Stuart Kauffman here presents a brilliant new paradigm for evolutionary biology, one that extends the basic concepts of Darwinian evolution to accommodate recent findings and perspectives from the fields of biology, physics, chemistry and mathematics. The book drives to the heart of the exciting debate on the origins of life and maintenance of order in complex biological systems. It focuses on the concept of self-organization: the spontaneous emergence of order widely observed throughout nature. Kauffman here argues that self-organization plays an (...) important role in the emergence of life itself and may play as fundamental a role in shaping life's subsequent evolution as does the Darwinian process of natural selection. Yet until now no systematic effort has been made to incorporate the concept of self-organization into evolutionary theory. The construction requirements which permit complex systems to adapt remain poorly understood, as is the extent to which selection itself can yield systems able to adapt more successfully. This book explores these themes. It shows how complex systems, contrary to expectations, can spontaneously exhibit stunning degrees of order, and how this order, in turn, is essential for understanding the emergence and development of life on Earth. Topics include the new biotechnology of applied molecular evolution, with its important implications for developing new drugs and vaccines; the balance between order and chaos observed in many naturally occurring systems; new insights concerning the predictive power of statistical mechanics in biology; and other major issues. Indeed, the approaches investigated here may prove to be the new center around which biologicalscience itself will evolve. The work is written for all those interested in the cutting edge of research in the life sciences. (shrink)

Theoretical physicist Nicolescu (CNRS and U. of Paris, France) employs a view of the universe found in quantum physics to build his argument as to how basic spiritual questions may be answered and the problems of humanity, such as greed and the dichotomy between rich and poor, can be overcome. His method is called transdisciplinarity because it requires a way of thinking that rises above and beyond the methods of individual disciplines, seeing multiple levels of meaning rather than simple dichotomies. (...) Annotation copyrighted by Book News, Inc., Portland, OR. (shrink)

This book offers a philosophy for error-prone humans trying to understand messy systems in the real world.

Acknowledgments THROUGHOUT this book I cite the many people who have provided information on individual programs and activities. ...

An analysis of two heuristic strategies for the development of mechanistic models, illustrated with historical examples from the life sciences. In Discovering Complexity, William Bechtel and Robert Richardson examine two heuristics that guided the development of mechanistic models in the life sciences: decomposition and localization. Drawing on historical cases from disciplines including cell biology, cognitive neuroscience, and genetics, they identify a number of "choice points" that life scientists confront in developing mechanistic explanations and show how different choices result in divergent (...) explanatory models. Describing decomposition as the attempt to differentiate functional and structural components of a system and localization as the assignment of responsibility for specific functions to specific structures, Bechtel and Richardson examine the usefulness of these heuristics as well as their fallibility—the sometimes false assumption underlying them that nature is significantly decomposable and hierarchically organized. When Discovering Complexity was originally published in 1993, few philosophers of science perceived the centrality of seeking mechanisms to explain phenomena in biology, relying instead on the model of nomological explanation advanced by the logical positivists (a model Bechtel and Richardson found to be utterly inapplicable to the examples from the life sciences in their study). Since then, mechanism and mechanistic explanation have become widely discussed. In a substantive new introduction to this MIT Press edition of their book, Bechtel and Richardson examine both philosophical and scientific developments in research on mechanistic models since 1993. (shrink)

Discussion of the cognitive division of labor has usually made very little contact with relevant materials from other disciplines, including theoretical biology, management science, and design theory. This article draws on these materials to consider some unavoidable conundrums faced by any attempt to present a particular way of dividing tasks among a labor team as the uniquely rational way of doing this, given the interdependence of the underlying evaluative standards by which the products of a system of division of labor (...) will be judged. Divisions of labor will typically cut across these interdependencies in ways which leave the outcomes of a process of labor hostage to path dependencies and suboptimalities. Some attempts to avoid these results are shown to be unsuccessful. All these difficulties are compounded by the fact that, in many cases, the division of labor has to be constructed over a ground of values that is itself being constructed simultaneously with the products which they are invoked to assess. Key Words: risk spreading • interdependency • NK fitness landscapes • complexity • epistasis • Kuhn • modularity • path dependency. (shrink)

The dominant theory of cross-disciplinarity represents multidisciplinarity as ‘lower’ or ‘less interesting’ than interdisciplinarity. In this paper, it is argued that this unfavorable representation of multidisciplinarity is ungrounded because it is an effect of the theory being incomplete. It is also explained that the unfavorable, ungrounded representation of multidisciplinarity is problematic: when someone adopts the dominant theory of cross-disciplinarity, the unfavorable representation supports the development of a preference for interdisciplinarity over multidisciplinarity. However, being ungrounded, the support the representation provides for (...) a preference for interdisciplinarity, is invalid. The issue is even more pressing because research policy makers and funding bodies are among the adopters of the theory, which means that there is a risk of policies reflecting an unjustified preference for interdisciplinarity over multidisciplinarity. This paper presents an improved version of the dominant theory of cross-disciplinarity, obtained by completing the original version with the information it was missing. Because the improved version is more neutral regarding the value of different types of cross-disciplinarity, it is better suited for use by research policy makers and funding bodies. (shrink)

We review the most prominent modeling approaches in social epistemology aimed at understand- ing the functioning of epistemic communities and provide a philosophy of science perspective on the use and interpretation of such simple toy models, thereby suggesting how they could be integrated with conceptual and empirical work. We highlight the need for better integration of such models with relevant findings from disciplines such as social psychology and organization studies.

According to the diversity-beats-ability theorem, groups of diverse problem solvers can outperform groups of high-ability problem solvers. We argue that the model introduced by Lu Hong and Scott Page is inadequate for exploring the trade-off between diversity and ability. This is because the model employs an impoverished implementation of the problem-solving task. We present a new version of the model that captures the role of ‘ability’ in a meaningful way, and we use it to explore the trade-offs between diversity and (...) ability in scientific problem solving. (shrink)