Date of Award


Document Type

Open Access Master's Thesis

Degree Name

Master of Science in Applied Cognitive Science and Human Factors (MS)

Administrative Home Department

Department of Cognitive and Learning Sciences

Advisor 1

Susan Amato-Henderson

Committee Member 1

Kelly Steelman

Committee Member 2

Michele Miller


This research addressed the effect of retirement plan task complexity on retirement plan earnings estimates. Past research has shown that increased task complexity results in more decision-making errors as well increased use of heuristics, or rules of thumb, which can result in non-optimal outcomes such as under-saving or disproportionate equity/income balances (Benartzi & Thaler, 2001, 2007; Maynard & Hakel, 1997). This research used two experiments to test whether individuals would judge a retirement investment plan with high task complexity to be more profitable than a plan with low task complexity - a non-normative and potentially costly bias. Experiment 1 used retirement plans based on theoretical models while Experiment 2 used materials that were ecologically representative. In both studies participants judged a retirement plan with high task complexity to be more likely to return higher earnings than a retirement plan with low task complexity; this finding was unaffected by financial literacy and numeracy, which were expected to have a de-biasing effect. Subjective task complexity was found to be a significant predictor of earnings estimates, independent from estimates of plan risk and stability. These findings have practical and theoretical implications. Individual investors may be susceptible to the high task complexity of retirement investment plans which could lead to paying more fees. Benefits administrators can use this information to design and present retirement investment plan options in a way that potentially can mediate this bias for complexity.