Pandora's Box Reopened: Robust Search and Choice Overload

2025-12-24

AI Summary

This paper introduces, for the first time within the Pandora’s Box framework, the minimax *ex post* regret criterion to study sequential search and stopping strategies under uncertainty. Methodologically, it integrates robust optimization, regret theory, and sequential decision analysis to rigorously characterize the optimal search depth, ranking order of options, and stopping threshold. The contributions are threefold: (1) It theoretically establishes a “choice overload” phenomenon—increasing the number of options systematically raises the probability of abandoning search in favor of an outside option; (2) It identifies the root cause as fear of making a *wrong choice*, rather than cognitive load; (3) It proposes two intervention mechanisms—recommendation-based signal design and heterogeneity in search cost—that significantly reduce worst-case regret and incentivize efficient exploration. These results offer a novel paradigm bridging behavioral decision theory and mechanism design.

Technology Category

• Search and Optimization: Other Foundations of Search & Optimization
• Reasoning under Uncertainty: Sequential Decision Making
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: The sharing economy
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations

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.

Problem

• Minimizing maximal ex-post regret in Pandora's box problem
• Analyzing how option availability increases likelihood of opting out
• Mitigating regret via recommendations or cost heterogeneity

Innovation

• Regret-minimizing search rule for option exploration
• Choice overload driven by fear of selection error
• Mitigation via recommendations or cost heterogeneity