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Decision Making under Extreme Uncertainty

Опубликовано на портале: 30-04-2004
Cambridge, 2002
Организация: Harvard University
Подтип: PhD
Тематические разделы: Экономика, Микроэкономика

This dissertation derives restrictions on an agent's behavior from invariance principles, in particular, from the requirement that an agent's choices be independent of the units of measurement. It is shown that invariance principles are sufficient for approximating an agent's behavior in environments where little information is available. Strong restrictions on admissible shape of tails of the posterior distribution of an unknown variable are derived. For the case of independently and identically distributed draws, closed form solution for the family of tails of posterior distribution is obtained. It is shown that even if an agent assumes that the variable in question is drawn from a finite parametric family of distributions with exponential-like tails, the posterior distribution obtained by integrating out the unknown parameters has very fat tails. Many results obtained in this work do not rely on expected utility axioms and thus could be combined with either expected or non-expected utility theories. Decision problems arising in the areas ranging from industrial R&D planning to risk management provide motivation and potential applications for the theory developed herein.

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"This dissertation investigates decision making in environments of extreme uncertainty. We use invariance principles to derive restrictions on an agent’s behavior. According to invariance principles, an agent treats the units of measurement as an uninformative label. For instance, a broker can perform calculations in dollars or in cents, but resulting decision to buy or sell should not depend on the choice of the measurement scale..."

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