Pandora's Box Reopened: Robust Search and Choice Overload

TL;DR

This paper analyzes how decision-makers manage search and choice overload in the Pandora's box problem, revealing that increased options can lead to higher opting-out rates due to fear of selection errors, and suggests mitigation strategies.

Contribution

It characterizes the regret-minimizing search rule and explains the impact of choice overload driven by fear of selection errors in the Pandora's box problem.

Findings

Likelihood of opting out increases with more options.

Choice overload is driven by fear of selection errors.

Recommendations and cost heterogeneity can reduce regret.

Abstract

This paper revisits the classic Pandora's box problem, studying a decision-maker (DM) who seeks to minimize her maximal ex-post regret. The DM decides how many options to explore and in what order, before choosing one or taking an outside option. We characterize the regret-minimizing search rule and show that the likelihood of opting out often increases as more options become available for exploration. We show that this ``choice overload" is driven by the DM's fear of ``selection error" -- the regret from searching the wrong options -- suggesting that steering choice via recommendations or cost heterogeneity can mitigate regret and encourage search.