Edited by James P. Crutchfield and Peter Schuster

Today evolution is analyzed at very different levels, from paleontology to molecular biology and even computer science; from the commercial use of evolutionary drug design to the innovation of new and highly abstract mathematics. Nonetheless, common phenomena and common problems relate evolutionary behaviors as they appear in these different arenas. Examples include stepwise rather than gradual time courses of evolutionary adaptation, the role of selectively neutral variants in optimization, the destabilization of evolutionary memory as a function of parameters (error thresholds), the emergence of novel dynamical behaviors induced by finite populations, and the lack of a theory for genotype-phenotype relations and for emergent functionality. New paradigms and metaphors--such as self-organization, complex adaptive systems, phase transitions, and stochastic dynamical systems--will help to achieve progress and, hopefully, a new level of integration in analyzing these difficult problems.

This book collects a wide range of research on these cross-cutting topics. The workshop out of which they came brought together researchers from different disciplines: physicists and computer scientists, on the one hand, and molecular, developmental, and macroevolutionary biologists on the other. The resulting contributions present conflicting views on a number of outstanding problems in order to stimulate and provoke multifocused discussions. Though a final integration of the deeper conflicts--such as those between selectionists, neutralists, and structuralists or those between macroevolutionists and microevolutionists--is still some distance in the future, the dialogue that emerges from the collection as a whole sheds new light on the richness and difficulty of evolutionary dynamics. A primary goal of the collection is to begin articulating a comprehensive dynamical theory--one that incorporates structural constraints, variational attainability, nonlinear population dynamics, neutrality, function, and modularity, all on an equal footing.

About the Editors

James P. Crutchfield is a theoretical physicist who, before becoming a Research Professor at the Santa Fe Institute, spent 14 years in the Physics Department at the University of California, Berkeley. Prior to this he received his B.A. in Physics and Mathematics from the University of California, Santa Cruz, and his Ph.D. in Physics there. Most recently he founded the Art and Science Laboratory, a nonprofit research center in Santa Fe, New Mexico, dedicated to fostering a dialogue between the arts and sciences.

Over the last twenty-five years Professor Crutchfield has worked in the areas of nonlinear dynamics, solid state physics, astrophysics, fluid mechanics, critical phenomena and phase transitions, chaos, and pattern formation. His current research interests center on emergence, the nature of patterns, natural computation, the dynamics of learning, and evolutionary dynamics--interpreted both as a novel class of optimization method and as the driver that produced today's biological diversity.

Peter Schuster is Professor of Theoretical Chemistry at the University of Vienna since 1973 and external faculty member of the Santa Fe Institute since 1991. He holds a Ph.D. in chemistry and physics from the University of Vienna. In the years 1991-1995 he was founding director of the Institute for Molecular Biotechnology in Jena, Germany, a research institute devoted to transfer of knowledge from basic research in the life sciences to technological applications.

Since the beginning of his Ph.D. work in 1963, Professor Schuster has worked in the areas of organic chemistry, theoretical chemistry, molecular spectroscopy, chemical kinetics, and molecular evolution. He has developed the concepts of molecular quasispecies and hypercycles together with the Nobel laureate Manfred Eigen. His current research is dealing with the role of neutrality in evolutionary dynamics. He developed the concept of neutral networks and designed the RNA model as a tool to study evolutionary phenomena. This tool is simple enough to allow for mathematical analysis and computer simulation but, at the same time, sufficiently detailed to be subjected to experimental tests.