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Testing Models of Complexity Aversion

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  • Konstantinos Georgalos
  • Nathan Nabil

Abstract

In this paper we aim to investigate how the complexity of a decision-task may change an agents strategic behaviour as a result of increased cognitive fatigue. In this framework, complexity is defined as a function of the number of outcomes in a lottery. Using Bayesian inference techniques, 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 result to heuristics when the complexity of a task overwhelms their cognitive load.

Suggested Citation

  • Konstantinos Georgalos & Nathan Nabil, 2023. "Testing Models of Complexity Aversion," Working Papers 400814269, Lancaster University Management School, Economics Department.
    • Handle: RePEc:lan:wpaper:400814269
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    References listed on IDEAS

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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
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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