Systems Ecology, Energy Networks, and a Path to Sustainability
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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. -
Emergy and Efficiency Analysis of Historical Bubbles
Emergy Synthesis 10, Proceedings of the 10th Biennial Emergy Conference (2019) 29 Emergy and Efficiency Analysis of Historical Bubbles Mark P. A. Ciotola ABSTRACT Human societies comprise energy-consuming beings, who are vast consumers of energy, as both individual organisms and organized societies. Hence, in principle, it should be possible to utilize thermodynamic approaches to generate models of historical phenomena. The existing energy-related efficiency-discounted exponential growth (EDEG) framework for historical resource bubbles and dynasties involving a nonrenewable resource is presented. As EDEG is presented, its relationship to emergy-related concepts and approaches are explored and analyzed. Finally, future research possibilities for expanding the physical, closed system framework behind EDEG into the open system emergy framework. CONTEXT A framework for a science of human history has been developed from a physics perspective utilizing no-renewable quantities and approximating historical dynasties as closed systems. However, it is important to also consider open systems and better constrain models with better physical data. The emergy approach offers both capabilities. This paper will explore emergy concepts as related to past work. INTRODUCTION Following the success of physical scientists such as Isaac Newton with the law of universal gravitation, it had been the dream of some social scientists to develop a science of society and history. Coming at the top of a wave of scientific discoveries, and excited also by the political and social changes in the European world around him, he [father of Sociology August Comte] conceived the idea, which was shared in varying degrees by other thinkers of his time, that science might be unified and used in such a way as to harmonize the discords of human life (Marvin, 1936). -
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. -
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). -
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. -
Developmental Systems Science 1
DEVELOPMENTAL SYSTEMS SCIENCE 1 Developmental Systems Science: Exploring the Application of Systems Science Methods to Developmental Science Questions Manuscript #: 8(1)_1 Date: October 27, 2010 Running head: Developmental Systems Science Urban, J.B., Osgood, N., & Mabry, P. (2011). Developmental systems science: Exploring the application of non- linear methods to developmental science questions. Research in Human Development, 8(1), 1-25. doi: 10.1080/15427609.2011.549686 DEVELOPMENTAL SYSTEMS SCIENCE 2 Abstract Developmental science theorists fully acknowledge the wide array of complex interactions between biology, behavior, and environment that together give rise to development. However, despite this conceptual understanding of development as a system, developmental science has not fully applied analytic methods commensurate with this systems perspective. This paper provides a brief introduction to systems science, an approach to problem-solving that involves the use of methods especially equipped to handle complex relationships and their evolution over time. Moreover, we provide a rationale for why and how these methods can serve the needs of the developmental science research community. A variety of developmental science theories are reviewed and the need for systems science methodologies is demonstrated. This is followed by an abridged primer on systems science terminology and concepts, with specific attention to how these concepts relate to similar concepts in developmental science. Finally, an illustrative example is presented to demonstrate the utility of systems science methodologies. We hope that this article inspires developmental scientists to learn more about systems science methodologies and to begin to use them in their work. Urban, J.B., Osgood, N., & Mabry, P. (2011). Developmental systems science: Exploring the application of non- linear methods to developmental science questions. -
Self-Organization and the Maximum Empower Principle in the Framework of Max-Plus Algebra
Self-organization and the Maximum Empower Principle in the Framework of max-plus Algebra Chams LAHLOU∗ and Laurent TRUFFETy Department of Automation, Production and Computer Sciences, IMT-A, Nantes, France August 31, 2021 Abstract Self-organization is a process where order of a whole system arises out of local interactions between small components of a system. Emergy, spelled with an 'm', defined as the amount of (solar) energy used to make a product or service, is becoming an important ecological indicator. The Maximum Empower Principle (MEP) was proposed as the fourth law of thermodynamics by the ecologist Odum in the 90's to explain observed self-organization of energy driven systems. But this principle suffers a lack of mathematical formulation due to an insufficiency of details about the underlying computation of empower (i.e. emergy per time). For empower computation in steady-state an axiomatic basis has been developed recently by Le Corre and the second author of this paper. In this axiomatic basis emergy is defined as a recursive max-plus linear function. Using this axiomatic basis and a correspondance between ecological theory and dynamic systems theory, we prove the MEP. In particular, we show that the empower computation in steady-state is equivalent to a combinatorial optimization problem. Keywords: Emergy, Graph, Max-plus algebra, Sustainability, Fourth law of thermodynamics. 1 Introduction It has been observed since a long time (see e.g. [30], [4]) that energy, as the ability to do work, plays an important role in our civilization. Nowadays, more and more people realize that complex systems such as ecological networks, social organizations, economic systems are energy driven systems. -
