Testing models of complexity aversion

In this study we aim to test behavioural models of complexity aversion. In this framework, complexity is defined as a function of the number of outcomes in a lottery. Using Bayesian inference techniques, we re-analyse data from a lottery-choice experiment. We quantitatively specify and estimate adap...

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Bibliographic Details
Published inJournal of behavioral and experimental economics Vol. 116; p. 102354
Main Authors Georgalos, Konstantinos, Nabil, Nathan
Format Journal Article
LanguageEnglish
Published Elsevier Inc 01.06.2025
Subjects
Bayesian modelling
Complexity aversion
Heuristics
Risky choice
Toolbox models
Toolbox models
Heuristics
Risky choice
C91
Complexity aversion
D81
D91
Bayesian modelling
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Summary:In this study we aim to test behavioural models of complexity aversion. In this framework, complexity is defined as a function of the number of outcomes in a lottery. Using Bayesian inference techniques, we re-analyse data from a lottery-choice experiment. We quantitatively specify and estimate adaptive toolbox models of cognition, which we rigorously test against popular expectation-based models; modified to account for complexity aversion. We find that for the majority of the subjects, a toolbox model performs best both in-sample, and with regards to its predictive capacity out-of-sample, suggesting that individuals resort to heuristics in the presense of extreme complexity. •We investigate whether decision makers resort to heuristics in the face of complex tasks.•We quantitatively specify and estimate adaptive toolbox models of cognition.•We compare the toolbox models against popular expectation-based models.•For the majority of the subjects, a toolbox model performs best both in-sample and out-of-sample.
ISSN:2214-8043
DOI:10.1016/j.socec.2025.102354