Frederick Lavington and Uncertainty in the Marshallian Tradition

Frederick Lavington (1881–1927) was a minor Marshallian economist. He began study at Cambridge as a mature student in 1908 (Groenewegen 2012: 100) and graduated from Cambridge in 1911 with a degree in Part II of the Economics and Politics Tripos; he worked in the British Board of Trade from 1912 to 1918, and became a lecturer in economics at Cambridge in 1918, but died early in 1927 (Groenewegen 2012: 99).

His work is of some interest in that he raised issues about fundamental uncertainty in relation to economic life, and may have anticipated Keynes to some limited extent.

Lavington’s most important work included the following:

Lavington, Frederick. 1911. “The Social Importance of Banking,” Economic Journal 21.81: 53–60.

Lavington, Frederick. 1912. “Uncertainty in its Relation to the Net Rate of Interest,” The Economic Journal 22.87: 398–409.

Lavington, Frederick. 1913. “The Social Interest in Speculation on the Stock Exchange,” The Economic Journal 23.89: 36–52.

Lavington, Frederick. 1921. The English Capital Market. Methuen & Co., London.

Lavington, Frederick. 1922. The Trade Cycle: An Account of the Causes Producing Rhythmical Changes in the Activity of Business. P. S. King, London.

Lavington, Frederick. 1925. “An Approach to the Theory of Business Risks I,” Economic Journal 35.138: 186–199.

Lavington, Frederick. 1926. “An Approach to the Theory of Business Risks II,” The Economic Journal 36.142: 192–203.

Lavington’s article “Uncertainty in its Relation to the Net Rate of Interest” (1912) has some interesting analysis of uncertainty in business life. Lavington defined uncertainty as a type of “ignorance” but also a form of “imperfect knowledge” (Lavington 1912: 400), so it is not clear that he understood the finer points of radical uncertainty. Nevertheless, Lavington also argued that uncertainty can badly affect investment and prevent capital investment happening (Lavington 1912: 401).

In a fascinating analysis, Lavington notes how societies can reduce uncertainty both through government interventions such as the provision of police services and law and order and by private market arrangements such as joint stock companies, speculators as intermediaries, and insurance companies (Lavington 1912: 401). Above all, business people deal with uncertainty by holding money as “reserve fund” to insure against an uncertain future (Lavington 1912: 401–402). As Keynes would have said, money can be held as a “hedge” against uncertainty, and this is an important insight.

In short, the work on uncertainty by Lavington can be seen as a sort of precursor to the later insights of Keynes.

BIBLIOGRAPHY
Groenewegen, Peter. 2012. The Minor Marshallians and Alfred Marshall: An Evaluation. Routledge, New York.

Lavington, Frederick. 1911. “The Social Importance of Banking,” Economic Journal 21.81: 53–60.

Lavington, F. 1912. “Uncertainty in its Relation to the Net Rate of Interest,” The Economic Journal 22.87: 398–409.

Lavington, Frederick. 1913. “The Social Interest in Speculation on the Stock Exchange,” The Economic Journal 23.89: 36–52.

Lavington, Frederick. 1921. The English Capital Market. Methuen & Co., London.

Lavington, Frederick. 1922. The Trade Cycle: An Account of the Causes Producing Rhythmical Changes in the Activity of Business. P. S. King, London.

Lavington, Frederick. 1926. “An Approach to the Theory of Business Risks,” The Economic Journal 36.142: 192–203.

Shackle on the Emergence of Uncertainty and Expectations in Modern Economics

From G. L. S. Shackle’s fascinating book The Years of High Theory: Invention and Tradition in Economic Thought 1926–1939 (1967):

“At the opening of the 1930s economic theory still rested on the assumption of a basically orderly and tranquil world. At their end it had come to terms with the restless anarchy and disorder of the world of fact. Partly this transformation was effected by the brutal force of events: by a slump without parallel and the unnerving spectacle of the rise of Nazism in a world cheated of the hope of peace. But partly it was the work of a mere handful of great theoreticians. One thing above all divided the new theory from the old: the discarding of the assumption (which had often been quite tacit) of universal perfect knowledge. What sense did it make to assume perfect knowledge in a world where every morning’s newspaper was opened in fear and scanned with foreboding? But the ferment had been working in the world of theory from the beginning of the 1920s. Frank Knight’s Risk Uncertainty and Profit of 1921 puts entrepreneurship in the forefront of a treatise on value theory which largely sets forth the old orthodoxy. But perhaps its title was a portent. It was in Sweden that expectation was first taken seriously as a prime mover in the economic process. (Marshall, as always, was with the angels, but he did not blow this particular trumpet very loud.) Erik Lindahl and, more incisively and with one brilliant and epoch-marking stroke, Gunnar Myrdal, developed the first ‘economics of expectation’. Myrdal’s essay, published in Swedish in 1931, in German in 1933, and in English only in 1939, would have served very well as the launching-pad for a theory of general output and employment, had the General Theory never been written. 1937 was the year of intensive Keynesian critical debate. In February Keynes himself declared in the Quarterly Journal of Economics that the General Theory was concerned with the consequences of our modes of coping with, or of concealing from our conscious selves, our ignorance of the future. Hugh Townshend, his intellectually most radical interpreter, simultaneously expressed the matter (in the Economic Journal for March) in terms, if anything, even more uncompromising. Uncertainty was the new strand placed gleamingly in the skein of economic ideas in the 1930s.” (Shackle 1967: 5–6).

Myrdal’s “essay” that Shackle refers to here was “Om penningteoretisk jämvikt. En studie över den ‘normala räntan’ i Wicksells penninglära” [“On Monetary Equilibrium. A Study of the ‘Normal Rate of Interest’ in Wicksell’s Monetary Theory”] (Ekonomisk Tidskrift 33 [1931]: 191–302), which was translated into English as Monetary Equilibrium (London, 1939). Myrdal had, according to Shackle, opened the debate about expectations before Keynes.

Later Shackle examines the significance of Gunnar Myrdal’s work Monetary Equilibrium (1931, English trans. 1939):

“In Monetary Equilibrium we have chapter IV. That chapter and its sequel are the battlefield where the decisive action occurs. We have examined their contents and effect in detail. But after them come passages of high interest, confirming and extending the conclusion that, had the General Theory never been written, Myrdal’s work would eventually have supplied almost the same theory.” (Shackle 1967: 124).

I am not sure whether this is a fair assessment, but it is high praise indeed coming from Shackle.

In Monetary Equilibrium, Myrdal seems to have criticised Wicksell’s concept of the “natural rate” and other aspects of the monetary equilibrium approach (Skaggs 1997: 473). Myrdal later advocated countercyclical fiscal policy and even had some understanding of the multiplier process (Skaggs 1997: 474).

Addendum
Philip Pilkington has a fascinating post here analysing Myrdal’s monetary equilibrium theory:

Philip Pilkington, “Gunnar Myrdal’s Monetary Equilibrium Theory: A Summarized Version,” Fixing the Economists, August 12, 2013.

Also, there is another post here on Myrdal and the General Theory:

Philip Pilkington, Gunnar Myrdal’s Prescient Criticisms of Keynes’ General Theory,” Fixing the Economists, August 10, 2013.

BIBLIOGRAPHY
Myrdal, Gunnar. 1931. “Om penningteoretisk jämvikt. En studie över den ‘normala räntan’ i Wicksells penninglära,” Ekonomisk Tidskrift 33: 191–302.

Myrdal, Gunnar. 1939 [1931]. Monetary Equilibrium. W. Hodge & Company, London.

Shackle, G. L. S. 1967. The Years of High Theory: Invention and Tradition in Economic Thought 1926–1939. Cambridge University Press, Cambridge.

Skaggs, Neil T. 1997. “Myrdal, Gunnar (1898–1987),” in D. Glasner and T. F. Cooley (eds). Business Cycles and Depressions: An Encyclopedia. Garland Pub., New York. 473–474.

Davidson on Nominal Contracts and Uncertainty

From Davidson (1988):

“In the absence of money production-hire contracts over time in a nonergodic environment, entrepreneurs would be foolish to start up a long duration production process, for they would not possess any knowledge of the ultimate future costs of production! (How could a profit-maximising manager calculate the marginal cost associated with varying production flows, in a nonergodic world, without fixed nominal wage contracts?) The institution of forward money contracts where delivery and payment is specified at a future date is an institutional arrangement which permits agents to deal with, and control the outcomes of, an otherwise uncertain future. Long-lived forward contracts are the way a free market economy, in an uncertain world, builds in institutional price and wage stickiness over time. In a nonergodic world, such explicit money contractual anchors for future events are necessary conditions for encouraging entrepreneurs to carry out economic activities in a market economy” (Davidson 1988: 335).

Of course, we can add mark-up pricing/administered pricing to nominal contracts as well.