Boltzmann Statistics As Founding Principle of Microbial Growth Elie Desmond-Le Quéméner, Théodore Bouchez
Boltzmann statistics as founding principle of microbial growth Elie Desmond-Le Quéméner, Théodore Bouchez To cite this version: Elie Desmond-Le Quéméner, Théodore Bouchez. Boltzmann statistics as founding principle of micro- bial growth. 2013. hal-00825781v1 HAL Id: hal-00825781 https://hal.archives-ouvertes.fr/hal-00825781v1 Preprint submitted on 24 May 2013 (v1), last revised 26 Aug 2015 (v3) 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. 1 Boltzmann statistics as founding principle of 2 microbial growth 3 4 5 6 Authors: Elie Desmond-Le Quéméner et Théodore Bouchez* 7 8 Affiliation : Irstea, UR HBAN, 1 rue Pierre-Gilles de Gennes, 92761 Antony cedex, France 9 * corresponding author : [email protected] 10 11 12 13 Keywords: microbial division, growth rate, exergy, Gibbs energy, statistical physics, flux-force 14 relationship. 15 16 17 SUMMARY: Microbes are the most abundant living forms on earth and major contributors to the 18 biogeochemical cycles. However, our ability to model their dynamics only relies on empirical laws, 19 fundamentally restricting our understanding and predictive capacity in many environmental systems. 20 Preeminent physicists such as Schrödinger and Prigogine have introduced a thermodynamic 21 interpretation of life, paving the way for a quantitative theory. -
Systems Theory"
General By the same author: System Modern Theories of Development (in German, English, Spani~h) Nikolaus von Kues Theory Lebenswissenschaft und Bildung Theoretische Biologie J Foundations, Development, Applications Das Gefüge des Lebens Vom Molekül zur Organismenwelt Problems of Life (in German, English, French, Spanish, Dutch, Japanese) Auf den Pfaden des Lebens I by Ludwig von Bertalanffy Biophysik des Fliessgleichgewichts t Robots, Men and Minds University of Alberta Edmonton) Canada GEORGE BRAZILLER New York MANIBUS Nicolai de Cusa Cardinalis, Gottfriedi Guglielmi Leibnitii, ]oannis Wolfgangi de Goethe Aldique Huxleyi, neenon de Bertalanffy Pauli, S.J., antecessoris, cosmographi Copyright © 1968 by Ludwig von Bertalanffy All rights in this hook are reserved. For information address the publisher, George Braziller, lnc. One Park Avenue New York, N.Y. 10016 Foreword The present volume appears to demand some introductory notes clarifying its scope, content, and method of presentation. There is a large number of texts, monographs, symposia, etc., devoted to "systems" and "systems theory". "Systems Science," or one of its many synonyms, is rapidly becoming part of the estab lished university curriculum. This is predominantly a develop ment in engineering science in the broad sense, necessitated by the complexity of "systems" in modern technology, man-machine relations, programming and similar considerations which were not felt in yesteryear's technology but which have become im perative in the complex technological and social structures of the modern world. Systems theory, in this sense, is preeminently a mathematica! field, offering partly novel and highly sophisti cated techniques, closely linked with computer science, and essentially determined by the requirement to cope with a new sort of problem that has been appearing. -
Two Kinds of General Theories in Systems Science
TWO KINDS OF GENERAL THEORIES IN SYSTEMS SCIENCE Stuart A. Umpleby Department of Management Science George Washington University Washington, DC 20052 USA January 2001 Presented at the American Society for Cybernetics conference Vancouver, Canada, May 26-28, 2001 TWO KINDS OF GENERAL THEORIES IN SYSTEMS SCIENCE STUART A. UMPLEBY Department of Management Science George Washington University Washington, DC 20052 USA ABSTRACT A key tenet of the philosophy of science is that science progresses when a more general theory is formulated. That is, an aim of science is to explain as many phenomena as possible with as few statements as possible. This idea, that more general theories are preferred over more specialized theories, lies at the heart of systems science. But are there different ways in which a theory can be more general? Is there a tradeoff between universality and testability? Does increasing generalization lead to a loss of information and utility? An examination of several principles from systems theory and cybernetics suggests three conclusions: there are two types of general theories; general theories requires "domain specific knowledge" to make the connection between theory and experiment or between theory and practice; and theories which are more general than those formulated in the traditional disciplines can be effective at facilitating communication among disciplines. 1. INTRODUCTION How does scientific knowledge develop? Is knowledge in systems science different from knowledge in other scientific fields? If it is different, in what way is it different? Are the systems sciences a revolution in one or more previous sciences, the addition of a new domain of inquiry, or a new type of inquiry lying perhaps between mathematics and the sciences? Karl Popper claimed that science advances by conjectures and refutations, and he preferred bold conjectures to more modest conjectures.