Optimal iterative pricing over social networks

  • Authors:

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

  • Affiliations:

  • Computer Engineering Department, Sharif University of Technology;Computer Engineering Department, Sharif University of Technology and Institute for Research in Fundamental Sciences, IPM, Tehran, Iran;Northwestern University, EECS Department;Google Research NYC, New York, NY;Computer Engineering Department, Sharif University of Technology;Carnegie Mellon University, Tepper School of Business

  • Venue:
  • WINE'10 Proceedings of the 6th international conference on Internet and network economics
  • Year:
  • 2010

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Abstract

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.