We already looked at Variables which are vaguely defined, in the sense that we can’t list or even precisely specify all their Levels. An example was “reading age”, which quite clearly covers the ages, say, 4 to 18 but it is not clear (or very important) if there is a reading age of 0, or 90, or 900.
In this chapter, we look at what happens when Statements are more or less vague.
Much of our data we get as evaluators consists of vague Statements.
The child's weight is around *40kg--* ((continuous)) The country with the most biodiversity in the world is *one of the poorest countries* ((list of all the world's countries))
Although in this example, the Variable is not vague (except: is Kosovo a country? Palestine?), but the factual Level is quite vague. What counts as poor? What does “one of the poorest” mean? One of the 100 poorest? 50?
The important thing about vague Statements is that we can still sometimes do important work with them, thanks to the magic of Soft Arithmetic.
More to come
We could use the fuzzy sets approach (Zadeh, 1973) to extend the way Levels are defined, but we would arrive at another kind of Variable - not the same as the vague Variables I describe here.