AAAI Publications, Thirty-Second AAAI Conference on Artificial Intelligence

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On Social Envy-Freeness in Multi-Unit Markets
Michele Flammini, Manuel Mauro, Matteo Tonelli

Last modified: 2018-04-25


We consider a market setting in which buyers are individuals of a population, whose relationships are represented by an underlying social graph. Given buyers valuations for the items being sold, an outcome consists of a pricing of the objects and an allocation of bundles to the buyers. An outcome is social envy-free if no buyer strictly prefers the bundles of her neighbors in the social graph. We focus on the revenue maximization problem in multi-unit markets, in which there are multiple copies of a same item being sold and each buyer is assigned a set of identical items. We consider the four different cases arising by considering different buyers valuations, i.e., single-minded or general, and by adopting different forms of pricing, that is item- or bundle-pricing. For all the above cases we show the hardness of the revenue maximization problem and give corresponding approximation results. All our approximation bounds are optimal or nearly optimal. Moreover, we provide an optimal allocation algorithm for general valuations with item-pricing, under the assumption of social graphs of bounded treewidth. Finally, we determine optimal bounds on the corresponding price of envy-freeness, that is on the worst case ratio between the maximum revenue that can be achieved without envy-freeness constraints, and the one obtainable in case of social relationships. Some of our results close hardness open questions or improve already known ones in the literature concerning the classical setting without sociality.


Markets; Envy-free pricing; Social Networks

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