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.