Information is usually in strings, like sentences, programs or data for a computer. Information is a much more general concept, however. Here I look at information systems that can be three or more dimensional, and examine how such systems can be arranged hierarchically so that each level has an associated entropy due to unconstrained information at a lower level. This allows self-organization of such systems, which may be biological, psychological, or social. Keywords: information; dynamics; self-organization; hierarchy Information is usually studied in terms of stings of characters, such as in the methods described by Shannon [1] which applies best to the information capacity of a channel, and Kolomogorov [2] and Chaitin [3], which applies best to the information in a system. The basic idea of all three, though they differ somewhat in method, is that the most compressed form of the information in something is a measure of its actual information content, an idea I will clarify shortly. This compressed form gives the minimum number of yes, no questions required to uniquely identify some system [4]. Collier [5] introduced the idea of a physical information system. The idea was to open up information from strings to multidimensional physical systems. Although any system can be mapped onto a (perhaps continuous) string, it struck me that it was more straightforward to go directly to multidimensional systems, as found in, say biology. -
Social Systems
Home Browse Authors Sources Documents Years Theories Subjects Find Sources Authors Search Simple Advanced Help Previous Source Document Document 1 Next Source Document Front Matter by Editor, in Social Systems. [by] Niklas Luhmann and translated by John Bednarz, Jr. with Dirk Baecker. (Stanford University Press, Stanford, CA, 1995). pp. [N pag]-11. [Bibliographic Details] [View Documents] -- [NA] -- Front Matter [Cover] SOCIAL SYSTEMS Niklas Luhmann TRANSLATED BY John Bcdnarz, Jr. WITH Dirk Baecker -- [NA] -- -- [NA] -- SOCIAL SYSTEMS -- [NA] -- -- [NA] -- WRITING SCIENCE EDITORS Timothy Lenoir and Hans Ulrich Gumbrecht -- [NA] -- -- [NA] -- [Title Page and Credits] SOCIAL SYSTEMS Niklas Luhmann TRANSLATED BY John Bednarz, Jr., with Dirk Baecker FOREWORD BY Eva M. Knodt STANFORD UNIVERSITY PRESS STANFORD, CALIFORNIA -- [NA] -- Assistance for the translation was provided by Inter Nationes Social Systems was originally published in German in 1984 as Soziale Systeme: Grundriβ einer allgemeinen Theorie, © 1984 Suhrkamp Verlag Frankfurt am Main. Stanford University Press, Stanford, California © 1995 by the Board of Trustees of the Leland Stanford Junior University Printed in the United States of America CIP data appear at the end of the book Original printing 1995 -- [NA] -- Contents Foreword ix Instead of a Preface to the English Edition: On the Concepts "Subject" and "Action" xxxvii Preface to the German Edition xlv Introduction: Paradigm Change in Systems Theory 1 1. System and Function 12 2. Meaning 59 3. Double Contingency 103 4. Communication and Action 137 5. System and Environment 176 6. Interpenetration 210 7. The Individuality of Psychic Systems 255 8. Structure and Time 278 9. Contradiction and Conflict 357 10. Society and Interaction 405 11. -
Second Order Cybernetics 31/08/2008 10:08
SECOND ORDER CYBERNETICS 31/08/2008 10:08 Search Print this chapter Cite this chapter SECOND ORDER CYBERNETICS Ranulph Glanville, CybernEthics Research, UK and Royal Melbourne Institute of Technology University, Australia Keywords: actor, analogy, argument, automata, autopoiesis, behavior, biology, Black_Box, causality, circularity, coding, cognition, communication, computation, concept, consciousness, control, conversation, cybernetics, description, design, distinction, ecology, education, environment, epistemology, error, ethics, feedback, form, function, generosity, goal, homeostasis, idea, identity, individual, information, interaction, internet, knowing, language, learning, logic, management, mathematics, meaning, mechanism, memory, meta-, metaphor, moment, mutualism, network, object, observe, organizational_closure, oscillation, person, phenomenon, philosophical, physics, positivist, (post-)_modernism, praxis, process, program, psychology, purpose, quality, reality, reason, recursion, relative, responsibility, science, second_order_cybernetics, (self-)_reference, semiotics, social, structure, subject, support, system, theory, thinking, time, truth, understanding, variety, whole_(and_part) Contents 1. Introduction: What Second Order Cybernetics is, and What it Offers 2. Background—the Logical Basis for Second Order Cybernetics 3. Second Order Cybernetics—Historical Overview 4. Theory of Second Order Cybernetics 5. Praxis of Second Order Cybernetics 6. A Note on Second Order Cybernetics and Constructivism 7. Cybernetics, Second Order -
Exploring the Nature of Computational Irreducibility Across Physical, Biological, and Human Social Systems
More complex complexity: Exploring the nature of computational irreducibility across physical, biological, and human social systems Brian Beckage1, Stuart Kauffman2, Asim Zia3, Christopher Koliba4, and Louis J. Gross5 1Department of Plant Biology University of Vermont, Burlington, VT 05405 2Department of Mathematics, Department of Biochemistry, and Complexity Group, University of Vermont, Burlington, VT 05405 3Department of Community Development and Applied Economics, University of Vermont, Burlington, VT 05405 4Department of Community Development and Applied Economics, University of Vermont, Burlington, VT 05405 5National Institute for Mathematical and Biological Synthesis University of Tennessee, Knoxville, TN 37996-1527 1 1 Abstract The predictability of many complex systems is limited by computational irre- ducibility, but we argue that the nature of computational irreducibility varies across physical, biological and human social systems. We suggest that the com- putational irreducibility of biological and social systems is distinguished from physical systems by functional contingency, biological evolution, and individ- ual variation. In physical systems, computationally irreducibility is driven by the interactions, sometimes nonlinear, of many different system components (e.g., particles, atoms, planets). Biological systems can also be computationally irreducible because of nonlinear interactions of a large number of system com- ponents (e.g., gene networks, cells, individuals). Biological systems additionally create the probability -
Dubberly & Pangaro, “Cybernetics and Design: Conversations for Action”
Chapter 3 Cybernetics and Design: Conversations for Action Hugh Dubberly and Paul Pangaro Abstract Ranulph Glanville came to believe that cybernetics and design are two sides of the same coin. The authors present their understanding of Glanville and the relationships they see between cybernetics and design. They argue that cyber- netics is a necessary foundation for 21st century design practice: If design, then systems: Due in part to the rise of computing technology and its role in human communications, the domain of design has expanded from giving form to creating systems that support human interactions; thus, systems literacy becomes a neces- sary foundation for design. If systems, then cybernetics: Interaction involves goals, feedback, and learning, the science of which is cybernetics. If cybernetics, then second-order cybernetics: Framing wicked problems requires making explicit one’s values and viewpoints, accompanied by the responsibility to justify them with ex- plicit arguments; this incorporates subjectivity and the epistemology of second-order cybernetics. If second-order cybernetics, then conversation: Design grounded in ar- gumentation requires conversations so that participants may understand, agree, and collaborate on effective action – that is, participants in a design conversation learn together in order to act together. The authors see cybernetics as a way of framing both the process of designing and the things being designed – both means and ends – not only design-as-conversation but also design-for-conversation. Second-order cybernetics frames design as conversation, and they explicitly frame “second-order design” as creating possibilities for others to have conversations. Hugh Dubberly Dubberly Design Office, 2501 Harrison Street, No.