On the Impact of Buyers Preselection in Pricing Problems

We investigate the problem of preselecting a subset of buyers participating in a market so as to optimize the performance of stable outcomes. We consider four scenarios arising from the combination of two stability notions, item and bundle envy-freeness, with the two classical objective functions, i.e., the social welfare and the seller's revenue. When adopting the notion of item envy-freeness, we prove that, for both the two objective functions, the problem cannot be approximated within n 1-ε for any ε > 0, and provide tight or nearly tight approximation algorithms. We also prove that maximizing the seller's revenue is NP-hard even for a single buyer, thus closing a longstanding open question. Under bundle envyfreeness, instead, we show how to transform in polynomial time any stable outcome for a market involving only a subset of buyers to a stable one for the whole market without worsening its performance, both for the social welfare and the seller's revenue. This transformation implies that, although in this case buyer preselection cannot improve the performance, it can still be used as an algorithmic tool for computing good stable outcomes when preselection is not allowed. In fact, it can be first exploited to simplify the combinatorics of the problem, and then for mapping back the computed solution to one encompassing all the buyers. Finally, we consider multi-unit markets, where all items are of the same type and are assigned the same price. For this specific case, we show that buyer preselection can improve the performance of stable outcomes in all of the four considered scenarios, and design corresponding approximation algorithms.