  Traditional approaches to non-monotonic reasoning fail to satisfy a number of
plausible axioms for belief revision and suffer from conceptual difficulties as
well. Recent work on ranked preferential models (RPMs) promises to overcome
some of these difficulties. Here we show that RPMs are not adequate to handle
iterated belief change. Specifically, we show that RPMs do not always allow for
the reversibility of belief change. This result indicates the need for
numerical strengths of belief.
