Due to their simple applicability score systems are in widespread use as a tool for decision making. Unfortunately, as we all feel, they are somehow not apt to take into account interdependencies among the values which are input to them when trying to decide an actual application case; a drawback which is overcome by the more powerful probabilistic systems. In order to analyze which assumptions are inherent in score systems, we translate them as faithfully as possible into probabilistic systems thus making available the technical machinery of the latter for this analysis task. Such a translation (as also given in1) reveals indeed some properties of score systems, but leads to an exponential number of probabilistic rules, which rules it out for practical use. For this reason we also developed further translations into probabilistic systems, which keep the simplicity of a score system (i.e. they use the same amount of rules as the score system). Moreover, the resulting probabilistic systems show their structure more explicitly than score systems, and they are also open to the addition of further knowledge.