Platforms use common fees to price search information effectively
Pricing Information in Search with Nonobligatory Inspection
Computer Science and Game Theory
Summary
When people search for options online, like shopping or ads, they often want extra information before choosing. This paper looks at how platforms can charge for giving that information when buyers can look but don’t have to pay to see details for all choices. The authors find that charging the same fee for all options works best in many cases, even though figuring out the perfect fees is sometimes very hard. Their method also gives a good approximation to the best possible pricing, making it practical.
What this means in practice
- •For online advertising platforms: Set uniform fees to sell information that helps advertisers better target choices without forcing them to inspect every option.$Commercial implications: Enables platforms to launch paid information products that optimize advertising revenue from user insights.
- •For ecommerce marketplace operators: Design pricing schemes for product information access that balance buyer freedom and platform revenue efficiently.
A theory result. No direct application yet.
Authors
Shuo Zhang, Boyu Liu, Zihe Wang
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.