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arXiv · 2610.34369

Pricing Information in Search with Nonobligatory Inspection

Shuo Zhang·Boyu Liu·Zihe Wang
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Abstract

As AI agents reshape Web search and advertising, platforms can monetize information that improves decisions. We study information pricing for sequential search among independent alternatives with nonobligatory inspection: the buyer may select any alternative without buying its report. We show that a common fee maximizes static revenue for identical priors, resolving a uniform-pricing conjecture. Our results also establish that exact optimization is NP-hard for heterogeneous binary priors. Despite this hardness, our polynomial-time static pricing algorithm achieves a $3$-approximation to the information surplus. More generally, a common static fee approximates optimal adaptive revenue within a logarithmic factor in the number of alternatives. This guarantee is asymptotically tight.

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Shuo Zhang, Boyu Liu, Zihe Wang. 2026-10-25. Pricing Information in Search with Nonobligatory Inspection. https://arxiv.org/abs/2610.34369

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