Overview

When can a finite, coarse-grained theory adequately describe an infinitely more complicated world? This question is central to the choice and calibration of evolutionary models. The same question was at the center of progress in condensed matter physics and particle theory in the 20th century, where it led to a reconceptualization of the nature of objects and interactions in statistical terms. The key concepts in this new understanding were the roles of symmetry and collective fluctuations. This review considers the problem of modeling stochastic evolutionary dynamics from the perspective that all evolutionary theories are ultimately effective theories: the robust properties and predictions of models are those that do not depend sensitively on the many parameters in any real system that are impossible to estimate or even identify. The tool to extract such robust properties is the large deviations theory of stochastic population processes. Games enter evolutionary modeling as a general framework to capture the constructive dynamics that map genotypes in their population context to phenotypes and fitness consequences.In this book the authors present methods to derive large-deviations limits for population processes, and apply these to game models illustrating the many roles of symmetry and collective fluctuations in evolutionary dynamics. Problems considered include the origin of dynamics that span large scales from individuals to populations, the spontaneous emergence of multilevel selection, subtleties of the gene concept, and corrections to fitness from evolutionary entropies in systems with neutral directions.

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Eric Smith received the Bachelor of Science in Physics and Mathematics from the California Institute of Technology in 1987, and a Ph.D. in Physics from The University of Texas at Austin in 1993, with a dissertation on problems in string theory and high-temperature superconductivity. From 1993 to 2000 he worked in physical, nonlinear, and statistical acoustics at the Applied Research Labs: U. T. Austin, and at the Los Alamos National Laboratory. From 2000 he has worked at the Santa Fe Institute on problems of self-organization in thermal, chemical, and biological systems. A focus of his current work is the statistical mechanics of the transition from the geochemistry of the early earth to the first levels of biological organization, with some emphasis on the emergence of the metabolic network. Supriya Krishnamurthy received her BSc and MSc degrees in Physics from Hansraj College, Delhi University and I.I.T Kanpur respectively and a PhD degree, in Non-Equilibrium Statistical Mechanics, from the Tata Institute of Fundamental Research in Bombay in 1998. After her PhD she worked as a post-doctoral researcher in Paris at the Ecole Superieure de Physique et de Chimie Industrielles, at the Theoretical Physics Department of the University of Oxford and at the Santa Fe Institute. She has worked at the Swedish Institute of Computer Science SICS , Stockholm as a senior researcher (2004-2009) and as an Assistant professor at the Royal Institute of Technology at Stockholm (2006-2008). Currently she is an Associate professor at the Department of Physics at the University of Stockholm. She has also been associated with the Santa Fe Institute during 2005-2011 as external faculty. Her research interests include understanding both fundamental as well as inter-disciplinary applications of non-equilibrium statistical mechanics.

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