Generalized Ranking Kinematics for Iterated Belief Revision

Probability kinematics is a leading paradigm in probabilistic belief change. It is based on the idea that conditional beliefs should be independent from changes of their antecedents' probabilities. In this paper, we propose a re-interpretation of this paradigm for Spohn's ranking functions which we call Generalized Ranking Kinematics as a new principle for iterated belief revision of ranking functions by sets of conditional beliefs. This general setting also covers iterated revision by propositional beliefs. We then present c-revisions as belief change methodology that satisfies Generalized Ranking Kinematics.