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On threshold behavior in query incentive networks
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Electronic Commerce archive
Proceedings of the 8th ACM conference on Electronic commerce table of contents
San Diego, California, USA
SESSION: Pass It On table of contents
Pages: 66 - 74  
Year of Publication: 2007
ISBN:978-1-59593-653-0
Authors
Esteban Arcaute  Stanford University, Stanford, CA
Adam Kirsch  Harvard University, Cambridge, MA
Ravi Kumar  Yahoo! Research, Sunnyvale, CA
David Liben-Nowell  Carleton College, Northfield, MN
Sergei Vassilvitskii  Stanford University, Stanford, CA
Sponsors
ACM: Association for Computing Machinery
SIGEcom: ACM Special Interest Group on Electronic Commerce
Publisher
ACM  New York, NY, USA
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ABSTRACT

Motivated by the role of incentives in large-scale information systems, Kleinberg and Raghavan (FOCS 2005) studied strategic games in decentralized information networks. Given a branching process that specifies the network, the rarity of answers to a specific question, and a desired probability of success, how much reward does the root node need to offer so that it receives an answer with this probability, when all of the nodes are playing strategically? For a specific family of branching processes and a constant failure probability, they showed that the reward function exhibited a threshold behavior that depends on the branching parameter b.

In this paper we study two factors that can contribute to this transition behavior, namely, the branching process itself and the failure probability. On one hand we show that the threshold behavior is robust with respect to the branching process: for all branching processes and any constant failure probability, if b > 2 then the required reward is linear in the expected depth of the search tree, and if b < 2 then the required reward is exponential in that depth. On the other hand we show that the threshold behavior is fragile with respect to the failure probability σ: if σ is inversely polynomial in the rarity of the answer, then all branching processes require rewards exponential in the depth of the search tree.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Esteban Arcaute: colleagues
Adam Kirsch: colleagues
Ravi Kumar: colleagues
David Liben-Nowell: colleagues
Sergei Vassilvitskii: colleagues