. . . Unlike Dewey, he has provided detailed incisive argumentation, and has shown just where the dogmas and dualisms break down." -- Richard Rorty, The Yale Review.

The philosophical theory of scientific explanation proposed here involves a radically new treatment of causality that accords with the pervasively statistical character of contemporary science. Wesley C. Salmon describes three fundamental conceptions of scientific explanation--the epistemic, modal, and ontic. He argues that the prevailing view is untenable and that the modal conception is scientifically out-dated. Significantly revising aspects of his earlier work, he defends a causal/mechanical theory that is a version of the ontic conception. Professor Salmon's theory furnishes a robust (...) argument for scientific realism akin to the argument that convinced twentieth-century physical scientists of the existence of atoms and molecules. To do justice to such notions as irreducibly statistical laws and statistical explanation, he offers a novel account of physical randomness. The transition from the "reviewed view" of scientific explanation to the causal/mechanical model requires fundamental rethinking of basic explanatory concepts. (shrink)

If the decades of the forties through the sixties were dominated by discussion of Hempel's “covering law“ explication of explanation, that of the seventies was preoccupied with Salmon's “statistical relevance” conception, which emerged as the principal alternative to Hempel's enormously influential account. Readers of Wesley C. Salmon's Scientific Explanation and the Causal Structure of the World, therefore, ought to find it refreshing to discover that its author has not remained content with a facile defense of his previous investigations; on the (...) contrary, Salmon offers an original account of different kinds of explications, advances additional criticisms of various alternative theories, and elaborates a novel “two-tiered“ analysis of explanation that tacitly depends upon a “two-tiered” account of homogeneity. Indeed, if the considerations that follow are correct, Salmon has not merely refined his statistical relevance account but has actually abandoned it in favor of a “causal/mechanistic“ construction. This striking development suggests that the theory of explanation is likely to remain as lively an arena of debate in the eighties as it has been in the past. (shrink)

The concept of mechanism is analyzed in terms of entities and activities, organized such that they are productive of regular changes. Examples show how mechanisms work in neurobiology and molecular biology. Thinking in terms of mechanisms provides a new framework for addressing many traditional philosophical issues: causality, laws, explanation, reduction, and scientific change.

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)

Scientists argue for their claims using many kinds of representations: text, mathematical formulas, and figures such as diagrams, graphs, charts, electron micrographs, etc. The prevalence of visual representations suggests that they play some important role in science that is different from that of linguistic representations. But the foundational questions about the presence of visual representations in scientific arguments have not yet been addressed. What do figures represent? Can they bear truth? How do they support inferences? My dissertation develops answers to (...) these questions, and uses them to explain how figures contribute to the expression and justification of scientific knowledge. ;Since philosophers think of arguments as consisting of statements or mathematical representations, the first task is to show that figures are symbols that can participate in arguments. Goodman's analysis of symbol systems provides semantic and syntactic criteria to compare and contrast different types of representations---visual as well as serial representations like text and mathematical expressions. The visual symbol systems used in science are characterized by systematic form-content relationships, which can be used to define Tarski-style concepts of truth. This is evidence that visual representations can bear truth, supporting my contention that they are genuine components of arguments. ;Analysis of figures in terms their form and symbol system properties shows how visual representations play particular roles in arguments, such as providing evidential support. Figures are also used to express conclusions: models of biological structures and mechanisms often appear in the form of diagrams. I show why the visual format is especially effective in representing the kind of explanation Cummins identified as functional analysis, and why linguistic formats are better for other types of explanations. ;While analysis in terms of symbol systems is essential to understanding the function of visual representations in scientific arguments, this method does not resolve every issue about the use of figures in science. As a final step, I relate current models of visual perception and cognition to their epistemic roles. The result is a coherent picture of the roles figures play in establishing scientific knowledge claims, and how those roles are adapted to human cognitive abilities. (shrink)