Discovering Complexity: Decomposition and Localization as Strategies in Scientific Research

Bechtel, William; Richardson, Robert C.

ISBN 10: 0691087628 / ISBN 13: 9780691087627

Edité par Princeton University Press, 1993

A propos de cet article

Hardcover and dust jacket are almost like new. No writing or names inside. Small bump to top corner. 1993 first printing.

N° de réf. du vendeur 042591

Détails bibliographiques

• Titre: Discovering Complexity: Decomposition and Localization as Strategies in Scientific Research
• Éditeur: Princeton University Press
• Date de parution: 1993
• Langue: anglais
• ISBN à 10 chiffres: 0691087628
• ISBN à 13 chiffres: 9780691087627
• Reliure: Hardcover
• État de l'article: Fine
• État de la jaquette: Near Fine
• Type de livre: Book

A propos de ce titre

Synopsis

Discovering Complexity offers an account of scientific discovery that aims to be psychologically and historically realistic. Drawing on cases from a number of life sciences, including biochemistry, genetics, and neuroscience, this study of the dynamics of theory development focuses on two psychological heuristics, decomposition and localization. William Bechtel and Robert Richardson identify a number of "choice-points" that scientists confront in developing mechanistic explanations and describe how different choices result in divergent explanatory models. According to Bechtel and Richardson's analysis, decomposition is the attempt to differentiate components of a system, while localization assigns responsibility for specific tasks to these components. The book examines in detail the usefulness of these heuristics in biological science, but also discusses their fallibility: underlying their use is the sometimes false assumption that nature is significantly decomposable and hierarchical. When a system does not appear to be decomposable, a classic response has been to abandon the pursuit of mechanistic explanation and to settle for accurate descriptions of phenomena. More recently, with advances in mathematical modeling, an alternative has emerged. Described in this work is an approach to explanation that appeals to interactions between simple components, rather than assigning functions to individual components.