Advertisement

An Analysis of Allocation Stability on Approximation-Based Pricing for Multi-unit Combinatorial Auctions

  • Naoki Fukuta
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 685)

Abstract

In this paper, a discussion and an analysis about the stability on pricing and allocation of resources are presented. On the discussion, an approximate auction which has VCG-like pricing mechanism is used when cancellation of winner bid(s) after its winner determination is considered. An analysis about stable approximate pricing mechanisms against cancellation of a winner after its winner determination is also presented. In there, a single-unit non-combinatorial reserve price bidding on a multi-unit combinatorial auction could also be employed as well. The pricing algorithm employs an approximate allocation and pricing algorithm that is capable of handling multi-unit auctions with reserve price biddings. This type of auction is expected to be applied to a situation when we consider an allocation of electricity while considering electricity generation costs on the power suppliers in more realistic configurations, i.e., some bidders might be untrustful in their ability. Based on the experimental analysis, the algorithm effectively produces approximation allocations that are necessary in the pricing phase, as well as yielding better stability in the case of single-winner cancellation. It also behaves as an approximation of VCG(Vickrey-Clarke-Groves) mechanism satisfying budget balance condition and bidders’ individual rationality without enforcing the single-minded bidders assumption.

Keywords

Multi-unit auctions Combinatorial auctions Approximation 

Notes

Acknowledgements

The work was partly supported by Grants-in-Aid for Young Scientists(B) 22700142, Grants-in-Aid for Challenging Exploratory Research 26540162, and JST CREST JPMJCR15E1.

