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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). -
Synergetic Environments Form, Social Behavior, and Coordination
Synergetic Environments Form, Social Behavior, and Coordination “In the most general sense, computation is the process of storing, transmitting, and transforming information from one form to another” (Santa Fe Institute). ARCH 6307/4050/6050, IT IS 6010, ITCS 5010 Wednesdays 6:00pm-8:45pm, Storrs 255 School of Architecture, UNC Charlotte Prof Dr. Dimitrios Papanikolaou / [email protected] Office hours: Wednesdays 10am-12pm by appointment Office: Storrs 146 Premise The seminar critically reviews the evolution and design principles of systems of urban and territorial intelligence, from the ancient networks of optical telegraphy to today’s internet of things. Through discussions, we will examine technologies, systems, and mechanisms for turning information into decision and action in large scales; we will question the role of the physical environment, resource scarcity, and human behavior in shaping equilibrium conditions; we will investigate the role of data, modeling, and simulation in exploring dynamics of urban systems; and we will critically speculate where the design field of urban computing might be heading in the future. Objectives By the end of the course, students will develop a broad yet critical understanding of what urban intelligence is, how it can be constructed, and what limits it may reach, from a sociotechnical and a systemic perspective. Topics include: Theory and technologies of computation, information, communication, and human factors in relation to the built environment; systems theory; cybernetics; 1 ARCH 4050/6050 FALL 2017, SoA, UNC Charlotte urban dynamics; ecology; social cooperation; collective intelligence; game theory; mechanism design; as well as applications in mobility, resource allocation, sustainability, and energy. Method The course combines discussions, lectures, and workshops. -
Paul Smolensky
Vita PAUL SMOLENSKY Department of Cognitive Science 11824 Mays Chapel Road 239A Krieger Hall Timonium, MD 21093-1821 Johns Hopkins University (667) 229-9509 Baltimore, MD 21218-2685 May 5, 1955 (410) 516-5331 Citizenship: USA [email protected] cogsci.jhu.edu/directory/paul-smolensky/ DEGREES Ph.D. in mathematical physics, Indiana University, 1981. M.S. in physics, Indiana University, 1977. A.B. summa cum laude in physics, Harvard University, 1976. PROFESSIONAL POSITIONS Partner Researcher, Microsoft Research Artificial Intelligence, Redmond WA, Dec. 2016−present. Krieger-Eisenhower Professor of Cognitive Science, Johns Hopkins University, 2006–present. Full Professor, Department of Cognitive Science, Johns Hopkins University, 1994–2006. Chair, Department of Cognitive Science, Johns Hopkins University, Jan. 1997−June 1998 (Acting), July 1998−June 2000 Professor, Department of Computer Science, University of Colorado at Boulder, Full Professor, 1994–95 (on leave, 1994–95). Associate Professor, 1990–94. Assistant Professor, 1985–90. Assistant Research Cognitive Scientist (Assistant Professor – Research), Institute for Cognitive Science, University of California at San Diego, 1982–85. Visiting Scholar, Program in Cognitive Science, University of California at San Diego, 1981–82. Adjunct Professor, Department of Linguistics, University of Maryland at College Park, 1994–2010. Assistant Director, Center for Language and Speech Processing, Johns Hopkins University, 1995–2008. Director, NSF IGERT Training Program, Unifying the Science of Language, 2006−2015. Director, NSF IGERT Training Program in the Cognitive Science of Language, 1999−2006. International Chair, Inria Paris (National Institute for Research in Computer Science and Automation), 2017−2021. Visiting Scientist, Inserm-CEA Cognitive Neuroimaging Unit, NeuroSpin Center, Paris, France, 2016. -
8418B0e55972ecac157afb730f8
Baltic Journal of Economic Studies Vol. 4, No. 2, 2018 DOI: https://doi.org/10.30525/2256-0742/2018-4-2-254-260 ANALYTIC OVERLOOK OF THE METHODOLOGY OF SYNERGETICS IN POSTNONCLASSICAL SCIENCE Viktor Yakimtsov1 Ukrainian National Forestry University, Ukraine Abstract. Purpose. This article reveals the main definitions of synergetics and methods that are being used in synergetic research. The differences-characteristics of classical, nonclassical, and postnonclassical science and their schematic illustration are described. There are criteria, by which the main methodological principles of synergetics are being chosen. The reasons that have caused an appearance of synergetics and its methodological apparatus and the framework of this apparatus are considered. The special aspects of nonlinearity of complicated systems, in our opinion, include the economic ones. Methodology. Such foreign and domestic scientists as Wiener N. (2003), Thom R. (1975, 1996), Prigogine I., Stengers I. (1986), Zang V.B. (1999), and Arnold V. (2004) have used methodological apparatus of synergetics in modern science. Methodologically synergetics is open for those new conceptions that are being formed in certain disciplines. Methodological principles of synergetics that cause the “colostral” principles are nonlinearity, nonclosure, and instability. The main principle – the rule of nonlinearity is a contravention of the principle of the super offer in the certain phenomenon (process): the result of adding the impacts on the system is not the adding these impacts’ results. The causes’ results cannot be added. This means that the result of adding the causes does not equal to the union of causes’ results. Results. For the synergy concept, the idea is typical that we see everything at once: the whole and its parts. -
