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Despite the increasing importance of multiple priors in various domains of economics and the significant theoretical advances concerning them, choice-based incentive-compatible multiple-prior elicitation largely remains an open problem. This paper develops a solution, comprising a preference-based identification of a subject’s probability interval for an event, and two procedures for eliciting it. The method does not rely on specific assumptions about subjects’ ambiguity attitudes or probabilistic sophistication. To demonstrate its feasibility, we implement it in two incentivized experiments to elicit the multiple-prior equivalent of subjects’ cumulative distribution functions over continuous-valued sources of uncertainty. We find a predominance of non-degenerate probability intervals among subjects for all explored sources, with intervals being wider for less familiar sources. Finally, we use our method to undertake the first elicitation of the mixture coefficient in the Hurwicz α-maxmin EU model that fully controls for beliefs.

This paper develops a belief update rule under ambiguity, motivated by the maxim: in the face of new information, retain those conditional beliefs in which you are more confident, and relinquish only those in which you have less confidence. We provide a preference-based axiomatisation, drawing on the account of confidence in beliefs developed in Hill (2013) . The proposed rule constitutes a general framework of which several existing rules for multiple priors (Full Bayesian, Maximum Likelihood) are special cases, but avoids the problems that these rules have with updating on complete ignorance. Moreover, it can handle surprising and null events, such as crises or reasoning in games, recovering traditional approaches, such as conditional probability systems, as special cases.

In the face of rising income inequality (Acemoglu & Autor, 2011; Atkinson, Piketty, & Saez, 2011; Piketty, 2014; World Economic Forum, 2014), one recent proposal is to provide consumers with information about the income inequality across those involved in the production of each good, at the point of purchase. This has been shown to depress overall inequality (Hill, 2020), though its impact depends crucially on whether people are willing to pay more for goods whose production involves less income inequality. Here we investigate this largely unexplored empirical question through an incentivised, behavioural choice experiment on a representative sample of the English population. We find that a large majority are willing to pay significantly more for goods associated with less inequality. How much more people are willing to pay varies with political leaning and increases with the extent of the inequality reduction, but is positive across the political spectrum and for all studied inequality differences. Moreover, it is typically higher when inequality is reported in more intuitive and informa- tive formats. Our results bode well for the effectiveness of product-level inequality information provision as a tool for moderating income inequality, promising impacts even in markets where all goods involve relatively high inequality levels and potential participation across the political spectrum.

The standard, Bayesian account of rational belief and decision is often argued to be unable to cope properly with severe uncertainty, of the sort ubiquitous in some areas of policy making. This paper tackles the question of what should replace it as a guide for rational decision making. It defends a recent proposal, which reserves a role for the decision maker’s confidence in beliefs. Beyond being able to cope with severe uncertainty, the account has strong normative credentials on the main fronts typically evoked as relevant for rational belief and decision. It fares particularly well, we argue, in comparison to other prominent non-Bayesian models in the literature.

International audience; Reports of the Intergovernmental Panel on Climate Change (IPCC) employ an evolving framework of calibrated language for assessing and communicating degrees of certainty in findings. A persistent challenge for this framework has been ambiguity in the relationship between multiple degree-of-certainty metrics. We aim to clarify the relationship between the likelihood and confidence metrics used in the Fifth Assessment Report (2013), with benefits for mathematical consistency among multiple findings and for usability in downstream modeling and decision analysis. We discuss how our proposal meshes with current and proposed practice in IPCC uncertainty assessment.

It is commonly argued that dynamic consistency, consequentialism and non-expected 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 non-expected utility. We then analyze the economic consequences of this new perspective. On the one hand, it provides a principled justification for restrictions on non-expected 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 non-expected utility is non-negative 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.