Music at the Edge of Chaos: a Complex Systems Perspective on File Sharing Deborah Tussey Oklahoma City University School of Law
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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). -
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. -
Navigating the Landscape of Conflict: Applications of Dynamical Systems Theory to Addressing Protracted Conflict
Dynamics of Intractable Conflict Peter T. Coleman, PhD Robin Vallacher, PhD Andrzej Nowak, PhD International Center for Cooperation and Conflict Resolution Teachers College, Columbia University New York, NY, USA Why are some intergroup conflicts impossible to solve and what can we do to address them? “…one of the things that frustrates me about this conflict, thinking about this conflict, is that people don’t realize the complexity… how many stakeholders there are in there…I think there is a whole element to this particular conflict to where you start the story, to where you begin the narrative, and clearly it’s whose perspective you tell it from…One of the things that’s always struck me is that there are very compelling narratives to this conflict and all are true, in as much as anything is true… I think the complexity is on so many levels…It’s a complexity of geographic realities…the complexities are in the relationships…it has many different ethnic pockets… and I think it’s fighting against a place, where particularly in the United States, in American culture, we want to simplify, we want easy answers…We want to synthesize it down to something that people can wrap themselves around and take a side on…And maybe sometimes I feel overwhelmed…” (Anonymous Palestinian, 2002) Four Basic Themes An increasing degree of complexity and interdependence of elements. An underlying proclivity for change, development, and evolution within people and social-physical systems. Extraordinary cognitive, emotional, and behavioral demands…anxiety, hopelessness. Oversimplification of problems. Intractable Conflicts: The 5% Problem Three inter-related dimensions (Kriesberg, 2005): Enduring Destructive Resistant Uncommon but significant (5%; Diehl & Goertz, 2000) 5% of 11,000 interstate rivalries between 1816-1992. -
Ergodicity, Decisions, and Partial Information
Ergodicity, Decisions, and Partial Information Ramon van Handel Abstract In the simplest sequential decision problem for an ergodic stochastic pro- cess X, at each time n a decision un is made as a function of past observations X0,...,Xn 1, and a loss l(un,Xn) is incurred. In this setting, it is known that one may choose− (under a mild integrability assumption) a decision strategy whose path- wise time-average loss is asymptotically smaller than that of any other strategy. The corresponding problem in the case of partial information proves to be much more delicate, however: if the process X is not observable, but decisions must be based on the observation of a different process Y, the existence of pathwise optimal strategies is not guaranteed. The aim of this paper is to exhibit connections between pathwise optimal strategies and notions from ergodic theory. The sequential decision problem is developed in the general setting of an ergodic dynamical system (Ω,B,P,T) with partial information Y B. The existence of pathwise optimal strategies grounded in ⊆ two basic properties: the conditional ergodic theory of the dynamical system, and the complexity of the loss function. When the loss function is not too complex, a gen- eral sufficient condition for the existence of pathwise optimal strategies is that the dynamical system is a conditional K-automorphism relative to the past observations n n 0 T Y. If the conditional ergodicity assumption is strengthened, the complexity assumption≥ can be weakened. Several examples demonstrate the interplay between complexity and ergodicity, which does not arise in the case of full information. -
On the Continuing Relevance of Mandelbrot's Non-Ergodic Fractional Renewal Models of 1963 to 1967
Nicholas W. Watkins On the continuing relevance of Mandelbrot's non-ergodic fractional renewal models of 1963 to 1967 Article (Published version) (Refereed) Original citation: Watkins, Nicholas W. (2017) On the continuing relevance of Mandelbrot's non-ergodic fractional renewal models of 1963 to 1967.European Physical Journal B, 90 (241). ISSN 1434-6028 DOI: 10.1140/epjb/e2017-80357-3 Reuse of this item is permitted through licensing under the Creative Commons: © 2017 The Author CC BY 4.0 This version available at: http://eprints.lse.ac.uk/84858/ Available in LSE Research Online: February 2018 LSE has developed LSE Research Online so that users may access research output of the School. Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. You may freely distribute the URL (http://eprints.lse.ac.uk) of the LSE Research Online website. Eur. Phys. J. B (2017) 90: 241 DOI: 10.1140/epjb/e2017-80357-3 THE EUROPEAN PHYSICAL JOURNAL B Regular Article On the continuing relevance of Mandelbrot's non-ergodic fractional renewal models of 1963 to 1967? Nicholas W. Watkins1,2,3 ,a 1 Centre for the Analysis of Time Series, London School of Economics and Political Science, London, UK 2 Centre for Fusion, Space and Astrophysics, University of Warwick, Coventry, UK 3 Faculty of Science, Technology, Engineering and Mathematics, Open University, Milton Keynes, UK Received 20 June 2017 / Received in final form 25 September 2017 Published online 11 December 2017 c The Author(s) 2017. This article is published with open access at Springerlink.com Abstract. -
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. -
Ergodicity and Metric Transitivity
