Download Re-Engineering Philosophy for Limited Beings: Piecewise by William C. Wimsatt PDF

By William C. Wimsatt

Analytic philosophers as soon as pantomimed physics: they attempted to appreciate the area through breaking it down into the smallest attainable bits. Thinkers from the Darwinian sciences now pose choices to this simplistic reductionism. during this highbrow tour--essays spanning thirty years--William Wimsatt argues that scientists search to atomize phenomena simply while useful within the seek to appreciate how entities, occasions, and methods articulate at diverse degrees. Evolution kinds the wildlife now not as Laplace's all-seeing demon yet as a backwoods mechanic solving and re-fashioning machines out of no matter what is to hand. W. V. Quine's misplaced look for a "desert ontology" leads as an alternative to Wimsatt's stroll via a tropical rain wooded area. This publication deals a philosophy for error-prone people attempting to comprehend messy platforms within the actual global. opposed to eliminative reductionism, Wimsatt pits new views to house rising typical and social complexities. He argues that our philosophy may be rooted in heuristics and versions that paintings in perform, not just in precept. He demonstrates the right way to do that with an research of the strengths, the bounds, and a recalibration of our reductionistic and analytic methodologies. Our goals are replaced and our philosophy is transfigured within the procedure.

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Extra resources for Re-Engineering Philosophy for Limited Beings: Piecewise Approximations to Reality

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An agent has a level of aspiration or of satisfaction and evaluates alternatives by whether they fall above or below this level. The level of aspiration is set and modified through experience. Alternatives aren't given in advance, but generated, or acquired (and sometimes lost) sequentially in real time. " This model demands much less in computational and cognitive resources than rational decision theory, and fits plausibly with decision mechanisms we actually use. Unlike the maximization model, one need not compare--or even generate--all choices (Goldstein and Gigerenzer, 1996).

We have engaged for over 350 years in futile searches for guaranteed ways. But what if a search for reliable knowledge is not best pursued as a search for guarantees? Maybe errors are okay-or tolerable-as long as they aren't too frequent, and if we have good ways of remembering, detecting, and recovering from common ones; learning from the patterns of our mistakes; and effectively maintaining, refining, and teaching what we have learned. More remarkable than our occasional failures is the fact that these common methods work so well as often as they do.

These are common patterns of failure in after-the-fact analyses of major disasters. There is seldom a single error, but a series of compounding factors: intersecting errors of design, manufacture, maintenance, training, knowledge, and use. Peterson (1995) has revealing case studies of diverse software errors-errors that often led too much larger accidents. Often the disaster develops over time and could have been better contained if operators had made the "right" decisions; Normative Idealizations versus the Metabolism of Error · 21 judged by hindsight, of course.

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