Testing models of complexity aversion - Georgalos, Konstantinos; Nabil, Nathan (Journal of behavioral and experimental economics, 2025)
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.