# Pandora's Problem with Nonobligatory Inspection

## TL;DR

This paper studies a variant of Pandora's search problem where inspection costs are nonobligatory, providing the first approximation guarantees for simple, computationally efficient policies that closely approximate the optimal solution.

## Contribution

It introduces a family of committing policies for the nonobligatory inspection problem and proves they approximate the optimal policy within a guaranteed factor.

## Key findings

- Optimal committing policy approximates the fully optimal policy within 63%.
- For two options, the approximation factor improves to 80%.
- The 80% approximation is tight for committing policies.

## Abstract

Martin Weitzman's "Pandora's problem" furnishes the mathematical basis for optimal search theory in economics. Nearly 40 years later, Laura Doval introduced a version of the problem in which the searcher is not obligated to pay the cost of inspecting an alternative's value before selecting it. Unlike the original Pandora's problem, the version with nonobligatory inspection cannot be solved optimally by any simple ranking-based policy, and it is unknown whether there exists any polynomial-time algorithm to compute the optimal policy. This motivates the study of approximately optimal policies that are simple and computationally efficient. In this work we provide the first non-trivial approximation guarantees for this problem. We introduce a family of "committing policies" such that it is computationally easy to find and implement the optimal committing policy. We prove that the optimal committing policy is guaranteed to approximate the fully optimal policy within a $1-\frac1e = 0.63\ldots$ factor, and for the special case of two boxes we improve this factor to 4/5 and show that this approximation is tight for the class of committing policies.