ABSTRACT. This paper examines the difference between strategic ambiguity, as in game theory, versus “nature” ambiguity, as in individual decisions. We identify a new, non-strategic, component underlying all strategic ambiguities, called social ambiguity. We recommend correcting for it so as to better identify strategic causes. Thus, we shed new light on Bohnet and Zeckhauser’s betrayal aversion in the trust game. Contrary to preceding claims in the literature, ambiguity attitudes do play a role there. Social ambiguity, rather than betrayal aversion, can explain the empirical findings. These results show the importance of controlling for ambiguity attitudes before speculating on strategic factors.
ABSTRACT. Savage's 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 way more accessible. Our derivations are self-contained. This helps to understand the depth and beauty of Savage's work and the foundations of Bayesianism better, to more easily teach it, and to more easily develop non-Bayesian generalizations incorporating ambiguity.
ABSTRACT. When measuring ambiguity attitudes one should control for subjective beliefs, but those are usually not directly observable. Hence, measurements focused on artificial events (secretized urns or researcher-specified probability intervals), where beliefs could be inferred from symmetry conditions. For application-relevant events, however, such symmetries are rarely available. This paper solves this problem. We show that ambiguity attitudes then can still be identified by using belief hedges—collections of events that protect against unknown beliefs. We define two indexes of ambiguity attitudes that can be used under all ambiguity models popular today. This solves a second problem: there are (too) many ambiguity models today, leaving practitioners at a loss to choose one. Our indexes are compatible with virtually all existing indexes and ambiguity orderings wherever those are defined, showing that they properly capture the general ambiguity concepts. We ensure that the indexes are directly observable from revealed preferences, and we axiomatize them.
ABSTRACT. This paper introduces the Prince incentive system for measuring preferences. Prince is a variation of the random incentive system devised to maximize the perception of isolation with subjects, and to minimize any perception of meta-lotteries. It combines the tractability of direct matching, allowing for the precise and direct elicitation of indifference values, with the clarity and validity of choice lists. It makes incentive compatibility completely transparent to subjects, avoiding the opaqueness of the Becker-DeGroot-Marschak mechanism. It can be used for adaptive experiments while avoiding any possibility of strategic behavior by subjects, and even avoiding any suggestion in that direction. To illustrate the wide applicability of Prince, we investigate preference reversals, the discrepancy between willingness to pay and willingness to accept, and the major components of decision under uncertainty: utilities, subjective beliefs, and ambiguity attitudes. In particular, Prince allows for measuring utility under risk and ambiguity even if expected utility is violated, in a tractable and incentive-compatible manner. Our empirical findings support modern views, e.g., confirming the endowment effect and showing that utility is closer to linear than classically thought. In a comparative study, Prince outperforms a classical implementation of the random incentive system.
Last updated: 4 October, 2019
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