Competing for Customers in a Social Network: The Quasi-linear Case

  • Pradeep Dubey
  • Rahul Garg
  • Bernard De Meyer
Conference paper

DOI: 10.1007/11944874_16

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4286)
Cite this paper as:
Dubey P., Garg R., De Meyer B. (2006) Competing for Customers in a Social Network: The Quasi-linear Case. In: Spirakis P., Mavronicolas M., Kontogiannis S. (eds) Internet and Network Economics. WINE 2006. Lecture Notes in Computer Science, vol 4286. Springer, Berlin, Heidelberg

Abstract

There are many situations in which a customer’s proclivity to buy the product of any firm depends not only on the classical attributes of the product such as its price and quality, but also on who else is buying the same product. We model these situations as games in which firms compete for customers located in a “social network”. Nash Equilibrium (NE) in pure strategies exist and are unique. Indeed there are closed-form formulae for the NE in terms of the exogenous parameters of the model, which enables us to compute NE in polynomial time.

An important structural feature of NE is that, if there are no a priori biases between customers and firms, then there is a cut-off level above which high cost firms are blockaded at an NE, while the rest compete uniformly throughout the network.

We finally explore the relation between the connectivity of a customer and the money firms spend on him. This relation becomes particularly transparent when externalities are dominant: NE can be characterized in terms of the invariant measures on the recurrent classes of the Markov chain underlying the social network.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Pradeep Dubey
    • 1
    • 2
  • Rahul Garg
    • 3
  • Bernard De Meyer
    • 2
    • 4
  1. 1.Center for Game TheoryStony Brook UniversityUSA
  2. 2.Cowles FoundationYale UniversityUSA
  3. 3.IBM India Research LabNew DelhiIndia
  4. 4.Cermsem, Univesité Paris 1ParisFrance

Personalised recommendations