The rise of a high degree of price and wage rigidity in modern economies is, contrary to neoclassical theory, a development that many businesses actively desire because it promotes stability and increases their ability to successfully plan for the future. In that sense, contrary to Austrian and neoclassical theory, relative price and wage rigidity greatly aids economic calculation, because such rigidity allows more successful forward planning.

Both fixed nominal contracts and administered prices are simply private sector institutions to decrease the uncertainty that economic agents face when dealing with an unknown future: they have emerged from within markets, and are not some artificial or unnatural imposition on markets.

BIBLIOGRAPHY
Davidson, P. 1988. “A Technical Definition of Uncertainty and the Long-Run Non-Neutrality of Money,” Cambridge Journal of Economics 12.3: 329-337.

The Sobering Truth about Business Failure Rates

A statistic on business failure in the United States: only 50% of new businesses will survive after 5 years, only 38% will last 10 years, and only 31% will last 15 years (from Birch 1987 cited in Lane and Schary 1991: 97).

Although the data here are from the years before 1991, subsequent empirical studies of both the US and other nations result in much the same findings: e.g., in Canada, 68% of businesses with fewer than 5 employees fail within 5 years, and 48% of business with 5 to 99 employees fail within five years too (Monk 2000: 12).

And matters are no better when you look at business failures in different, more laissez faire systems like the gold standard era: what good data from 19th century that does exist suggests that business failure rates were very high back then as well (Lerner et al. 1975: p. 913, “Series V 20-30. Business Formation and Business Failures: 1875 to 1970”).

If anything, the data in the Historical Statistics of the United States, Colonial Times to 1970. Part 2 (pp. 912–913) shows that business failure rates – though high – actually fell after 1945 under modern, interventionist monetary and fiscal regimes, so that the post-1945 record is better than the pre-1939 or pre-1914 one.

If one looks at these failure rates, one can only conclude that decentralised, private decision-making on production and investment mostly turns out to be wrong and a failure; it is not some incredibly efficient system at all, for a 50% failure rate after 5 years for new businesses means massive wasted resources and lost opportunity costs.

This is curious given how frequently we hear paeans to the alleged extraordinary success of private enterprise over government production, and the power of the private sector to efficiently decide what and how much to produce as demanded by consumers in businesses that are viable.

All in all, the claims made for the private sector seem grossly overrated when one looks at the sobering facts on how high business failures rates actually are.

Governments can fail too of course and sometimes fail badly, but I see no evidence that their failures are any worse than the stunning and abysmal failure rates that plague the private sector.

I hasten to add that none of this is meant to be an argument for a command economy over private production of most goods and services; it is merely a sobering look at why the private sector is not necessarily better than government.

The main reason for high business failure rates throughout modern history is most probably this: fundamental uncertainty.

You cannot predict many aspects of the future with objective probability scores, and decision-making on private investment is made under subjective expectations. Of course, some businesses can reduce their degree of uncertainty and risk, and we have institutions – both public and private – that reduce uncertainty and stabilise the system.

But a great deal of private investment is simply a type of gambling, where some few get lucky and many others lose.

And once we realise that investment itself is unstable given that business confidence can fluctuate between waves of pessimism and optimism, we can understand the essence of Keynes’ revolution in economics as summarised by George L. S. Shackle:

“[sc. Keynes’s] ... theory of involuntary unemployment is perfectly simple and can be expressed in a paragraph, or in a sentence. If you express it in a sentence, you simply say that enterprise is the launching of resources upon a project whose outcome you do not, and cannot, know. The business of enterprise involves investment, the investing of large amounts of resources--huge sums of money--in things whose outcome you cannot be certain of, which could perfectly well turn into a disaster or a brilliant success.

The people who do this kind of investing are essentially gamblers and they can lose their nerve. And if they decide to withdraw from trade, they sweep their chips up from the table. If they decide it’s too risky, if their nerve gives out and they can’t bring themselves to go on investing, they cease to give employment and that is the explanation.”
“An Interview with G.L.S. Shackle,” The Austrian Economics Newsletter, Spring 1983.

BIBLIOGRAPHY
Birch, David L. 1987. Job Creation in America. Free Press, New York.

Lane, Sarah J. and Martha Schary. 1991. “Understanding the Business Failure Rate,” Contemporary Policy Issues 9.4: 93–105.

Lerner, W. et al. 1975. Historical Statistics of the United States, Colonial Times to 1970. Part 2 (bicentennial edn.). U.S. Dept. of Commerce, Bureau of the Census, Washington, D.C.

Monk, Richard. 2000. “Why Small Businesses Fail,” CMA Management 74.6: 12–13.

Bayesian Subjectivists and Expected Utility Theory abolish Uncertainty

This is a straightforward point, but an important one.

The subjectivist theory of probability was developed by Frank Ramsey, Bruno de Finetti (1906–1985), and Leonard Jimmie Savage (1917–1971).

Bruno de Finetti (1937) and Frank Ramsey seem to have independently developed the subjectivist approach to probability. In turn, Savage drew on the work of Bruno de Finetti (Feduzi, Runde and Zappia 2014: 3).

The subjectivist theory renders a person’s subjective beliefs about probability numerically determinate and then consistent with the rules of the probability calculus (Feduzi, Runde and Zappia 2014: 3), and the key to this are the concepts of “coherence,” the “Dutch book argument,” and “exchangeable” events. But the subjectivist theory of probability in effect abolishes unquantifiable uncertainty by expressing probabilities in terms of numerical “point” probabilities by means of betting quotients that can supposedly be given to all conceivable subjective probabilities (Feduzi, Runde and Zappia 2014: 3).

Neoclassical decision-making theory in the form of subjective expected utility theory has adopted the subjectivist theory of probability, and Leonard J. Savage’s The Foundations of Statistics (1954; 2nd edn. 1972) was a crucial text for this.

Bayesian subjectivist probabilities are often an important part of this.

The result of this in modern neoclassical economics has been to abolish the distinction between risk and uncertainty. As Davidson notes,

“… immutable reality models typically employ a subjectivist orientation. Agents form subjective expectations (usually, but not necessarily in the form of Bayesian subjective probabilities). In the short run, subjective probabilities need not coincide with the presumed immutable objective probabilities. Today’s decision makers, therefore, can make short-run errors regarding the uncertain (i.e., probabilistic risky) future. Agents ‘learn’ from these short-run mistakes so that subjective probabilities or decision weights tend to converge onto an accurate description of the programmed external reality.” (Davidson 1996: 486).

The trouble with this is that such a Darwinian “selection” of agents or entrepreneurs will not work if the future is not determinate and yet to be created, and subject to ontological uncertainty. Just because a decision made in the past turned out to be successful on the basis of some subjective probability estimate, it does not follow that decisions today by the same agent on the basis of subjective probability estimates will continue to be successful in the future.

BIBLIOGRAPHY
Davidson, Paul. 1996. “Reality and Economic Theory,” Journal of Post Keynesian Economics 18.4: 479–508.

de Finetti, B. 1937. “La prévision: ses lois logiques, ses sources subjectives,” Annales de l’Institut Henri Poincaré 7: 1–68.

de Finetti, B. 1964 [1937]. “Foresight: Its Logical Laws, its Subjective Sources,” in H. E. Kyburg, and H. E. Smokler (eds.), Studies in Subjective Probability. John Wiley & Sons, New York. 93–158.

Feduzi, Alberto, Runde, Jochen and Carlo Zappia. 2014. “De Finetti on Uncertainty,” Cambridge Journal of Economics 38.1: 1–21.

Savage, Leonard J. 1954. The Foundations of Statistics. Wiley, New York.

Savage, Leonard J. 1972. The Foundations of Statistics (2nd. rev. edn.). Dover Publications, New York.

My Posts on Uncertainty

Below are links to my various posts on fundamental uncertainty, often with discussion of probability theory:

(1) Bibliography
“Bibliography on Keynesian Uncertainty,” March 3, 2011.

(2) General
“Davidson on ‘Reality and Economic Theory,’” July 10, 2013.

“Post Keynesians and Degrees of Uncertainty,” July 31, 2013.

“Probability and Uncertainty,” July 11, 2013.

“Degrees of Uncertainty,” November 24, 2011.

“How Can Government Overcome Uncertainty?,” October 4, 2011.

“Skidelsky on Keynesian Uncertainty,” May 1, 2011.

“Uncertainty and Non-Ergodic Stochastic Systems,” March 2, 2011.

(3) Austrians and Uncertainty
“Mises, Action and Uncertainty,” December 4, 2013.

“Austrians Mangle Business Uncertainty,” July 22, 2012.

“Peter Lewin on Expectations and Uncertainty,” July 2, 2012.

“Michael Emmett Brady on Hayek’s Concept of Uncertainty,” October 11, 2011.

“A Note on Mises and the Concept of Uncertainty,” August 1, 2011.

“Davidson on the Austrian Concept of Uncertainty,” March 4, 2011.

