Quantifying the Tape of Life: Ancestry-Based Metrics Provide
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Guiding Organic Management in a Service-Oriented Real-Time Middleware Architecture
Guiding Organic Management in a Service-Oriented Real-Time Middleware Architecture Manuel Nickschas and Uwe Brinkschulte Institute for Computer Science University of Frankfurt, Germany {nickschas, brinks}@es.cs.uni-frankfurt.de Abstract. To cope with the ever increasing complexity of today's com- puting systems, the concepts of organic and autonomic computing have been devised. Organic or autonomic systems are characterized by so- called self-X properties such as self-conguration and self-optimization. This approach is particularly interesting in the domain of distributed, embedded, real-time systems. We have already proposed a service-ori- ented middleware architecture for such systems that uses multi-agent principles for implementing the organic management. However, organic management needs some guidance in order to take dependencies between services into account as well as the current hardware conguration and other application-specic knowledge. It is important to allow the appli- cation developer or system designer to specify such information without having to modify the middleware. In this paper, we propose a generic mechanism based on capabilities that allows describing such dependen- cies and domain knowledge, which can be combined with an agent-based approach to organic management in order to realize self-X properties. We also describe how to make use of this approach for integrating the middleware's organic management with node-local organic management. 1 Introduction Distributed, embedded systems are rapidly advancing in all areas of our lives, forming increasingly complex networks that are increasingly hard to handle for both system developers and maintainers. In order to cope with this explosion of complexity, also commonly referred to as the Software Crisis [1], the concepts of Autonomic [2,3] and Organic [4,5,6] Computing have been devised. -
Organic Computing Doctoral Dissertation Colloquium 2018 B
13 13 S. Tomforde | B. Sick (Eds.) Organic Computing Doctoral Dissertation Colloquium 2018 Organic Computing B. Sick (Eds.) B. | S. Tomforde S. Tomforde ISBN 978-3-7376-0696-7 9 783737 606967 V`:%$V$VGVJ 0QJ `Q`8 `8 V`J.:`R 1H@5 J10V`1 ? :VC Organic Computing Doctoral Dissertation Colloquium 2018 S. Tomforde, B. Sick (Editors) !! ! "#$%&'&$'$(&)(#(&$* + "#$%&'&$'$(&)(#$&,*&+ - .! ! /)!/#0// 12#$%'$'$()(#$, 23 & ! )))0&,)(#$' '0)/#5 6 7851 999!& ! kassel university press 7 6 Preface The Organic Computing Doctoral Dissertation Colloquium (OC-DDC) series is part of the Organic Computing initiative [9, 10] which is steered by a Special Interest Group of the German Society of Computer Science (Gesellschaft fur¨ Informatik e.V.). It provides an environment for PhD students who have their research focus within the broader Organic Computing (OC) community to discuss their PhD project and current trends in the corresponding research areas. This includes, for instance, research in the context of Autonomic Computing [7], ProActive Computing [11], Complex Adaptive Systems [8], Self-Organisation [2], and related topics. The main goal of the colloquium is to foster excellence in OC-related research by providing feedback and advice that are particularly relevant to the students’ studies and ca- reer development. Thereby, participants are involved in all stages of the colloquium organisation and the review process to gain experience. The OC-DDC 2018 took place in -
Teaching and Learning with Digital Evolution: Factors Influencing Implementation and Student Outcomes
TEACHING AND LEARNING WITH DIGITAL EVOLUTION: FACTORS INFLUENCING IMPLEMENTATION AND STUDENT OUTCOMES By Amy M Lark A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Curriculum, Instruction, and Teacher Education – Doctor of Philosophy 2014 ABSTRACT TEACHING AND LEARNING WITH DIGITAL EVOLUTION: FACTORS INFLUENCING IMPLEMENTATION AND STUDENT OUTCOMES By Amy M Lark Science literacy for all Americans has been the rallying cry of science education in the United States for decades. Regardless, Americans continue to fall short when it comes to keeping pace with other developed nations on international science education assessments. To combat this problem, recent national reforms have