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\lhead{{\it J. J. Bryson }}
\chead{Demography and Culture}
\rhead{Coursework 2}
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\title{Coursework 2:  Demography and Culture}
\author{Joanna J. Bryson}


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\section{Introduction}
All of your courseworks are designed primarily to give you experience
in developing intelligent control and/or cognitive systems. This
course also gives you experience and feedback in writing about
research.  However, this year we are attempting to make the second and
third coursework lighter-touch.  Therefore, the writeup for this
submission should be at {\em most} two pages, including the figures,
and does not need to have all the normal sections of an academic
paper.

\section{Approach}


There are many different types of social simulation, but in keeping
with the emphasis of ICCS, you will be doing a {\em spatial
  simulation}, where you are meant to be simulating some approximation
of:
\begin{itemize}
\item real time,
\item real space,
\item real animal capacities, e.g. motion, perception, and metabolism.
\end{itemize}

The idea here is to test whether it is possible to replicate the results of \citet{Powell09} using
more spatial, cognitive agents.  A replication  means that you should briefly review the scientific
reason for the model as well as checking the model's outcomes.  In
this case, we have given you the code for an existing replication (see
Moodle).  You are not obliged to use this code, but it will give you a
substantial head start if you do.  If you want more information than you
had in lecture about replication, you may want to read \citet{King95}
or \citet{BrysonPTRS07}.  The starting-point code for the replication
was actually a quite different paper written by an MSc student
visiting Bath that was subsequently published, see \citep{Cace07}.

Note that spatial reasoning is not really necessary in this task, at
least for the simulated agents, but for {\em you}, learning to reason
about how agents move in virtual space may help you with the game AI
in CW3. More importantly, this helps you learn about social learning,
social behaviour and its outcomes.

This coursework is due {\bf  24 March 2017}.


\section{Results}

In this coursework, please just document one hypothesis you had, and
show one graph testing that hypothesis.   You should probably state your
hypothesis and where it came from as an Introduction, and explain your
code briefly in the Approach. 

NetLogo provides a tool for running experiments (BehaviourSpace) and
more tools for drawing graphs.  Thus it is pretty easy to get NetLogo to run
experiments for you, if you can think of parameters you might like to
vary or code you might want to add that would be experimentally interesting. 

With respect to your own results, if you describe a reasonably-well
working system in a comprehensible manner, you will pass. If you can
justify your hypothesis from the literature (e.g. say which result you
are trying to replicate, and what you thought was needed to do so) you
will get at least 55.  Getting a mark over 70 requires demonstrating
insight, creativity and/or understanding that goes beyond the basics
laid out for you in this document.  For example, you could relate your
outcome to other scientific articles, or describe further experiments
you would like to do if you had time.  Doing a little more statistical
analysis is another way to get over 70.

\section{Discussion and Conclusions}

You are not obliged in this coursework to come to a definite conclusion, just to
show that you can design and execute an experiment in a social
software simulation that might validate or improve our understanding
of one of the outcomes in \citet{Powell09}.  Of course, ideally, you
would conclude that their results could or could not be replicated
with more spatially-situated agents.


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