Backhouse, Bateman, the Post Keynesians, and the General Theory - An Examination of Their Failure to Understand the Languages of English and Mathematics When Attempting to Read and Understand the General Theory
18 Pages Posted: 6 Jun 2015
Date Written: June 4, 2015
Backhouse and Bateman attempt to evaluate Keynes’s written words in the General Theory concerning his view of how useful mathematical analysis is in economics. They simply lack the basic tools needed to accomplish the task.
This failure , however ,is not an isolated one. The same failure is pandemic among Post Keynesians, Institutionalists , and economists who accept the work in decision theory of Ramsey, de Finetti ,Savage, M. Friedman or J. Tobin. Their understanding of what mathematics is is limited to the linearity, additivity, and complementarity assumptions accepted in quantitative analysis/econometrics classes that underlie the manipulation of decision trees and tree diagrams when applying the purely mathematical laws of the calculus of probabilities upon which Subjective Expected Utility (SEU) theory is built. Keynes, correctly, viewed this type of mathematical modeling to be a very special case of a much, much ,more general, nonlinear, non- additive approach to mathematical modeling that leads directly to Keynes’s indeterminate, interval valued approach to probability based on Boole.
It is interesting that Backhouse and Bateman apparently now acknowledge the superiority of Keynes’s imprecise view concerning the application of formal mathematical analysis in economics. This, of course ,represents a rejection of Ramsey’s precise, exact, numerical approach to specifying probabilities and means that Bateman has capitulated to Keynes after 30 years of making the opposite claim.
Keynes’s major condition for full employment in chapter 21 of the General Theory, that e=1, follows directly from Keynes’s major result from chapter 26 of the A Treatise on Probability, that w, the weight of the evidence, must equal 1 in order for precise numerical probabilities or probability density functions to be specified.
Keywords: intervals, indeterminate, weight, uncertainty, probability, decision theory
JEL Classification: B10, B12, B20, B22
Suggested Citation: Suggested Citation