Chapter 25 Ergodicity and Metric Transitivity Section 25.1 explains the ideas of ergodicity (roughly, there is only one invariant set of positive measure) and metric transivity (roughly, the system has a positive probability of going from any- where to anywhere), and why they are (almost) the same. Section 25.2 gives some examples of ergodic systems. Section 25.3 deduces some consequences of ergodicity, most im- portantly that time averages have deterministic limits ( 25.3.1), and an asymptotic approach to independence between even§ts at widely separated times ( 25.3.2), admittedly in a very weak sense. § 25.1 Metric Transitivity Definition 341 (Ergodic Systems, Processes, Measures and Transfor- mations) A dynamical system Ξ, , µ, T is ergodic, or an ergodic system or an ergodic process when µ(C) = 0 orXµ(C) = 1 for every T -invariant set C. µ is called a T -ergodic measure, and T is called a µ-ergodic transformation, or just an ergodic measure and ergodic transformation, respectively. Remark: Most authorities require a µ-ergodic transformation to also be measure-preserving for µ. But (Corollary 54) measure-preserving transforma- tions are necessarily stationary, and we want to minimize our stationarity as- sumptions. So what most books call “ergodic”, we have to qualify as “stationary and ergodic”. (Conversely, when other people talk about processes being “sta- tionary and ergodic”, they mean “stationary with only one ergodic component”; but of that, more later. Definition 342 (Metric Transitivity) A dynamical system is metrically tran- sitive, metrically indecomposable, or irreducible when, for any two sets A, B n ∈ , if µ(A), µ(B) > 0, there exists an n such that µ(T − A B) > 0. -
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. -
Introductory Course on Dynamical Systems Theory and Intractable Conflict DST Course Objectives
Introductory Course on Dynamical Systems Theory and Intractable Conflict Peter T. Coleman Columbia University December 2012 This self-guided 4-part course will introduce the relevance of dynamical systems theory for understanding, investigating, and resolving protracted social conflict at different levels of social reality (interpersonal, inter-group, international). It views conflicts as dynamic processes whose evolution reflects a complex interplay of factors operating at different levels and timescales. The goal for the course is to help develop a basic understanding of the dynamics underlying the development and transformation of intractable conflict. DST Course Objectives Participants in this class will: Learn the basic ideas and methods associated with dynamical systems. Learn the relevance of dynamical systems for personal and interpersonal processes. Learn the implications of dynamical models for understanding and investigating conflict of different types and at different levels of social reality. Learn to think about conflict in a manner that allows for new and testable means of conflict resolution. Foundational Texts Nowak, A. & Vallacher, R. R. (1998). Dynamical social psychology. New York: Guilford Publications. Vallacher, R., Nowak, A., Coleman, P. C., Bui-Wrzosinska, L., Leibovitch, L., Kugler, K. & Bartoli, A. (Forthcoming in 2013). Attracted to Conflict: The Emergence, Maintenance and Transformation of Malignant Social Relations. Springer. Coleman (2011). The Five Percent: Finding Solutions to Seemingly Impossible Conflicts. Perseus Books. Coleman, P. T. & Vallacher, R. R. (Eds.) (2010). Peace and Conflict: Journal of Peace Psychology, Vol. 16, No. 2, 2010. (Special issue devoted to dynamical models of intractable conflict). Ricigliano, R. (2012).Making Peace Last. Paradigm. Burns, D. (2007). Systemic Action Research: A Strategy for Whole System Change. -
Strategies and Rubrics for Teaching Chaos and Complex Systems Theories As Elaborating, Self-Organizing, and Fractionating Evolutionary Systems Lynn S
Strategies and Rubrics for Teaching Chaos and Complex Systems Theories as Elaborating, Self-Organizing, and Fractionating Evolutionary Systems Lynn S. Fichter1,2, E.J. Pyle1,3, S.J. Whitmeyer1,4 ABSTRACT To say Earth systems are complex, is not the same as saying they are a complex system. A complex system, in the technical sense, is a group of ―agents‖ (individual interacting units, like birds in a flock, sand grains in a ripple, or individual units of friction along a fault zone), existing far from equilibrium, interacting through positive and negative feedbacks, forming interdependent, dynamic, evolutionary networks, that possess universality properties common to all complex systems (bifurcations, sensitive dependence, fractal organization, and avalanche behaviour that follows power- law distributions.) Chaos/complex systems theory behaviors are explicit, with their own assumptions, approaches, cognitive tools, and models that must be taught as deliberately and systematically as the equilibrium principles normally taught to students. We present a learning progression of concept building from chaos theory, through a variety of complex systems, and ending with how such systems result in increases in complexity, diversity, order, and/or interconnectedness with time—that is, evolve. Quantitative and qualitative course-end assessment data indicate that students who have gone through the rubrics are receptive to the ideas, and willing to continue to learn about, apply, and be influenced by them. The reliability/validity is strongly supported by open, written student comments. INTRODUCTION divided into fractions through the addition of sufficient Two interrelated subjects are poised for rapid energy because of differences in the size, weight, valence, development in the Earth sciences.