Equilibrium pricing with positive externalities (extended abstract)

Nima Anari, Shayan Ehsani, Mohammad Ghodsi, Nima Haghpanah, Nicole Immorlica, Hamid Mahini, Vahab S. Mirrokni

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

15 Scopus citations


We study the problem of selling an item to strategic buyers in the presence of positive historical externalities, where the value of a product increases as more people buy and use it. This increase in the value of the product is the result of resolving bugs or security holes after more usage. We consider a continuum of buyers that are partitioned into types where each type has a valuation function based on the actions of other buyers. Given a fixed sequence of prices, or price trajectory, buyers choose a day on which to purchase the product, i.e., they have to decide whether to purchase the product early in the game or later after more people already own it. We model this strategic setting as a game, study existence and uniqueness of the equilibria, and design an FPTAS to compute an approximately revenue-maximizing pricing trajectory for the seller in two special cases: the symmetric settings in which there is just a single buyer type, and the linear settings that are characterized by an initial type-independent bias and a linear type-dependent influenceability coefficient.

Original languageEnglish (US)
Title of host publicationInternet and Network Economics - 6th International Workshop, WINE 2010, Proceedings
Number of pages8
StatePublished - Dec 1 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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