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SUMMARY:Modeling Crowdsourcing Systems: Design and Analysis of Incentive a
 nd Reputation Systems - John C.S. Lui (The Chinese University of Hong Kong
 )
DTSTART:20140703T140000Z
DTEND:20140703T150000Z
UID:TALK53252@talks.cam.ac.uk
CONTACT:Felix Fischer
DESCRIPTION:Crowdsourcing systems like Yahoo!Answers\, Amazon Mechanical T
 urk\, and Google Helpouts\, etc.\, have seen an increasing importance and 
 prevalence in the past few years.  Crowdsourcing system serves as an effic
 ient platform for "requesters" to outsource "tasks" to a crowd of distribu
 ted "workers\," who in return solve the assigned tasks and reply to reques
 ters with solution.  The participation of users (requesters and workers)\,
  high quality solutions\, and a fair rating system are critical to the rev
 enue of a crowdsourcing system.  To address these challenges\, we develop 
 simple models to characterize workers' strategic behavior\, which allows d
 ifferent levels of contribution.  We design a class of simple but effectiv
 e incentive mechanisms\, which consist of a "task bundling scheme" and a "
 rating system"\, and pay workers according to solution ratings from reques
 ters.  We also propose a probabilistic model to capture various human fact
 ors\, e.g.\, bias\, in rating\, as well as quantify its impact on the ince
 ntive mechanism\, which is shown to be highly robust.  We characterize the
  design space of rating systems\, and quantify the impact of rating system
 s and the bundling scheme on the incentive mechanism.  We show that the de
 sign of a rating system is a fundamental tradeoff between "crowdsourcing s
 ystem efficiency" (i.e.\, the number of tasks can be solved for the given 
 rewards) and the "cost in expressing solution ratings" (i.e.\, the time or
  cognitive cost).  We also study a class of “threshold based rating syst
 ems\," which allows to vary the minimum requirement on task solutions.
LOCATION:MR14\, Centre for Mathematical Sciences\, Wilberforce Road\, Camb
 ridge
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