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CHIpaper/MarketPaper.tex View file @ 0e37d66
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137 137 \section{Introduction}
138 138 Human-computation and crowd-sourcing are now perceived as valuable techniques to help solving difficult computational problems. In order to make the best use of human skills in these systems, it is important to be able to characterize the expertise and performance of humans as individual and even more importantly as groups.
139 139  
140   -Currently, popular crowd-computing platform such as Amazon Mechanical Turk~\cite{Buhrmester01012011, Paolacci} or Crowdcrafting~\cite{Crowdcrafting} are based on similar divide-and-conquer architectures, where the initial problem is decomposed into smaller sub-tasks that are distributed to individual workers and then aggregated to build a solution. In particular, these systems prevent any interaction between workers in order to prevent groupthink phenomena and bias in the solution \cite{Lorenz:2011aa}.
  140 +Currently, popular crowd-computing platform such as Amazon Mechanical Turk (AMT) \cite{Buhrmester01012011, Paolacci} or Crowdcrafting \cite{Crowdcrafting} are based on similars divide-and-conquer architectures, where the initial problem is decomposed into smaller sub-tasks that are distributed to individual workers and then aggregated to build a solution. In particular, these systems prevent any interaction between workers in order to prevent groupthink phenomena and bias in the solution \cite{Lorenz:2011aa}.
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142 142 However, such constraints are necessarily limiting the capacity of the system to harness the cognitive power of crowds and make full benefit of collective intelligence. In order to gain expressivity and improve their performance, the next generation of human-computation platforms will undoubtedly need to relax these constraints and build market systems in which workers can collaborate. Nonetheless, before transitioning to this new model, it is important to first estimate the gain of productivity and quantify the usefulness of the mechanisms and incentives to promote collaborative solving and prevent groupthink.
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  144 +In \cite{DBLP:conf/chi/Little10}, G. Little \text{et al.} compared the performances of iterative and parallel work processes on AMT.
  145 +
  146 +%A multiagent perspective is taken to examine the principles of both gameplay and mechanism design for productive games. \cite{DBLP:conf/atal/TsaiLCHH08}
  147 +
  148 +%Shepherding the crowd yields better work \cite{DBLP:conf/cscw/DowKKH12}
  149 +
  150 +%Human computation tasks with global constraints \cite{DBLP:conf/chi/ZhangLMGPH12}
143 151  
144 152 Historically, computation on graphs has proven to be a good model to study the performance of humans in solving complex combinatorial problems \cite{Kearns:2006aa}. Experiments have been conducted to evaluate the dynamics of crowds collaborating at solving graph problems \cite{DBLP:journals/cacm/Kearns12} but still, little is known about the efficiency of various modes of interaction.
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