Evolutionary Dynamics, Cognitive Processes, and Bayesian Inference Jordan W
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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. -
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. -
Converging Cognitive Enhancements
Converging Cognitive Enhancements ANDERS SANDBERGa AND NICK BOSTROMb aOxford Uehiro Centre for Practical Ethics, Faculty of Philosophy, bOxford Future of Humanity Institute, Faculty of Philosophy & James Martin 21st Century School, Oxford University, Oxford, OX1 1PT, United Kingdom ABSTRACT: Cognitive enhancement, the amplification or extension of core capacities of the mind, has become a major topic in bioethics. But cognitive enhancement is a prime example of a converging technology where individual disciplines merge and issues transcend particular local discourses. This article reviews currently available methods of cognitive enhancement and their likely near-term prospects for convergence. KEYWORDS: cognitive enhancement; cognition; intelligence; biotechnol- ogy; collective enhancement; mental training; converging technologies CONVERGING COGNITIVE ENHANCEMENTS There are few resources more useful than cognitive ability. While other resources are necessary or desirable, cognition enables them to be used for achieving personal goals. While there is little evidence that high intelli- gence causes happiness there appears to be ample evidence that low intel- ligence increases the risk for accidents, negative life events, and low income (Gottfredson 1997, 2004) while higher intelligence promotes health (Whalley and Deary 2001) and wealth. We also need better cognition in order to bal- ance an increasingly complex society where information becomes more avail- able and our actions have more far-reaching consequences (Heylighen 2002a, 2002b). There may also be an intrinsic existential value in being able to per- ceive, understand, and interact well with the world. Cognitive enhancement may be defined as the amplification or extension of core capacities of the mind through improvement or augmentation of internal or external information processing systems. -
The Psychology of Creativity
History of Creativity Research 1 The Psychology of Creativity: A Historical Perspective Dean Keith Simonton, PhD Professor of Psychology University of California, Davis Davis, CA 95616-8686 USA Presented at the Green College Lecture Series on The Nature of Creativity: History Biology, and Socio-Cultural Dimensions, University of British Columbia, 2001. Originally planned to be a chapter in an edited volume by the same name, but those plans were usurped by the events following the 9/11 terrorist attack, which occurred the day immediately after. History of Creativity Research 2 The Psychology of Creativity: A Historical Perspective Psychologists usually define creativity as the capacity to produce ideas that are both original and adaptive. In other words, the ideas must be both new and workable or functional. Thus, creativity enables a person to adjust to novel circumstances and to solve problems that unexpectedly arise. Obviously, such a capacity is often very valuable in everyday life. Yet creativity can also result in major contributions to human civilization. Examples include Michelangelo’s Sistine Chapel, Beethoven’s Fifth Symphony, Tolstoy’s War and Peace, and Darwin’s Origin of Species. One might conclude from these observations that creativity has always been one of the central topics in the field. But that is not the case. Although psychology became a formal discipline in the last few decades of the 19th century, it took several generations before the creativity attracted the attention it deserves. This neglect was even indicated in the 1950 Presidential Address that J. P. Guilford delivered before the American Psychological Association. Nevertheless, in the following half century the field could claim two professional journals – the Journal of Creative Behavior and the Creativity Research Journal – several handbooks (e.g., Sternberg, 1999), and even a two-volume Handbook of Creativity (Runco & Pritzker, 1999). -
Stories to Make Us Human: Twenty-First-Century Dystopian
MELISSA CRISTINA SILVA DE SÁ Stories to Make Us Human: Twenty-First-Century Dystopian Novels by Women BELO HORIZONTE 2020 MELISSA CRISTINA SILVA DE SÁ Stories to Make Us Human: Twenty-First-Century Dystopian Novels by Women Tese de doutorado apresentada ao Programa de Pós-Graduação em Estudos Literários da Facul- dade de Letras da Universidade Federal de Minas Gerais, como requisito parcial para obtenção do título de Doutora em Letras: Estudos Literários. BELO HORIZONTE 2020 To the ones who dare to dream new worlds. Acknowledgements Research as extensive as the one required for a Ph.D. dissertation cannot be done without the support of many people and institutions. I want to express my gratitude to all the ones that were part of this process for their patience and unconditional dedication. Without any particular order, I recognize the importance of the following: Instituto Federal de Minas Gerais – IFMG – for the eighteen-month paid leave that allowed me to do my research. I also thank my fellow professors at the institution, namely Anderson de Souto and Thadyanara Martinelli, who spent their time talking to me about the crazy new worlds I studied between classes. Professor Julio Jeha, my advisor, who helped me to refine all my arguments and consider diverse viewpoints. I thank you for making me a better and more mature researcher. Also, the meetings at Intelligenza were quite memorable. Diego Malachias, my husband and colleague, whom I met at the beginning of this journey. What a story we have to tell! Thank you for being such a fantastic companion – both in life and academia. -
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. -
Openness to Experience: a Four-Factor Model and Relations to Creative Achievement in the Arts and Sciences
