Semi-Separable Mechanisms in Multi-Item Robust Screening (2408.13580v1)

Abstract: It is generally challenging to characterize the optimal selling mechanism even when the seller knows the buyer's valuation distributions in multi-item screening. An insightful and significant result in robust mechanism design literature is that if the seller knows only marginal distributions of the buyer's valuation, then separable mechanisms, in which all items are sold independently, are robustly optimal under the maximin revenue objectives. While the separable mechanism is simple to implement, the literature also indicates that separate selling can not guarantee any substantial fraction of the potential optimal revenue for given distributions. To design a simple mechanism with a good performance guarantee, we introduce a novel class of mechanisms, termed "semi-separable mechanism". In these mechanisms, the allocation and payment rule of each item is a function solely of the corresponding item's valuation, which retains the separable mechanism's practical simplicity. However, the design of the allocation and payment function is enhanced by leveraging the joint distributional information, thereby improving the performance guarantee against the hindsight optimal revenue. We establish that a semi-separable mechanism achieves the optimal performance ratio among all incentive-compatible and individually rational mechanisms when only marginal support information is known. This result demonstrates that the semi-separable mechanisms ensure both the interpretation and implementation simplicity, and performance superiority. Our framework is also applicable to scenarios where the seller possesses information about the aggregate valuations of product bundles within any given partition of the product set. Furthermore, our results also provide guidelines for the multi-item screening problem with non-standard ambiguity sets.