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.
