The Evidential Foundations of Probabilistic Reasoning (Wiley Series in Systems Engineering and Management) (Hardcover)

~ (Author) "Perhaps I should begin this discourse with a careful definition of the word evidence..." (more)
Key Phrases: inferential force, evidential subtleties, conditional nonindependence, Sherlock Holmes, Officer Connolly, Conan Doyle (more...)

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Product Description

A detailed treatment regarding the diverse properties and uses of evidence and the judgmental tasks they entail. Examines various processes by which evidence may be developed or discovered. Considers the construction of arguments made in defense of the relevance and credibility of individual items and masses of evidence as well as the task of assessing the inferential force of evidence. Includes over 100 numerical examples to illustrate the workings of diverse probabilistic expressions for the inferential force of evidence and the subtleties they reveal.

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A detailed treatment regarding the diverse properties and uses of evidence and the judgmental tasks they entail. Examines various processes by which evidence may be developed or discovered. Considers the construction of arguments made in defense of the relevance and credibility of individual items and masses of evidence as well as the task of assessing the inferential force of evidence. Includes over 100 numerical examples to illustrate the workings of diverse probabilistic expressions for the inferential force of evidence and the subtleties they reveal.

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Product Details

• Hardcover: 568 pages
• Publisher: Wiley-Interscience (April 1994)
• Language: English
• ISBN-10: 047157936X
• ISBN-13: 978-0471579366
• Product Dimensions: 9.6 x 6.5 x 1.3 inches
• Shipping Weight: 2.2 pounds
• Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
• Amazon.com Sales Rank: #2,857,448 in Books (See Bestsellers in Books)

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3 of 6 people found the following review helpful:

4.0 out of 5 stars Interesting but incomplete, July 15, 2006

By	Michael Emmett Brady "mandmbrady" (Bellflower, California ,United States) - See all my reviews (TOP 1000 REVIEWER)    (REAL NAME)

This review is from: The Evidential Foundations of Probabilistic Reasoning (Paperback)

Schum (S) has put together some very interesting commentary and analysis of questions about the role played by evidence related issues in the history of probability and decision science,as well as current evaluations concerning evidence in recent work done in fuzzy logic,possibility theory,Bayesian conditionalization,networks,etc.
The most interesting chapter is chapter 5 .Its major flaw is a very incomplete overview of Keynes's contributions to questions about the role played by evidence in probability and decision theory in chapter 26 of the TP CONCERNING HIS INDEX,W, TO MEASURE THE COMPLETENESS OF THE EVIDENCE ,as well as Keynes's decision rule,c,the conventional coefficient of risk and weight;the other major omission is the failure to provide any discussion whatsoever of the important contribution made by D.Ellsberg in his famous 1961 article on ambiguity in decision making in the Quarterly Journal of Economics.The book's positive aspects are a very wide ranging coverage of (a) the grading of probabilities,(b) the standard numerical,tree diagram Pascalian probabilities approach,(c) a discussion of L Jonathan Cohen's Keynes like Baconian probabilities in section 5.5, (d)coverage of Dempster's(Shafer)belief functions,(e)the question of precision versus imprecision in probabilistic elicitation and assessment,and(f)fuzzy set- logic theory.


The author is certainly correct when he concludes that Keynes was the first to explicitly differentiate between the weight and the probability of an outcome(see p.256).Carnap's 1950 belief ,that Peirce was the first, overlooks the fact that the standard error of the estimate is a probabilistically related term.Of course,Keynes discussed the fact that hypotheses with high evidential weight also usually would have minimal standard errors of the estimate,meaning that the standard error could sometimes be used as an approximation of the weight in hard science related areas, where there is a great deal of replication and duplication of work ,and where the basic phenomenon studied consists of data which is stable,uniform,homogeneous, and repetitive throughout time.

On the other hand,the author commits a stunning error in his claim(see p.314) that Keynes used a single Pascalian probability to grade the credibility-related characteristics of an evidence source.He completely overlooks the interval valued nature of Keynesian probabilities presented by Keynes in chapters 15 and 17 of his 1921 A Treatise on Probability(TP).Keynes's interval estimate approach is directly based on the work of George Boole.HE CONSIDERS ONLY CHAPTER 16 IN ISOLATION FROM THE REST OF THE BOOK.The failure to integrate a discussion of Keynes's chapter 26 extension of his chapter 6 accounts of the weight of the argument(evidence)is most likely the reason why this error was committed .This error means that the author has no real understanding of the fundamental foundations underlying Keynes's general,logical approach to probability.The Pascalian calculus has a role to play but it is subservient to the Baconian approach.Note that it was Frank P. Ramsey who first introduced the error that Keynes used no numbers in his analysis.Ramsey's false conclusion,that Keynes's theory only allows ordinal comparisons some of the time,is the primary reason why Keynes's interval estimate approach has been ignored since 1921. Only Theodore Hailperin,a well recognized world class mathematician at Lehigh University,realized what Keynes had done.