BTW, I just saw something about this regarding fuzzy uncertainty. They made a distinction between vagueness and ambiguity. Both of which might make it difficu

BTW, I just saw something about this regarding fuzzy uncertainty. They made a distinction between vagueness and ambiguity. Both of which might make it difficult to assign a probability distribution among the possible values. Ambiguous is when the boundaries of the sets are not clearly defined. In the thermometer example, it would be not knowing exactly what temperatures are meant by "cold". Or maybe like the supreme court definition of porn, you know it when you see it. Vague is when there are clear definitions, but you're not sure how well your data fits into those definitions. For the temperature example, it would be what a crappy thermometer would tell you. It'd roughly correlate (it wouldn't return boiling if it's freezing), but it'd be pretty inaccurate. In true/false values, maybe 0/.5 and .5/0 could be construed as ambiguous, while 1/.5 and .5/1 could be construed as vague.