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CHAPTER 2 Complex Systems and Mechanistic Explanations 1. MECHANISTIC EXPLANATION Our aim is to develop a cognitive model of the dynamics ofscientific theorizing that is grounded in actual scientific practice. Our focus is on one kind of explanation, one involved in understanding the behavior of complex systems in biology and psychology. Examples ofthe complex systems we have in mind are the physiological system in yeast that is responsible for alcoholic fermentation, and the psychological system responsible for memory of spatial locations. As we shall discuss in this section, these explanations , which we refer to as mechanistic explanations, propose to account for the behavior of a system in terms of the functions performed by its parts and the interactions between these parts. The heuristics of decomposition and localization are central to our analysis of the development of mechanistic explanations. We shall discuss these heuristics in some detail in this chapter, especially in sections 2 and 3 of this chapter. Parts II and III will be concerned with illustrating their role, and also their development under other influences. These heuristics, or family of heuristics, can be thought ofas imposing assumptions about the organization of the systems being explained. We shall examine these assumptions and the kinds of organization of actual systems for which these assumptions are likely to succeed and those on which they will likely fail. By calling the explanations mechanistic, we are highlighting the fact that they treat the systems as producing a certain behavior in a manner analogous to that of machines developed through human technology. A machine is a composite of interrelated parts, each performing its own functions, that are combined in such a way that each contributes to producing a behavior of the system. A mechanistic explanation identifies these parts and their organization, showing how the behavior of the machine is a consequence ofthe parts and their organization. What counts as mechanistic, though, changes with social context. Scientists will appeal analogically to the principles they know to be operative in artificial contrivances as well as in natural systems that are already adequately understood . The state of technology and natural science at any given time thus plays a significant role in determining the plausibility and limits ofmechanistic explanations (cf. Gregory 1981). From the universe of the Timaeus, through the Archimedian analogues of Galileo and the clockwork universe 18 . I. Scientific Discovery of Newton, to the recent focus on servo-mechanisms and computers, the available analogues were important factors in determining which mechanistic models scientists advanced. The nature and plausibility of mechanistic models is also influenced by characteristics of human thinking, especially by the proclivity of humans to trace operations in a linear or step-by-step fashion. This is especially evident when we consider the forms of organization possible. Many machines are simple, consisting of only a handful of parts that interact minimally or in a linear way. In these machines we can trace and describe the events occurring straightforwardly, relating first what is done by one component , then how this affects the next. Such machines induce little cognitive strain. Some machines, however, are much more complex: one component may affect and be affected by several others, with a cascading effect; or there may be significant feedback from "later" to "earlier" stages. In the latter case, what is functionally dependent becomes unclear. Interaction among components becomes critical. Mechanisms of this latter kind are complex systems. In the extreme they are integrated systems. In such cases, attempting to understand the operation of the entire machine by following the activities in each component in a brute force manner is liable to be futile. A major part of developing a mechanistic explanation is simply to determine what the components of a system are and what they do. In broad outline, there are two strategies available to analyze and isolate component functions. The first is to isolate components physically within the system and then determine what each does (the goal is to use the knowledge of components to reconstruct how the system as a whole operates). The second strategy is to conjecture how the behavior ofthe system might be performed by a set ofcomponent operations, and then to identifY components within the system responsible for the several subtasks. The former is the analytic method of Etienne Condillac, which played a role in the development and promotion of both Lavoisier's chemistry and Cuvier's functional anatomy. The latter is the explanatory program of functionalism in contemporary philosophy of mind (see Cummins 1983...

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