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Testing models of complexity aversion

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  • Georgalos, Konstantinos
  • Nabil, Nathan

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.

Suggested Citation

  • Georgalos, Konstantinos & Nabil, Nathan, 2025. "Testing models of complexity aversion," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 116(C).
    • Handle: RePEc:eee:soceco:v:116:y:2025:i:c:s2214804325000217
    DOI: 10.1016/j.socec.2025.102354
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    More about this item

    Keywords

    Complexity aversion; Toolbox models; Heuristics; Risky choice; Bayesian modelling;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

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