Testing models of complexity aversion

Konstantinos Georgalos and Nathan Nabil

Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), 2025, vol. 116, issue C

Abstract: 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.

Keywords: Complexity aversion; Toolbox models; Heuristics; Risky choice; Bayesian modelling (search for similar items in EconPapers)
JEL-codes: C91 D81 D91 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:soceco:v:116:y:2025:i:c:s2214804325000217

DOI: 10.1016/j.socec.2025.102354

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