SCOTT BARRY KAUFMAN Opening up Openness to Experience: A Four-Factor Model and Relations to Creative Achievement in the Arts and Sciences ABSTRACT Openness to experience is the broadest personality domain of the Big Five, includ- ing a mix of traits relating to intellectual curiosity, intellectual interests, perceived intelligence, imagination, creativity, artistic and aesthetic interests, emotional and fan- tasy richness, and unconventionality. Likewise, creative achievement is a broad con- struct, comprising creativity across the arts and sciences. The aim of this study was to clarify the relationship between openness to experience and creative achievement. Toward this aim, I factor analyzed a battery of tests of cognitive ability, working mem- ory, Intellect, Openness, affect, and intuition among a sample of English Sixth Form students (N = 146). Four factors were revealed: explicit cognitive ability, intellectual engagement, affective engagement, and aesthetic engagement. In line with dual- process theory, each of these four factors showed differential relations with personality, impulsivity, and creative achievement. Affective engagement and aesthetic engagement were associated with creative achievement in the arts, whereas explicit cognitive ability and intellectual engagement were associated with creative achievement in the sciences. The results suggest that the Intellectual and Openness aspects of the broader openness to experience personality domain are related to different modes of information processing and predict different -
Evolutionary Dynamics on Graphs
letters to nature Received 24 September; accepted 16 November 2004; doi:10.1038/nature03211. often individuals place offspring into adjacent vertices. The 3 1. MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton Univ. Press, homogeneous population, described by the Moran process ,is Princeton, 1969). the special case of a fully connected graph with evenly weighted 2. Fisher, R. A., Corbet, A. S. & Williams, C. B. The relation between the number of species and the number of individuals in a random sample of an animal population. J. Anim. Ecol. 12, 42–58 (1943). edges. Spatial structures are described by graphs where vertices 3. Preston, F. W. The commonness, and rarity, of species. Ecology 41, 611–627 (1948). are connected with their nearest neighbours. We also explore 4. Brown, J. H. Macroecology (Univ. Chicago Press, Chicago, 1995). evolution on random and scale-free networks5–7. We determine 5. Hubbell, S. P. A unified theory of biogeography and relative species abundance and its application to the fixation probability of mutants, and characterize those tropical rain forests and coral reefs. Coral Reefs 16, S9–S21 (1997). 6. Hubbell, S. P. The Unified Theory of Biodiversity and Biogeography (Princeton Univ. Press, Princeton, graphs for which fixation behaviour is identical to that of a 7 2001). homogeneous population . Furthermore, some graphs act as 7. Caswell, H. Community structure: a neutral model analysis. Ecol. Monogr. 46, 327–354 (1976). suppressors and others as amplifiers of selection. It is even 8. Bell, G. Neutral macroecology. Science 293, 2413–2418 (2001). possible to find graphs that guarantee the fixation of any 9. -
Diversity and Coevolutionary Dynamics in High-Dimensional Phenotype
Diversity and coevolutionary dynamics in high-dimensional phenotype spaces Michael Doebeli∗ & Iaroslav Ispolatov∗∗ ∗ Departments of Zoology and Mathematics, University of British Columbia, 6270 University Boulevard, Vancouver B.C. Canada, V6T 1Z4; [email protected] ∗∗ Departamento de Fisica, Universidad de Santiago de Chile Casilla 302, Correo 2, Santiago, Chile; [email protected] October 15, 2018 Supporting Material: Appendix A arXiv:1701.07861v2 [q-bio.PE] 10 Feb 2017 RH: Diversity and coevolutionary dynamics Corresponding author: Michael Doebeli, Department of Zoology and Department of Math- ematics, University of British Columbia, 6270 University Boulevard, Vancouver B.C. Canada, V6T 1Z4, Email: [email protected]. 1 Abstract We study macroevolutionary dynamics by extending microevolutionary competition models to long time scales. It has been shown that for a general class of competition models, gradual evolu- tionary change in continuous phenotypes (evolutionary dynamics) can be non-stationary and even chaotic when the dimension of the phenotype space in which the evolutionary dynamics unfold is high. It has also been shown that evolutionary diversification can occur along non-equilibrium trajectories in phenotype space. We combine these lines of thinking by studying long-term co- evolutionary dynamics of emerging lineages in multi-dimensional phenotype spaces. We use a statistical approach to investigate the evolutionary dynamics of many different systems. We find: 1) for a given dimension of phenotype space, the coevolutionary dynamics tends to be fast and non-stationary for an intermediate number of coexisting lineages, but tends to stabilize as the evolving communities reach a saturation level of diversity; and 2) the amount of diversity at the saturation level increases rapidly (exponentially) with the dimension of phenotype space. -
Temporal and Spatial Effects Beyond Replicator Dynamics
Evolutionary game theory: Temporal and spatial effects beyond replicator dynamics Carlos P. Roca a,∗, José A. Cuesta a, Angel Sánchez a,b,c a Grupo Interdisciplinar de Sistemas Complejos (GISC), Departamento de Matemáticas, Universidad Carlos III de Madrid, Avenida de la Universidad 30, 28911 Leganés, Madrid, Spain b Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), Universidad de Zaragoza, Corona de Aragón 42, 50009 Zaragoza, Spain c Instituto de Ciencias Matemáticas CSIC-UAM-UC3M-UCM, 28006 Madrid, Spain Abstract Evolutionary game dynamics is one of the most fruitful frameworks for studying evolution in different disciplines, from Biology to Economics. Within this context, the approach of choice for many researchers is the so-called replicator equation, that describes mathematically the idea that those individuals performing better have more offspring and thus their frequency in the population grows. While very many interesting results have been obtained with this equation in the three decades elapsed since it was first proposed, it is important to realize the limits of its applicability. One particularly relevant issue in this respect is that of non-mean- field effects, that may arise from temporal fluctuations or from spatial correlations, both neglected in the replicator equation. This review discusses these temporal and spatial effects focusing on the non-trivial modifications they induce when compared to the outcome of replicator dynamics. Alongside this question, the hypothesis of linearity and its relation to the choice of the rule for strategy update is also analyzed. The discussion is presented in terms of the emergence of cooperation, as one of the current key problems in Biology and in other disciplines.