Computer-based Simulation Modelling for Anthropologists
Michael D. Fischer

Simulation Home


Evaluating results

The results of a simulation can be evaluated in a number of ways. If the simulation is
basically an empirical one, which has a number of random or statistically generated events
within it, we can often evaluate the results by using a statistical test such as chi square.
Many times though we are principally interested in using the data to establish some point
for which we don't have direct data. Here we are exploring the structural possibilities
given what we do know. Simulations are useful for 'what if' situations, where we are
attempting to extrapolate from what we do know to areas with which we have little or no
data. One method of some use in evaluating simulations is examining its structural
stability. This is useful where we are (sometimes grossly) estimating a number of values
for the different models because the information is simply not available. This is common in
ethnography, because of necessity we collect an account of events that are idiosyncratic to
the time which we are in residence, in most studies less than two years, and often less than
a year. We have however some confidence that the behaviour that we observe during our
tenure in the situation is not simply idiosyncratic behaviour. The particular events and
situations we observer are, but we assume that the responses to these is derived from
general principles of the society, and this is usually the object of an ethnographic analysis.
Simulation can give us an opportunity to validate some of these assumptions and analyses,
because with simulation we can create contexts and situations that did not occur during our
study. If the various 'solutions' we find in the social group are likely to represent general
processes, we expect that they will work, and the social group will survive in a wide range
of possibilities. For example, agricultural practices that lead to the loss on one crop in three
are not likely to be considered successful, and probably represent at least a situation where
we did not collect enough data. While we can't be sure of our simulation model, we can
establish the various limits which the simulation can adapt to. That is the various points at
which it breaks down.