The Kent Uncertainty Principle - On Data and Reality

The more precisely the position of some particle is determined, the less precisely its momentum can be predicted from initial conditions, and vice versa. There is a similar uncertainty relation between time and energy: a state that only exists for a short time cannot have a definite energy because the frequency of the state must be defined accurately, and this requires the state to hang around for many cycles.1

In the last chapter of Data and Reality2, Bill Kent presents an uncertainty relation between (a) “the” human way of perceiving information, and (b) the durability of a conceptual data model. “Truth” versus utility. Theory versus tool.

He argues that language “is merely an incidental means of solving specific problems of communication or reflection”:

we are most prepared to identify as entities or relationships those things for which our vocabulary happens to contain a word. The presence of such a word focuses our thinking onto what then appears as a singular phenomenon. The absence of such a word renders the thought diffuse, non-specific, non-singular.

It seems to me that the use of opaque identifiers can allow one to record statements about concepts without having to first make a decision about a certain word being singularly acceptable as a surrogate for a concept. But I digress.

Kent uses the terms “scope” and “purpose” for the two observables in his uncertainty relation, and “chance of reconciliation” for the limit term. By scope, he means “the number of people whose views have to be reconciled”. By reconciliation, he means “a state in which the parties involved have negligible differences in that portion of their world views that is relevant to the purpose at hand.” The chances of achieving a shared view become poorer “when we try to encompass broader purposes, and to involve more people.”

Technology has fostered interaction among greater numbers of people, and has enabled integration of processes into monoliths serving wider and wider purposes. It is natural then that “discrepancies in fundamental assumptions will become increasingly exposed.”

The duality is this: in an absolute sense, there may be no singular objective reality. However, groups of people can share a common enough view of it for relatively narrow purposes, so that reality appears to be objective and stable in most of our working environments.

This post was adapted from a note sent to my email list on Scientific Data Unification.
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  2. W. Kent, Data and reality, 2nd ed. Bloomington, Ind: 1stBooks Library, 2000. ↩︎