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Flexible estimation of parametric prospect models using hierarchical Bayesian methods.

Balcombe, Kelvin, Fraser, Iain M (2025) Flexible estimation of parametric prospect models using hierarchical Bayesian methods. Experimental Economics, . ISSN 1386-4157. E-ISSN 1573-6938. (In press) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:109839)

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Abstract

In this paper, we present a flexible approach to estimating parametric cumulative Prospect Theory using hierarchical Bayesian methods. Bayesian methods allow us to include prior knowledge in estimation and heterogeneity in individual responses. The model employs a generalized parametric specification of the value function allowing each individual to be risk-seeking in low-stakes mixed prospects. In addition, it includes parameters accounting for varying levels of model noise across domains (gain, loss, and mixed) and several aspects of lottery design that can influence respondent behaviour. Our results indicate that enhancing value function flexibility leads to improved model performance. Our analysis reveals that choices within the gain domain tend to be more predictable. This implies that respondents find tasks in the gain domain cognitively less challenging in comparison to making choices within the loss and mixed domains.

Item Type: Article
Uncontrolled keywords: cumulative prospect theory; hierarchical Bayesian methods; complexity.
Subjects: H Social Sciences > HB Economic Theory
Institutional Unit: Schools > School of Economics and Politics and International Relations > Economics
Former Institutional Unit:
Divisions > Division of Human and Social Sciences > School of Economics
Funders: British Academy (https://ror.org/0302b4677)
Depositing User: Iain Fraser
Date Deposited: 05 May 2025 01:58 UTC
Last Modified: 20 May 2025 12:43 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/109839 (The current URI for this page, for reference purposes)

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