The Effects of Feedback Interventions on Performance: a Historical Review, a Meta-Analysis, and a Preliminary Feedback Intervention Theory
Psychological Bulletin Copyright 1996 by the American Psychological Association, Inc. 1996, Vol. II9, No. 2, 254-284 0033-2909/96/S3.00 The Effects of Feedback Interventions on Performance: A Historical Review, a Meta-Analysis, and a Preliminary Feedback Intervention Theory Avraham N. Kluger Angelo DeNisi The Hebrew University of Jerusalem Rutgers University Since the beginning of the century, feedback interventions (FIs) produced negative—but largely ignored—effects on performance. A meta-analysis (607 effect sizes; 23,663 observations) suggests that FIs improved performance on average (d = .41) but that over '/3 of the FIs decreased perfor- mance. This finding cannot be explained by sampling error, feedback sign, or existing theories. The authors proposed a preliminary FI theory (FIT) and tested it with moderator analyses. The central assumption of FIT is that FIs change the locus of attention among 3 general and hierarchically organized levels of control: task learning, task motivation, and meta-tasks (including self-related) processes. The results suggest that FI effectiveness decreases as attention moves up the hierarchy closer to the self and away from the task. These findings are further moderated by task characteristics that are still poorly understood. To relate feedback directly to behavior is very confusing. Results able has been historically disregarded by most FI researchers. This are contradictory and seldom straight-forward. (Ilgen, Fisher, & disregard has led to a widely shared assumption that FIs consis- Taylor, 1979, p. 368) tently improve performance. Fortunately, several FI researchers The effects of manipulation of KR [knowledge of results] on motor (see epigraphs) have recently recognized that FIs have highly vari- learning. -
An Analysis of Power and Stress Using Cybernetic Epistemology Randall Robert Lyle Iowa State University
Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 1992 An analysis of power and stress using cybernetic epistemology Randall Robert Lyle Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the Educational Psychology Commons, Family, Life Course, and Society Commons, and the Philosophy Commons Recommended Citation Lyle, Randall Robert, "An analysis of power and stress using cybernetic epistemology " (1992). Retrospective Theses and Dissertations. 10131. https://lib.dr.iastate.edu/rtd/10131 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely afreet reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand corner and continuing from lefr to right in equal sections with small overlaps. -
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). -
1 © Stafford Beer December 1992 WORLD in TORMENT a TIME
ã Stafford Beer December 1992 WORLD IN TORMENT A TIME WHOSE IDEA MUST COME You will remember the beginning of humankind. Our first parents were quick to get themselves into trouble. They were expelled from the garden of Eden. I understand that Adam took Eve's hand, and said: 'My dear, we are living in a time of transition'. Perhaps people have always felt like that. We certainly do today. Have you ever tried to list the components of contemporary change? It is easy enough to cite the marvels of modern science and technology - how the computer, and television, and medical science have changed our lives. If you start with such matters, it becomes a 'profound insight' to observe that there has been a change in the rate of change. But that was obvious twenty to thirty years ago, for I was writing books about it then. Components of Contemporary Change Today, my list is different. At the top is the spectacular advance in human misery. I estimate that more human beings are enduring agony today than ever before; the number could be greater than the sum of sufferers throughout history. I speak of starvation and epidemic; war and terrorism; deprivation, exploitation, and physical torture. I repeat the word agony; I am not talking about 'hard times'. Second on my list is the collapse of the civilisation we have known in our lifetime. We are looking at the rubble that remains of two competing empires. Soviet communism has accepted its own demise; Western capitalism has not accepted it yet. But I am not making a forecast. -
“The Action of the Brain” Machine Models and Adaptive Functions in Turing and Ashby
