The Reputation Server is a platform for multiple scoring functions, and each domain can choose the kinds of scoring used and the functions that compute the scores.
A number of reputation metrics have been proposed in the literature. Some simply provide ad hoc scales, dividing reputations into discrete steps or assigning boundaries and steps arbitrarily. While ad hoc definitions of reputation can seem reasonable at first, they can have undesirable properties. For example, simply incrementing reputation by one for each good transaction and decrementing by one for each bad transaction allows a reputation to keep growing indefinitely if a seller cheats one buyer out of every four. If the seller does a lot of volume, she could have a higher reputation in this system than someone who trades perfectly but has less than three quarters the volume. Other reputation metrics can have high sensitivity to lies or losses of information.
Other approaches to reputation are principled. One of the approaches to reputation that I like is working from statistical models of behavior, in which reputation is an unbound model parameter to be determined from the feedback data, using Maximum Likelihood Estimation (MLE). MLE is a standard statistical technique: it chooses model parameters that maximize the likelihood of getting the sample data.
The reputation calculation can also be performed with a Bayesian approach. In this approach, the Reputation Server makes explicit prior assumptions ...