What is life? For four centuries, it has been believed that the only possible scientific approach to this question proceeds from the Cartesian metaphor -- organism as machine. Therefore, organisms are to be studied and characterized the same way "machines" are; the same way any inorganic system is. Robert Rosen argues that such a view is neither necessary nor sufficient to answer the question. He asserts that life is not a specialization of mechanism, but rather a sweeping generalization of it. (...) Above all, Rosen argues that renouncing mechanism does not mean abandoning science. A radical alternative is proposed, drawn equally from experience in biology, physics, and mathematics; an alternative which draws attention to a new class of complex systems, which are radically different from mechanism. (shrink)

Accounts of mechanistic explanation have emphasized the importance of looking down—decomposing a mechanism into its parts and operations. Using research on visual processing as an exemplar, I illustrate how productive such research has been. But once multiple components of a mechanism have been identified, researchers also need to figure out how it is organized—they must look around and determine how to recompose the mechanism. Although researchers often begin by trying to recompose the mechanism in terms of sequential operations, they frequently (...) find that the components of a mechanism interact in complex ways involving positive and negative feedback and that the organization often exhibits highly interactive local networks linked by a few long-range connections (small-worlds organization) and power law distributions of connections. The mechanisms are themselves active systems that are perturbed by inputs but not set in motion by them. Researchers also need to look up —situate a mechanism in its context, which may be a larger mechanism that modulates its behavior. When looking down is combined with looking around and up, mechanistic research results in an integrated, multi-level perspective. (shrink)

Two widely accepted assumptions within cognitive science are that (1) the goal is to understand the mechanisms responsible for cognitive performances and (2) computational modeling is a major tool for understanding these mechanisms. The particular approaches to computational modeling adopted in cognitive science, moreover, have significantly affected the way in which cognitive mechanisms are understood. Unable to employ some of the more common methods for conducting research on mechanisms, cognitive scientists’ guiding ideas about mechanism have developed in conjunction with their (...) styles of modeling. In particular, mental operations often are conceptualized as comparable to the processes employed in classical symbolic AI or neural network models. These models, in turn, have been interpreted by some as themselves intelligent systems since they employ the same type of operations as does the mind. For this paper, what is significant about these approaches to modeling is that they are constructed specifically to account for behavior and are evaluated by how well they do so—not by independent evidence that they describe actual operations in mental mechanisms. (shrink)

What makes a living system a living system? What kind of biological phenomenon is the phenomenon of cognition? These two questions have been frequently considered, but, in this volume, the authors consider them as concrete biological questions. Their analysis is bold and provocative, for the authors have constructed a systematic theoretical biology which attempts to define living systems not as objects of observation and description, nor even as interacting systems, but as self-contained unities whose only reference is to themselves. The (...) consequence of their investigations and of their living systems as self-making, self-referring autonomous unities, is that they discovered that the two questions have a common answer: living systems are cognitive systems, and living as a process is a process of cognition. The result of their investigations is a completely new perspective of biological (human) phenomena. During the investigations, it was found that a complete linguistic description pertaining to the ‘organization of the living’ was lacking and, in fact, was hampering the reporting of results. Hence, the authors have coined the word ‘autopoiesis’ to replace the expression ‘circular organization’. Autopoiesis conveys, by itself, the central feature of the organization of the living, which is autonomy. (shrink)

The mechanistic perspective has dominated biological disciplines such as biochemistry, physiology, cell and molecular biology, and neuroscience, especially during the 20th century. The primary strategy is reductionist: organisms are to be decomposed into component parts and operations at multiple levels. Researchers adopting this perspective have generated an enormous body of information about the mechanisms of life at scales ranging from the whole organism down to genetic and other molecular operations.

This highly readable theory of life and its origins offers a non-technical discussion of a chemical perspective on the fundamental organisation of living systems. Essays on the biological and philosophical significance of Ganti's work of thirty years indicate not only its enduring theoretical significance, but also the continuing relevance and heuristic power of Ganti's insights.

The basic principles of scientific research from the great French physiologist whose contributions in the 19th century included the discovery of vasomotor nerves; nature of curare and other poisons in human body; more.

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)