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October 17, 2019

Cover of Decision (small) If you have ever encountered difficulty deciding among many options, you are not alone.

In 2017, a private think tank called Netspar found that Sweden’s citizens were less willing to save money using the government-sponsored retirement plan when a large variety of investment options was presented to them. Instead, a small set of default options resulted in the most responsible saving habits.

This example demonstrates the choice overload effect, where individuals become less likely to make a selection as the number of options increases.

A common explanation for choice overload is that the decision is too difficult due to conflict arising from the number of tradeoffs (e.g., a dish that tastes rich but is not good for one’s health).

Ryan Jessup, Levi Ritchie, and John Homer’s article in Decision (advance online publication — August 19, 2019) compares that explanation against the more prosaic idea that the effect is merely driven by the time set aside for choosing.

Previous work by Jessup and others used a cognitive process model called decision field theory to generate predictions via simulation regarding the choice overload effect.

Cognitive process models seek to mimic underlying cognitive principles when generating predictions, as opposed to statistical curve fitting which merely finds the best statistical model without reference to cognitive principles.

The prior work took advantage of the principle that choices emerge probabilistically over time in the theory — rather than instantaneously and deterministically — to create (1) a tradeoff-based explanation for not choosing and (2) a time-based no-choice explanation.

The results of simulations from the previous work indicate where and when to look for the choice overload effect.

When comparing both explanations, Jessup and colleagues concluded that time pressure — rather than the difficulty of deciding due to the presence of tradeoffs — is more likely to produce the choice overload effect.

In this study, participants engaged in a virtual hiring task considering a set of job applicants on four relevant factors. Sets of applicants were distributed such that there was either one dominant option or many tradeoffs with no superior option. Each trial consisted of either a large or small set of candidates.

Additionally, participants either choose under high or no time pressure. Participants could hire any of the applicants or reject them all. The authors then examined the likelihood of choosing between the small and large option sets in light of the theory-derived predictions.

Applications of their research are quite practical.

For example, any location that caters to a time-pressured crowd — drive through windows or supermarkets whose clientele often bring children — might see an uptick in sales by reducing or otherwise simplifying their available choices.

Given too many alternatives, these time-pressured shoppers may defer choice to a later time or to another business with fewer options.

Citation

  • Jessup, R. K., Ritchie, L. E., & Homer, J. (2019). Hurry up and decide: Empirical tests of the choice overload effect using cognitive process models. Decision. Advance online publication. http://dx.doi.org/10.1037/dec0000115

Note: This article is in the Neuroscience and Cognition topic area. View more articles in the Neuroscience and Cognition topic area.

About the Authors

Ryan Jessup, PhD, is an associate professor of marketing at Abilene Christian University. His research utilizes mathematical models of decision making and its interaction with learning in fields such as cognitive psychology, consumer behavior, and neuroscience.

Levi Ritchie, MS, is a professional data scientist working at Docket Navigator, a patent law analytics firm. His work focuses on building machine learning applications for natural language processing and data cleansing.

John Homer, PhD, is a professor of computer science at Abilene Christian University. His research applies logic-driven approaches to problem-solving in a variety of fields, such as enterprise network security management, risk assessment, and decision making.

Date created: October 2019
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