On ‘ Money, Uncertainty and Time’ by Giuseppe Fontana

1. On ‘Money, Uncertainty and Time’ by Giuseppe Fontana Alberto Feduzi

2. Money, Uncertainty and Time Three parts: (I): Keynes, the 'Classics' and the Modern Keynesian Dissent 2 The Historical Development of Dissent in Keynesian Economics 3 Methodology and Economic Theory in Keynes's General Theory (II): From Rationality to Unemployment and the Monetary Circuit 4 A Two-Dimensional Theory of Probability and Knowledge 5 Uncertainty and Money (III): Understanding Endogenous Money 6 Hicks as An Early Precursor of Endogenous Money Theory 7 Horizontalist and Structuralist Analyses of Endogenous Money 8 A General Theory of Endogenous Money

3. From Rationality to Unemployment and the Monetary Circuit • Main contributions: • It develops a general theory of knowledge based on Keynes’s theory of probability. • It relates this theory of knowledge to the theories of money developed by the Monetary circuit theorists and the Non-ergodic/Monetary Post Keynesians.

4. A general theory of knowledge based on Keynes’s theory of probability

5. Knowledge and Reality • Two paradigms in Economics (Davidson, 1996): • (1) The Economics of a predetermined, immutable and ergodically knowable reality; • (2) The Economics of an unknownable, transmutable and Non-ergodic reality. • Relationship between the proposed theory of knowledge and the two paradigms: • “The first paradigm describing the economics of a predetermined, immutable and ergodically knowable reality is covered by columns 2 (Certainty) and 3 (Risk)…” (p. 56). • “The case of uncertain knowledge described by Davidson is represented by column 5 (Uncertainty2)…” (p. 57).

6. Uncertainty and Money

7. Keynes’s Theory of Probability Probability is conceived as a logical relation between a set of evidential propositions and a conclusion. If E is a set of evidential premises and H is the conclusion of an argument, then p = H/E is the degree of rational belief that the probability relation between E and H justifies. Fig. 1. Numerical, comparable and non-comparable probabilities Although probabilities are nowadays usually regarded as bearing a definite numerical value in the interval [0,1], Keynes held that, in general, degrees of belief can be measured numerically only in two particular situations: when it is possible to apply the ‘Principle of Indifference’ and when it is possible to estimate statistical frequencies. But he also argued: ‘‘Many probabilities, which are incapable of numerical measurement, can be placed nevertheless between numerical limits”(1921, p. 176).

8. Keynes’s conception of the Weight of Evidence Keynes’s definition of evidential weight as‘the degree of completeness of the information upon which a probability is based’: Example In order to explain the relevance of the notion of evidential weight, Keynes provides a pedagogical example of drawing a white ball from two different urns: “…in the first case we know that the urn contains black and white in equal proportions; in the second case the proportion of each colour is unknown, and each ball is as likely to be black as white. It is evident that in either case the probability of drawing a white ball is 1/2, but that the weight of the argument in favour of this conclusion is greater in the first case” (1921, p. 82).

9. Weight of Evidence (1) Page 9

10. Weight of Evidence (2) Page 10

11. On the proposed theory of knowledge • Certainty/ Risk • Is there a difference between the concept of evidential weight in situations of ‘certainty’ and ‘risk’? Is there a difference between ‘high’ and ‘maximum’ weight? • Uncertainty1 /Uncertainty2 • It is argued that uncertainty2 depends on: (a) unknown probabilities; and (b) numerically incalculable or incomparable probabilities. • ‘The problem with numerically incalculable or incomparable probabilities is that the evidence h upon which individuals should base their beliefs is inconclusive, and hence the secondary proposition p cannot be either estimated or used for comparison with other secondary propositions. For this reason, individuals cannot rationally hold any probable degree of belief p in the primary proposition a, i.e. p is simply non-existent’. • ‘When the evidential base of the degrees of belief is inconclusive, a probability relation cannot be conceived. This means that there is nothing to guide individuals in their practical decision-making. This situation describes the notion of uncertainty on which the demand for a liquid store of wealth is based’. • But (1) Keynes’s theory of probability is mainly non-numerical and (2) people seem to act on the basis of non-numerical probabilities. Therefore a number of questions arise e.g.: • By p, do we always mean ‘sharp’ numerical probabilities? • Are there cases in which the weight lies somewhere between a ‘high’ and ‘low’ level? • When the weight is high, can we always rely on sharp numerical probabilities? • Do interval probabilities fit with this classification? • Can we deal with the notion of unexpected events?

12. Weight of Evidence and Reality • Q:Why is the concept of evidential weight ‘non-existent’ in situations of uncertainty2? • Possible explanation: • It is difficult to use the notion of evidential weight as the degree of completeness of the information because of fundamental uncertainty. In situations of fundamental uncertainty at least some essential information about future events cannot be known at the moment of decision because this information simply does not exist. Decision-makers therefore cannot precisely establish how complete their information is about the future. • But: • (a) Fundamental uncertainty does not seem to be a necessary condition for people not being able to establish how complete their information is. • (b) Fundamental uncertainty does not seem to be a sufficient condition for difficulties in the application of the theory of evidential weight.

13. Weight of Evidence and the ‘stopping problem’ ‘when our knowledge is slight but capable of increase, the course of action, which will, relative to such knowledge, probably produce the greatest amount of good, will often consist in the acquisition of more knowledge. But there clearly comes a point when it is no longer worth while to spend trouble, before acting, in the acquisition of further information, and there is no evident principle by which to determine how far we ought to carry our maxim of strengthening the weight of our argument’ (Keynes, 1973, p. 83-84). The arbitrary nature of the stopping rules might help us understand why different people, with the same evidence and similar beliefs, show different propensities to act. This might help explaining different economic behaviour including the agent’s demand for liquid assets. Page 13