Skip to main content

The Contest Game for Crowdsourcing Reviews

  • Conference paper
  • First Online:
Algorithmic Game Theory (SAGT 2023)

Abstract

We consider a contest game modelling a contest where reviews for a proposal are crowdsourced from n players. Player i has a skill \(s_{i}\), strategically chooses a quality \(q \in \{ 1, 2, \ldots , Q \}\) for her review and pays an effort \(\textsf{f}_{q} \ge 0\), strictly increasing with q. Under voluntary participation, a player may opt to not write a review, paying zero effort; mandatory participation does not provide this option. For her effort, she is awarded a payment per her payment function, which is either player-invariant, like, e.g., the popular proportional allocation, or player-specific; it is oblivious when it does not depend on the numbers of players choosing a different quality. The utility to player i is the difference between her payment and her cost, calculated by a skill-effort function \(\mathsf{\Lambda } (s_{i}, \textsf{f}_{q})\). Skills may vary for arbitrary players; anonymous players means \(s_{i} = 1\) for all players i. In a pure Nash equilibrium, no player could unilaterally increase her utility by switching to a different quality. We show the following results about the existence and the computation of a pure Nash equilibrium:

  • We present an exact potential to show the existence of a pure Nash equilibrium for the contest game with arbitrary players and player-invariant and oblivious payments. A particular case of this result provides an answer to an open question from [6]. In contrast, a pure Nash equilibrium might not exist (i) for player-invariant payments, even if players are anonymous, (ii) for proportional allocation payments and arbitrary players, and (iii) for player-specific payments, even if players are anonymous; in the last case, it is \(\mathcal{N}\mathcal{P}\)-hard to tell. These counterexamples prove the tightness of our existence result.

  • We show that the contest game with proportional allocation, voluntary participation and anonymous players has the Finite Improvement Property, or FIP; this yields two pure Nash equilibria. The FIP carries over to mandatory participation, except that there is now a single pure Nash equilibrium. For arbitrary players, we determine a simple sufficient condition for the FIP in the special case where the skill-effort function has the product form \(\mathsf{\Lambda } (s_{i}, \textsf{f}_{q}) = s_{i}\, \textsf{f}_{q}\).

  • We introduce a novel, discrete concavity property of player-specific payments, namely three-discrete-concavity, which we exploit to devise, for constant Q, a polynomial-time \(\varTheta (n^{Q})\) algorithm to compute a pure Nash equilibrium in the contest game with arbitrary players; it is a special case of a \(\varTheta \left( n\, Q^{2}\, \left( {\begin{array}{c}\textstyle n+Q-1\\ \textstyle Q-1\end{array}}\right) \right) \) algorithm for arbitrary Q that we present. Thus, the problem is \({\mathcal{X}\mathcal{P}}\)-tractable with respect to the parameter Q. The computed equilibrium is contiguous: players with higher skills are contiguously assigned to lower qualities. Both three-discrete-concavity and the algorithm extend naturally to player-invariant payments.

The first author is supported by research funds at University of Cyprus. The second author is supported by ESPRC grant EP/P02002X/1.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abbassi, Z., Hedge, N., Massoulié, L.: Distributed content curation on the web. ACM Trans. Internet Technol. 14(2–3) (2014). Article 9

    Google Scholar 

  2. Armosti, N., Weinberg, S.M.: Bitcoin: a natural oligopoly. Manage. Sci. 68(7), 4755–4771 (2022)

    Article  Google Scholar 

  3. Baye, M.R., Kovenock, D., De Vries, C.G.: The all-pay auction with complete information. Econ. Theor. 8(2), 291–305 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  4. Benadè, G., Nath, S., Procaccia, A., Shah, N.: Preference elicitation for participatory budgeting. Manage. Sci. 67(5), 2813–2827 (2021)

    Article  Google Scholar 

  5. Bilò, V., Gourvés, L., Monnot, J.: Project games. Theoret. Comput. Sci. 940, 97–111 (2023)

    Article  MathSciNet  MATH  Google Scholar 

  6. Birmpas, G., Kovalchuk, L., Lazos, P., Oliynykov, R.: Parallel contests for crowdsourcing reviews: existence and quality of equilibria. In: Proceedings of the 4th ACM Conference on Financial Technologies, pp. 268–280 (2022)

    Google Scholar 

  7. Charalambides, Ch.A.: Enumerative Combinatorics. Chapman & Hall/CRC, Boca Raton (2002)

    Google Scholar 

  8. Chawla, S., Hartline, J.D., Sivan, B.: Optimal crowdsourcing contests. Games Econ. Behav. 113, 80–96 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  9. Chen, M., Tang, P., Wang, Z., Xiao, S., Yang, X.: Optimal anonymous independent reward scheme design. In: Proceedings of the 31st International Joint Conference on Artificial Intelligence, pp. 165–171 (2022)

    Google Scholar 

  10. Cheng, Y., Deng, X., Qi, Q., Yan, X.: Truthfulness of a proportional sharing mechanism in resource exchange. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence, pp. 187–193 (2016)

    Google Scholar 

  11. Cohen, C., Sela, A.: Contests with ties. B. E. J. Theoret. Econ. 7 (2007). Article 43

    Google Scholar 

  12. Deng, X., Li, N., Li, W., Qi, Q.: Competition among parallel contests. ArXiv:2210.06866, October 2022

  13. Di Palantino, D., Vojnovic̀, M.: Crowdsourcing and all-pay auctions. In: Proceedings of the 10th ACM Conference on Electronic Commerce, pp. 119–128 (2009)

