Advertisement

Game-Theoretic Analysis on the Number of Participants in the Software Crowdsourcing Contest

  • Pengcheng Peng
  • Chenqi MouEmail author
  • Wei-Tek Tsai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11110)

Abstract

In this paper a game theoretic model of multiple players is established to relate the reward from the outsourcer and the number of participants in the software crowdsourcing contest in the winner-take-all mode via Nash equilibria of the game. We show how to construct the payoff function of each participant in this game by computing his expected probability of winning sequential pairwise challenges. Preliminary experimental results with our implementations are provided to illustrate the relationships between the reward and the number of participants for three typical participant compositions.

Keywords

Software crowdsourcing Game theory Nash equilibrium Payoff function 

Notes

Acknowledgements

The first author wishes to thank his supervisor, Professor Dongming Wang, for his support and encouragement.

References

  1. 1.
    Archak, N., Sundararajan, A.: Optimal design of crowdsourcing contests. In: Proceedings of International Conference on Information Systems 2009, p. 200 (2009)Google Scholar
  2. 2.
    Avis, D., Rosenberg, G.D., Savani, R., Von Stengel, B.: Enumeration of Nash equilibria for two-player games. Econ. Theor. 42(1), 9–37 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Brabham, D.C.: Crowdsourcing as a model for problem solving: an introduction and cases. Convergence 14(1), 75–90 (2008)CrossRefGoogle Scholar
  4. 4.
    Chawla, S., Hartline, J.D., Sivan, B.: Optimal crowdsourcing contests. Games Econ. Behav. (2015, in press)Google Scholar
  5. 5.
    Datta, R.S.: Finding all Nash equilibria of a finite game using polynomial algebra. Econ. Theor. 42(1), 55–96 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    DiPalantino, D., Vojnovic, M.: Crowdsourcing and all-pay auctions. In: Proceedings of the 10th ACM Conference on Electronic Commerce, pp. 119–128. ACM (2009)Google Scholar
  7. 7.
    Fudenberg, D., Tirole, J.: Game Theory. MIT Press, Cambridge, Massachusetts (1991)zbMATHGoogle Scholar
  8. 8.
    Hu, Z., Wu, W.: A game theoretic model of software crowdsourcing. In: Proceedings of IEEE 8th International Symposium on Service Oriented System Engineering, pp. 446–453. IEEE (2014)Google Scholar
  9. 9.
    Kalra, A., Shi, M.: Designing optimal sales contests: a theoretical perspective. Mark. Sci. 2(20), 170–193 (2001)CrossRefGoogle Scholar
  10. 10.
    Lakhanih, K., Garvin, D.A., Lonstein, E.: Topcoder (A): developing software through crowdsourcing. Harvard Business School General Management Unit Case No. 610–032 (2010)Google Scholar
  11. 11.
    Li, W., Huhns, M.N., Tsai, W.-T., Wu, W. (eds.): Crowdsourcing: Cloud-Based Software Development. Springer, Heidelberg (2015).  https://doi.org/10.1007/978-3-662-47011-4CrossRefGoogle Scholar
  12. 12.
    Liang, X., Yan, Z.: A survey on game theoretical methods in human-machine networks. Future Gener. Comput. Syst. (2017, in press)Google Scholar
  13. 13.
    Liu, T.X., Yang, J., Adamic, L.A., Chen, Y.: Crowdsourcing with all-pay auctions: a field experiment on Taskcn. Manage. Sci. 60(8), 2020–2037 (2014)CrossRefGoogle Scholar
  14. 14.
    McKelvey, R.D., McLennan, A.M., Turocy, T.L.: Gambit: Software tools for game theory (2006)Google Scholar
  15. 15.
    Moldovanu, B., Sela, A.: The optimal allocation of prizes in contests. Am. Econ. Rev. 3(91), 542–558 (2001)CrossRefGoogle Scholar
  16. 16.
    Moshfeghi, Y., Rosero, A.F.H., Jose, J.M.: A game-theory approach for effective crowdsource-based relevance assessment. ACM Trans. Intell. Syst. Technol. 7(4), 55 (2016)CrossRefGoogle Scholar
  17. 17.
    Nash, J.: Non-cooperative games. Ann. Math. 54(2), 286–295 (1951)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Nisan, N., Roughgarden, T., Tardos, E., Vazirani, V.V.: Algorithmic Game Theory. Cambridge University Press, Cambridge (2007)CrossRefGoogle Scholar
  19. 19.
    Wu, W., Tsai, W.-T., Li, W.: Creative software crowdsourcing: from components and algorithm development to project concept formations. Int. J. Creative Comput. 1(1), 57–91 (2013)CrossRefGoogle Scholar
  20. 20.
    Wu, W., Tsai, W.-T., Li, W.: An evaluation framework for software crowdsourcing. Front. Comput. Sci. 7(5), 694–709 (2013)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.LMIB – School of Mathematics and Systems Science / Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijingChina
  2. 2.School of Computer Science and EngineeringBeihang UniversityBeijingChina
  3. 3.School of Computing, Informatics, and Decision Systems EngineeringArizona State UniversityTempeUSA

Personalised recommendations