Skyrms, Brian - Causal Necessity: Pragmatic Investigation of th
Schrijver: | Skyrms, Brian |
---|---|
Titel: | Causal Necessity: Pragmatic Investigation of the Necessity of Laws Hardcover – July 1, 1980 |
Taal: | Engels |
Uitgever: | Description New Haven : Yale University Press, 1980 |
Bijzonderheden: | gebonden boek, zwart gekartonneerd, met stof omslag |
Prijs: | € 33,00 (Excl. verzendkosten) |
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trefwoorden Causal Necessity: Pragmatic Investigation of the Necessity of Laws Hardcover – July 1, 1980 by Brian Skyrms (Author) Product details Hardcover: 176 pages Publisher: Yale University Press; First Edition edition (July 1, 1980) Language: English ISBN-10: 0300023391 ISBN-13: 978-0300023398 Shipping Weight: 14.1 ounces Format BookBook Author Skyrms, Brian Description New Haven : Yale University Press, 1980 xii, 205 p. ; 22 cm. ISBN 0300023391 Notes Includes index. Bibliography: p. 189-201. Subjects Necessity (Philosophy) | Law -- Philosophy. | Causation. | Pragmatics. RICHARD OTTE CRITICAL REVIEW: BRIAN SKYRMS, CA USAL NECESSITY (Received 15 November, 1982) In a recent book, Causal Necessity, Brian Skyrms has attempted to deal with many of the major problems facing philosophers today. 1 The central idea of his book is that invariance is the key to understanding many of these prob- lems. The idea of invariance is applied to problems such as randomness, epistemic probabilities, confirmation, conditionals, and decision theory. In this article I will briefly present the essence of his position on invariance, and then critically analyze it. The first part of Skyrms' book deals with propensities and statistical laws. Skyrms believes that propensities are the probabilities that play a role in statistical laws, and his discussion of statistical laws assumes this. Statistical laws tell us that certain systems have a stable probability; these stable proba- bilities are propensities. Skyrrns discusses in detail what it means for a proba- bility to be stable, and he defines a notion of resiliency which is supposed to capture the idea of invariance and stability. Resiliency is defined as: Resiliency of Pt(q)s being a = 1 - Maxila -Pt-(q)[ over Pl " Pn (where the Ptis are 1 . "'" gotten by conditionalizing on some truth-functional compound of the pi s which is logically consistent with both q and its negation) (pp. 11-12). In this definition the PiS are properties or experimental factors which are considered relevant to the occurrence of q. Resiliency measures the indepen- dence of q and these factors; thus resiliency is a measure of stability, in- dependence, and invariance. If the resiliency of a certain proposition is 1, then we know that the proposition is necessary or invariant. Degrees of resiliency less than one correspond to cases of approximate independence or approximate invariance. Thus we can look at the resiliency of a proposition to determine how close we are to the ideal. There are, however, some problems that arise with this def'mition of resiliency. One problem concerns the way in which the degree of resiliency is measured. Resiliency is measured by the maximum difference between a and the probability of q conditional on various truth functional cornbina- 426 RICHARD OTTE tions of the Pi s. But there are cases in which this difference is not an adequate measure of the degree of invariance of a proposition. Events with a very low probability can be quite variant, and yet be highly resilient according to Skyrms' definition. Suppose that we have a certain atom with a large half life; the probability of this atom decaying in a certain time interval, say one year, will be quite small. Even if conditional on one of the Pi s the probability of decay is doubled, the decay of the atom is highly resilient, because of the small difference between the actual probability values. If Pr(q) is low enough, the difference between Pr(q) and 2Pr(q) will be very small. A small difference between two numbers is compatible with a large ratio between them. This seems to indicate that the difference between these two numbers cannot be an adequate measure of resiliency or invariance. Contrast the previous example with another example of an atom which has a much shorter half life. The probability that this atom may decay in the same time period may be much higher, let us say around 0.25. If, relative to one of the Pi s, the probability of this atom decaying also doubles, it will not be a very resilient probability. In this case the resiliency would be 0.75, which is not nearly as high as the resiliency of the other example. The only difference between this example and the previous one is the actual probabili- ty values involved. It seems that if one of these is highly invariant, so should the other be. The same atomic theory is behind both of them: if one of these probabilities is a propensity, they should both be propensities. Another example which makes the same point is as follows. The probabili- ty of an atom decaying in a certain time interval may be very low, and thus even if there is a Pi which doubles that probability, it will be highly resilient. However, the probability that the same atom will decay in a much larger time interval will be much larger, in which case it is likely that the decay of the atom will not be highly resilient. The only difference in this example is the longer time intervals involved, which results in different probability values. But it seems clear that the longer time interval does not affect the indepen- dence of the propositions involved; all it does is change the |
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