Inequity Aversion Pricing over Social Networks: Approximation Algorithms and Hardness Results
Abstract
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 large, or even when the number of allowed distinct prices is as small as three. Finally, we provide some extensions of the model, regarding either the allowed set of prices or the demand type of the clients.
Keywords
Cite
@article{arxiv.1606.06664,
title = {Inequity Aversion Pricing over Social Networks: Approximation Algorithms and Hardness Results},
author = {Georgios Amanatidis and Peter Fulla and Evangelos Markakis and Krzysztof Sornat},
journal= {arXiv preprint arXiv:1606.06664},
year = {2019}
}
Comments
A preliminary conference version of this work appeared in MFCS 2016