The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Discovering complexity: decomposition and localization as strategies in scientific research
Abstract
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.
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Cites
Statistical mechanics of complex networks
Albert, R., & Barabási, A.-L. (2002) · Reviews of Modern Physics · doi:10.1103/revmodphys.74.47 · Cited by 20,471
Strogatz, S. H. (2001) · Nature · doi:10.1038/35065725 · Cited by 8,302
Axten, N., Newell, A., & Simon, H. A. (1973) · Contemporary Sociology · doi:10.2307/2063712 · Cited by 6,834
Judgment Under Uncertainty: Heuristics and Biases.
Shafer, G. R., Kahnerman, D., Slovic, P., & Tversky, A. (1984) · Journal of the American Statistical Association · doi:10.2307/2288362 · Cited by 5,657
The Evolution of Random Graphs
(1986) · CBMS Regional Conference Series in Mathematics · doi:10.1090/cbms/062/06 · Cited by 5,543
Cybernetics or Control and Communication in the Animal and the Machine
Wiener, N. (2019) · The MIT Press eBooks · doi:10.7551/mitpress/11810.001.0001 · Cited by 5,009
Some Studies in Machine Learning Using the Game of Checkers
Samuel, A. L. (1959) · IBM Journal of Research and Development · doi:10.1147/rd.33.0210 · Cited by 4,443
Nagel, E., & Hawkins, D. (1961) · American Journal of Physics · doi:10.1119/1.1937571 · Cited by 3,667
What we can do and what we cannot do with fMRI
Logothetis, N. K. (2008) · Nature · doi:10.1038/nature06976 · Cited by 3,612
Machamer, P., Darden, L., & Craver, C. F. (2000) · Philosophy of Science · doi:10.1086/392759 · Cited by 3,179
Cited by
Machamer, P., Darden, L., & Craver, C. F. (2000) · Philosophy of Science · doi:10.1086/392759 · Cited by 3,179
Being There: Putting Brain, Body and World Together Again.
van Gelder, T., & Clark, A. (1998) · The Philosophical Review · doi:10.2307/2998391 · Cited by 3,128
On the relationship between emotion and cognition
Pessoa, L. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2317 · Cited by 2,350
Causal Mechanisms in the Social Sciences
Hedström, P., & Ylikoski, P. (2010) · Annual Review of Sociology · doi:10.1146/annurev.soc.012809.102632 · Cited by 1,630
Neural reuse: A fundamental organizational principle of the brain
Anderson, M. L. (2010) · Behavioral and Brain Sciences · doi:10.1017/s0140525x10000853 · Cited by 1,212
Metadata
- Venue
- Choice Reviews Online
- Publisher
- American Library Association
- Published
- 1993-07-01
- Volume
- 30
- Issue
- 11
- Pages
- 30-6134-30-6134
- ISSN
- 0009-4978 (ISSN-L)
- 1523-8253
- OpenAlex
- W1580911780
- Cited by
- 1,294
- References
- 46
- Type
- book review
- Language
- en
- Open access
- closed
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