Optimal Auctions with Positive Network Externalities

  • Authors:
  • Nima Haghpanah;Nicole Immorlica;Vahab Mirrokni;Kamesh Munagala

  • Affiliations:
  • Northwestern University;Northwestern University;Google Research;Duke University

  • Venue:
  • ACM Transactions on Economics and Computation - Special Issue on Algorithmic Game Theory
  • Year:
  • 2013

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Abstract

We consider the problem of designing auctions in social networks for goods that exhibit single-parameter submodular network externalities in which a bidder’s value for an outcome is a fixed private type times a known submodular function of the allocation of his friends. Externalities pose many issues that are hard to address with traditional techniques; our work shows how to resolve these issues in a specific setting of particular interest. We operate in a Bayesian environment and so assume private values are drawn according to known distributions. We prove that the optimal auction is NP-hard to approximate pointwise, and APX-hard on average. Thus we instead design auctions whose revenue approximates that of the optimal auction. Our main result considers step-function externalities in which a bidder’s value for an outcome is either zero, or equal to his private type if at least one friend has the good. For these settings, we provide a e/e + 1-approximation. We also give a 0.25-approximation auction for general single-parameter submodular network externalities, and discuss optimizing over a class of simple pricing strategies.