Foundations of neo-Bayesian statistics

Amarante, Massimiliano

doi:10.1016/j.jet.2009.04.001

Cited by 42 publications

(49 citation statements)

References 15 publications

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“…A(C). Thus, the characterization of D-preferences becomes an easy consequence of a theorem that we proved in [1].…”

Section: D-preferencesmentioning

confidence: 82%

“…Rather, we are interested in the model of conditional EU preferences per se and in its relation with non-necessarily additive unconditional preferences such as those studied in [14], [9], [7] and [1]. This motivates the following assumption which implies that the set of acts is "rich".…”

Section: Settingmentioning

confidence: 99%

“…We employ three main tools. A theorem on the representation of monotone, translation invariant functionals, which we proved in [1], a theorem of Samet [13] on the characterization of common priors and a theorem of Phelps [12,Theorem 1] on the uniqueness of the Hahn-Banach extension.…”

Section: Introductionmentioning

confidence: 99%

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Conditional expected utility

Amarante

2017

Theory Decis

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Abstract. Let E be a class of event. Conditionally Expected Utility decision makers are decision makers whose conditional preferences %E, E 2 E, satisfy the axioms of Subjective Expected Utility theory (SEU). We extend the notion of unconditional preference that is conditionally EU to unconditional preferences that are not necessarily SEU. We give a representation theorem for a class of such preferences, and show that they are Invariant Bi-separable in the sense of Ghirardato et al. [7]. Then, we consider the special case where the unconditional preference is itself SEU, and compare our results with those of Fishburn [6].

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“…A(C). Thus, the characterization of D-preferences becomes an easy consequence of a theorem that we proved in [1].…”

Section: D-preferencesmentioning

confidence: 82%

Section: Settingmentioning

confidence: 99%

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Conditional expected utility

Amarante

2017

Theory Decis

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“…See Amarante (2009) andEichberger, Grant, Kelsey, andKoshevoy (2011) for further insights on this approach.…”

Section: Alternative Approachesmentioning

confidence: 99%

Ambiguity and Nonexpected Utility

Karni

Maccheroni

Marinacci

2015

Handbook of Game Theory With Economic Applications

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“…Ghirardato, Maccheroni and Marinacci [10] propose a general model of decision under ambiguity that includes that of [9]. Amarante [11] introduces a model of decision under ambiguity that includes the aforementioned ones. We refer to the recent survey of Gilboa and Marinacci [12] for more on this topic, including other models of decision under ambiguity and applications of these models to several problems in economic theory.…”

Section: Introductionmentioning

confidence: 99%

Optimal Insurance for a Minimal Expected Retention: The Case of an Ambiguity-Seeking Insurer

Amarante

Ghossoub

2016

Risks

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Abstract:In the classical expected utility framework, a problem of optimal insurance design with a premium constraint is equivalent to a problem of optimal insurance design with a minimum expected retention constraint. When the insurer has ambiguous beliefs represented by a non-additive probability measure, as in Schmeidler, this equivalence no longer holds. Recently, Amarante, Ghossoub and Phelps examined the problem of optimal insurance design with a premium constraint when the insurer has ambiguous beliefs. In particular, they showed that when the insurer is ambiguity-seeking, with a concave distortion of the insured's probability measure, then the optimal indemnity schedule is a state-contingent deductible schedule, in which the deductible depends on the state of the world only through the insurer's distortion function. In this paper, we examine the problem of optimal insurance design with a minimum expected retention constraint, in the case where the insurer is ambiguity-seeking. We obtain the aforementioned result of Amarante, Ghossoub and Phelps and the classical result of Arrow as special cases.

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Foundations of neo-Bayesian statistics

Cited by 42 publications

References 15 publications

Conditional expected utility

Conditional expected utility

Ambiguity and Nonexpected Utility

Optimal Insurance for a Minimal Expected Retention: The Case of an Ambiguity-Seeking Insurer

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