  A stochastic model checker is presented for analysing the performance of
game-theoretic learning algorithms. The method enables the comparison of
short-term behaviour of learning algorithms intended for practical use. The
procedure of comparison is automated and it can be tuned for accuracy and
speed. Users can choose from among various learning algorithms to select a
suitable one for a given practical problem. The powerful performance of the
method is enabled by a novel behaviour-similarity-relation, which compacts
large state spaces into small ones. The stochastic model checking tool is
tested on a set of examples classified into four categories to demonstrate the
effectiveness of selecting suitable algorithms for distributed decision making.