(4) Uncertainty and Climate
“Is Long Term Climate Non-Ergodic?,” July 18, 2013.

Commentary on Brady and Arthmar (2010)

Brady and Arthmar (2010) (according to this, to appear in History of Economic Ideas as “Boole, Keynes, and the Interval Approach to Probability”) is a fascinating paper. I have not yet properly digested it, but two posts here on various other blogs discuss the paper or issues related to it:

Dave Marsay, 2012. “Brady+ on Keynes’ Interval Probability,” Djmarsay, Reasoning about Reasoning, Mathematical, July 28.

Isaac Marmolejo, 2012. Michael Brady on Shackle and Post Keynesianism, The Radical Subjectivist, July 28.

Isaac Marmolejo raises a fundamental issue: how in the face of true uncertainty can government diminish it?

The answer, quite simply, is that uncertainty comes in degrees and is capable of being diminished or increased, even though, generally speaking, one does not eliminate it.

First, what is certainty?

True certainty exists only in very limited cases: for example, it can be derived from valid and sound deductive arguments. It might be found in mathematical proofs. Alternatively, if you have proven that a fair and non-fraudulent game of dice is in progress, you can be certain that the probability of rolling a 3 is 1 in 6.

But outside of valid and sound deduction, mathematics, or strict objective numerical probabilities, one must use inductive reasoning, which yields only probability and some degree of uncertainty. That is, in virtually all other areas of argument and knowledge, even in the natural sciences or history, we do not obtain certainty, but probability mixed with uncertainty, at varying degrees, for all inductive arguments yield conclusions which are only, at most, extremely probable.

Consider the following propositions:

Proposition (1) the sun will rise tomorrow (understood scientifically as the earth will continue to rotate on its axis tomorrow).

Proposition (2) Napoleon was defeated at the battle of Waterloo.

Proposition (3) Alexander the Great did not die by poisoning in a conspiracy in 323 BC.

Proposition (4) on January 15, 2014, it will be raining in London at Cleopatra’s needle at 12 noon.

All these propositions are or will be true or false. But do we have absolute certainty that they are (or will be) true or false? No.

Their truth can only be defended with inductive argument, but the truth is probable only.

The probability of (1) seems extremely high, and one faces a very low degree of uncertainty.

Proposition (2) is a historical fact, but even these are ultimately only probable to some degree. As it happens, proposition (2) has a very high degree of historical evidence, and only a low degree of uncertainty, but the degree of uncertainty can be judged greater to some slight degree than that involved in Proposition (1).

Proposition (3) is another historical fact, but its probability of truth is less than Proposition (2), since historians judge, on the basis of good evidence, that there is a small chance Alexander the Great was poisoned, and thus the uncertainty about its truth can be judged higher than Proposition (2).

Finally we move to Proposition (4): this must be judged highly uncertain: the degree of uncertainty is much greater than in all earlier propositions.

It follows that uncertainty does indeed come in degrees, and it can be greater or smaller depending on the proposition and arguments.

It is furthermore the case the uncertainty can be reduced. For example, with regard to economic life, the private sector can, and historically undoubtedly has, reduced the uncertainty faced by agents, e.g.,

(1) holding money is, amongst other things, a method by which agents can reduce (though not eliminate) uncertainty about future liquidity concerns; or money can reduce the uncertainties associated with direct barter trading (N.B. I am not endorsing the universal Mengerian account of money’s origins by this statement);

(2) private sector insurance is a fundamental way in which uncertainty is reduced, though not eliminated.

(3) forward and futures contracts reduce uncertainty, by, for example, locking the price of good to be delivered at a future date, when decentralised production and exchange of many goods in flexprice markets means you cannot predict the future price.

So the private sector is perfectly capable of reducing uncertainty, and so is the government sector, e.g.,:

(1) government provision of law and order reduces uncertainty associated with the threat of crime as faced by businesses and virtually all business activities (e.g., theft during transportation of goods to markets);

(2) government justice systems reduce uncertainty from potential conflicts and failure to fulfil private contracts;

(3) a central bank acting as a lender of last resort reduces uncertainty associated with fractional reserve banking systems;

(4) government social security reduces uncertainty associated with what happens when people are unemployed and have lost their major source of income, and so on.

In these cases, both the private sector and government sector can reduce uncertainty, but not eliminate it, and uncertainty comes in degrees.

The problem with private, decentralised decision-making in capitalism is that it still leaves a significant degree of uncertainty in decisions about investment (i.e., aggregate investment can fluctuate wildly and expectations can collapse), and governments can overcome that through fiscal policy and other interventions.


BIBLIOGRAPHY
Brady, Michael Emmett and Rogério Arthmar, 2010. “Keynes’ Lower-Upper Bound Interval Approach to Probability,” SSRN Working Paper Series, February 2
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1546726

Probability and Uncertainty

There are two fundamental types of probability with subcategories:

(1) Physical/Objective probabilities (class probabilities), divided into:

(i.) A priori probabilities (mathematical/Classical probabilities)
(ii.) Relative frequency probabilities (or a posteriori/empirical/experimental probabilities), and

(2) Subjective probability (or evidential/Bayesian probability).

These are discussed below, with the issue of uncertainty in section (3).

(1) Physical/Objective Probabilities
Again, these are divided into:

(i.) A priori probabilities (mathematical/Classical probabilities), and
(ii.) Relative frequency probabilities (or a posteriori/empirical/experimental probabilities).

Objective probabilities are either in practice or in theory quantifiable with a numerical value (or numerical coefficient of probability). The numerical value that describes the likelihood of an occurrence or event can range from 0 (impossibility) to 1 (certainty).

A priori probabilities can be calculated from antecedent information and before the experiment or the event in question, such as probabilities of coin tosses.

Relative frequency probabilities, on the other hand, are derived from the empirical data of a sufficiently representative, random sample. Usually a reference class and attribute of interest are involved, and in theory the probability can be expressed as a numerical value, calculated as a fraction where the denominator is the number of members in the reference class and the numerator is the number of members of the reference class who have the attribute involved. Probabilities are assigned to events on the basis of available evidence or sample, and therefore may be different when people have different sized data.

But is also possible to view a priori probabilities as relative frequency probabilities: hence the probability of heads in a fair coin toss at 0.5 can be conceived as the relative frequency of that outcome in repeated experiments of coin tosses over many instances, and as the repeated experiments approach infinity supposedly the numerical value will approach 0.5.

Advocates of the frequentist interpretation of probability might contend that a priori probabilities do not exist, but are ultimately explained by relative frequencies. It is interesting that Ludwig von Mises referred to objective probabilities as “class probabilities,” under the influence of his brother Richard von Mises (1883–1953), a proponent of the frequentist interpretation of physical probabilities.

I assume (I could be wrong) that if Bayesian probability uses frequency probabilities, it may also be able to yield objective probabilities.

A fundamental point is that risk (as opposed to uncertainty) is associated with objective probabilities, either a priori probabilities or relative frequency probabilities, when a numerical value can be assigned, as Frank Knight argued (though Knight’s terminology was potentially misleading as he also called risk “measurable uncertainty”).

Post Keynesians would argue that risk is not the relevant concept in many entrepreneurial investment decisions, but uncertainty.

(2) Subjective Probability (or evidential probability/Bayesian probability)
In instances where probabilities of events cannot be analysed in terms of relative frequencies or because the events are unique and cannot be included in a reference class, probability theory has been developed that measures “degrees of belief,” and that can be termed “subjective probability.” The usual procedure for this is some form of Bayesian probability theory.

In neoclassical economics, subjective probability theory was developed from the work of John von Neumann, Oskar Morgenstern, Frank Ramsey, Bruno de Finetti, and Leonard J. Savage, the latter of whom (drawing on Bayesian probability theory as well) formulated a formal model of decision-making where optimal decisions are made to maximise expected utility, and probability distributions are given by subjective evaluations.

But even here uncertainty is seen as a state of the mind, not as a state of the world, and ultimately Walrasian general equilibrium theory in its various forms requires real, objective probabilities to actually exist for events in economic decision making, and for the subjective probabilities of agents to converge towards these objective probabilities over time.

Curiously, though being subjectivists, Austrian economists reject the expected-utility representation of decision making under uncertainty in neoclassical economics (Langlois 1994: 118). We should also note that Ludwig von Mises’s “case probability” is not really the same thing as Bayesian subjective probability. Case probability is a purely subjective form of probability and Mises argued that “case probability is not open to any kind of numerical evaluation” (Mises 1998: 113). By contrast, Bayesianism does give numerical values to evidential probabilities, even if these are deemed subjective, but are updated and revised in light of new evidence.