reinvigorated the discussion of what and how we should teach science, advocating for the integration of disciplinary core ideas, crosscutting concepts, and science practices. In the biological sciences, teaching the core idea of evolution in ways consistent with reforms is fraught with challenges. Not only is it difficult to observe biological evolution in action, it is nearly impossible to engage students in authentic science practices in the context of evolution. One way to overcome these challenges is through the use of evolving populations of digital organisms. Avida-ED is digital evolution software for education that allows for the integration of science practice and content related to evolution. The purpose of this study was to investigate the effects of Avida-ED on teaching and learning evolution and the nature of science. To accomplish this I conducted a nationwide, multiple-case study, documenting how instructors at various institutions were using Avida-ED in their classrooms, factors influencing implementation decisions, and effects on student outcomes. -
Ecological and Evolutionary Dynamics of Interconnectedness and Modularity
Ecological and evolutionary dynamics of interconnectedness and modularity Jan M. Nordbottena,b, Simon A. Levinc, Eörs Szathmáryd,e,f, and Nils C. Stensethg,1 aDepartment of Mathematics, University of Bergen, N-5020 Bergen, Norway; bDepartment of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544; cDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544; dParmenides Center for the Conceptual Foundations of Science, D-82049 Pullach, Germany; eMTA-ELTE (Hungarian Academy of Sciences—Eötvös University), Theoretical Biology and Evolutionary Ecology Research Group, Department of Plant Systematics, Ecology and Theoretical Biology, Eötvös University, Budapest, H-1117 Hungary; fEvolutionary Systems Research Group, MTA (Hungarian Academy of Sciences) Ecological Research Centre, Tihany, H-8237 Hungary; and gCentre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, N-0316 Oslo, Norway Contributed by Nils C. Stenseth, December 1, 2017 (sent for review September 12, 2017; reviewed by David C. Krakauer and Günter P. Wagner) In this contribution, we develop a theoretical framework for linking refinement of this inquiry is to look for compartmentalization (i.e., microprocesses (i.e., population dynamics and evolution through modularity) in ecosystems. In food webs, for example, compartments natural selection) with macrophenomena (such as interconnectedness (which are subgroups of taxa with many strong interactions in each and modularity within an -
What Genomic Data Can Reveal About Eco-Evolutionary Dynamics
PERSPECTIVE https://doi.org/10.1038/s41559-017-0385-2 What genomic data can reveal about eco-evolutionary dynamics Seth M. Rudman 1*, Matthew A. Barbour2, Katalin Csilléry3, Phillip Gienapp4, Frederic Guillaume2, Nelson G. Hairston Jr5, Andrew P. Hendry6, Jesse R. Lasky7, Marina Rafajlović8,9, Katja Räsänen10, Paul S. Schmidt1, Ole Seehausen 11,12, Nina O. Therkildsen13, Martin M. Turcotte3,14 and Jonathan M. Levine15 Recognition that evolution operates on the same timescale as ecological processes has motivated growing interest in eco-evo- lutionary dynamics. Nonetheless, generating sufficient data to test predictions about eco-evolutionary dynamics has proved challenging, particularly in natural contexts. Here we argue that genomic data can be integrated into the study of eco-evo- lutionary dynamics in ways that deepen our understanding of the interplay between ecology and evolution. Specifically, we outline five major questions in the study of eco-evolutionary dynamics for which genomic data may provide answers. Although genomic data alone will not be sufficient to resolve these challenges, integrating genomic data can provide a more mechanistic understanding of the causes of phenotypic change, help elucidate the mechanisms driving eco-evolutionary dynamics, and lead to more accurate evolutionary predictions of eco-evolutionary dynamics in nature. vidence that the ways in which organisms interact with their evolution plays a central role in regulating such dynamics. environment can evolve fast enough to alter ecological dynam- Particularly relevant is information gleaned from efforts to under- Eics has forged a new link between ecology and evolution1–5. A stand the genomic basis of adaptation, a burgeoning field in evolu- growing area of study, termed eco-evolutionary dynamics, centres tionary biology that investigates the specific genomic underpinnings on understanding when rapid evolutionary change is a meaningful of phenotypic variation, its response to natural selection and effects driver of ecological dynamics in natural ecosystems6–8. -
