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\title{Monkey Word Learning NDL Simulation}
\author{Robert Geirhos (3827808), Klara Grethen (3899962), David-Elias Künstle (3822829), Felicia Saar (3818590)}
\affiliation{Linguistics for Cognitive Science Course, University of Tübingen}
\shorttitle{}
\abstract{our abstract goes here!}


\begin{document}

\maketitle{}

% \cite{}
\section{Introduction}
statement of the problem

\section{Simulations}
\subsection{Experimental Code}
why we didn't use the given code, what we improved, how the result is structured - Goal: modular and comprehensive experiment.

\subsection{Choice of Parameters}
choice

\subsection{Running Parallelized Experiments}
Running an experiment with a single combination of $ \alpha $ and $ \beta $ on a normal desktop computer took about 75 minutes. Therefore, the parameter space one could explore within a reasonable amount of time was quite restricted. We decided to write a parallelized version of the code to reduce the overall runtime. Using the R packages foreach, parallel and doParallel (TODO: Cite them properly), we restructured the experiment. Since conflicts can easily occur when more than one core is trying to access a data structure at the same time, we implemented a parallelized version that is able to run without even having critical sections. Instead, each thread has its own data structure, a .txt file), and in the end the results are harvested and combined. This version of the experiment ran on a cluster with 15 cores, each performing a total amount of eight experiments. Alltogether, 120 combinations of $ \alpha $ and $ \beta $ were explored overnight, which would have taken about 150 hours in a non-parallelized version.

\section{Results}
results

\begin{figure*}
\includegraphics[width=0.9\textwidth]{plots/plot_accuracy}
\end{figure*}

\begin{figure*}
\includegraphics[width=0.9\textwidth]{plots/plot_numwords}
\end{figure*}


\section{Discussion}
discussion



\newpage
\appendix
\section{Code}
%TODO change back to single column here
%\input{code.tex}


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\end{document}
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