Savage for Dummies and Experts Abstract   Savageu0027s foundation of expected utility is considered to be the most convincing justification of Bayesian expected utility and the crowning glory of decision theory. It combines exceptionally appealing axioms with deep mathematics. Despite the wide influence and deep respect that Savage received in economics and statistics, virtually no one touched his mathematical tools. We provide an updated analysis that is more general and more accessible. Our derivations are selfcontained. This helps to better appreciate the depth and beauty of Savageu0027s work and the foundations of Bayesianism, to teach it more easily, and to develop nonBayesian generalizations incorporating ambiguity more efficiently.

Dynamic Consistency and Ambiguity: A Reappraisal It is commonly argued that dynamic consistency, consequentialism and nonexpected utility are incompatible. The first aim of this paper is to rebut such arguments, by targeting the implicit assumption that the relevant contingencies correspond to objective resolutions of uncertainty (that is, events in a state space). These are not necessarily the same as the contingencies that the decision maker envisages, and we argue that any reasonable notion of dynamic consistency involves the latter, rather than the former, sort of contingency. We formulate such a version of dynamic consistency and show it to be compatible with consequentialism and nonexpected utility. We then analyze the economic consequences of this new perspective. On the one hand, it provides a principled justification for restrictions on nonexpected utility models (such as that proposed by Epstein and Schneider (2003)) in applications to dynamic choice problems. On the other hand, it provides a new analysis of the issue of attitude to information; contra standard arguments, the value of information under nonexpected utility is nonnegative as long as the information offered does not compromise information that the decision maker had otherwise expected to receive. Finally, we give a representation theorem for the contingencies the decision maker envisages, in the case where he uses the maxmin expected utility rule.