ABSTRACT. This paper introduces source theory, a new theory for decision under ambiguity. It shows how probability weighting functions can be used to model ambiguity. It can do so in the Savage (& Gilboa) framework, and does not need complex two-stage gambles, multistage optimization principles, expected utility for risk (descriptively problematic), or any linear algebra. Still the mathematical analysis is simple, with intuitive preference axioms, tractable empirical implementations and calculations, and convenient graphical representations of ambiguity attitudes. It gives new ways to compare weighting functions, not between persons as is common, but within one person and between sources, giving Arrow-Pratt-like transformations “within” rather than “outside” the functions. Within-person between-sources comparisons are the main novelty of ambiguity over risk, first demonstrated by Ellsberg’s paradox.
ABSTRACT. This paper provides, first, the most general preference axiomatization of average utility (AU) maximization over infinite sequences presently available, reaching almost complete generality (only restriction: all periodic sequences should be contained in the domain). Here, infinite sequences may designate intertemporal outcomes streams where AU models patience, or welfare allocations where AU models fairness, or decision under ambiguity where AU models complete ignorance. Second, as a methodological contribution, this paper shows that infinite-dimensional representations can be simpler, rather than more complex, than finite-dimensional ones: infinite dimensions provide a richness that is convenient rather than cumbersome. In particular, (empirically problematic) continuity assumptions are not needed. Continuity is optional.
ABSTRACT. This paper proposes a unified framework for optimization over two or more components (risk/time; risk/welfare; etc). Using a century-old theorem on macro-micro aggregation, we show that many existing debates, on incentive compatibility of random incentives, hedging confoundings in ambiguity measurements, equity in Harsanyi’s veil of ignorance, multiattribute risk aversion, and many others, all concern the same bifurcation question “row-first or column-first aggregation?” For a single component, behavioral models typically relax separability while maintaining monotonicity. For two or more components, this is, surprisingly, no longer possible. Then at least one monotonicity must be violated. The question of which one is equivalent to the above bifurcation question. Our analysis clarifies many ongoing debates in many fields, including the aforementioned ones. We provide diagnoses and techniques for overcoming undesirable violations of monotonicity. A mathematical online appendix shows how our framework can be used theoretically to generalize many well-known preference axiomatizations.
ABSTRACT. Bernheim & Sprenger (2020), BS henceforth, claimed to falsify rank dependence. Abdellaoui, Li, Wakker, & Wu (2020), AL henceforth, criticized BS. Bernheim, Royer, & Sprenger (2022), BRS henceforth, redid part of BS’s experiment. I first criticize BRS’s experiment, and then criticize them for ignoring AL’s other criticisms of BS.
ABSTRACT. Bernheim and Sprenger (2020, Econometrica) presented experimental evidence aimed to falsify rank dependence (and, thus, prospect theory). We argue that their experiment captured heuristics and not preferences. The same tests, but with procedures that avoid heuristics, have been done before, and they confirm rank dependence. Many other violations of rank dependence have been published before. Bernheim and Sprenger recommend rank-independent probability weighting with complexity aversion, but this is theoretically unsound and empirically invalid. In view of its many positive results, prospect theory with rank dependence remains the best model of probability weighting and the existing model that works best for applied economics.
Last updated: 6 June, 2023
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