Free-riding and free-labor in combinatorial agency

Moshe Babaioff*, Michal Feldman, Noam Nisan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations


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.

Original languageAmerican English
Title of host publicationAlgorithmic Game Theory - Second International Symposium, SAGT 2009, Proceedings
Number of pages13
StatePublished - 2009
Event2nd International Symposium on Algorithmic Game Theory, SAGT 2009 - Paphos, Cyprus
Duration: 18 Oct 200920 Oct 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5814 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2nd International Symposium on Algorithmic Game Theory, SAGT 2009


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