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littledot5566 committed 23217e5

Updated latex.

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 *.war
 *.ear
 
+#eclipse
 .classpath
+
+#latex
+*.acn
+*.acr
+*.alg
+*.aux
+*.bbl
+*.blg
+*.dvi
+*.fdb_latexmk
+*.glg
+*.glo
+*.gls
+*.idx
+*.ilg
+*.ind
+*.ist
+*.lof
+*.log
+*.lot
+*.maf
+*.mtc
+*.mtc0
+*.nav
+*.nlo
+*.out
+*.pdfsync
+*.ps
+*.snm
+*.synctex.gz
+*.toc
+*.vrb
+*.xdy
+*.tdo

latex/CS886PAPER.aux

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 \begin{abstract}
 
-Online labor markets is a new alternative for task givers to find interim workers and for workers to earn extra pocket money. Amazon Mechanical Turk, MicroTask and ClickWorker are just some examples of online labor markets. However, the quality of the work produced is dependent on the quality of the worker. Because tasks are usually designed to be completed multiple times, it makes sense to encourage quality workers to come back and do more tasks while discouraging low-quality workers to find other tasks that are better-suited to the workers' skill set. We propose a ranking system to do exactly that. The system would classify workers into different tiers or brackets based on their past work quality. We then adjust the payout to the workers based on the tier they are in. We show that.......
+Online labor markets is a new alternative for task givers to find interim workers and for workers to earn extra pocket money. Amazon Mechanical Turk, MicroTask and ClickWorker are just some examples of online labor markets. However, the quality of the work produced is dependent on the quality of the worker. Because tasks are usually designed to be completed multiple times, it makes sense to encourage quality workers to come back and do more tasks while discouraging low-quality workers to find other tasks that are better-suited to the workers' skill set. We propose a ranking system to do exactly that. The system would classify workers into different tiers or brackets based on their past work quality. We then adjust the payout to the workers based on the tier they are in. We show that by adjusting the payout based on the workers' previous history, we can indeed lower the number of low-quality workers, thereby increasing overall task quality and at the same time lowering the amount of capital spent.
 
 \end{abstract}
 
 
 In online labor markets, we believe with less low-quality workers contributing more noise than signal, the task would converge much quicker to a stable answer. We also believe with less low-quality workers taking up less task quota, the requester could save substantial amounts of money. Thus, it is reasonable to design incentives for quality workers to encourage them to complete more tasks while discouraging low-quality workers to find other tasks that are better-suited for their skill set. We believe our contributions in this paper can raise task accuracy and save budget spending at the same time.
 
-In this paper, we propose a method to de-incentivise low-quality workers. Our system, LadderMT, first ranks the workers by the quality of their work. Rather than paying a flat reward like traditional online markets, the system adjusts the reward as a function of the workers' rank. The higher a worker is ranked, the higher the reward, and vice versa. We evaluate our system based on its effectiveness to attract top-tier workers to return and complete more tasks while repelling bottom-tier workers, and the total amount of capital spent. We compare these metrics with traditional online labor markets and show that........
+In this paper, we propose a method to de-incentivise low-quality workers. Our system, LadderMT, first ranks the workers by the quality of their work. Rather than paying a flat reward like traditional online markets, the system adjusts the reward as a function of the workers' rank. The higher a worker is ranked, the higher the reward, and vice versa. We evaluate our system based on its effectiveness to attract top-tier workers to return and complete more tasks while repelling bottom-tier workers, the total amount of capital spent and the overall quality of completed tasks. We compare these metrics with traditional online labor markets and show that by adjusting the payout based on the workers' previous history, we can indeed lower the number of low-quality workers, thereby increasing overall task quality and at the same time lowering the amount of capital spent.
 
-\section{LadderMT}
+\section{Related Work}
 
+LadderMT borrows ideas from Games with a Purpose.
 
+\subsection*{Games with a Purpose}
 
+Traditional crowdsourcing methods try to attract workers with financial incentives to make up for completing tasks that are tedious and boring. Von Ahn et al. identified this problem and proposed a novel approach that channels human computation power through computer games, called Games with a Purpose (GWAP). In their experiments, they conclude that people play GWAPs not because they are personally interested in solving a computational problem, but because they wish to be entertained\cite{VonAhn}.
+
+GWAPs are successful because workers' utility is compensated with fun rather than money. However, this shrinks the pool of potential workers because workers who are driven completely by monetary incentives will not be attracted to participate at all. LadderMT tries to incorporate GWAP mechanics into traditional online labor markets. We think by leveraging both financial incentives and entertainment, we can produce a crowdsourcing environment where both work and play are involved, thus appealing to people who are either financially-oriented or entertainment-oriented. People who are looking to be entertained could have fun while making some money on the side; people who treat online labor markets as a serious occupation could enjoy the process because of GWAP's mechanics.
+
+
+\section{Ranking System}
+
+Our ranking system, LadderMT, has two goals. First, is to generate a score or elo for each participating worker based on their previous work quality history. Second, is to adjust payout to workers based on their score when they participate in future tasks.
+
+\subsection{Worker Scoring Rules}
+
+
+\subsection{Adjusted Payout Rules}
+
+\section{Simulation}
+
+\section{Evaluation}
 
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