    Google Scholar 

  14. Easley, D., Ghosh, A.: Incentives, gamification, and game theory: an economic approach to badge design. ACM Trans. Econ. Comput. 4(3), 16.1–16.26 (2016)

    Google Scholar 

  15. Elkind, E., Ghosh, A., Goldberg, P.W.: Contest design with threshold objectives. In: Proceedings of the 17th Conference on Web and Internet Economics, p. 554, December 2021. Also: arXiv:2109.03179v2

  16. Elkind, E., Ghosh, A., Goldberg, P.W.: Simultaneous contests with equal sharing allocation of prizes: computational complexity and price of anarchy. In: Proceedings of the 15th International Symposium on Algorithmic Game Theory, pp. 133–150, September 2022

    Google Scholar 

  17. Elkind, E., Ghosh, A., Goldberg, P.: Contests to incentivize a target group. In: Proceedings of the 31st International Joint Conference on Artificial Intelligence, pp. 279–285, July 2022

    Google Scholar 

  18. Feldmann, M., Lai, K., Zhang, L.: The proportional share allocation market for computational resources. IEEE Trans. Parallel Distrib. Syst. 20(8), 1075–1088 (2009)

    Article  Google Scholar 

  19. Gairing, M., Monien, B., Tiemann, K.: Selfish routing with incomplete information. Theory Comput. Syst. 42(1), 91–130 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  20. Gairing, M., Monien, B., Tiemann, K.: Routing (Un-)splittable flow in games with player-specific affine latency functions. ACM Trans. Algorithms 7(3) (2011). Article 31

    Google Scholar 

  21. Ghosh, A., McAfee, P.: Incentivizing high-quality user-generated content. In: Proceedings of the 20th International Conference on World Wide Web, pp. 137–146 (2011)

    Google Scholar 

  22. Jain, S., Chen, Y., Parkes, D.C.: Designing incentives for question-and-answer forums. Games Econom. Behav. 86, 458–474 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  23. Johari, R., Tsitsiklis, J.N.: Efficiency loss in a network resource allocation game. Math. Oper. Res. 29(3), 402–435 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  24. Karlin, A., Peres, Y.: Game Theory. American Mathematical Society, Alive (2017)

    MATH  Google Scholar 

  25. May, A., Chaintreau, A., Korula, N., Lattanzi, S.: Filter & follow: how social media foster content curation. ACM SIGMETRICS Perform. Eval. Rev. 42(1), 43–55 (2014)

    Article  Google Scholar 

  26. Milchtaich, I.: Congestion games with player-specific payoff functions. Games Econom. Behav. 13(1), 111–124 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  27. Monderer, D., Shapley, L.S.: Potential games. Games Econom. Behav. 14(1), 124–143 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  28. Murota, K.: Discrete Convex Analysis, SIAM Monographs on Discrete Mathematics and Applications, vol. 10 (2003)

    Google Scholar 

  29. Pálvölgyi, D., Peters, H., Vermeulen, D.: A strategic approach to multiple estate division problems. Games Econom. Behav. 88, 135–152 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  30. Quint, Th., Shubik, M.: A Model of Migration, Cowles Foundation Discussion Paper 1088, Yale University (1994)

    Google Scholar 

  31. Rosenthal, R.W.: A class of games possessing pure-strategy nash equilibria. Internat. J. Game Theory 2(1), 65–67 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  32. Sahni, S.: Computationally related problems. SIAM J. Comput. 3(4), 262–279 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  33. Segev, E.: Crowdsourcing contests. Eur. J. Oper. Res. 281, 241–255 (2020)

    Google Scholar 

  34. Shapley, L.: A value for \(n\)-player games. In: Kuhn, H., Tucker, A. (eds.) Contributions to the Theory of Games. Princeton University Press (1962)

    Google Scholar 

  35. Shubick, M.: Incentives, decentralized control, the assignment of joint costs and internal pricing. Manage. Sci. 8(3), 325–343 (1962)

    Article  MathSciNet  Google Scholar 

  36. Siegel, R.: All-pay auctions. Econometrica 77(1), 71–92 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  37. Skaperdas, S.: Contest success functions. Econ. Theor. 7(2), 283–290 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  38. Vetta, A.: Nash equilibria in competitive societies, with applications to facility location, traffic routing and auctions. In: Proceedings of the 43rd IEEE Symposium on Foundations of Computer Science, pp. 416–425 (2002)

    Google Scholar 

  39. Vojnovic̀, M.: Contest Theory - Incentive Mechanisms and Ranking Methods, Cambridge University Press, Cambridge (2015)

    Google Scholar 

  40. Xia, Y., Qin, T., Yu, N., Liu, T.Y.: Incentivizing high-quality content from heterogeneous users: on the existence of nash equilibrium. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence, pp. 819–825 (2014). Also: arXiv:1404.5155v1

  41. Zhang, L.: The efficiency and fairness of a fixed budget resource allocation game. In: Proceedings of the 32nd International Colloquium on Automata, Languages and Programming, pp. 485–496 (2005)

    Google Scholar 

Download references

Acknowledgements

We would like to thank all anonymous referees to this and previous versions of the paper for some very insightful comments they offered.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marios Mavronicolas .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mavronicolas, M., Spirakis, P.G. (2023). The Contest Game for Crowdsourcing Reviews. In: Deligkas, A., Filos-Ratsikas, A. (eds) Algorithmic Game Theory. SAGT 2023. Lecture Notes in Computer Science, vol 14238. Springer, Cham. https://doi.org/10.1007/978-3-031-43254-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-43254-5_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43253-8

  • Online ISBN: 978-3-031-43254-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics