Abstract
It was common in the past to evaluate projects by their expected outcome, until Saint Petersburg Paradox emerges. The well-known Saint Petersburg Paradox has led Bernoulli in 1738 to develop a theory asserting that investor make choices based on expected utility from wealth and not based on the expected wealth itself. Note that the paradox emerges mainly from experimental observations: investors who face a specific game with an infinite expected monetary value are willing to pay only a few dollars to participate in such a game-hence the paradox. Indeed employing the log utility function suggested by Bernoulli solves the paradox, as the value of the game with this function is indeed only a few dollars, which is consistent with the experimental observations. In 1944, von Neumann and Morgenstern have developed a formal theoretical model which supports the Bernoulli’s solution of the paradox. The expected utility paradigm proved by them can be justified if one is willing to accept some set of very appealing axioms. One may calculate the expected utility with either objective probabilities or subjective probabilities.
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The payoff is determined by the outcome offer deleting one zero and stating it in Israeli Shekels. Thus, –$100 is involved with a loss of 10/4.5 ≅ $2.2 where 4.5 is the exchange rate between Israeli Shekels and US dollars. The subjects received an initial endowment such that even if −100 occurs, they end up with zero net balance.
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The 260 subjects are composed from undergraduate students, MBA students, Executive MBS students and Fund managers. For the differences in the FSD violation s of the various groups, see Levy, footnote 33.
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See footnote 6.
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References
Kahneman, D. and A. Tversky, “Prospect Theory: An Analysis of Decision Under Risk,” Econometrica, 47, 1979, 263–291.
Levy, H., and Wiener, Z., “Stochastic Dominance and Prospect Dominance with Subjective Weighting Functions,” Journal of Risk and Uncertainty, 16, 1998, pp. 147–163.
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Levy, H. (2016). Non-expected Utility and Stochastic Dominance. In: Stochastic Dominance. Springer, Cham. https://doi.org/10.1007/978-3-319-21708-6_15
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