Paper Info
Title
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 relatively large. Finally, we also provide some extensions of the model of Alon et al., regarding the allowed set of prices.
Year
DOI
Venue
2016
10.4230/LIPIcs.MFCS.2016.9
mathematical foundations of computer science
DocType
Volume
Citations 
Conference
abs/1606.06664
0
PageRank 
References 
Authors
0.34
8
3
Name
Order
Citations
PageRank
georgios amanatidis18613.32
Evangelos Markakis2122586.93
Krzysztof Sornat300.68