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Testing models of complexity aversion

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Testing models of complexity aversion. / Georgalos, Konstantinos; Nabil, Nathan.
In: Journal of Behavioral and Experimental Economics, Vol. 116, 102354, 30.06.2025.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Georgalos, K & Nabil, N 2025, 'Testing models of complexity aversion', Journal of Behavioral and Experimental Economics, vol. 116, 102354. https://doi.org/10.1016/j.socec.2025.102354

APA

Georgalos, K., & Nabil, N. (2025). Testing models of complexity aversion. Journal of Behavioral and Experimental Economics, 116, Article 102354. https://doi.org/10.1016/j.socec.2025.102354

Vancouver

Georgalos K, Nabil N. Testing models of complexity aversion. Journal of Behavioral and Experimental Economics. 2025 Jun 30;116:102354. Epub 2025 Mar 15. doi: 10.1016/j.socec.2025.102354

Author

Georgalos, Konstantinos ; Nabil, Nathan. / Testing models of complexity aversion. In: Journal of Behavioral and Experimental Economics. 2025 ; Vol. 116.

Bibtex

@article{e6125d657b6044c98defb7c28bfe3eab,
title = "Testing models of complexity aversion",
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 = "Bayesian modelling, Complexity aversion, Heuristics, Risky choice, Toolbox models",
author = "Konstantinos Georgalos and Nathan Nabil",
year = "2025",
month = jun,
day = "30",
doi = "10.1016/j.socec.2025.102354",
language = "English",
volume = "116",
journal = "Journal of Behavioral and Experimental Economics",
issn = "2214-8043",
publisher = "Elsevier Inc.",

}

RIS

TY - JOUR

T1 - Testing models of complexity aversion

AU - Georgalos, Konstantinos

AU - Nabil, Nathan

PY - 2025/6/30

Y1 - 2025/6/30

N2 - 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.

AB - 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.

KW - Bayesian modelling

KW - Complexity aversion

KW - Heuristics

KW - Risky choice

KW - Toolbox models

U2 - 10.1016/j.socec.2025.102354

DO - 10.1016/j.socec.2025.102354

M3 - Journal article

AN - SCOPUS:86000575718

VL - 116

JO - Journal of Behavioral and Experimental Economics

JF - Journal of Behavioral and Experimental Economics

SN - 2214-8043

M1 - 102354

ER -