So with some think time off line. I am happy with the notion of "Calibration propositions" (described in the original question) as a means for establishing a fuzzy sets membership and domain scale relationships. The Truth Series, along with a number of other systems, described by Cox in his book- Fuzzy Systems Handbook validate this method for discovering a fuzzy sets membership & value pairing. So is it probabilistic ? .... In method I would have say that it is. BUT From an intent and outcome stance there are enormous differences. Fuzzy is not a variation of a probability math because you need to discard a basic foundation of classical set theory required of probability to accept the legitimacy of Fuzzy Math. When Voting about the probability that a height is tall in order to yield a probability distribution curve the voters are working to producing a membership value (% population Yes OR No) that a value is tall OR is not tall.......... not both. When Voting if a height is Fuzzy tall the voters accept the notion that the height is also not tall. The vote goes to the degree of tallness. The % of population that vote Tall and that give a fuzzy height value its membership may also vote that the height is not tall. The law of non contradiction must be discarded by those accepting Fuzzy logic as a valid mathematics. Fuzzy means that nothing is either OR --- probability needs only either OR. This is not to say that the mathematics of Probability are not useful. It is a math method accustomed to processing "degree". In that it doesn't compromise Fuzzy set contradiction has some useful method. Rob W Wise wrote in message <9kia7r$kc9$1@perki.connect.com.au>...> The original question that launched this stream > talked about > ########## > defining a membership function of a set by using > ``calibrational propositions'' -- the idea is that if > you ask 100 people if John is tall, and 70 of them say ``yes,'' then > mu(tall) = .7. While this seems to do a good job of capturing common > word usage, it's not at all clear to me that it captures the fuzzy > behavior of the ``tall'' set; > > it seems probabilistic rather than fuzzy. > > ########### > > > So can a "fuzzy expert" panel of 100 vote on a particular elements inclusion > in a fuzzy set (ie that a height is tall) and the extent to which they agree > (say 70%) on the inclusion of that element in the set be converted to a > membership value ? > > Then IF the answer is yes why then is this not probabilistic ? > > Rob W > > >> > >