Martin A. Nowak
EVOLUTIONARY DYNAMICS EXPLORING THE EQUATIONS OF LIFE MARTIN A. NOWAK THE BELKNAP PRESS OF HARVARD UNIVERSITY PRESS CAMBRIDGE, MASSACHUSETTS, AND LONDON, ENGLAND 2006 Copyright © 2006 by the President and Fellows of Harvard College All rights reserved Printed in Canada Library of Congress Cataloging-in-Publication Data Nowak, M. A. (Martin A.) Evolutionary dynamics : exploring the equations of life / Martin A. Nowak. p. cm. Includes bibliographical references and index. ISBN-13: 978-0-674-02338-3 (alk. paper) ISBN-10: 0-674-02338-2 (alk. paper) 1. Evolution (Biology)—Mathematical models. I. Title. QH371.3.M37N69 2006 576.8015118—dc22 2006042693 Designed by Gwen Nefsky Frankfeldt CONTENTS Preface ix 1 Introduction 1 2 What Evolution Is 9 3 Fitness Landscapes and Sequence Spaces 27 4 Evolutionary Games 45 5 Prisoners of the Dilemma 71 6 Finite Populations 93 7 Games in Finite Populations 107 8 Evolutionary Graph Theory 123 9 Spatial Games 145 10 HIV Infection 167 11 Evolution of Virulence 189 12 Evolutionary Dynamics of Cancer 209 13 Language Evolution 249 14 Conclusion 287 Further Reading 295 References 311 Index 349 PREFACE Evolutionary Dynamics presents those mathematical principles according to which life has evolved and continues to evolve. Since the 1950s biology, and with it the study of evolution, has grown enormously, driven by the quest to understand the world we live in and the stuff we are made of. Evolution is the one theory that transcends all of biology. Any observation of a living system must ultimately be interpreted in the context of its evolution. Because of the tremendous advances over the last half century, evolution has become a dis- cipline that is based on precise mathematical foundations. -
Network Complexity of Foodwebs
Network Complexity of Foodwebs Russell Standish School of Mathematics and Statistics, University of New South Wales Abstract Complexity as Information The notion of using information content as a complexity In previous work, I have developed an information theoretic measure is fairly simple. In most cases, there is an ob- complexity measure of networks. When applied to several vious prefix-free representation language within which de- real world food webs, there is a distinct difference in com- plexity between the real food web, and randomised control scriptions of the objects of interest can be encoded. There networks obtained by shuffling the network links. One hy- is also a classifier of descriptions that can determine if two pothesis is that this complexity surplus represents informa- descriptions correspond to the same object. This classifier is tion captured by the evolutionary process that generated the commonly called the observer, denoted O(x). network. To compute the complexity of some object x, count the In this paper, I test this idea by applying the same complex- number of equivalent descriptions ω(ℓ, x) of length ℓ that ity measure to several well-known artificial life models that map to the object x under the agreed classifier. Then the exhibit ecological networks: Tierra, EcoLab and Webworld. Contrary to what was found in real networks, the artificial life complexity of x is given in the limit as ℓ → ∞: generated foodwebs had little information difference between C(x) = lim ℓ log N − log ω(ℓ, x) (1) itself and randomly shuffled versions. ℓ→∞ where N is the size of the alphabet used for the representa- Introduction tion language. -
Evolutionary Dynamics on Any Population Structure