References

  1. 1.
    An, B., Lesser, V., Irwin, D., Zink, M.: Automated negotiation with decommitment for dynamic resource allocation in cloud computing. In: Proceedings International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), vol. 1, pp. 981–988 (2010)Google Scholar
  2. 2.
    Cavallo, R.: Incentive compatible two-tiered resource allocation without money. In: Proceedings the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014). pp. 1313–1320 (2014)Google Scholar
  3. 3.
    Constantin, F., Feldman, J., Muthukrishnan, S., Pal, M.: An online mechanism for ad slot reservations with cancellations. In: Proceedings of ACM-SIAM Symposium on Discrete Algorithms (SODA 2009). pp. 1265–1274 (2009)Google Scholar
  4. 4.
    Cramton, P., Shoham, Y., Steinberg, R.: Combinatorial Auctions. The MIT Press (2006)Google Scholar
  5. 5.
    Edelman, B., Ostorvsky, M., Schwarz, M.: Internet advertising and the generalized second price auction: selling billions of dollars worth of keywords. Am. Econ. Rev. 97(1), 242–259 (2007)CrossRefGoogle Scholar
  6. 6.
    Fujishima, Y., Leyton-Brown, K., Shoham, Y.: Taming the computational complexity of combinatorial auctions: optimal and approximate approarches. In: Proceeding of the 16th International Joint Conference on Artificial Intelligence (IJCAI 1999). pp. 548–553 (1999)Google Scholar
  7. 7.
    Fukuta, N.: Toward a VCG-like approximate mechanism for large-scale multi-unit combinatorial auctions. In: Proceeding IEEE/ACM/WIC International Conference on Intelligent Agent Technology(IAT 2011). pp. 317–322 (2011)Google Scholar
  8. 8.
    Fukuta, N.: A mobile agent approach for p2p-based semantic file retrieval. J. Inf Process. 20(3), 607–613 (2012)Google Scholar
  9. 9.
    Fukuta, N.: An approach to VCG-like approximate allocation and pricing for large-scale multi-unit combinatorial auctions. J. Inf. Process. 21(1), 9–15 (2013)Google Scholar
  10. 10.
    Fukuta, N.: A market-based agent-mediated resource control framework for middle-scale smart grids. In: Proceeding the 2013 IEEE/WIC/ACM International Conference on Web intelligence/Intelligent Agent Technology (WI-IAT 2013). vol. 3, pp. 292–293 (2013)Google Scholar
  11. 11.
    Fukuta, N.: A preliminary implementation on pricing mechanisms for electric resource control markets. In: Proceedings the 3rd International Workshop on Knowledge and Service Technology for Life, Environment, and Sustainability (KASTLES 2013). pp. 338–342 (2013)Google Scholar
  12. 12.
    Fukuta, N.: An approximation approach for large-scale multi-unit combinatorial auctions with reserve-price biddings. In: Proceeding the 2nd International Conference on Smart Computing and Artificial Intelligence(ICSCAI 2014). pp. 487–492 (2014)Google Scholar
  13. 13.
    Fukuta, N.: A preliminary analysis of allocation stability on approximation-based pricing for multi-unit combinatorial auctions–a single-winner cancellation scenario. In: Proceedings KICSS 2015 International Workshop on Collective Intelligence and Crowd/Social Computing. pp. 236–246. Phuket, Thailand (2015)Google Scholar
  14. 14.
    Fukuta, N.: Toward efficient approximation for large-scale multi-unit combinatorial auctions with reserve-price bidding. IEICE Trans. Inf. Syst. J98-D(6), 948–961 (Jun 2015), (In Japanese.)Google Scholar
  15. 15.
    Fukuta, N.: A pre-processing approach for fast and stable allocations on approximation-based pricing for multi-unit combinatorial auctions. Inf. Eng. Express 2(4), 21–30 (2016)Google Scholar
  16. 16.
    Fukuta, N.: Toward fast approximation of stable allocation and pricing on combinatorial auctions. In: Proceedings of 1st IEEE International Conference on Agents(ICA 2016). pp. 74–77 (Sep 2016)Google Scholar
  17. 17.
    Fukuta, N., Ito, T.: Towards better approximation of winner determination for combinatorial auctions with large number of bids. In: Proceedings of The 2006 WIC/IEEE/ACM International Conference on Intelligent Agent Technology(IAT 2006). pp. 618–621 (2006)Google Scholar
  18. 18.
    Fukuta, N., Ito, T.: Fine-grained efficient resource allocation using approximated combinatorial auctions-a parallel greedy winner approximation for large-scale problems. Web Intel. Agent Syst. Int. J. 7(1), 43–63 (2009)Google Scholar
  19. 19.
    Fukuta, N., Ito, T.: An experimental analysis of biased parallel greedy approximation for combinatorial auctions. Int. J. Intel. Inf. Database Syst. 4(5), 487–508 (2010)Google Scholar
  20. 20.
    Hoos, H.H., Boutilier, C.: Solving combinatorial auctions using stochastic local search. In: Proceedings of 17th National Conference on Artificial Intelligence (AAAI 2000). pp. 22–29 (2000)Google Scholar
  21. 21.
    Ishikawa, T., Fukuta, N.: A prototype system for federated cloud-based resource allocation by automated negotiations using strategy changes. In: Proceedings the 6th International Workshop on Agent-based Complex Automated Negotiations (ACAN 2013) (2013)Google Scholar
  22. 22.
    Ito, T., Imi, Y., Ito, T., Hideshima, E.: COLLAGREE: A faciliator-mediated large-scale consensus support system. In: Proceedings of the International Conference on Collective Intelligence (2014)Google Scholar
  23. 23.
    Ito, T., Parkes, D.C.: Instantiating the contingent bids model of truthful interdependent value auctions. In: Proceedings of the International Joint Conference on Autonomous Agents and Multi Agent Systems (AAMAS 2006). pp. 1151–1158 (2006)Google Scholar
  24. 24.
    Krysta, P., orestis Teleis, Ventre, C.: Mechanisms for multi-unit combinatorial auctions with a few distinct goods. In: Proceedings the 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013). pp. 691–698 (2013)Google Scholar
  25. 25.
    Lavi, R., Nisan, N.: Competitive analysis of incentive compatible on-line auctions. In: Proceedings of the 2nd ACM conference on Electronic commerce(EC 2000). pp. 233–241. ACM (2000)Google Scholar
  26. 26.
    Lehmann, D., O’Callaghan, L.I., Shoham, Y.: Truth revelation in rapid, approximately efficient combinatorial auctions. J. ACM 49, 577–602 (2002)MathSciNetCrossRefMATHGoogle Scholar
  27. 27.
    Leyton-Brown, K., Pearson, M., Shoham, Y.: Towards a universal test suite for combinatorial auction algorithms. In: Proceedings of ACM Conference on Electronic Commerce (EC 2000). pp. 66–76 (2000)Google Scholar
  28. 28.
    McMillan, J.: Selling spectrum rights. J. Econ. Perspect. 8, 145–162 (1994)CrossRefGoogle Scholar
  29. 29.
    Nisan, N., Ronen, A.: Computationally feasible VCG mechanisms. In: Proceeding of ACM Conference on Electronic Commerce. pp. 242–252 (2000)Google Scholar
  30. 30.
    Sandholm, T., Suri, S., Gilpin, A., Levine, D.: Cabob: a fast optimal algorithm for winner determination in combinatorial auctions. Manag. Sci. 51(3), 374–390 (2005)CrossRefMATHGoogle Scholar
  31. 31.
    Shoham, Y., Leyton-Brown, K.: Multiagent Systems: algorithmic, game-theoretic, and logical foundations. Cambridge University Press (2008)Google Scholar
  32. 32.
    Todo, T., Iwasaki, A., Yokoo, M., Sakurai, Y.: Characterizing false-name-proof allocation rules in combinatorial auctions. In: Proceedings 8th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2009) (2009)Google Scholar
  33. 33.
    Vickrey, W.: Counterspeculation, auctions, and competitive sealed tenders. J. Finance 16(1), 8–37 (1961)MathSciNetCrossRefGoogle Scholar
  34. 34.
    de Vries, S., Vohra, R.V.: Combinatorial auctions: a survey. Informs J. Comput. 15(3), 284–309 (2003)MathSciNetCrossRefMATHGoogle Scholar
  35. 35.
    Yokoo, M.: The characterization of strategy/false-name proof combinatorial auction protocols: Price-oriented, rationing-free protocol. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence. pp. 733–739 (2003)Google Scholar
  36. 36.
    Zurel, E., Nisan, N.: An efficient approximate allocation algorithm for combinatorial auctions. In: Proceeding of the 3rd ACM Conference on Electronic Commerce (EC 2001). pp. 125–136 (2001)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Shizuoka UniversityHamamatsu, ShizuokaJapan

Personalised recommendations