Optimal iterative pricing over social networks (extended abstract)

Hessameddin Akhlaghpour, Mohammad Ghodsi, Nima Haghpanah, Vahab S. Mirrokni, Hamid Mahini, Afshin Nikzad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

45 Scopus citations


We study the optimal pricing for revenue maximization over social networks in the presence of positive network externalities. In our model, the value of a digital good for a buyer is a function of the set of buyers who have already bought the item. In this setting, a decision to buy an item depends on its price and also on the set of other buyers that have already owned that item. The revenue maximization problem in the context of social networks has been studied by Hartline, Mirrokni, and Sundararajan [4], following the previous line of research on optimal viral marketing over social networks [5,6,7]. We consider the Bayesian setting in which there are some prior knowledge of the probability distribution on the valuations of buyers. In particular, we study two iterative pricing models in which a seller iteratively posts a new price for a digital good (visible to all buyers). In one model, re-pricing of the items are only allowed at a limited rate. For this case, we give a FPTAS for the optimal pricing strategy in the general case. In the second model, we allow very frequent re-pricing of the items. We show that the revenue maximization problem in this case is inapproximable even for simple deterministic valuation functions. In the light of this hardness result, we present constant and logarithmic approximation algorithms when the individual distributions are identical.

Original languageEnglish (US)
Title of host publicationInternet and Network Economics - 6th International Workshop, WINE 2010, Proceedings
Number of pages9
StatePublished - 2010
Event6th International Workshop on Internet and Network Economics, WINE 2010 - Stanford, CA, United States
Duration: Dec 13 2010Dec 17 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6484 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other6th International Workshop on Internet and Network Economics, WINE 2010
Country/TerritoryUnited States
CityStanford, CA

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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