Mechanism Design on Trust Networks

  • Arpita Ghosh
  • Mohammad Mahdian
  • Daniel M. Reeves
  • David M. Pennock
  • Ryan Fugger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4858)


We introduce the concept of a trust network—a decentralized payment infrastructure in which payments are routed as IOUs between trusted entities. The trust network has directed links between pairs of agents, with capacities that are related to the credit an agent is willing to extend another; payments may be routed between any two agents that are connected by a path in the network. The network structure introduces group budget constraints on the payments from a subset of agents to another on the trust network: this generalizes the notion of individually budget constrained bidders.

We consider a multi-unit auction of identical items among bidders with unit demand, when the auctioneer and bidders are all nodes on a trust network. We define a generalized notion of social welfare for such budget-constrained bidders, and show that the winner determination problem under this notion of social welfare is NP-hard; however the flow structure in a trust network can be exploited to approximate the solution with a factor of 1 − 1/e. We then present a pricing scheme that leads to an incentive compatible, individually rational mechanism with feasible payments that respect the trust network’s payment constraints and that maximizes the modified social welfare to within a factor 1 − 1/e.


Social Welfare Greedy Algorithm Link Capacity Trust Network Prediction Market 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ahuja, R.K., Magnanti, T.L., Orlin, J.B.: Network Flows: Theory, Algorithms, and Applications. Prentice-Hall, Englewood Cliffs (1993)Google Scholar
  2. 2.
    Archer, A., Tardos, E.: Truthful mechanisms for one-parameter agents. In: IEEE Symposium on Foundations of Computer Science, pp. 482–491 (2001)Google Scholar
  3. 3.
    Borgs, C., Chayes, J., Immorlica, N., Mahdian, M., Saberi, A.: Multi-unit auctions with budget-constrained bidders. In: Proceedings of the 6th ACM Conference on Electronic Commerce (EC), pp. 44–51 (2005)Google Scholar
  4. 4.
    Chandra, R., Qiu, L., Jain, K., Mahdian, M.: Optimizing the placement of integration points in multi-hop wireless networks. In: Proceedings of the 12th IEEE International Conference on Network Protocols (ICNP) (2004)Google Scholar
  5. 5.
    Dash, R.K., Jennings, N.R., Parkes, D.C.: Computational mechanism design: A call to arms. IEEE Intelligent Systems 18, 40–47 (2003)CrossRefGoogle Scholar
  6. 6.
    Feige, U.: A threshold of ln n for approximating set cover. J.ACM 45, 634–652 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Fugger, R.: The ripple project (2004),
  8. 8.
    Håstad, J.: Clique is hard to approximate within n 1 − ε. Acta Mathematica 182, 105–142 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Hanson, R.D.: Combinatorial information market design. Information Systems Frontiers 5(1), 107–119 (2003)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Hanson, R.D.: Logarithmic market scoring rules for modular combinatorial information aggregation. Journal of Prediction Markets 1(1), 1–15 (2007)Google Scholar
  11. 11.
    Pennock, D.M.: A dynamic pari-mutuel market for hedging, wagering, and information aggregation. In: David, M. (ed.) Proceedings of the Fifth ACM Conference on Electronic Commerce (EC 2004) (May 2004)Google Scholar
  12. 12.
    Reeves, D.M., Soule, B.M., Kasturi, T.: Yootopia! SIGecom Exchanges 6, 1–26 (2006)CrossRefGoogle Scholar
  13. 13.
    Wolfers, J., Zitzewitz, E.: Prediction markets. Journal of Economic Perspective 18(2), 107–126 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Arpita Ghosh
    • 1
  • Mohammad Mahdian
    • 1
  • Daniel M. Reeves
    • 1
  • David M. Pennock
    • 1
  • Ryan Fugger
    • 2
  1. 1.Yahoo! Research 

Personalised recommendations