(3) Uncertainty
In understanding uncertainty, the distinction between ergodic and non-ergodic processes is important. For neoclassical theory, reliable knowledge of the future requires the assumption of the ergodic axiom. Ergodicity is a property of some process or phenomenon in which time and/or space averages or attributes of that system either coincide for an infinite series or converge as the finite number of observations increases (Dunn 2012: 434). Thus a sufficient sample of the past can be said to reveal the future in an ergodic process.

But, for Post Keynesians, the complications involved in assessing the ergodic or non-ergodic nature of an economic process might be considerable, especially as there exist:

(1) genuinely ergodic economic processes/phenomena;

(2) genuinely non-ergodic economic processes/phenomena;

(3) economic processes/phenomena that appear ergodic for short periods of calendar time, but may change. (Dunn 2012: 435).

For example, non-stationarity and Shackle’s “crucial decision” concept in decision making are sufficient conditions for non-ergodicity, but not necessary conditions (Dunn 2012: 435–436). Future events or processes that are created by human agency are, above all, candidates for non-ergodicity.

Events where objective probabilities exist imply an ergodic world or a justified use of the ergodic axiom. Information from past and present data series should allow a probability estimate that approaches the objective numerical value as the data increases, even for future events.

Keynesian uncertainty (in the sense of Keynes and Post Keynesianism) stresses the unknowable nature of the future and the inappropriateness or profound limitations of probability theory.

Although there is not an exact equivalence between all the various concepts below (and perhaps some important differences), these concepts of uncertainty are roughly similar to Keynesian uncertainty:

(1) Knightian (unmeasurable) uncertainty;

(2) Misesian case probability;

(3) G. L. S. Shackle’s radical uncertainty;

(4) Ludwig Lachmann’s radical uncertainty;

(5) Austrian “structural uncertainty” (Langlois 1994: 120);

(6) Loasby’s partial ignorance, and

(7) O’Driscoll and Rizzo’s genuine uncertainty.

When the idea of fundamental uncertainty is understood as a crucial one for economic science, the next question is: how do economic agents act and make decisions under uncertain conditions?

George L. S. Shackle developed a theory of decision making under uncertainty that dispensed with probability theories in describing such behaviour, and this was a project derived from the work of Frank Knight and Keynes. In contrast, as we have seen, mainstream neoclassical economics via Arrow adopted the use of subjective probability in decision making theory, and effectively denied the (1) risk versus (2) Knightian/Keynesian uncertainty distinction.

Neoclassical theory was influenced by the work of Frank Ramsay and Leonard J. Savage and essentially went down the path of subjective probability theory with a Bayesian flavour.

I conclude by posing some other questions that seem important to me:

(1) what is the contribution and value of Gilboa and Schmeidler’s non-additive probability approach to decision-making under uncertainty?

(2) to what extent did Ludwig von Mises follow the frequentist interpretation of probability of his brother Richard von Mises?

(3) Knight made a distinction between “statistical probability” and “estimated probability.” Is “estimated probability” more or less “subjective probability”?

(4) What is the significance of Daniel Kahneman and Amos Tversky’s critiques of standard economic decision making theory, and that of Daniel Ellsberg in Risk, Ambiguity and Decision (2001)?

BIBLIOGRAPHY
Copi, Irving, Cohen, Carl and Kenneth McMahon. 2011. Introduction to Logic (14th edn.). Prentice Hall, Boston, Mass. and London.

Dunn, S. P. 2012. “Non-Ergodicity,” in J. E. King (ed.), The Elgar Companion to Post Keynesian Economics (2nd edn.), Edward Elgar, Cheltenham, UK and Northampton, MA. 434–439.

Langlois, R. 1994. “Risk and Uncertainty,” in Peter J. Boettke (ed.), The Elgar Companion to Austrian Economics. E. Elgar, Aldershot. 118–122.

Mises, L. 1998. Human Action: A Treatise on Economics. The Scholar's Edition. Mises Institute, Auburn, Ala.

Runde, Jochen. 2000. “Shackle on Probability,” in Stephen F. Frowen and Peter Earl (eds.), Economics as an Art of Thought: Essays in Memory of G. L. S. Shackle. Routledge, New York.

Skyrms, B. 2010. “Probability, Theories of,” in Jonathan Dancy, Ernest Sosa, and Matthias Steup (eds.), A Companion to Epistemology (2nd edn.). Wiley-Blackwell, Oxford. 622–626.

Hayek on the “Use of Knowledge in Society”

The “knowledge problem” according to Hayek consists in explaining how a market economy overcomes the problem of dispersed, decentralised knowledge of supply and demand and the many individual plans and preferences of both consumers and producers. His classic article on the subject was published in 1945 in the American Economic Review (35.4 [1945]: 519–530).

But, curiously, various Austrians – mainly Misesians and Rothbardians – are unsatisfied with Hayek’s paper. Even Kirzner complains that in the “Use of Knowledge in Society” Hayek made “it appear that the function of prices in communicating knowledge was a function that is filled, in principle, also in the state of equilibrium” (Kirzner 2000: 157).

Salerno argues that Hayek’s “knowledge problem” is different from Mises’s “socialist economic calculation” problem, and even Kirzner concedes that Mises did not formulate the “calculation problem” in terms of knowledge (Kirzner 2000: 158).

What is Hayek’s argument?

The economic data for a whole society are never given to a single mind (Hayek 1945: 519) or given to any person “in its totality” (Hayek 1945: 520). A rational economic order is characterised by a state of affairs in which the relevant information exists “solely as the dispersed bits of incomplete and frequently contradictory knowledge which all the separate individuals possess” (Hayek 1945: 519). Planning is obviously done by very many individuals in a decentralised manner (Hayek 1945: 521).

Therefore the “various ways in which the knowledge on which people base their plans is communicated to them is the crucial problem for any theory explaining the economic process” (Hayek 1945: 520). At the same time, specific knowledge of “particular circumstances of time and place” is also very important (Hayek 1945: 521): that is, the knowledge that individual business people have of their markets, stocks, trades and capital goods, and so on.

In dealing with economic change, the best people to make the decisions are those who know their businesses, resources and markets: the “man on the spot,” as it were (Hayek 1945: 524).

Yet business people need to fit their decisions into the “whole pattern of changes of the larger economic system” (Hayek 1945: 525).

For Hayek, the price system communicates this knowledge (Hayek 1945: 526). As an example, Hayek thinks of an increase in demand for tin. The price of tin rises: buyers of tin now know they must economise tin.

What are the problems with Hayek’s theory?

We can set the problems out below:

(1) the implied assumption of Hayek’s argument (so his Misesian critics argue, perhaps not unfairly) is an economy near equilibrium, or (that is to say) in a “proximal equilibrium” state.

(2) the failure of Hayek to understand the role of Knightian uncertainty;

(3) the role of fixprice markets and administered prices, and

(4) the destabilising role of speculation in prices.

First, the role of “proximal equilibrium.” Hayek briefly nods his head at the reality that price adjustments in the real world are never “perfect” as in equilibrium analysis (Hayek 1945: 527), but his actual theory does require reasonably flexible – if not perfect – price adjustments in all markets. It is no surprise that even Austrian critics of Hayek complained that his theory treats the world as if it is in a state of “proximal equilibrium.”

Salerno contends that:

“as Hayek points out, in order for prices to fulfill their knowledge-disseminating and plan-coordinating functions, the economy must subsist in a state of what I will call ‘proximal equilibrium,’ wherein realized prices are always fairly accurate indicators of future prices.” (Salerno 1993: 128; cf. Horwitz 2004: 314–315).

That is a wholly unrealistic idea.

Secondly, more serious difficulties emerge in Hayek’s neglect of Knightian uncertainty:

“Uncertainty for Hayek means that each individual decision maker only has a small piece of the puzzle. However, as a whole, the aggregated set of all decision makers have a complete set of all relevant knowledge. There are no pieces missing, lacking or unavailable from the puzzle. Market prices organize and synthesize the aggregate amount of knowledge so that market price signals, understood only by savvy, knowledgeable entrepreneurs, [eliminate] … any uncertainty.” (Brady 2011: 14).

“Keynes, Knight and Schumpeter deny Hayek’s claim that the market generates price vectors which concentrate the knowledge so that savvy, knowledgeable entrepreneurs can act on this information and solve the problem of uncertainty. Uncertainty means vital important information is missing. Pieces from the puzzle are missing and will not turn up in the future” (Brady 2011: 14).

“Hayek could not accept the standard concept of uncertainty as defined by Keynes, Knight and Schumpeter because it would then be impossible for market prices to concentrate knowledge that did not exist. In conclusion, nowhere in any of Hayek’s three articles on Knowledge in Economics in 1937, 1945 and 1947 does Hayek deal with the standard view that uncertainty means knowledge that is not there.” (Brady 2011: 15).

Thirdly, factor (3) above – the extensive role of fixprice markets – means Hayek’s vision is deeply flawed.

Prices that remain essentially rigid in response to demand changes (quite frequent in the real world) cannot have the knowledge-communicating role of Hayek’s theory.

