Hungarian SugarScape

[manor]

Hi Nelson,

Assuming the students in Hungary are going to start out
by reproducing the results of Epstein and Axtell I have
a project that suggested itself while reading their book.

Right in Chapter II E&A discuss their ability to generate
a skewed wealth distribution despite the simplicity of
the Agent movement rule, replacement rule and growth rule
(so called M,R,G). In the summary of the chapter they
write:

  "For example, only one agent rule, M, has been used,
   and it is about as primitive a rule as we could construct.
   ... And yet, all sorts of unexpected things emerge from
   the interactions of these agents: basic principles like
   the existence of environmental carrying capacities;
   skewed distributions of wealth etc. etc. ... And that
   strikes us as surprising."

However, I couldn't help noticing the rather large source
of heterogeneity in their model: the resource is distributed
unevenly, the maximum age of the agents is not unique and
neither is their hunting capacity (vision). Under these
circumstances it is hardly surprising that a skewed wealth
distribution arises.

So, I was wondering whether one of the students working on
this wouldn't like to investigate exactly what factor is
most responsible for the inequity in the model. In other
words, set all these factors to be uniform and then take
alternative subsets (distribute the sugar unevenly but keep
the maximum age constant, and allow heterogeneous vision
etc. etc.) and see which combinations yield the highest
Gini coefficients.

This could be an interesting little project...

Regards,

Manor.

PS> I know it seems kind of tedious, and normally the tendency
    is to want to add on features (pollution, culture, sex, trade
    etc. etc. etc.) but it would be nice to know a little more
    about the simplest configuration of SugarScape.

    Note-- E&A do point out that:

     "Such skewed wealth distributions are produced for wide ranges
      of agent and environment specifications."

    But this seems a bit short on detail...

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