  Computational models of complex systems are usually elaborate and sensitive
to implementation details, characteristics which often affect their
verification and validation. Model replication is a possible solution to this
issue. It avoids biases associated with the language or toolkit used to develop
the original model, not only promoting its verification and validation, but
also fostering the credibility of the underlying conceptual model. However,
different model implementations must be compared to assess their equivalence.
The problem is, given two or more implementations of a stochastic model, how to
prove that they display similar behavior? In this paper, we present a model
comparison technique, which uses principal component analysis to convert
simulation output into a set of linearly uncorrelated statistical measures,
analyzable in a consistent, model-independent fashion. It is appropriate for
ascertaining distributional equivalence of a model replication with its
original implementation. Besides model-independence, this technique has three
other desirable properties: a) it automatically selects output features that
best explain implementation differences; b) it does not depend on the
distributional properties of simulation output; and, c) it simplifies the
modelers' work, as it can be used directly on simulation outputs. The proposed
technique is shown to produce similar results to the manual or empirical
selection of output features when applied to a well-studied reference model.
