Crowdsourcing and all-pay auctions

ABSTRACT

In this paper we present and analyze a model in which users select among, and subsequently compete in, a collection of contests offering various rewards. The objective is to capture the essential features of a crowdsourcing system, an environment in which diverse tasks are presented to a large community. We aim to demonstrate the precise relationship between incentives and participation in such systems.

We model contests as all-pay auctions with incomplete information; as a consequence of revenue equivalence, our model may also be interpreted more broadly as one in which users select among auctions of heterogeneous goods. We present two regimes in which we find an explicit correspondence in equilibrium between the offered rewards and the users' participation levels. The regimes respectively model situations in which different contests require similar or unrelated skills. Principally, we find that rewards yield logarithmically diminishing returns with respect to participation levels. We compare these results to empirical data from the crowdsourcing site Taskcn.com; we find that as we condition the data on more experienced users, the model more closely conforms to the empirical data.

REFERENCES

• 1: I. Ashlagi, D. Monderer, and M. Tennenholtz. Competing ad auctions. In Proc. of the Fourth Workshop on Ad Auctions (in conjuction with ACM, 2008.
• 2: M.R. Baye, D. Kovenock, and C.G. de Vries. The all-pay auction with complete information. Economic Theory, 8(2):291--305, June 1996.
• 3: Y. Chen, T.-H. Ho, and Y.-M. Kim. Knowledge market design: A field experiment at google answers. Working Paper, November 2008.
• 4: D. DiPalantino and M. Vojnovic. Crowdsourcing and all-pay auctions. Technical Report MSR-TR-2009-9, Microsoft Research Cambridge, 2009.
• 5: A. Hernando-Veciana. Competition among auctioneers in large markets. Journal of Economic Theory, 121(1):107--127, March 2005.
• 6: V. Krishna. Auction Theory. Academic Press, 2002.
• 7: R.P. McAfee. Mechanism design by competing sellers. Econometrica, 61(6):1281--1312, November 1993.
• 8: P.R. Milgrom and R.J. Weber. A theory of auctions and competitive bidding. Econometrica, 50(5):1089--1121, September 1982.
• 9: P.R. Milgrom and R.J. Weber. Distributional strategies for games with incomplete information. Mathematics of Operations Research, 10(4):619--632, November 1985.
• 10: B. Moldovanu and A. Sela. The optimal allocation of prizes in contests. American Economic Review, 91(3):542--558, 2001.
• 11: M. Peters. Competition among mechanism designers in a common value environment. Review of Economic Design, 4(3):273--292, 1999.
• 12: M. Peters and S. Severinov. Competition among sellers who offer auctions instead of prices. Journal of Economic Theory, 75:141--179, 1997.
• 13: A. Wolinsky. Dynamic markets with competitive bidding. Review of Economic Studies, 55(1):71--84, January 1988.
• 14: Jiang Yang , Lada A. Adamic , Mark S. Ackerman, Crowdsourcing and knowledge sharing: strategic user behavior on taskcn, Proceedings of the 9th ACM conference on Electronic commerce, July 08-12, 2008, Chicago, Il, USA [doi>10.1145/1386790.1386829]

INDEX TERMS

Primary Classification:
  J. Computer Applications
  J.m MISCELLANEOUS

General Terms:
Design, Economics

Keywords:
all-pay auctions, contests, crowdsourcing, game theory