Flexible estimation of parametric prospect models using hierarchical Bayesian methods.

Balcombe, K. and Fraser, I. (2025) Flexible estimation of parametric prospect models using hierarchical Bayesian methods. Experimental Economics, 28 (2). pp. 354-378. ISSN 1573-6938 doi: 10.1017/eec.2025.10012

Abstract/Summary

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 domain

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/122646
Identification Number/DOI	10.1017/eec.2025.10012
Refereed	Yes
Divisions	Life Sciences > School of Agriculture, Policy and Development > Department of Agri-Food Economics & Marketing
Publisher	Springer
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