Dear All,
I am trying to generate a simulated population with a particular
distribution attributes. The snag is that some of these attributes
have a "biographical" or historical dimension ie the social class of
a child is the social class of its father - I know, sexist, but the
research norm _when the child is born_. This involves some "children"
with "fictional" parents at t=0 but these fictional parents also need
to have sensible values of the biographical attributes. It is quite
tricky to "match" initial attribute distributions with those that
then emerge in the evolution of the population. Either it takes
forever for any initial distortion to "work out" or it never does and
the population is unstable.
Has anyone done something like this? Is there an algorithmic "trick"
I'm missing? I'd like to be able to use biggish populations (1000+)
so any solution can't be too computationally lazy.
ATB,
Edmund
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Edmund Chattoe: Department of Sociology, University of Oxford, 3 George
Street Mews, Oxford, Oxon, OX1 2AA, tel: 01865-278833, fax: 01865-278831,
http://www.sociology.ox.ac.uk, Review Editor, J. Artificial Societies
and Social Simulation (JASSS) http://www.soc.surrey.ac.uk/JASSS/,
"So act as
to treat humanity, whether in your own person or in another, always as an
end, and never as only a means." (Immanuel Kant, Fundamental Principles)
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