Multiagent Resource Allocation in the Presence of Externalities

  • Paul E. Dunne
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3690)

Abstract

In studies of settings concerning the allocation of a finite resource collection among a set of agents it is, usually, assumed that each agent associates a value with each subset of resources via a utility function that is free from so-called externalities, i.e. that these values are independent of the distribution of the remaining resources among the other agents. While this assumption is valid in many application domains, it is, however, by no means universally so. Thus, one can identify a number of circumstances wherein an agent’s assessment of a given subset is dependent not only on the elements of this set but also on the context in which it is held, i.e. on the resources owned by other agents. In this paper a general model for considering resource allocation settings with externalities is presented and its properties reviewed with reference to a select number of issues that have been widely-studied in externality–free settings.

Keywords

Multiagent Resource allocation Computational Complexity 

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References

  1. 1.
    Bouveret, S., Lang, J.: Efficiency and envy–freeness in fair division of indivisible goods: logical representation and complexity. In: Proc. 19th International Joint Conf. on A.I. (IJCAI 2005), Edinburgh (2005) (to appear)Google Scholar
  2. 2.
    Dignum, F. (ed.): ACL 2003. LNCS (LNAI), vol. 2922. Springer, Heidelberg (2004)Google Scholar
  3. 3.
    Dunne, P.E.: Extremal behaviour in multiagent contract negotiation. Jnl. of Artificial Intelligence Research 23, 41–78 (2005)MATHMathSciNetGoogle Scholar
  4. 4.
    Dunne, P.E., Wooldridge, M.J., Laurence, M.: The complexity of contract negotiation. Artificial Intelligence 164, 23–46 (2005)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Endriss, U., Maudet, N.: On the Communication Complexity of Multilateral Trading. In: Proc. 3rd International Joint Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2004), pp. 622–629. ACM Press, New York (2004)Google Scholar
  6. 6.
    Endriss, U., Maudet, N.: Welfare Engineering in Multiagent Systems. In: Omicini, A., Petta, P., Pitt, J. (eds.) ESAW 2003. LNCS (LNAI), vol. 3071, pp. 93–106. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Kraus, S.: Strategic negotiation in multiagent environments. MIT Press, Cambridge (2001)MATHGoogle Scholar
  8. 8.
    McBurney, P., Parsons, S.: Games that agents play: A formal framework for dialogues between autonomous agents. J. Logic, Language and Information 11, 315–334 (2002)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Parkes, D.C., Ungar, L.H.: Iterative combinatorial auctions: theory and practice. In: Proc. 17th National Conf. on Artificial Intelligence (AAAI 2000), pp. 74–81 (2000)Google Scholar
  10. 10.
    Reed, C.: Dialogue frames in agent communications. In: Demazeau, Y. (ed.) Proc. 3rd International Conference on Multi-agent systems (ICMAS 1998), pp. 246–253 (1998)Google Scholar
  11. 11.
    Sandholm, T.W.: Contract types for satisficing task allocation: I theoretical results. In: AAAI Spring Symposium: Satisficing Models (1998)Google Scholar
  12. 12.
    Sandholm, T.W.: Distributed rational decision making. In: Weiß, G. (ed.) Multiagent Systems, pp. 201–258. MIT Press, Cambridge (1999)Google Scholar
  13. 13.
    Tennenholz, M.: Some tractable combinatorial auctions. In: Proc. 17th National Conf. on Artificial Intelligence (AAAI 2000) (2000)Google Scholar
  14. 14.
    Yokoo, M., Sakurai, Y., Matsubara, S.: The effect of false-name bids in combinatorial auctions: new fraud in internet auctions. Games and Economic Behavior 46(1), 174–188 (2004)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Paul E. Dunne
    • 1
  1. 1.Dept. of Computer ScienceUniversity of LiverpoolLiverpoolUK

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