Evolutionary dynamics on any population structure Benjamin Allen1,2,3, Gabor Lippner4, Yu-Ting Chen5, Babak Fotouhi1,6, Naghmeh Momeni1,7, Shing-Tung Yau3,8, and Martin A. Nowak1,8,9 1Program for Evolutionary Dynamics, Harvard University, Cambridge, MA, USA 2Department of Mathematics, Emmanuel College, Boston, MA, USA 3Center for Mathematical Sciences and Applications, Harvard University, Cambridge, MA, USA 4Department of Mathematics, Northeastern University, Boston, MA, USA 5Department of Mathematics, University of Tennessee, Knoxville, TN, USA 6Institute for Quantitative Social Sciences, Harvard University, Cambridge, MA, USA 7Department of Electrical and Computer Engineering, McGill University, Montreal, Canada 8Department of Mathematics, Harvard University, Cambridge, MA, USA 9Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA December 23, 2016 Abstract Evolution occurs in populations of reproducing individuals. The structure of a biological population affects which traits evolve [1, 2]. Understanding evolutionary game dynamics in structured populations is difficult. Precise results have been absent for a long time, but have recently emerged for special structures where all individuals have the arXiv:1605.06530v2 [q-bio.PE] 22 Dec 2016 same number of neighbors [3, 4, 5, 6, 7]. But the problem of deter- mining which trait is favored by selection in the natural case where the number of neighbors can vary, has remained open. For arbitrary selection intensity, the problem is in a computational complexity class which suggests there is no efficient algorithm [8]. Whether there exists a simple solution for weak selection was unanswered. Here we provide, surprisingly, a general formula for weak selection that applies to any graph or social network. -
Evolving Quorum Sensing in Digital Organisms
Evolving Quorum Sensing in Digital Organisms Benjamin E. Beckmann and Phillip K. McKinley Department of Computer Science and Engineering 3115 Engineering Building Michigan State University East Lansing, Michigan 48824 {beckma24,mckinley}@cse.msu.edu ABSTRACT 1. INTRODUCTION For centuries it was thought that bacteria live asocial lives. The human body is made up of 1013 human cells. Yet, However, recent discoveries show many species of bacteria this number is an order of magnitude smaller than the num- communicate in order to perform tasks previously thought ber a bacterial cells living within the gastrointestinal tract to be limited to multicellular organisms. Central to this ca- of a single adult [4]. Although it was previously assumed pability is quorum sensing, whereby organisms detect cell that bacteria and other microorganisms rarely interact [24], density and use this information to trigger group behav- in 1979 Nealson and Hastings found evidence that bacterial iors. Quorum sensing is used by bacteria in the formation communities of Vibrio fischeri and Vibrio harveyi were able of biofilms, secretion of digestive enzymes and, in the case to perform a coordinated behavioral change, namely emit- of pathogenic bacteria, release of toxins or other virulence ting light, when the cell density rose above a certain thresh- factors. Indeed, methods to disrupt quorum sensing are cur- old [20]. This type of density-based behavioral change is rently being investigated as possible treatments for numer- called quorum sensing [27]. In quorum sensing (QS), con- ous diseases, including cystic fibrosis, epidemic cholera, and tinual secretion and detection of chemicals provide a way methicillin-resistant Staphylococcus aureus. -
Digital Irreducible Complexity: a Survey of Irreducible Complexity in Computer Simulations
Critical Focus Digital Irreducible Complexity: A Survey of Irreducible Complexity in Computer Simulations Winston Ewert* Biologic Institute, Redmond, Washington, USA Abstract Irreducible complexity is a concept developed by Michael Behe to describe certain biological systems. Behe claims that irreducible complexity poses a challenge to Darwinian evolution. Irreducibly complex systems, he argues, are highly unlikely to evolve because they have no direct series of selectable intermediates. Various computer models have been published that attempt to demonstrate the evolution of irreducibly complex systems and thus falsify this claim. How- ever, closer inspection of these models shows that they fail to meet the definition of irreducible complexity in a num- ber of ways. In this paper we demonstrate how these models fail. In addition, we present another designed digital sys- tem that does exhibit designed irreducible complexity, but that has not been shown to be able to evolve. Taken together, these examples indicate that Behe’s concept of irreducible complexity has not been falsified by computer models. Cite as: Ewert W (2014) Digital irreducible complexity: A survey of irreducible complexity in computer simulations. BIO-Complexity 2014 (1):1–10. doi:10.5048/BIO-C.2014.1. Editor: Ola Hössjer Received: November 16, 2013; Accepted: February 18, 2014; Published: April 5, 2014 Copyright: © 2014 Ewert. This open-access article is published under the terms of the Creative Commons Attribution License, which permits free distribution and reuse in derivative works provided the original author(s) and source are credited. Notes: A Critique of this paper, when available, will be assigned doi:10.5048/BIO-C.2014.1.c. -