And finally Hayek’s theory never considers the destabilising role of commodity speculation. How can relevant information be communicated if prices are distorted by speculative activity, and therefore are not related to underlying supply and demand?

Finally, there is one statement in the paper that Hayek never develops:

“We must look at the price system as such a mechanism for communicating information if we want to understand its real function—a function which, of course, it fulfils less perfectly as prices grow more rigid. (Even when quoted prices have become quite rigid, however, the forces which would operate through changes in price still operate to a considerable extent through changes in the other terms of the contract.)” (Hayek 1945: 526).

It never occurs to Hayek that, in the advanced capitalist economies of his day, prices had already grown rigid in many markets because of administered prices. Yet economic coordination continued to occur, and economic growth and markets continued to function, and after 1945 far better than in previous periods.

And what other forms of the business contract allow economic coordination without flexible prices? Hayek never tells us, but in reality it is “quantity signals” that are the fundamental factor in fixprice markets that equate supply and demand, but Hayek never understood that.

Probably he never properly understood it at any point in his life.

In short, the price system mostly has a secondary coordination role in modern markets: “in actual adjustment of supply and demand, prices play only a very subordinate role, if any” (Kaldor 1985: 25).

BIBLIOGRAPHY

Brady, Michael Emmett. 2011. “Comparing J.M. Keynes’s and F. von Hayek’s Differing Definitions of Uncertainty as it Relates to Knowledge,” January 30.
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1751569

Hayek, F. A. von. 1945. “The Use of Knowledge in Society,” American Economic Review 35.4: 519–530.

Horwitz, S. 2004. “Monetary Calculation and the Unintended Extended Order: The Misesian Microfoundations of the Hayekian Great Society,” Review of Austrian Economics 17.4: 307–321.

Kaldor, Nicholas. 1985. Economics Without Equilibrium. M.E. Sharpe, Armonk, N.Y.

Kirzner, Israel M. 2000. The Driving Force of the Market: Essays in Austrian Economics. Routledge, London and New York.

Salerno, Joseph T. 1993. “Mises and Hayek Dehomogenized,” Review of Austrian Economics 6.2: 113–146.

Austrians Mangle Business Uncertainty

I recommend this post at the ThinkMarkets blog for the Austrian view of uncertainty:

Chidem Kurdas, “Uncertainty and the Keynesians,” ThinkMarkets, July 20, 2012.

The argument of this post is flawed for the following reasons:

(1) Keynesians do not ignore uncertainty, and the heterodox Keynesians, above all, have developed Keynes’s ideas on uncertainty to a very great extent (e.g., G. L. S. Shackle and Paul Davidson).

(2) The implied Austrian belief that having the government “do nothing” would decrease uncertainty, and restore business confidence, is both ridiculous and risible. A “do nothing” policy in 2008 would have collapsed the financial system, caused millions to lose their savings, induced a depression, and shocked business expectations to an extent far greater than any “regime uncertainty” caused by the Obama administration.

(3) Whatever uncertainty is caused by the factors Kurdas lists (Obamacare, regulatory explosion, giant budget deficits, anti-business rhetoric and threats of increasing taxes) – and no doubt to some business people these things are a matter of concern – would be overcome by sufficient aggregate demand and a surge in demand for the products of business.

Paul Davidson has recently addressed this very issue:

“Recently I went to a well-known restaurant in Evanston, Illinois. ... But the night I was there, it was less than half full. I asked the manager if he would he hire more waiters and chefs if his taxes were reduced and/or government removed the existing regulations controlling the way his restaurant could operate. His answer was that even if his taxes were reduced and regulations eliminated, he would only hire more staff if more customers came in for dinner. On the other hand, if there were twice as many customers for dinners than there were on this night (and there were many more customers before the recession began in 2007) he would gladly double the number of workers he employed even if his taxes were not reduced or regulations changed.

That’s how things work in the Real World. This simple case illustrates clearly that entrepreneurs will have confidence to expand and hire more workers only if they find the market demand for their products and services strong and growing.”

Paul Davidson, “Restoring Trust in the American Economy: The Real World v. The Confidence Fairy,” Alternet.org, July 11, 2012.

This is the reality of business confidence – Austrian fairy tales of “evil” government notwithstanding.

Peter Lewin on Expectations and Uncertainty

Peter Lewin (Professor of economics at the University of Texas at Dallas) talks here about expectations and uncertainty. Lewin claims an affiliation with the Austrian School, and in particular Ludwig M. Lachmann.

It seems to me that he misses a fundamental point about radical uncertainty here: this type of uncertainty concerns the inability of agents to calculate risk in the sense of assigning objective probability scores to outcomes in the future.

Roger Koppl on Modern Austrian Economics

Continuing on from themes raised in the last post, I was struck by an insightful comment by Roger Koppl here on Rizzo’s ThinkMarkets post (“Yes, Paul: It is Hayek versus Keynes,” December 7, 2011). The comment is here (slightly edited):

“[sc. Austrians] ... need to deal with the epistemic Keynes that Shackle so rightly emphasized. But the thing is that we have and we do! The O’Driscoll & Rizzo volume certainly does that. My Big Players work with Butos, Yeager, and others is all about engaging the epistemic Keynes as was my HOPE [sc. History of Political Economy] article with Butos on Hayek vs. Keynes. And the Post Keynesian responded [quite] ... a bit to that HOPE article. Butos and I got several published comments from Post Keynesians and responded to them. Steve Horwitz pointed out ... over at Coordinationproblem that this year’s SDAE best-paper prize went to Dave Prychitko’s paper on Minsky, a Post Keynesian. My recent RAE paper with Will Luther addresses Minsky and animal spirits. And so on. If you re-read Karen Vaughn’s book on “Austrian Economics in America,” you will see that it is a story about how, basically, Lachmann won the day. Lachmann taught us that we need to deal with the epistemic Keynes and we got the lesson. It was a huge change in the American branch of Austrian economics. ...”

Roger Koppl @December 8, 2011 at 8:09 am, ThinkMarkets

Both Karen Vaughn and Ludwig Lachmann rate highly on my list of Austrians. Indeed, of all the Austrians, Lachmann’s work is the most congenial (as it were) to Post Keynesians. The issues of radical uncertainty and subjective expectations, and how they influence the level of investment are at the heart of Post Keynesian economics and were also taken seriously by Lachmann. Roger Koppl has put his finger on the problem that modern Austrian economics has from the Post Keynesian perspective.

Roger Koppl refers to recent Austrian literature worth reading:

Butos, W. N. and R. Koppl, 1997. “The Varieties of Subjectivism: Keynes and Hayek on Expectations,” History of Political Economy 29.2: 327–359.

Koppl, R. and J. B. Rosser, 2002. “All That I Have to Say has Already Crossed Your Mind,” Metroeconomica 53.4: 339-360.

Koppl, R. and W. J. Luther, 2011. “Hayek, Keynes, and Modern Macroeconomics,” Review of Austrian Economics (July): 1-19.
This paper can also be downloaded through the Social Science Research Network (SSRN).

Prychitko, David L. 2010. “Competing Explanations of the Minsky Moment: The Financial Instability Hypothesis in Light of Austrian Theory,” Review of Austrian Economics 23: 199-221.

I am currently reading these and hope to comment on them soon.

How Can Government Overcome Uncertainty?

S. D. Parsons poses the following question:

“Post Keynesian economists can, with considerable justification, criticize the view in some Austrian circles that it is possible to emphasize both uncertainty and market coordination. However, it would also seem that the Post Keynesian emphasis on uncertainty raises problems for the argument that governments can resolve coordination problems. ... Keynes may well have correctly identified problems of market coordination when he wrote, and correctly identified policy instruments to resolve them. However, given uncertainty, the past is a fickle guide to the future and, given transmutation, the world is now a different place. In conclusion, Post Keynesians have a valid point when they argue that an emphasis on economic uncertainty raises problems for the assumption that market coordination can occur in the absence of governmental intervention. However, it can also be argued that the emphasis on uncertainty raises problems for the assumption that market coordination can occur through government intervention.” (Parsons 2003: 9).

It is not, however, difficult to answer these charges.

When you introduce an intervention to influence the state of a nonergodic stochastic system, that process and outcome is not in the same ontological category or status as the future of that system, without intervention. The past data from which one draws inferences about what the intervention will do consist of examples of past such interventions, ideally of the same type. For example, there is no doubt that induction from past data will not be a reliable method to predict the future value of certain shares on the stock market or the future value of the whole market itself measured by some index, but predicting what happens when an entity with the power to influence certain shares or the whole system is a different matter. If the Treasury bought up the stock of a certain promising company, making the shares scarce when demand is high, announcing it will even support the value of the shares, we can make a empirical prediction about the outcome, which can be falsified. How? I have already addressed the question of the epistemological justification for such things and even Keynesian stimulus (and other government interventions) here:

“Risk and Uncertainty in Post Keynesian Economics,” December 8, 2010.

