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Preventing Under-Reporting in Social Task Allocation

  • Mathijs de Weerdt
  • Yingqian Zhang
Conference paper
  • 329 Downloads
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 44)

Abstract

In games where agents are asked to declare their available resources, they can also strategize over this declaration. Surprisingly, not in all such games a VCG payment can be applied to construct a truthful mechanism using an optimal algorithm, though such payments can prevent under-reporting of resources. We show this for the problem of allocating tasks in a social network (STAP).

Since STAP is NP-hard, we introduce an approximation algorithm as well. However for such an approximation, a VCG payment cannot prevent under-reporting anymore. Therefore we introduce an alternative payment function that motivates agents to fully declare their resources. We also demonstrate by experiments that the approximation algorithm works well in different types of social networks.

Keywords

Problem Instance Allocation Algorithm Task Allocation Resource Type Combinatorial Auction 
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.

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References

  1. 1.
    de Vries, S., Vohra, R.: Combinatorial Auctions: A Survey. INFORMS Journal on Computing 15(3), 284–309 (2003)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Blumrosen, L., Nisan, N.: Combinatorial auctions. In: Algorithmic Game Theory, pp. 209–242. Cambridge University Press, Cambridge (2007)Google Scholar
  3. 3.
    Chevaleyre, Y., Dunne, P.E., Endriss, U., Lang, J., Lemaitre, M., Maudet, N., Padget, J., Phelps, S., Rodriguez-Aguilar, J.A., Sousa, P.: Issues in multiagent resource allocation. Informatica 30, 3–31 (2006)zbMATHGoogle Scholar
  4. 4.
    de Weerdt, M., Zhang, Y., Klos, T.B.: Distributed task allocation in social networks. In: Proc. of 6th Int. Conf. on AAMAS, pp. 17–24. ACM, New York (2007)Google Scholar
  5. 5.
    Vickrey, W.: Counterspeculation, auctions, and competitive sealed tenders. The Journal of Finance 16(1), 8–37 (1961)CrossRefGoogle Scholar
  6. 6.
    Clarke, E.H.: Multipart pricing of public goods. Public Choice 11(1) (1971)Google Scholar
  7. 7.
    Groves, T.: Incentives in teams. Econometrica 41(4), 617–631 (1973)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Nisan, N.: Introduction to mechanism design (for computer scientists). In: Algorithmic Game Theory, pp. 209–242. Cambridge University Press, Cambridge (2007)Google Scholar
  9. 9.
    Nisan, N., Ronen, A.: Algorithmic mechanism design (extended abstract). In: Proc. of 31th ACM Symposium on Theory of Computing, pp. 129–140. ACM, New York (1999)Google Scholar
  10. 10.
    Makhorin, A.: GLPK. GNU Linear Programming Kit (2004)Google Scholar
  11. 11.
    Dantzig, G.: Discrete variable problems. Operations Research 5, 266–277 (1957)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small world’ networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar
  13. 13.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Archer, A., Tardos, E.: Truthful mechanisms for one-parameter agents. In: Proc. of 42nd IEEE Symposium on FOCS, pp. 482–491 (2001)Google Scholar
  15. 15.
    Babaioff, M., Lavi, R., Pavlov, E.: Mechanism design for single-value domains. In: Proc. of 20th Nat. Conf. on Artificial intelligence, pp. 241–247. AAAI, Menlo Park (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Mathijs de Weerdt
    • 1
  • Yingqian Zhang
    • 1
  1. 1.Delft University of Technology 

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