Applying Evolutionary Computation to Mitigate Uncertainty in Dynamically-Adaptive, High-Assurance Middleware
J Internet Serv Appl (2012) 3:51–58 DOI 10.1007/s13174-011-0049-4 SI: FOME - THE FUTURE OF MIDDLEWARE Applying evolutionary computation to mitigate uncertainty in dynamically-adaptive, high-assurance middleware Philip K. McKinley · Betty H.C. Cheng · Andres J. Ramirez · Adam C. Jensen Received: 31 October 2011 / Accepted: 12 November 2011 / Published online: 3 December 2011 © The Brazilian Computer Society 2011 Abstract In this paper, we explore the integration of evolu- systems need to perform complex, distributed tasks despite tionary computation into the development and run-time sup- lossy wireless communication, limited resources, and uncer- port of dynamically-adaptable, high-assurance middleware. tainty in sensing the environment. However, even systems The open-ended nature of the evolutionary process has been that operate entirely within cyberspace need to account for shown to discover novel solutions to complex engineering changing network and load conditions, component failures, problems. In the case of high-assurance adaptive software, and exposure to a wide variety of cyber attacks. however, this search capability must be coupled with rigor- A dynamically adaptive system (DAS) monitors itself and ous development tools and run-time support to ensure that its execution environment at run time. This monitoring en- the resulting systems behave in accordance with require- ables a DAS not only to detect conditions warranting a re- ments. Early investigations are reviewed, and several chal- configuration, but also to determine when and how to safely lenging problems and possible research directions are dis- reconfigure itself in order to deliver acceptable behavior be- cussed. fore, during, and after adaptation [2]. -
Evolutionary Game Theory: a Renaissance
games Review Evolutionary Game Theory: A Renaissance Jonathan Newton ID Institute of Economic Research, Kyoto University, Kyoto 606-8501, Japan; [email protected] Received: 23 April 2018; Accepted: 15 May 2018; Published: 24 May 2018 Abstract: Economic agents are not always rational or farsighted and can make decisions according to simple behavioral rules that vary according to situation and can be studied using the tools of evolutionary game theory. Furthermore, such behavioral rules are themselves subject to evolutionary forces. Paying particular attention to the work of young researchers, this essay surveys the progress made over the last decade towards understanding these phenomena, and discusses open research topics of importance to economics and the broader social sciences. Keywords: evolution; game theory; dynamics; agency; assortativity; culture; distributed systems JEL Classification: C73 Table of contents 1 Introduction p. 2 Shadow of Nash Equilibrium Renaissance Structure of the Survey · · 2 Agency—Who Makes Decisions? p. 3 Methodology Implications Evolution of Collective Agency Links between Individual & · · · Collective Agency 3 Assortativity—With Whom Does Interaction Occur? p. 10 Assortativity & Preferences Evolution of Assortativity Generalized Matching Conditional · · · Dissociation Network Formation · 4 Evolution of Behavior p. 17 Traits Conventions—Culture in Society Culture in Individuals Culture in Individuals · · · & Society 5 Economic Applications p. 29 Macroeconomics, Market Selection & Finance Industrial Organization · 6 The Evolutionary Nash Program p. 30 Recontracting & Nash Demand Games TU Matching NTU Matching Bargaining Solutions & · · · Coordination Games 7 Behavioral Dynamics p. 36 Reinforcement Learning Imitation Best Experienced Payoff Dynamics Best & Better Response · · · Continuous Strategy Sets Completely Uncoupled · · 8 General Methodology p. 44 Perturbed Dynamics Further Stability Results Further Convergence Results Distributed · · · control Software and Simulations · 9 Empirics p.