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Modelling the Effects of Decision Complexity on Choice Behaviour Under Risk

This paper is concerned with how the effect of task complexity on behavioural error is better modelled and how the better performance of a stochastic model can be understood in a behavioural sense. Three behavioural error models are considered in this study; each accommodates the complexity effect by specifying the disturbance’s standard deviation as a function of ad hoc measures of task complexity. The first two stochastic models are Contextual Utility and the Decision Field Theory. We introduce an alternative specification borrowed from the consumer behaviour literature and built upon the Entropy function. The specification, also called the Entropy model, allows one to incorporate the effects of task complexity on behavioural errors more flexibly than the former two. We empirically evaluate the performance of the three heteroskedastic error models relative to the classical Fechner specification, using data from a longitudinal field experiment conducted in Denmark. Our analyses reject homoskedasticity in favour of one of the three heteroskedastic models. The Entropy model provides the best fit to the data under the Expected Utility Theory (EUT), and the Decision Field Theory delivers the best fit under the Rank Dependent Utility (RDU). Our results also show that the superior performance of a stochastic model can be explained by its ability to capture extreme behaviours in terms of utility curvature and decision weight at an individual level. Finally, we find that the results under EUT are robust across alternative stochastic models, whereas the results under RDU show some sensitivity.