The Power of Small Coalitions in Cost Sharing

  • Florian Schoppmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5385)


In a cost-sharing problem, finitely many players have an unknown preference for some public excludable good (service), and the task is to determine which players to serve and how to distribute the incurred cost. Therefore, incentive-compatible mechanisms are sought that elicit truthful bids, charge prices that recover the cost, and are economically efficient in that they reasonably balance cost and valuations. A commonplace notion of incentive-compatibility in cost sharing is group-strategyproofness (GSP), meaning that not even coordinated deceit is profitable. However, GSP makes strong implications on players’ coordination abilities and is known to impose severe limitations on other goals in cost sharing. There is hence good reason to seek for a weaker axiom: In this work, we study the following question: Does relaxing GSP to resilience only against coalitions of bounded size yield a richer set of possible mechanisms? Surprisingly, the answer is essentially “no”: We prove that already a mechanism resilient to coalitions of size only two (“2-GSP”) is GSP, once we require that cost shares must only depend on the service allocation (and not directly on the bids). Moreover, we show that even without additional requirements, 2-GSP implies weak group-strategyproofness (WGSP). Consequently, our results give some justification that GSP may, after all, still be desirable in various scenarios. As another benefit, we believe that our characterizations will facilitate devising and understanding new GSP cost-sharing mechanisms. Finally, we relate our findings to other concepts of non-manipulability such as (outcome) non-bossiness [19] and weak utility non-bossiness [13].


Cost Sharing True Valuation Coalition Size Service Allocation Small Coalition 
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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© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Florian Schoppmann
    • 1
    • 2
  1. 1.Faculty of Computer Science, Electrical Engineering and MathematicsUniversity of PaderbornPaderbornGermany
  2. 2.International Graduate School of Dynamic Intelligent SystemsGermany

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