The problem revolves around whether induction can be rationally justified. If one thinks that induction can be defended rationally, then inductive arguments using past empirical evidence can be used to provide justification for policy interventions. Induction can be reliable when used outside of nonergodic stochastic systems or events. If one thinks that induction has no rational justification, then Karl Popper’s falsificationism by hypothetico-deduction can be used to test predictive hypotheses about what will happened in the future under government intervention. In the absence of falsification, we have empirical support for such polices.

Fundamentally, if Austrians or neoclassicals think that they can evade their own such epistemological problems, they are deeply mistaken. How, for example, does the Austrian praxeologist justify his belief that that the axiom of disutility of labour will continue to be true in the future? Mises explicitly tells us that this axiom is “not of a categorial and aprioristic character”, but “experience teaches that there is disutility of labor” (Mises 1998: 65). In other words, it is a synthetic proposition and its truth is only known a posteriori. Praxeologists require either induction or Popper’s falsificationism by hypothetico-deduction using empirical evidence to justify their belief in its truth now and for the future.

The concept of radical uncertainty in the Post Keynesian or Knightian sense applies to non-ergodic, stochastic systems. But human life does not just consist only of non-ergodic systems. The economic system we know as capitalism, where most commodities are produced by decentralised investment decision-making by millions of agents and consumption by other agents with shifting subjective utilities, is not the only institution of modern life. We have government and quasi-government entities, private non-profit organisations, private voluntary organisations, and at the basic level families.

The free market itself has attempted to overcome uncertainty by certain institutions. Government interventions in economies are merely a much more powerful and more effective instrument for reducing uncertainty than what has emerged on the market.

Its many institutions that exist alongside and influence modern capitalism (such as law courts that enforce contracts, buffer stocks, and even central banks) have developed precisely to deal with uncertainty, as “outside” entities capable of reducing uncertainty by interventions designed to influence the state of the system. Law and order is a basic human institution without which commerce would be impossible. It has been enforced through the ages essentially by governments, not by private enterprise. When, for example, the trade of the Roman Republic was threatened by pirates in the east Mediterranean, it was the state that ended that threat and allowed commerce to resume with confidence. Indeed, some conventions or institutions that reduce uncertainty (for example, forward/future markets for commodities, and even money) are so deeply ingrained that we think of them now as a fundamental part of capitalism. A futures market was developed to reduce uncertainty for producers of commodities, often primary commodities. There is a great deal of evidence that standardised coinage in Western European civilisation was essentially the invention of the state. Indeed, the state had a great role in monetising economies.

Central banks developed in the 19th and 20th centuries precisely because business and financial interests wanted a system that would reduce the uncertainty caused by liquidity crises and financial panics, because they were frightened by the potentially disastrous consequences of unregulated financial markets and banking systems.

It is interesting that the Austrian Ludwig Lachmann’s view that institutions have an important part to play in free market systems is similar to the view I have had described above. It is important to note the logical consequences these ideas had for Lachmann as well:

“Because of his focus on uncertainty, Lachmann came to doubt that, in a laissez-faire society, entrepreneurs would be able to achieve any consistent meshing of their plans. The economy, instead of possessing a tendency toward equilibrium, was instead likely to careen out of control at any time. Lachmann thought that the government had a role to play in stabilizing the economic system and increasing the coordination of entrepreneurial plans. We call his position ‘intervention for stability.’” (Callahan 2004: 293).

While I doubt whether Lachmann’s interventions would have been anything but minimal by Post Keynesian standards, nevertheless his intellectual journey is actually a lesson for his fellow Austrians: once they take fundamental uncertainty and subjective expectations seriously they would find themselves forced to much the same conclusions that he eventually drew.


BIBLIOGRAPHY

Barkley Rosser, J. 2010. “How Complex are the Austrians?,” in R. Koppl, S. Horwitz, and P. Desrochers (eds), What is So Austrian About Austrian Economics?, Emerald Group Publishing Limited, Bingley, UK. 165–180.

Callahan, G. 2004. Economics for Real People: An Introduction to the Austrian School (2nd edn), Ludwig von Mises Institute, Auburn, Ala.

Parsons, S. D. 2003. “Austrian School of Economics,” in J. E. King (ed.), The Elgar Companion to Post Keynesian Economics, E. Elgar Pub., Cheltenham, UK and Northhampton, MA. 5–10.

A Note on Mises and the Concept of Uncertainty

A rather good question is posed here about Mises and the concept of uncertainty in the Knightian sense.

The earliest work by Ludwig von Mises on uncertainty that I can think of is in the English edition of Human Action (1949) in Chapter VI, long after Keynes had published “The General Theory of Employment,” Quarterly Journal of Economics 51 (1937): 209–223.

Both Keynes and Frank Knight came to their views on uncertainty largely independently without any influence from Mises, as far as I am aware.

Frank H. Knight’s Risk, Uncertainty and Profit (Houghton Mifflin, Boston) was published in 1921. I see no evidence Knight was influenced by Mises. Curiously, in the introduction to the scholar’s edition of Human Action, we have this account of Mises’s inspiration for the chapter on uncertainty:

“Several commentators have noted the similarity of Mises’s distinction between class probability and case probability and that between risk and uncertainty introduced by Knight in Risk, Uncertainty and Profit in 1921.

Yet, it does not appear that Mises was influenced by Knight in this regard. Mises had been long familiar with Knight’s work, and had already made reference to Risk, Uncertainty and Profit in Nationalökonomie in conjunction with his discussion of profit and uncertainty.

Rather, it appears more likely that Mises’s Chapter VI was stimulated and influenced by his younger brother, Richard von Mises (1883–1953). A professor of aerodynamics and applied mathematics at Harvard University, Richard von Mises’s most outstanding theoretical achievement was his contribution, from 1919 onward, to the frequency theory of probability. In principle, Ludwig accepted Richard’s frequency interpretation of probability, but Ludwig provided a new definition of randomness, and thus significantly improved on Richard’s theory. (Mises 1998: xvi).

But the idea that Mises was not influenced by Knight in regard to his chapter on uncertainty rings hollow to me. If Mises had been “long familiar with Knight’s work” and actually cited it in Nationalökonomie (first published in Geneva in 1940), then the view that there was some influence from Knight on Mises’s thinking on uncertainty seems plausible.


BIBLIOGRAPHY

Knight, F. H. 1921. Risk, Uncertainty and Profit, Houghton Mifflin, Boston.

Mises, L. 1940. Nationalökonomie, Theorie des Handelns und Wirtschaftens, Éditions Union, Geneva.

Mises, L. 1998. Human Action: A Treatise on Economics. The Scholar’s Edition, Mises Institute, Auburn, Ala.

Keynes, J. M. “The General Theory of Employment,” Quarterly Journal of Economics 51 (1937): 209–223.

Uncertainty and Non-Ergodic Stochastic Systems

The concept of uncertainty in economic life was used by Keynes in the General Theory (1936) and also in an article defending his new theory the next year (see Keynes, “The General Theory of Employment,” Quarterly Journal of Economics 51 [1937]: 209–223).

Paul Davidson notes the nature of uncertainty in the Keynesian/Knightian sense:

“Keynes’s description of uncertainty matches technically what mathematical statisticians call a nonergodic stochastic system. In a nonergodic system, one can never expect whatever data set exists today to provide a reliable guide to future outcomes. In such a world, markets cannot be efficient” (Davidson 2002: 187).

“Keynes … rejected this view that past information from economic time-series realizations provides reliable, useful data which permit stochastic predictions of the economic future. In a world where observations are drawn from a non-ergodic stochastic environment, past data cannot provide any reliable information about future probability distributions. Agents in a non-ergodic environment ‘know’ they cannot reliably know future outcomes. In an economy operating in a non-ergodic environment, therefore – our economic world – liquidity matters, money is never neutral, and neither Say’s Law nor Walras’s Law is relevant. In such a world, Keynes’s revolutionary logical analysis is relevant” (Davidson 2006: 150).

Certain types of phenomena in our universe are what mathematicians call non-ergodic stochastic systems. The concept of radical uncertainty applies to such systems, like medium term weather events, financial markets, and economies, and other natural systems studied in physics.

In these systems, past data is not a useful tool from which one can derive an objective probability score for some specific, future state of a quantitative variable in the system. Of course, such a system can still have trends, cycles and oscillations, both in the past and future. For example, stock markets certainly have cycles of bull and bear phases, but trying to predict the specific value of some stock x, say, two years from now with an objective probability score is not possible.

But the fundamental point is that it is still possible for a powerful agency or entity to reduce uncertainty in these systems, or at least in theory in some of them. It is entirely possible that in the future – with a far more advanced human civilization – we could use technology to control local, regional or perhaps even global weather.

