Inequity aversion pricing over social networks: Approximation algorithms and hardness results

Georgios Amanatidis, Peter Fulla, Evangelos Markakis, Krzysztof Sornat

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We study a revenue maximization problem in the context of social networks. Namely, we generalize a model introduced by Alon, Mansour, and Tennenholtz [2] that captures inequity aversion, i.e., it captures the fact that prices offered to neighboring nodes should not differ significantly. We first provide approximation algorithms for a natural class of instances, where the total revenue is the sum 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 instance, to settings where the seller will only consider a fixed number of discount types or special offers. To complement our positive results, we resolve one of the open questions posed in [2] 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 study extensions of the model regarding the demand type of the clients.

Original languageEnglish
Pages (from-to)62-78
Number of pages17
JournalTheoretical Computer Science
Volume871
DOIs
StatePublished - 6 Jun 2021
Externally publishedYes

Keywords

  • Approximation algorithms
  • Inequity aversion
  • Pricing
  • Revenue maximization
  • Social networks

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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