Template-Type: ReDIF-Paper 1.0
Author-Name: Konstantinos Georgalos
Author-Name-First: Konstantinos
Author-Name-Last: Georgalos
Author-Name: Nathan Nabil
Author-Name-First: Nathan
Author-Name-Last: Nabil
Title: Testing Models of Complexity Aversion
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.
Creation-Date: 2023
File-URL: http://www.lancaster.ac.uk/media/lancaster-university/content-assets/documents/lums/economics/working-papers/LancasterWP2023_011.pdf
File-Format: application/pdf
Number: 400814269
Classification-JEL: C91, D91, D81
Keywords: Complexity aversion, Toolbox models, Heuristics, Risky choice, Bayesian modelling
Handle: RePEc:lan:wpaper:400814269