And even today a powerful entity like the government can intervene to reduce uncertainty in the non-ergodic stochastic system we call the economy.


Is Climate a Non-Ergodic Stochastic System?

Does the earth’s climate system have the property of non-ergodicity? This question has occurred to me more than once, but I am actually unsure of the answer.

Some quick research suggests that climate models appear to make an ergodicity assumption about climate systems:

“Thus, it is perfectly valid to consider our climate a realization of a continuous stochastic process even though the time-evolution of any particular path is governed by physical laws. In order to apply this fact to our diagnostics of the observed and simulated climate we have to assume that the climate is ergodic. That is, we have to assume that every trajectory will eventually visit all parts of phase space and that sampling in time is equivalent to sampling different paths through phase space. Without this assumption about the operation of our physical system the study of the climate would be all but impossible.

The assumption of ergodicity is well founded, at least on shorter time scales, in the atmosphere and the ocean. In both media, the laws of physics describe turbulent fluids with limited predictability (ie, small perturbations grow quickly, so two paths through phase space diverge quickly) (von Storch and Zwiers 1999: 29–30).

But then what about longer time scales? If “the laws of physics describe turbulent fluids with limited predictability” on short time scales, what sort of predictability can they provide on medium or long term time scales?

Let’s assume, for the sake of argument, that long term climate is non-ergodic, in the way that a free market economy is. Does that mean all intervention would be useless and ineffective in such a system to affect the state of it? Does it mean that we are all doomed to (in a manner of speaking) live in a “free market” climate forever?

In fact, that does not follow at all. It is probably very likely that our future technology, when it becomes sophisticated and powerful enough, will be used by humans to intervene and control climate, e.g., by preventing ice ages.


BIBLIOGRAPHY

David, P. A. 2007. “Path Dependence, its Critics and the Quest for ‘Historical Economics,’” in G. M. Hodgson, The Evolution of Economic Institutions: A Critical Reader, Edward Elgar, Cheltenham. 120–144.

Davidson, P. 2002. Financial Markets, Money, and the Real World, Edward Elgar, Cheltenham.

Davidson, P. 2004. “Uncertainty and Monetary Policy,” in P. Mooslechner, H. Schuberth, and M. Schürz (eds), Economic Policy under Uncertainty: The Role of Truth and Accountability in Policy Advice, Edward Elgar, Cheltenham. 233–260.

Davidson, P. 2006. “Keynes and Money,” in P. Arestis and M. Sawyer (eds), A Handbook of Alternative Monetary Economics, Edward Elgar, Cheltenham, UK and Northampton, Mass. 139–153.

Keynes, J. M. 1937. “The General Theory of Employment,” Quarterly Journal of Economics 51 (February): 209–223.

Storch, H. von and F. W. Zwiers, 1999. Statistical Analysis in Climate Research, Cambridge University Press, Cambridge, UK and New York.

Post Keynesian Concept of Uncertainty goes Mainstream?

There is an interesting article in the Economist attacking the efficient market hypothesis, citing work by Roman Frydman and Michael Goldberg in Beyond Mechanical Markets: Asset Price Swings, Risk and the Role of the State (Princeton University Press, 2011), which relies, to some extent, on the concept of uncertainty in economic systems and the inability to calculate objective probabilities for important factors in the investment decision:

“A New Attempt to Explain Market Inefficiency,” Economist, February 24th, 2011.

This follows some good work by the New Keynesians G. A. Akerlof and R. J. Shiller in Animal Spirits: How Human Psychology drives the Economy, and Why it Matters for Global Capitalism (Princeton University Press, 2009) that clearly approaches the Post Keynesian concept of subjective expectations.

All this is good news: the mainstream is catching up with Post Keynesians!

Addendum: The Concept of Uncertainty in Other Schools

Uncertainty in the sense of not being able to assign objective probabilities to economic outcomes and events in the future is an idea fundamental to Post Keynesian economics.

Daniel Kuehn in a post called “More on Keynesian Uncertainty” (March 1, 2011) makes some comments on my post, and wonders why I connect “Keynesian uncertainty to Post-Keynesians specifically.”

It is perfectly true that the concept is held by other economists. The neoclassical Chicago school economist Frank Knight drew the distinction between risk and uncertainty. But have the neoclassicals ever adopted his ideas? They have not. The New Classicals and monetarists adopt the ergodic axiom and rational expectations, which are not compatible with the Keynesian/Knightian concept of uncertainty which is not measureable.

Radical uncertainty – uncertainty in the Knightian sense – was also stressed by Keynes and made a fundamental part of his macroeconomic theory in the The General Theory of Employment, Interest, and Money (1936).

G. L. S. Shackle (1903–1992) also adopted the idea from Keynes, and Shackle was generally classified as a hybrid Austrian/Post Keynesian (or someone who drew “Keynesian conclusions from Austrian premises”).

The Austrian radical subjectivists in the tradition of Ludwig Lachmann also use the idea of uncertainty in economic systems, and correctly draw from this the conclusion that expectations are subjective. But it is quite clear that Lachmann was influenced to a considerable extent by Keynes and Shackle in this, as well as Mises, and that Lachmann criticised Mises for failing to extend subjectivism to the realm of expectations (Gloria-Palermo 1999: 128).

Lachmann and the Austrian radical subjectivists supported the subjectivist nature of value, expectations, and knowledge, and Lachmann denied that free market systems tend to equilibrium or have coordinating processes, which follows logically from understanding the role of money, time and uncertainty in economic processes.

But then Lachmann’s position is rejected by other Austrians like Kirzner as “nihilism,” so there are clear limits to the extent to which Austrian economics really takes these ideas seriously.

BIBLIOGRAPHY

Gloria-Palermo, S. 1999. The Evolution of Austrian Economics: From Menger to Lachmann, Routledge, London and New York.

Risk and Uncertainty in Post Keynesian Economics

The distinction between risk and uncertainty is fundamental in Post Keynesian economics, as it was in the economic thinking of John Maynard Keynes. While risk can be quantified, uncertainty simply cannot be quantified.

The notion of future uncertainty is partly related to David Hume’s difficult philosophical problem of induction. According to Hume, we have no basis for believing that induction is rational. Human beings use induction out of habit, but there is no rational justification for this belief in induction. Although a full discussion of the problem of induction is not what I want to do here, a few comments can be made.

In brief, induction is the use of a finite number of specific observations to make a general conclusion. The most common form is the use of past observations of events to infer a general conclusion about how nature will behave in the future (e.g., gravity will continue to operate tomorrow or 10 minutes from now). But we can just as easily appeal to observed events in the recent past or present to make a general conclusion about the past (e.g., miracles do not happen in the past). The conclusion of an inductive argument is only probable and never certain.

The trouble is that observation of a finite number of events in the past does not seem to justify the belief that events will continue to happen in this way in the future. One solution to the problem is to assume the uniformity of nature. But even here the assumption that nature is uniform requires an inductive argument, so the reasoning is circular (and note that the radical sense of uncertainty that emerges in the absence of a justification for induction and the uniformity of nature is rather different from Keynesian uncertainty, which relates to future events for which no measurable probability can be given, as we will see below). Various philosophers have tried to solve the problem of induction (Will 1953; Edwards 1965; Strawson 1952; BonJour 1998; Salmon 1974), but many believe there is no satisfactory justification. Hume concluded that we use induction out of habit but with no legitimate basis, and modern evolutionary psychologists might argue that our ability to instinctively reason inductively is an innate property of the brain (like language or moral sentiments), which we have acquired by evolution, even though there is no rational justification for it.

Probably the best solution to the problem of induction (for both the natural and social sciences) is the use of Karl Popper’s critical rationalism (even though Popper’s critical rationalism seems to be widely criticised in modern analytic philosophy [Musgrave 2004: 16–17], and another complaint is that, while many working scientists claim to be Popperians in method, in practice they behave like good Bayesian inductivists [Evans 2007: 33]).

Popper adopts the hypothetico-deductive method, with falsification (not verification) of hypotheses by empirical evidence the key. In hypothetico-deduction, we use the method of forming a hypothesis, deducing predictions or conclusions from it, then empirically testing the predictions or conclusions, and thereby attempting to falsify the hypothesis. Popper comes to an astonishing conclusion:

“I go further than Hume: I hold that inductive procedures simply do not exist (not even low-level ones) and that the story of their existence is a myth” (Popper 1983: 118; see also Musgrave 2004: 18).

Induction is also unnecessary: we simply do not need it. In the natural sciences and other scientific inquiry, what really happens is that we use deduction, especially by means of the modus tollens and the elimination of erroneous hypotheses by empirical evidence, in a process of trial and error (more on this below).

John Maynard Keynes’ Treatise on Probability (1921) was an attempt to answer Hume on the problem of induction. Keynes took the view that induction was justified by a relation of probability which was objective (although later he conceded that probability was not an objective relation). But Keynes also came to argue that there must be a distinction between risk and uncertainty.

