Parametric preference functionals under risk in the gain domain: A Bayesian analysis

Balcombe, Kelvin; Fraser, Iain

doi:10.1007/s11166-015-9213-8

Cited by 21 publications

(30 citation statements)

References 44 publications

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“…The procedure was recommended by Nilsson et al (2011) and Scheibehenne and Pachur (2015). It has been applied in several other studies for estimating RDU and PT components (Balcombe and Fraser 2015;Kellen et al 2016;Lejarraga et al 2016). We estimated the Goldstein and Einhorn (1987) weighting function, as given by w(q) = q q +(1−q) .…”

Section: Parametric Estimationsmentioning

confidence: 99%

Experience and rationality under risk: re-examining the impact of sampling experience

Aydogan

Gao

2020

Exp Econ

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A recent strand of the literature on decision-making under uncertainty has pointed to an intriguing behavioral gap between decisions made from description and decisions made from experience. This study reinvestigates this description-experience gap to understand the impact that sampling experience has on decisions under risk. Our study adopts a complete sampling paradigm to address the lack of control over experienced probabilities by requiring complete sampling without replacement. We also address the roles of utilities and ambiguity, which are central in most current decision models in economics. Thus, our experiment identifies the deviations from expected utility due to over-(or under-) weighting of probabilities. Our results confirm the existence of the behavioral gap, but they provide no evidence for the underweighting of small probabilities within the complete sampling treatment. We find that sampling experience attenuates rather than reverses the inverse S-shaped probability weighting under risk.

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Section: Parametric Estimationsmentioning

confidence: 99%

Experience and rationality under risk: re-examining the impact of sampling experience

Aydogan

Gao

2020

Exp Econ

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“…Other than this, the ordering of parametric fit found is different than for risk (Balcombe & Fraser 2015). The reason is that insensitivity plays a more central role for ambiguity than for risk.…”

Section: Parametric Fittingsmentioning

confidence: 75%

The Rich Domain of Ambiguity Explored

Müller

Wakker

et al. 2018

Management Science

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. ABSTRACT. Ellsberg and others suggested that decision under ambiguity is a rich empirical domain with many phenomena to be investigated beyond the Ellsberg urns. We provide a systematic empirical investigation of this richness by varying both the uncertain events, the outcomes, and combinations of these. Although ambiguity aversion is prevailing, we also find systematic ambiguity seeking, confirming insensitivity. We find that ambiguity attitudes depend on the source of uncertainty (the kind of uncertain event) but not on the outcomes. Ambiguity attitudes are closer to rationality (ambiguity neutrality) for natural uncertainties than for the Ellsberg urns, as appearing from the reductions of monotonicity violations and of insensitivity. Terms of use: Documents in EconStor mayOur rich domain serves well to test families of weighting functions for fitting ambiguity attitudes.We find that two-parameter families, capturing not only aversion but also insensitivity, are desirable for ambiguity even more than for risk. The Goldstein-Einhorn family performs best for ambiguity.

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“…Many alternative learning mechanisms have been suggested for estimating probabilities. For recent examples, see Fennell and Baddeley (), Hayashi (), Di Caprio, Santos‐Arteaga, and Tavana (), Balcombe and Fraser (), and Bisière, Décamps, and Lovo (). However, neuroscience research (e.g., Schwartenbeck, FitzGerald, Dolan, & Friston, ) suggests that individuals aim to reduce surprises.…”

Section: The Modelmentioning

confidence: 99%

Learning and dynamic choices under uncertainty: From weighted regret and rejoice to expected utility

Zagonari

2019

Manage Decis Econ

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This paper identifies the globally stable conditions under which an individual facing the same choice in many subsequent times learns to behave as prescribed by the expected‐utility model. The analysis moves from the relevant behavioural models suggested by psychology, by updating probability estimations and outcome preferences according to the learning models suggested by neuroscience, in a manner analogous to Bayesian updating. The search context is derived from experimental economics, whereas the learning framework is borrowed from theoretical economics. Analytical results show that the expected‐utility model explains real behaviours in the long run whenever bad events are more likely than good events.

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Parametric preference functionals under risk in the gain domain: A Bayesian analysis

Cited by 21 publications

References 44 publications

Experience and rationality under risk: re-examining the impact of sampling experience

Experience and rationality under risk: re-examining the impact of sampling experience

The Rich Domain of Ambiguity Explored

Learning and dynamic choices under uncertainty: From weighted regret and rejoice to expected utility

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