Encarta defines veracity as “the truth, accuracy or precision of something” and that seems like a pretty good place to start.
Our systems don’t model uncertainty very well, and yet that is exactly what we deal with on a day-to-day basis. This paper examines one aspect of modeling certainty, namely veracity, and begins a dialog on how to represent it.
Veracity
Encarta defines veracity as “the truth, accuracy or precision of something” and that seems like a pretty good place to start. In our case we will primarily be dealing with whether a symbolic representation of something in the real world faithfully represents the item in the real world. Primarily we are dealing with these main artifacts of systems:
- Measurements – is the measurement recorded in the system an accurate reflection of what it was meant to measure in the real
world? - Events – do the events recorded in the system accurately record what really happened?
- Relationships – do the relationships as represented in the system accurately reflect the state of affairs in the world?
- Categorization – are the categories that we have assigned things to useful and defensible?
- Cause – do our implied notions of causality really bear out in the world? (This also includes predictions and hypotheses.)
Only the first has ever received systematic attention. Fuzzy numbers are a way of representing uncertainty in measurements, as is “interval math” and the uncertainty calculations used in Chemistry (2.034 +/ -.005 for instance).
But in business systems, all of these are recorded as if we are certain of them, and then as events unfold, we eventually may decide not only that we are not certain, but that we are certain of an opposite conclusion. We record an event as if it occurred and until we have
proof that it didn’t, we believe that it did.
