SAGT 2009: Algorithmic Game Theory pp 109-121 | Cite as

Free-Riding and Free-Labor in Combinatorial Agency

  • Moshe Babaioff
  • Michal Feldman
  • Noam Nisan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5814)

Abstract

This paper studies a setting where a principal needs to motivate teams of agents whose efforts lead to an outcome that stochastically depends on the combination of agents’ actions, which are not directly observable by the principal. In [1] we suggest and study a basic “combinatorial agency” model for this setting. In this paper we expose a somewhat surprising phenomenon found in this setting: cases where the principal can gain by asking agents to reduce their effort level, even when this increased effort comes for free. This phenomenon cannot occur in a setting where the principal can observe the agents’ actions, but we show that it can occur in the hidden-actions setting. We prove that for the family of technologies that exhibit “increasing returns to scale” this phenomenon cannot happen, and that in some sense this is a maximal family of technologies for which the phenomenon cannot occur. Finally, we relate our results to a basic question in production design in firms.

Keywords

Nash Equilibrium Boolean Function Action Space Success Probability Success Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Babaioff, M., Feldman, M., Nisan, N.: Combinatorial Agency. In: ACM Conference on Electronic Commerce (EC 2006), pp. 18–28 (2006)Google Scholar
  2. 2.
    Babaioff, M., Feldman, M., Nisan, N.: Mixed strategies in combinatorial agency. In: Spirakis, P.G., Mavronicolas, M., Kontogiannis, S.C. (eds.) WINE 2006. LNCS, vol. 4286, pp. 353–364. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Che, Y.K., Yoo, S.W.: Optimal Incentives in Teams. American Economic Review 91, 525–541 (2001)CrossRefGoogle Scholar
  4. 4.
    Chen, N., Karlin, A.R.: Cheap labor can be expensive. In: ACM-SIAM Symposium on Discrete Algorithms (SODA 2007), pp. 707–715 (2007)Google Scholar
  5. 5.
    Cole, R., Dodis, Y., Roughgarten, T.: Pricing Networks with Selfish Routing. In: Workshop on Economics of Peer-to-Peer Systems (2003)Google Scholar
  6. 6.
    Eidenbenz, R., Schmid, S.: Combinatorial agency with audits. In: IEEE International Conference on Game Theory for Networks, GameNets (2009)Google Scholar
  7. 7.
    Elkind, E.: The Costs of Cheap Labor Are Hard to Measure: Edge Deletion and VCG Payments in Graphs. In: ACM Conference on Electronic Commerce (2005)Google Scholar
  8. 8.
    Holmstrom, B.: Moral Hazard in Teams. Bell Journal of Economics 13, 324–340 (1982)CrossRefGoogle Scholar
  9. 9.
    Itoh, H.: Incentives to Help Multi-Agent Situations. Econometrica 59, 611–636 (1991)MATHCrossRefGoogle Scholar
  10. 10.
    Malone, T.W., Smith, S.A.: Modeling the Performance of Organizational Structures. Operations Research 36, 421–436 (1988)CrossRefGoogle Scholar
  11. 11.
    Mass-Colell, A., Whinston, M., Green, J.: Microeconomic Theory. Oxford University Press, Oxford (1995)Google Scholar
  12. 12.
    Rotemberg, J.J.: Process- Versus Function-Based Hierarchies. Journal of Economics and Management Strategy 8, 453–487 (1999)CrossRefGoogle Scholar
  13. 13.
    Strausz, R.: Moral Hazard in Sequential Teams. Departmental Working Paper. Free University of Berlin (1996)Google Scholar
  14. 14.
    Winter, E.: Collocation and Incentives: Function vs. Process-Based Teams. In: Working Papaer (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Moshe Babaioff
    • 1
  • Michal Feldman
    • 2
  • Noam Nisan
    • 3
  1. 1.Microsoft Research - Silicon Valley 
  2. 2.School of Business AdministrationHebrew University of Jerusalem 
  3. 3.School of Computer ScienceHebrew University of Jerusalem 

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