Testing Models of Complexity Aversion

Konstantinos Georgalos and Nathan Nabil

No 400814269, Working Papers from Lancaster University Management School, Economics Department

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.

Keywords: Complexity aversion; Toolbox models; Heuristics; Risky choice; Bayesian modelling (search for similar items in EconPapers)
JEL-codes: C91 D81 D91 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-cbe, nep-evo, nep-exp, nep-inv, nep-neu and nep-upt
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Persistent link: https://EconPapers.repec.org/RePEc:lan:wpaper:400814269

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