Skip to main content
Log in

A tractable multiple agents protocol and algorithm for resource allocation under price rigidities

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

In many resource allocation problems, economy efficiency must be taken into consideration together with social equality, and price rigidities are often made according to some economic and social needs. We investigate the computational issues of dynamic mechanisms for selling multiple indivisible objects under price rigidities. We propose a multiple agents protocol and algorithm with polynomial time complexity that can achieve the over-demanded sets of items, and then introduce a dynamic mechanism with rationing to discover constrainedWalrasian equilibria under price rigidities in polynomial time. We also address the computation of buyers’ expected profits and items’ expected prices, and discuss strategical issues in the sense of expected profits.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. Note that since upper bound prices are often set for the sake of equality between social members (who have some but limited pay ability), they generally accompany a limit to the number of resources one member can get.

  2. Suppose there are k buyers drawing lots for the right to buy item a. Then the lot is fair if each one of these buyers has 1/k chance of winning the lot.

References

  1. Ausubel LM (2006) An efficient dynamic auction for heterogeneous commodities. Amer Econ Rev:602–629

  2. Bahrammirzaee A, Chohra A, Madani K (2013) An adaptive approach for decision making tactics in automated negotiation. Appl Intell 39(3):583–606

    Article  Google Scholar 

  3. Bouveret S, Lang J (2011) A general elicitation-free protocol for allocating indivisible goods. In: Proceedings of international joint conference on artificial intelligence, pp 73–78

  4. Brams S, Fishburn P (2002) Fair division of indivisible items between two people with identical preferences. SocialChoice Welfare 17:247–267

    Article  MathSciNet  Google Scholar 

  5. Brams SJ, Feldman M, Lai JK, Morgenstern J, Procaccia AD (2012) On maxsum fair cake divisions. In: Proceedings of the 26th national conference on artificial intelligence. Toronto, pp 1285–1291

  6. Branzei S, Procaccia AD, Zhang J (2013) Externalities in cake cutting. In: Proceedings of the twenty-third international joint conference on artificial intelligence. AAAI Press, pp 55–61

  7. Brazier FM, Cornelissen F, Gustavsson R, Jonker CM, Lindeberg O, Polak B, Treur J (2004) Compositional verification of a multi-agent system for one-to-many negotiation. Appl Intell 20(2):95–117

    Article  MATH  Google Scholar 

  8. Chen Y, Lai JK, Parkes DC, Procaccia AD (2013) Truth, justice, and cake cutting. Games Econ Behav 77(1):284–297

    Article  MathSciNet  MATH  Google Scholar 

  9. Chevaleyre Y, Endriss U, Maudet N (2010) Simple negotiation schemes for agents with simple preferences: sufficiency, necessity and maximality. Auton Agents Multi-Agent Syst 20(2):234–259

    Article  Google Scholar 

  10. Cohler YJ, Lai JK, Parkes DC, Procaccia AD (2011) Optimal envy-free cake cutting. In: Proceedings of the 25th national conference on artificial intelligence. San Francisco, pp 626–631

  11. Cramton P, Shoham Y, Steinberg R (2006) Combinatorial auctions. MIT press

  12. Fahad M, Boissier O, Maret P, Moalla N, Gravier C (2014) Smart places: multi-agent based smart mobile virtual community management system. Appl Intell:1–19

  13. Gul F, Stacchetti E (1999) Walrasian equilibrium with gross substitutes. J Econ Theory 87(1):95–124

    Article  MathSciNet  MATH  Google Scholar 

  14. Gul F, Stacchetti E (2000) The english auction with differentiated commodities. J Econ Theory 92(1):66–95

    Article  MathSciNet  MATH  Google Scholar 

  15. Guo M, Deligkas A (2013) Revenue maximization via hiding item attributes. In: Proceedings of the twenty-third international joint conference on artificial Intelligence. AAAI Press, pp 157–163

  16. Hartline J, Yan Q (2011) Envy, truth, and profit. In: Proceedings of the 12th ACM conference on electronic commerce. ACM, pp 243–252

  17. Kalinowski T, Narodytska N, Walsh T (2013) A social welfare optimal sequential allocation procedure. In: Proceedings of the twenty-third international joint conference on artificial intelligence. AAAI Press, pp 227–233

  18. Kalinowski T, Narodytska N, Walsh T, Xia L (2012) Strategic behavior in a decentralized protocol for allocating indivisible goods. In: Proceedings of the 4th international workshop on computational social choice, vol 12. Kraków, pp 251–262

  19. Kalinowski T, Narodytska N, Walsh T, Xia L (2013) Strategic behavior when allocating indivisible goods sequentially. In: Proceedings of the 27th national conference on artificial intelligence. Bellevue, pp 452–458

  20. Lehmann B, Lehmann D, Nisan N (2001) Combinatorial auctions with decreasing marginal utilities. In: Proceedings of the 3rd ACM conference on electronic commerce. ACM, pp 18–28

  21. Likhodedov A, Sandholm T (2005) Approximating revenue-maximizing combinatorial auctions. In: Proceedings of the 20th national conference on artificial intelligence, vol 5. Pittsburgh, pp 267–274

  22. Lumet C, Bouveret S, Lemaitre M (2012) Fair division of indivisible goods under risk. In: Proceedings of the twentieth European conference on artificial intelligence. IOS Press, pp 564–569

  23. Mirchevska V, Luṡtrek M, BeŻek A, Gams M (2014) Discovering strategic behaviour of multi-agent systems in adversary settings. Comput Inf 33(1):79–108

    Google Scholar 

  24. Procaccia AD (2013) Cake cutting: not just child’s play. Commun ACM 56(7):78–87

    Article  Google Scholar 

  25. Rothkopf MH, Pekeċ A, Harstad RM (1998) Computationally manageable combinational auctions, vol 44

  26. Sandholm T (2002) Algorithm for optimal winner determination in combinatorial auctions, vol 135

  27. Schrijver A (2003) Combinatorial optimization: polyhedra and efficiency, vol 24. Springer

  28. Sun N, Yang Z (2009) A double-track adjustment process for discrete markets with substitutes and complements. Econometrica 77(3):933–952

    Article  MathSciNet  MATH  Google Scholar 

  29. Talman D, Yang Z (2008) A dynamic auction for differentiated items under price rigidities. Econ Lett 99(2):278–281

    Article  MathSciNet  MATH  Google Scholar 

  30. Wang JJD, Zender JF (2002) Auctioning divisible goods. Econ Theory 19:673–705

    Article  MathSciNet  MATH  Google Scholar 

  31. Zhang D, Huang W, Perrussel L (2010) Dynamic auction: a tractable auction procedure. In: Proceedings of the 24th national conference on artificial intelligence. Atlanta, pp 935–940

Download references

Acknowledgments

This work is supported partly by the National Natural Science Foundation of China (Grant No. 61105039,61173035,61472058), and the Program for New Century Excellent Talents in University (Grant No. NCET-11-0861).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hongbo Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, W., Liu, H., Dai, G. et al. A tractable multiple agents protocol and algorithm for resource allocation under price rigidities. Appl Intell 43, 564–577 (2015). https://doi.org/10.1007/s10489-015-0663-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-015-0663-0

Keywords

Navigation