J M Keynes, 'The State of the News (The Change in the Weight of the Evidence)' and the Weight of the Evidence: From Keynes's December, 1933 Student Lectures to Chapter 12 of the General Theory in February, 1936

25 Pages Posted: 6 Sep 2017 Last revised: 30 Dec 2017

See all articles by Michael Emmett Brady

Michael Emmett Brady

California State University, Dominguez Hills

Date Written: September 2, 2017

Abstract

J M Keynes’s “the State of the News” variable, W, which he integrated into his formal, simultaneous, four equation IS-LP( LM) model in his December 1933 student lectures, represented the change, either of an increase in the amount of relevant Knowledge, K, (evidence, data, information) in the Weight of the Evidence, w, or an increase in the amount of relevant ignorance,I, in the Weight of the Evidence, w. Keynes formalized his w variable in chapter 26 of the A Treatise on Probability on page 315 for insertion into his c coefficient model, the first decision weight rule constructed in the history of decision science, w=K/(K plus I). The completeness of the relevant evidence, w, was defined as being an index between 0 and 1, so that the completeness of the relevant evidence could be graded as 0 ≤ w ≤ 1.

Keynes’s W is a technical innovation that represents the change in w, which Keynes defined in the first paragraph of chapter 6 as a quantitative measure that had to be normalized, as probability had to be normalized, on the unit interval [0,1], as w= K/(K plus I) or 0 ≤ K/(K plus I)≤ 1. Thus, W=∆w =∆K/(∆K plus ∆I). As in the A Treatise on Probability, an increase in K, where Keynes used information, data, and evidence as synonyms for K, always leads to an increase in w. On the other hand, an increase in I, Ignorance, will always lead to a decrease in w. Keynes’s W allows a decision maker to figure out the change in w over time so as to deal with changing circumstances by adjusting his level of confidence and liquidity preference. Keynes’s innovation refutes the claim of Stephen Stigler, made in 2002 , that Keynes made no more contributions in the area of probability and statistics after 1921.

It is not surprising that Keynes chose capital W in December, 1933 to represent the change in w, given that Keynes had used w to define the weight of the evidence in his c coefficient model in chapter 26 of A Treatise on Probability on p. 315. Keynes’s IS -LP(LM) model of chapters 15 and 21 of the General Theory is built to integrate Keynes’s weight of the evidence into both the IS and LM equations in his formal model. W deals with the change in w over time Keynes changed his emphasis from changes in w, as given by W, to w itself in the General Theory when he defined uncertainty to be an inverse function of weight on p.148 of chapter 12 of the General Theory.

A large and erroneous literature had grown since 1990 among heterodox economists that is based on a confused and confusing article published by J Runde in 1990 that claims that Keynes gave three conflicting definitions of weight. Bertrand Russell, F.Y. Edgeworth, C. D. Broad, and William Ernest Johnson make no mention of any such inconsistencies or conflicts in Keynes’s analysis in their book reviews of the A Treatise on Probability. That is because there are no inconsistencies or conflicts in Keynes’s analysis. Needless to say, Runde has never cited any of these reviews in any of his published work.

Keywords: weight, interval valued probability, upper and lower probabilities, normative, descriptive, Savage, de Finetti

JEL Classification: B10, B12, B14, B16, B20, B22

Suggested Citation

Brady, Michael Emmett, J M Keynes, 'The State of the News (The Change in the Weight of the Evidence)' and the Weight of the Evidence: From Keynes's December, 1933 Student Lectures to Chapter 12 of the General Theory in February, 1936 (September 2, 2017). Available at SSRN: https://ssrn.com/abstract=3031314 or http://dx.doi.org/10.2139/ssrn.3031314

Michael Emmett Brady (Contact Author)

California State University, Dominguez Hills ( email )

1000 E. Victoria Street, Carson, CA
Carson, CA 90747
United States

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