Risk is something where a measurable probability can be given to outcomes, e.g., the probability of rolling a 3 when throwing a dice is 1 in 6 (Glickman 2003: 366). In contrast, we face fundamental uncertainty about many other events, and no measurable probabilities can be assigned (e.g., what the interest rate will be in 10 years time). This distinction between risk and uncertainty was also made by Frank Knight.

Keynes’s conception of uncertainty applies to what is technically called a nonergodic stochastic system (Davidson 2002: 187). Our economies are such complex systems, as are many other economic phenomena (e.g., stock markets and financial markets).

We face fundamental ontological or metaphysical uncertainty about many events in the future, a state of affairs which – when applied to economics – can be called “Keynesian uncertainty” (or uncertainty in the sense of Frank Knight, Keynes, George L. S. Shackle and Ludwig Lachmann).

Since there is fundamental uncertainty about many economic variables in the future (particularly in the long-term future), investment decisions cannot be based on a firm calculation of probabilities of future earnings. Since subjective preferences can change in the future, not all investment decisions today will be profitable in the future. The process involved in decision-making about investment and the action of investment itself is not rational calculation.

There is a time difference between production of commodities and the successful sale of those commodities at a profit, and so even the production of commodities has a speculative element, in which decisions to invest in production involve habits of minds, instincts, and conventions.

Thus the investment decisions of a firm are based on expectations of future earnings, which are not necessarily rational at all. When investment decisions are made, they are done under conditions of subjective expectations by business, and the expectations depend on what Keynes called “animal spirits” (for the original concept, see Gerrard 1994). Keynes discussed the factors influencing long-run expectations in Chapter 12 of the General Theory. Because of uncertainty about the future, expectations in investment decisions is not a matter of mathematical calculation. Decisions to invest are taken

“as a result of animal spirits – of a spontaneous urge to action rather than inaction, and not as the outcome of a weighted average of quantitative benefits multiplied by quantitative probabilities” (Keynes 2008 [1936]: 144).

The term “animal spirits” was borrowed by Keynes from Descartes (Gerrard 1994: 15), and whether or not modern psychology provides support for Keynes’ idea that we have a “spontaneous urge to action rather than inaction” is really irrelevant. The fundamental point here is that, because of uncertainty about the future and changing subjective preferences of consumers and other exogenous factors driving supply and demand, there can be no genuine rationality in expectations. Expectations are subjective, and the investment decision is essentially non-rational.

There has been a resurgence of interest in subjective expectations since the great recession of 2008/2009, and most notably the New Keynesians George A. Akerlof and Robert J. Shiller have published a book studying this subject (Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism, 2009).

Neoclassical economics in its various forms (e.g., general equilibrium analysis, the efficient market hypothesis, and New Classical economics) assumes the ergodic axiom, the belief that the future can be known or events in the future given objective probabilities. But recognition of the ontological uncertainty of events in the future and changing subjective preferences means that events cannot be quantified in terms of probabilities. This destroys the basis of rational expectations and other neoclassical theories.

In the face of the shock of the financial crisis in 2008, the severe global recession in 2008–2009, and debt deflation, business expectations across the world plummeted. The destruction of optimistic expectations is undoubtedly an important factor that will mire many economies around the world in periods of stagnation with low growth and high unemployment in years to come. The Keynesian resurgence and stimulus packages in many countries in 2008–2009 prevented a depression, but much more stimulus is needed in the major economies to restore better growth and to bring unemployment down (there are of course other problems too, such as poorly regulated financial markets and loss of manufacturing in countries like the US that will have to be fixed). Government spending and fiscal stimulus can help to overcome the poor business expectations in many countries.

To return to an earlier point above, how can we justify government intervention in the face of uncertainty? If one accepts that induction can be justified (perhaps along the lines of Will 1953 or Strawson 1952), then obviously one could use an inductive argument for government intervention.

But, if we argue that induction has severe philosophical problems and cannot be justified, how in fact do we justify the hypothesis that government stimulus packages should be used and will work? My answer is that we do not need inductive arguments. We can use Karl Popper’s hypothetico-deductive method to test Keynesian hypotheses about stimulus. Our Keynesian models of stimulus will make predictions about what will happen to an economy, and, if they are not falsified by the empirical evidence, they will have passed the test of falsification, just as they have many times in the past. Popper argued that the hypothetico-deductive method was fundamental to both the natural sciences and social sciences, and he makes a good case for this (Popper 1976: 130–143; Milonakis and Fine 2009: 262–263). In short, induction is unnecessary, and economic methodology can be hypothetico-deductive with falsification of hypotheses by empirical evidence, as M. Blaug (1992) has also argued.

The issue of how Popper’s critical rationalism should be properly applied to economics is not my purpose, and the issue is a difficult one.

Of course, there are numerous economists who do not think that a strict Popperian methodology of economics is workable (Caldwell 1985: 126), and on this subject one can consult as a starting point the critical essays in N. De Marchi (ed.). The Popperian Legacy in Economics: Papers Presented at a Symposium in Amsterdam, December 1985 (Cambridge and New York, 1988), with a response by L. A. Boland (1990–1992).

Lawrence A. Boland contends that economists promoting a Popperian methodology for economics have not fully understood Popper’s critical rationalism, and have been misled by Imre Lakatos’s alleged distortion of Popper’s thought by overemphasizing the role of falsificationism (Boland 2006: 222–223). Boland (2006: 223–224) believes that an Imre Lakatos-inspired and pseudo-Popperian method has been adopted by some proponents of a Popperian methodology for economics.

Although this question requires a post in its own right, there is no doubt that Popper argued for the unity of method and the importance of hypothetico-deduction in the The Poverty of Historicism:

“I … propose a doctrine of the unity of method; that is to say, the view that all theoretical or generalizing sciences make use of the same method, whether they are natural sciences or social sciences …. I do not intend to assert that there are no differences whatever between the methods of the theoretical sciences of nature and of society …. But I agree with Comte and Mill—and with many others, such as C. Menger—that the methods in the two fields are fundamentally the same (though what I understand by them may not be what they had in mind). The methods always amount to deductive causal explanation, prediction, and testing, as sketched in the foregoing section. This has sometimes been called the hypothetico-deductive method, or more often the method of hypothesis, for it does not achieve absolute certainty for any of the scientific statements which it tests; rather, these statements always retain the character of tentative hypotheses, even though their character of tentativeness may cease to be obvious after they have passed a great number of severe tests” (Popper 1976: 130–131).

I will note in conclusion that a Critical Realist methodology is often proposed for Post Keynesianism (King 2002: 197–200; Jespersen 2009), and that this position might be compatible with the core elements of a Popperian methodology too (King 2002: 253–254; Jespersen 2009: 57–62), and that in his later work Popper appears to have moved closer to Critical Realism (Lawson 1999: 8–9).

APPENDIX 1: THE PARADOX OF INDUCTION?

I noted above that an intuitive ability to reason inductively is probably an innate trait of the human mind, given to us by evolution by natural selection. Animals, for example, appear to use rudimentary induction in probability calculations when they forage for food (Real 1991).

Even though there is no rational justification for it, why then has induction been so successful and obviously selected for as a survival trait? Even if Popper and Hume are right, we are faced with the paradox that we appear use induction very frequently and that it is a successful form of reasoning, by and large (the fact that induction is of limited use in non-ergodic stochastic systems and that we can err in our inductive arguments does not change this fact [for a list of common fallacies in defective inductive reasoning, see Copi and Cohen 2005: 140–145]).

That inductive reasoning has been highly successful and useful in increasing our chances of survival (and was thus selected by evolution) does not necessarily mean that is it rationally justified, of course.

For example, the belief in life after death might very well help some human beings overcome the trauma and stress of losing a loved one or even contemplating their own death (perhaps it might even make them healthier and better able to survive?), but it is still an irrational, unjustified idea.

But the success of induction is presumably explained by the uniformity of nature which has persisted since the first living things with rudimentary inductive reasoning evolved (for example, if the law of gravity had stopped working 1 million years ago, then all land animals would simply have floated off the planet into space and been killed, and there would be no human beings today; the fact that we are here strongly suggests that basic natural laws have remained uniform).

Human beings have also evolved to use induction. I am aware that this is somewhat similar to Willard Van Orman Quine’s (1908–2000) naturalized epistemology and his explanation of why induction is successful. Quine argues that, while Darwinian evolution does not justify induction, it must explain why it is so effective (Derksen 2000: 27–28; Quine 1975; as an aside, Quine also argues that there is no synthetic/analytic distinction. Thus analytic propositions are just firmly-held synthetic propositions, and there are no real a priori propositions. Quine agrees with Popper that the essence of science is the falsificationist hypothetico-deductive method). And, if the uniformity of nature continues to hold in the future, then presumably inductive reasoning will continue to be successful in those areas where it works well.


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