Achieving Allocatively-Efficient and Strongly Budget-Balanced Mechanisms in the Network Flow Domain for Bounded-Rational Agents

  • Yoram Bachrach
  • Jeffrey S. Rosenschein
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3937)


Vickrey-Clarke-Groves (VCG) mechanisms are a well-known framework for finding a solution to a distributed optimization problem in systems of self-interested agents. VCG mechanisms have received wide attention in the AI community because they are efficient and strategy-proof; a special case of the Groves family of mechanisms, VCG mechanisms are the only direct-revelation mechanisms that are allocatively efficient and strategy-proof. Unfortunately, VCG mechanisms are only weakly budget-balanced.

We consider self-interested agents in a network flow domain, and show that in this domain, it is possible to design a mechanism that is both allocatively-efficient and almost completely budget-balanced. This is done by choosing a mechanism that is not strategy-proof but rather strategy-resistant. Instead of using the VCG mechanism, we propose a mechanism in which finding the most beneficial manipulation is an NP-complete problem, and the payments from the agents to the mechanism may be minimized as much as desired. This way, the mechanism is virtually strongly budget-balanced: for any ε> 0, we find a mechanism that is ε-budget-balanced.


Layer Graph Total Utility Combinatorial Auction Coalition Game Source Vertex 


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  1. 1.
    Green, J., Laffont, J.J.: Characterization of satisfactory mechanisms for the revelation of preferences for public goods. Econometrica 45(2), 427–438 (1977)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Hurwicz, L.: On the existence of allocation systems whose manipulative Nash equilibria are pareto-optimal. In: 3rd World Congress of the Econometric Society (unpublished, 1975)Google Scholar
  3. 3.
    Conitzer, V., Sandholm, T.: Universal voting protocol tweaks to make manipulation hard. In: Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI), Acapulco, Mexico, pp. 781–788 (2003)Google Scholar
  4. 4.
    Bartholdi, J.J.: The computational difficulty of manipulating an election. Social Choice and Welfare 6(3), 227–241 (1989)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Conitzer, V., Sandholm, T.: Computing shapley values, manipulating value division schemes, and checking core membership in multi-issue domains. In: Proceedings of the 19th National Conference on Artificial Intelligence (AAAI), San Jose, California, USA, pp. 219–225 (2004)Google Scholar
  6. 6.
    Shapley, L.S.: A value for n-person games. Contributions to the Theory of Games, 31–40 (1953)Google Scholar
  7. 7.
    Sandholm, T., Lesser, V.R.: Coalitions among computationally bounded agents. Artificial Intelligence 94(1-2), 99–137 (1997)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Sanghvi, S., Parkes, D.C.: Hard-to-manipulate combinatorial auctions. Technical report, Harvard University (2004)Google Scholar
  9. 9.
    Lavi, R., Mu’alem, A., Nisan, N.: Towards a characterization of truthful combinatorial auctions (extended abstract). In: Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science (FOCS) (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yoram Bachrach
    • 1
  • Jeffrey S. Rosenschein
    • 1
  1. 1.Hebrew UniversityJerusalemIsrael

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