Skip to main content
SpringerLink
Log in

A network based mechanism for managing decomposable tasks via crowdsourcing

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

Abstract

If a task is decomposable in a competitive crowdsourcing environment, thereby allowing collaboration, rational workers may choose to divide it into multiple sub-tasks among themselves. But as the winners are selected independently, there is no benefit out of this decomposition. We show that by the appropriate combination of such decomposed solutions, obtained from multiple workers, we can achieve a better solution for a given task. We present a network based mechanism to choose the best mixture of sub-tasks in a competitive environment for selecting collaborating winners.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Amazon mechanical turk. https://www.mturk.com. Accessed June 2017.

  2. Freelancer. https://www.freelancer.in. Accessed June 2017.

  3. 99designs. https://99designs.com. Accessed June 2017.

  4. Flightfox. https://www.flightfox.com. Accessed June 2017.

  5. Ipeirotis, P. G. (2010). Analyzing the amazon mechanical turk marketplace. XRDS: Crossroads, The ACM Magazine for Students, 17(2), 16–21.

    Article  Google Scholar 

  6. Ross, J, Irani, L, Silberman, M. S., Zaldivar, A. & Tomlinson, B. (2010). Who are the crowdworkers?: Shifting demographics in mechanical turk. Atlanta, Georgia, USA, April 10–15.

  7. Kittur, A. (2010). Crowdsourcing, collaboration and creativity. XRDS: Crossroads, the ACM magazine for students, 17(2), 22–26.

    Article  Google Scholar 

  8. Pan, Z., Yu, H., Miao, C., & Leung, C. (2016). Efficient collaborative crowdsourcing. In AAAI (pp. 4248–4249).

  9. Boudreau, K. J., Lacetera, N., & Lakhani, K. L. (2011). Incentives and problem uncertainty in innovation contests: An empirical analysis. Management Science, 57(5), 843–863.

    Article  Google Scholar 

  10. Tang, J. C., Cebrian, M., Giacobe, N. A., Kim, H.-W., Kim, T., & Wickert, D. B. (2011). Reflecting on the DARPA red balloon challenge. Communications of the ACM, 54(4), 78.

    Article  Google Scholar 

  11. Nima Anari, Gagan Goel, & Afshin Nikzad. (2014). Mechanism design for crowdsourcing: An optimal 1-1/e competitive budget-feasible mechanism for large markets. In Foundations of Computer Science (FOCS), 2014 IEEE 55th Annual Symposium on (pp. 266–275). IEEE.

  12. Yang, Y, & Saremi, R. (2015). Award vs. worker behaviors in competitive crowdsourcing tasks. In Empirical Software Engineering and Measurement (ESEM), 2015 ACM/IEEE International Symposium on (pp. 1–10). IEEE.

  13. Mridha, S. K., & Bhattacharyya, M. (2017). Is dutch auction suitable for decomposable tasks in competitive crowdsourcing markets? Human Computation, 4(1), 71–77.

    Article  Google Scholar 

  14. Goel, G., Nikzad, A., & Singla, A. (2014). Mechanism design for crowdsourcing markets with heterogeneous tasks. In Second AAAI Conference on Human Computation and Crowdsourcing.

  15. Satzger, B., Psaier, H., Schall, D., & Dustdar, S. (2013). Auction-based crowdsourcing supporting skill management. Information Systems, 38(4), 547–560.

    Article  Google Scholar 

  16. Yang, D., Xue, G., Fang, X., & Tang, J. (2012). Crowdsourcing to smartphones: Incentive mechanism design for mobile phone sensing. In Proceedings of the 18th Annual International Conference on Mobile Computing And Networking (pp. 173–184). Istanbul Turkey, August 22–26.

  17. Badanidiyuru, A., Kleinberg, R., & Singer, Y. (2012). Learning on a budget: Posted price mechanisms for online procurement. In Proceedings of the 13th ACM Conference on Electronic Commerce (pp. 128–145). Valenca, Spain, June 4–8.

  18. Chawla, S., Hartline, J. D., Malec, D. L., & Sivan, B. (2010). Multi-parameter mechanism design and sequential posted pricing. In Proceedings of the Forty-Second ACM Symposium on Theory of Computing (pp. 311–320). Cambridge, Massachusetts, UK, June 6–8.

  19. Kleinberg, R., & Leighton, T. (2003). The value of knowing a demand curve: Bounds on regret for online posted-price auctions. In Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science (p. 594). Cambridge, MA, October 11–14.

  20. Mridha, S. K., & Bhattacharyya, M. (2018). Network based mechanisms for competitive crowdsourcing. In Proceedings of the ACM India joint international conference on data science and management of data (pp. 318–321). ACM.

  21. Heer, J., & Bostock, M. (2010). Crowdsourcing graphical perception: Using mechanical turk to assess visualization design. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 203–212). ACM.

  22. Chen, N., Gravin, N., & Lu, P. (2011). On the approximability of budget feasible mechanisms. In Proceedings of the 22nd annual ACM-SIAM symposium on discrete algorithms (pp. 685–699). San Francisco, California, USA, January 22.

  23. Singer, Y. (2010). Budget feasible mechanisms. In Foundations of computer science (FOCS), 2010 51st annual IEEE symposium on (pp. 765–774). IEEE.

  24. Kulkarni, A. P., Can, M., & Hartmann, B. (2011). Turkomatic: Automatic recursive task and workflow design for mechanical turk. In CHI’11 extended abstracts on human factors in computing systems (pp. 2053–2058). ACM.

  25. Kittur, A., Smus, B., Khamkar, S., & Kraut, R. E. (2011). Crowdforge: Crowdsourcing complex work. In Proceedings of the 24th annual ACM symposium on user interface software and technology (pp. 43–52). ACM.

  26. Morris, M.R., Bigham, J. P., Brewer, R., Bragg, J., Kulkarni, A., Li, J., & Savage, S. (2017). Subcontracting microwork. In Proceedings of the 2017 CHI conference on human factors in computing systems (pp. 1867–1876). ACM.

  27. Fredman, M. L., & Tarjan, R. E. (1987). Fibonacci heaps and their uses in improved network optimization algorithms. Journal of the ACM, 34(3), 596–615.

    Article  Google Scholar 

Download references

Acknowledgements

The authors wish to thank the anonymous reviewers for their invaluable suggestions that greatly helped to improve the presentation of this paper. This publication is an outcome of the R&D work undertaken in the project under the Visvesvaraya Ph.D. Scheme of Ministry of Electronics and Information Technology, Government of India, being implemented by Digital India Corporation (formerly Media Lab Asia).

Author information

Authors and Affiliations

  1. Department of Information Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, 711103, India

    Sankar Kumar Mridha & Malay Bhattacharyya

Authors
  1. Sankar Kumar Mridha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Malay Bhattacharyya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Malay Bhattacharyya.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mridha, S.K., Bhattacharyya, M. A network based mechanism for managing decomposable tasks via crowdsourcing. Electron Commer Res 18, 869–881 (2018). https://doi.org/10.1007/s10660-018-9317-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10660-018-9317-8

Keywords

Search

Navigation