Pricing Problems with Buyer Preselection

We investigate the problem of preselecting a subset of buyers (also called agents) participating in a market so as to optimize the performance of stable outcomes. We consider four scenarios arising from the combination of two tability notions, namely market envy-freeness and agent envy-freeness, with the two state-of-the-art objective functions of ocial welfare and seller’s revenue. When insisting on market envy-freeness, we prove that the problem cannot be pproximated within n 1−ε (with n being the number of buyers) for any ε > 0, under both objective functions; we also provide approximation algorithms with an approximation ratio tight up to subpolynomial multiplicative factors for social welfare and the seller’s revenue. The negative result, in particular, holds even for markets with single-minded buyers. We also prove that maximizing the seller’s revenue is NP-hard even for a single buyer, thus closing a previous open question. Under agent envy-freeness and for both objective functions, instead, we design a polynomial time lgorithm transforming any stable outcome for a market involving any subset of buyers into a stable outcome for the whole market without worsening its performance. This result creates an interesting middle-ground situation where, if on the one hand buyer preselection cannot improve the performance of agent envy-free outcomes, on the other one it can be used as a tool for simplifying the combinatorial structure of the buyers’ valuation functions in a given market. Finally, we consider the restricted case of multi-unit markets, where all items are of the same type and are assigned the same price. For these markets, we show that preselection may improve the performance of stable outcomes in all of the four considered scenarios, and design corresponding approximation algorithms.