“The Action of the Brain” Machine Models and Adaptive Functions in Turing and Ashby Hajo Greif1,2 1 Munich Center for Technology in Society, Technical University of Munich, Germany 2 Department of Philosophy, University of Klagenfurt, Austria [email protected] Abstract Given the personal acquaintance between Alan M. Turing and W. Ross Ashby and the partial proximity of their research fields, a comparative view of Turing’s and Ashby’s works on modelling “the action of the brain” (in a 1946 letter from Turing to Ashby) will help to shed light on the seemingly strict symbolic / embodied dichotomy: while it is a straightforward matter to demonstrate Turing’s and Ashby’s respective commitments to formal, computational and material, analogue methods of modelling, there is no unambiguous mapping of these approaches onto symbol-based AI and embodiment-centered views respectively. Instead, it will be argued that both approaches, starting from a formal core, were at least partly concerned with biological and embodied phenomena, albeit in revealingly distinct ways. Keywords: Artificial Intelligence, Cybernetics, Models, Embodiment, Darwinian evolution, Morphogenesis, Functionalism 1 Introduction Not very much has been written to date on the relation between Alan M. Turing and W. Ross Ashby, both of whom were members of the “Ratio Club” (1949– 1958).1 Not much of the communication between the two seems to have been preserved or discovered either, the major exception being a letter from Turing to Ashby that includes the following statement: In working on the ACE [an early digital computer] I am more interested in the possibility of producing models of the action of the brain than in the practical applications of computing. -
HOMEOSTASIS: Negative Feedback and Breathing
HOMEOSTASIS: Negative Feedback and Breathing Homeostasis refers to the maintenance of relatively constant internal conditions. For example, your body shivers to maintain a relatively constant body temperature when the external environment gets colder. These body responses are an example of negative feedback. Negative feedback occurs when a change in a regulated variable triggers a response which reverses the initial change and brings the regulated variable back to the set point. Here is an experiment to better understand homeostasis and negative feedback mechanism. Your brain regulates the rate and depth of your breathing to match the needs of your body for O2 intake and CO2 removal. Breathing rate refers to the number of breaths per minute. Depth of breathing refers to the amount of air taken in with each breath. Developing Your Experimental Procedures For a scientific investigation to yield accurate results, students need to begin by developing reliable, valid methods of measuring the variables in the investigation. Divide into groups where each group has at least four students. Each person in your group of four students should get an 8 gallon plastic bag (can be a garbage bag). Your group will also need some way to time 30 second intervals during the experiment. During the experiment, you will need to breathe into the bag while holding it in a way that minimizes any tendency of the bag to flop over your nose or mouth as you breathe in. To accomplish this, open your bag completely and swish it through the air until the bag is nearly full of air; then gather the top of the bag in both hands and use your finger to open a small hole in the center just big enough to surround your nose and mouth; hold this opening tightly over your nose and mouth. -
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. -
Cybernetica-The Law of Requisite Variety
The Law of Requisite Variety “The Principles of Cybernetica Web” at http://pespmc1.vub.ac.be/REQVAR.HTML The larger the variety of actions available to a control system, the larger the variety of perturbations it is able to compensate. Control or regulation is most fundamentally formulated as a reduction of variety: perturbations with high variety affect the system's internal state, which should be kept as close as possible to the goal state, and therefore exhibit a low variety. So in a sense control prevents the transmission of variety from environment to system. This is the opposite of information transmission, where the purpose is to maximally conserve variety. In active (feedforward and/or feedback) regulation, each disturbance D will have to be compensated by an appropriate counteraction from the regulator R. If R would react in the same way to two different disturbances, then the result would be two different values for the essential variables, and thus imperfect regulation. This means that if we wish to completely block the effect of D, the regulator must be able to produce at least as many counteractions as there are disturbances in D. Therefore, the variety of R must be at least as great as the variety of D. If we moreover take into account the constant reduction of variety K due to buffering, the principle can be stated more precisely as: V(E) ³ V(D) - V(R) - K Ashby has called this principle the law of requisite variety: in active regulation only variety can destroy variety. It leads to the somewhat counterintuitive observation that the regulator must have a sufficiently large variety of actions in order to ensure a sufficiently small variety of outcomes in the essential variables E.