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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| Published in | Journal of behavioral and experimental economics Vol. 116; p. 102354 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.06.2025
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| Online Access | Get full text |
| ISSN | 2214-8043 |
| DOI | 10.1016/j.socec.2025.102354 |
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| 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.
•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. |
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| AbstractList | 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. |
| ArticleNumber | 102354 |
| Author | Nabil, Nathan Georgalos, Konstantinos |
| Author_xml | – sequence: 1 givenname: Konstantinos orcidid: 0000-0002-2655-4226 surname: Georgalos fullname: Georgalos, Konstantinos email: k.georgalos@lancaster.ac.uk – sequence: 2 givenname: Nathan surname: Nabil fullname: Nabil, Nathan email: n.nabil@lancaster.ac.uk |
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| Cites_doi | 10.1016/j.joep.2023.102681 10.1287/mnsc.31.12.1499 10.1016/j.socec.2022.101950 10.1007/s11166-016-9243-x 10.1007/s11166-015-9213-8 10.1037/xge0001323 10.1016/j.jebo.2022.08.013 10.3982/ECTA16646 10.1037/rev0000062 10.1287/mnsc.2021.4090 10.1287/mnsc.1050.0404 10.2139/ssrn.4603034 10.1287/mnsc.29.9.987 10.1016/j.cognition.2011.12.002 10.1007/s11166-018-9278-2 10.1093/qje/qjad025 10.1007/s11166-015-9226-3 10.1007/s11166-015-9229-0 10.1037/0022-3514.79.6.995 10.1002/bs.3830250306 10.1007/s11166-005-5831-x 10.1007/BF00209389 10.1287/mnsc.2018.3224 10.1007/s11238-009-9163-1 10.1007/BF00122574 10.1037/xge0000741 10.1007/s11166-022-09391-y 10.1002/bdm.683 10.1037/0033-295X.113.2.409 10.1111/1468-0297.00073 10.1016/j.obhdp.2014.06.003 10.1016/S0167-4870(99)00031-8 10.1162/rest_a_00700 10.1037/a0030777 |
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| Keywords | Toolbox models Heuristics Risky choice C91 Complexity aversion D81 D91 Bayesian modelling |
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| Title | Testing models of complexity aversion |
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