Unknowns taxonomy

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Purpose: To distinguish different kinds of unknowns.

Description: It is useful to characterise different kinds of unknowns and to show their relationships in a taxonomy.

A taxonomy of unknowns is shown in the figure below. The catch-all term used in this taxonomy to encompass all kinds of unknowns is “ignorance” and the first distinction is between passive and active ignorance. Passive ignorance involves “being ignorant of”, whereas active ignorance refers to “ignoring”. The term ‘error’ is used for the unknowns encompassed by passive ignorance and ‘irrelevance’ for active ignorance.

Each of these terms is then further divided into other kinds of ignorance or unknowns. The key issue is that the taxonomy demonstrates that there are multiple kinds of unknowns, many, if not all, of which will be inherent in any complex societal or environmental problem.

smithson_different-kinds-of-ignorance

Different kinds of ignorance or unknowns (Smithson, 1989, p. 6). Also described in Bammer et al. (2008) and Bammer (2013).

Having differentiated passive (error) from active (irrelevance) ignorance, let us examine the types of unknowns under ‘error’. Two primary sources of error are “distortion” and “incompleteness.”

One type of distortion, ‘confusion’, involves wrongful substitution, mistaking one attribute for another. Mistaking a block of cheese for a bar of soap is an example of confusion. The other, ‘inaccuracy’, is distortion in degree or bias. Assuming that all swans are white is an example of inaccuracy.

Moving on to ‘incompleteness’, it is useful to differentiate between “incompleteness in degree” or ‘uncertainty’, and “incompleteness in kind” or ‘absence’.

‘Uncertainty’ refers to partial information and can be subdivided into three categories:

  1. vagueness, which relates to a range of possible values on a continuum
  2. probability, which refers to the laws of chance
  3. ambiguity, which refers to a finite number of distinct possibilities.

Vagueness can then be subdivided into ‘fuzziness’ and ‘non-specificity’. Fuzziness refers to fine-grade distinctions and blurry boundaries. For example, an object may be dark, but there is no clear boundary where darkness begins and ends. Non-specificity is another kind of vagueness. An example relates to geographical location. To say that someone lives near a school does not give any indication of whether they are a 5- minute walk away or a 5-minute drive away.

Moving on to probability, the classic example refers to numerous tosses of a fair coin and the likely outcome that half of the tosses will land heads and half tails. Much statistics involves tackling problems which combine vagueness and probability. While probability does not help us with the vague statements provided as illustrations in the previous paragraph, it can assist with other vague statements, such as ‘this ticket may win money in the lottery’ or ‘today some drivers will be injured in an accident’. Probability then helps us calculate the chance of winning or being injured.

Ambiguity is best demonstrated though a linguistic example. To say that food is hot does not clearly tell us if this refers to temperature or spiciness.

The final item in the ‘error’ side of the taxonomy is “absence”. Absence is simply gaps in knowledge, which can be known or unknown gaps. This is where the matrix of three kinds of unknowns fits.

The ‘irrelevance’ arm of the taxonomy refers to issues that are deliberately or unconsciously overlooked. It is useful to identify three subcategories:

  1. ‘untopicality’, where, in the consideration of any particular issue, some things will be generally agreed to be off topic. In defence policy decisions, for example, the price of children’s toys would generally not be considered topical.
  2. ‘taboo,’ which refers to matters people must not know or even enquire about. This is socially enforced irrelevance.
  3. ‘undecidability,’ which happens when a matter cannot be designated true or false or when deciding on truth/falsity is not pertinent.

References:

  • Bammer, G. (2013). Disciplining Interdisciplinarity: Integration and Implementation Sciences for Researching Complex Real-World Problems. ANU Press: Canberra, Australia. (Online): http://press.anu.edu.au/publications/disciplining-interdisciplinarity
  • Bammer G., Smithson, M. and the Goolabri Group. (2008). The Nature of Uncertainty. In Bammer, G. and Smithson, M. (eds.) Uncertainty and Risk: Multi-Disciplinary Perspectives. Earthscan: London, United Kingdom, pp: 289-303
  • Smithson, M. (1989). Ignorance and uncertainty: Emerging paradigms. New York: Springer Verlag.

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Posted: June 2011
Last modified: March 2021