  The Iterated Prisoner's Dilemma (IPD) is a well studied framework for
understanding direct reciprocity and cooperation in pairwise encounters.
However, measuring the morality of various IPD strategies is still largely
lacking. Here, we partially address this issue by proposing a suit of plausible
morality metrics to quantify four aspects of justice. We focus our closed-form
calculation on the class of reactive strategies because of their mathematical
tractability and expressive power. We define reactive means as a tool for
studying how actors in the IPD and Iterated Snowdrift Game (ISG) behave under
typical circumstances. We compute reactive means for four functions intended to
capture human intuitions about ``goodness'' and ``fair play''. Two of these
functions are strongly anticorrelated with success in the IPD and ISG, and the
other two are weakly anticorrelated with success. Our results will aid in
evaluating and comparing powerful IPD strategies based on machine learning
algorithms, using simple and intuitive morality metrics.
