"Statistical Modeling, Analysis and Management of Fuzzy Data," or SMFD
for short, is an important contribution to a better understanding of a
basic issue -an issue which has been controversial, and still is though
to a lesser degree. In substance, the issue is: are fuzziness and
randomness distinct or coextensive facets of uncertainty? Are the
theories of fuzziness and random- ness competitive or complementary? In
SMFD, these and related issues are addressed with rigor, authority and
insight by prominent contributors drawn, in the main, from probability
theory, fuzzy set theory and data analysis com- munities. First, a
historical perspective. The almost simultaneous births -close to half a
century ago-of statistically-based information theory and cybernetics
were two major events which marked the beginning of the steep ascent of
probability theory and statistics in visibility, influence and
importance. I was a student when information theory and cybernetics were
born, and what is etched in my memory are the fascinating lectures by
Shannon and Wiener in which they sketched their visions of the coming
era of machine intelligence and automation of reasoning and decision
processes. What I heard in those lectures inspired one of my first
papers (1950) "An Extension of Wiener's Theory of Prediction," and led
to my life-long interest in probability theory and its applications to
information processing, decision analysis and control.
