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An investigation into sources of network externalities in the packaged PC software market
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On Some Tighter Inapproximability Results (Extended Abstract)
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Maximizing Non-Monotone Submodular Functions
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Stochastic analyses for online combinatorial optimization problems
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Optimal marketing strategies over social networks
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Set Covering with our Eyes Closed
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A Cascade Model for Externalities in Sponsored Search
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Sponsored Search Auctions with Markovian Users
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On the Equilibria and Efficiency of the GSP Mechanism in Keyword Auctions with Externalities
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Externalities in Keyword Auctions: An Empirical and Theoretical Assessment
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Online Stochastic Matching: Beating 1-1/e
FOCS '09 Proceedings of the 2009 50th Annual IEEE Symposium on Foundations of Computer Science
Optimal pricing in the presence of local network effects
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Optimal iterative pricing over social networks
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Equilibrium pricing with positive externalities
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Online bipartite matching with unknown distributions
Proceedings of the forty-third annual ACM symposium on Theory of computing
Online bipartite matching with random arrivals: an approach based on strongly factor-revealing LPs
Proceedings of the forty-third annual ACM symposium on Theory of computing
Submodular maximization by simulated annealing
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Online stochastic matching: online actions based on offline statistics
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
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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.