At a time of unprecedented expansion in the life sciences, 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. Evolutionary change is the consequence of mutation and natural selection, which are two concepts that can be described by mathematical equations. Evolutionary Dynamics is concerned with these equations of life. In this book, Martin A. Nowak draws on the languages of biology and mathematics to outline the mathematical principles according to which life evolves. His work introduces readers to the powerful yet simple laws that govern the evolution of living systems, no matter how complicated they might seem. Evolution has become a mathematical theory, Nowak suggests, and any idea of an evolutionary process or mechanism should be studied in the context of the mathematical equations of evolutionary dynamics. His book presents a range of analytical tools that can be used to this end: fitness landscapes, mutation matrices, genomic sequence space, random drift, quasispecies, replicators, the Prisoner’s Dilemma, games in finite and infinite populations, evolutionary graph theory, games on grids, evolutionary kaleidoscopes, fractals, and spatial chaos. Nowak then shows how evolutionary dynamics applies to critical real-world problems, including the progression of viral diseases such as AIDS, the virulence of infectious agents, the unpredictable mutations that lead to cancer, the evolution of altruism, and even the evolution of human language. His book makes a clear and compelling case for understanding every living system—and everything that arises as a consequence of living systems—in terms of evolutionary dynamics.

Evolution is the one theory that transcends all of biology. Nowak draws on the languages of biology and mathematics to outline the mathematical principles according to which life evolves. His book makes a case for understanding every living system—and everything that arises as a consequence of living systems—in terms of evolutionary dynamics.

The 14 chapters of this volume, which present an overview of new research in evolutionary dynamics, were first presented at a conference held in October 1998 at the Santa Fe Institute. The main divisions of the book are macroevolution; epochal evolution; population genetics, dynamics, and optimization; and evolution of cooperation. Individual topics include spectral landscape theory, external triggers in biological evolution, and evolutionary dynamics of asexual reproduction. Several of the contributors, like the editors, are affiliated with the Sante Fe Institute; others teach or work in physics, genetics, biology, computational neuroscience, and theoretical chemistry at universities and private institutions in the US, UK, Austria, Sweden, Australia, Israel, and Germany. Annotation copyrighted by Book News, Inc., Portland, OR

Evolutionary game theory studies the behaviour of large populations of strategically interacting agents & is used by economists to predict in settings where traditional assumptions about the rationality of agents & knowledge may be inapplicable.

The central topic of this book is the mathematical analysis of social systems, understood in the following rather classical way: social systems consist of social actors who interact according to specific rules of interactions; the dynamics of social systems is then the consequences of these interactions, viz., the self-organization of social systems. According to particular demands of their environment, social systems are able to behave in an adaptive manner, that is they can change their rules of interaction by certain meta rules and thus generate a meta dynamics. It is possible to model and analyse mathematically both dynamics and meta dynamics, using cellular automata and genetic algorithms. These tools allow social systems theory to be carried through as precisely as the theories of natural systems, a feat that has not previously been possible. Readership: Researchers and graduate students in the fields of theoretical sociology and social and general systems theory and other interested scientists. No specialised knowledge of mathematics and/or computer science is required.

The onset of cancer presents one of the most fundamental problems in modern biology. In Dynamics of Cancer, Steven Frank produces the first comprehensive analysis of how particular genetic and environmental causes influence the age of onset. The book provides a unique conceptual and historical framework for understanding the causes of cancer and other diseases that increase with age. Using a novel quantitative framework of reliability and multistage breakdown, Frank unifies molecular, demographic, and evolutionary levels of analysis. He interprets a wide variety of observations on the age of cancer onset, the genetic and environmental causes of disease, and the organization of tissues with regard to stem cell biology and somatic mutation. Frank uses new quantitative methods to tackle some of the classic problems in cancer biology and aging: how the rate of increase in the incidence of lung cancer declines after individuals quit smoking, the distinction between the dosage of a chemical carcinogen and the time of exposure, and the role of inherited genetic variation in familial patterns of cancer. This is the only book that presents a full analysis of the age of cancer onset. It is a superb teaching tool and a rich source of ideas for new and experienced researchers. For cancer biologists, population geneticists, evolutionary biologists, and demographers interested in aging, this book provides new insight into disease progression, the inheritance of predisposition to disease, and the evolutionary processes that have shaped organismal design.

Every form of behaviour is shaped by trial and error. Such stepwise adaptation can occur through individual learning or through natural selection, the basis of evolution. Since the work of Maynard Smith and others, it has been realised how game theory can model this process. Evolutionary game theory replaces the static solutions of classical game theory by a dynamical approach centred not on the concept of rational players but on the population dynamics of behavioural programmes. In this book the authors investigate the nonlinear dynamics of the self-regulation of social and economic behaviour, and of the closely related interactions between species in ecological communities. Replicator equations describe how successful strategies spread and thereby create new conditions which can alter the basis of their success, i.e. to enable us to understand the strategic and genetic foundations of the endless chronicle of invasions and extinctions which punctuate evolution. In short, evolutionary game theory describes when to escalate a conflict, how to elicit cooperation, why to expect a balance of the sexes, and how to understand natural selection in mathematical terms.