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Optimal iterative pricing over social networks (extended abstract)

Akhlaghpour, H ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-642-17572-5_34
  3. Publisher: 2010
  4. Abstract:
  5. 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
  6. Keywords:
  7. Bayesian ; Digital goods ; Extended abstracts ; Hardness result ; Logarithmic approximation ; Network externality ; Pricing models ; Pricing strategy ; Prior knowledge ; Revenue maximization ; Revenue maximization problem ; Social Networks ; Valuation function ; Viral marketing ; Approximation algorithms ; Costs ; Internet ; Optimization ; Probability distributions ; Sales ; Social networking (online) ; Wine ; Economics
  8. Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13 December 2010 through 17 December 2010 ; Volume 6484 LNCS , December , 2010 , Pages 415-423 ; 03029743 (ISSN) ; 3642175716 (ISBN)
  9. URL: http://link.springer.com/chapter/10.1007%2F978-3-642-17572-5_34