Inequity Aversion Pricing over Social Networks: Approximation Algorithms and Hardness Results

Authors Georgios Amanatidis, Evangelos Markakis, Krzysztof Sornat

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Georgios Amanatidis
Evangelos Markakis
Krzysztof Sornat

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Georgios Amanatidis, Evangelos Markakis, and Krzysztof Sornat. Inequity Aversion Pricing over Social Networks: Approximation Algorithms and Hardness Results. In 41st International Symposium on Mathematical Foundations of Computer Science (MFCS 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 58, pp. 9:1-9:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)


We study a revenue maximization problem in the context of social networks. Namely, we consider a model introduced by Alon, Mansour, and Tennenholtz (EC 2013) that captures inequity aversion, i.e., prices offered to neighboring vertices should not be significantly different. We first provide approximation algorithms for a natural class of instances, referred to as the class of single-value revenue functions. Our results improve on the current state of the art, especially when the number of distinct prices is small. This applies, for example, to settings where the seller will only consider a fixed number of discount types or special offers. We then resolve one of the open questions posed in Alon et al., by establishing APX-hardness for the problem. Surprisingly, we further show that the problem is NP-complete even when the price differences are allowed to be relatively large. Finally, we also provide some extensions of the model of Alon et al., regarding the allowed set of prices.
  • inequity aversion
  • social networks
  • revenue maximization


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