Discovering Complexity Decomposition and Localization As Strategies in Scientific Research

Bechtel, William und Robert C. Richardson:

ISBN 10: 0691087628 / ISBN 13: 9780691087627

Edité par Princeton, New Jersey : Princeton University Press, 1993

A propos de cet article

XIV, 286 Pages ; With Figures Original Leineneinband mit Bibliotheksrückenschild. Etwas verschossen. Innen mit Bibliotheksstempeln, Legende, sauberer, sehr guter Zustand. Englische Sprache - Original Cloth-Binding with Library label. Little faded. Inside with Library stamps, Legend, in very good condition. English Language B03-03-05F|S6 Sprache: Englisch Gewicht in Gramm: 595.

N° de réf. du vendeur 69672

Détails bibliographiques

• Titre: Discovering Complexity Decomposition and Localization As Strategies in Scientific Research
• Éditeur: Princeton, New Jersey : Princeton University Press
• Date de parution: 1993
• Langue: anglais
• ISBN à 10 chiffres: 0691087628
• ISBN à 13 chiffres: 9780691087627
• Reliure: 24 x 16 cm
• État de l'article: Gut
• Catalogues du vendeur:
  • Modernes Antiquariat

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.