Uncertainty is part of every decision; ambiguity should never be. There are at least 4 significant sources of uncertainty in any decision:
- Stakeholders’ inability to express the relative importance of design margin associated with criteria (weights)
- Inability to estimate the effectiveness of alternatives against a criterion (scores) – no facts about future!
- Impact of probabilistic events (risks, opportunities) that could significantly change the effectiveness of an alternative (including the decision-makers’ ability to manage these events and their consequences)
- Variance between the “As-chosen” alternative and the “As-built” one (poor post-decision execution)
Many techniques exist to manage these uncertainties, but driving uncertainty to zero is almost never practical. There is a point of diminishing returns at which further effort to reduce uncertainty isn’t warranted by the value at risk in the decision.
Embrace uncertainty, but avoid ambiguity like the plague. Ambiguity implies doubt associated with the meaning of something; a statement that can have multiple interpretations. I found one definition of ambiguity that describes it as “uncertainty in intention“, leaving something fuzzy on purpose (e.g. the politician’s tactic to enable plausible deniability) or through carelessness or lack of communication/thinking skill.
In the context of decisions, ambiguity can creep into:
- Criteria – sloppy definitions with unclear units of measurement that increase the chance of redundant and overlapping factors
- Alternatives – incomplete descriptions that lead to a “moving target” definition when scoring or poor handoff to the implementation team (“What did we really commit to implement?”)
The Decision Driven® Solutions Framework (DDSF) can help decision-makers avoid ambiguity by providing:
- Separate Title and Description fields that encourage a succinct name for each criterion or alternative that distinguishes it from other siblings, supplemented by an expanded explanation of what makes each object unique
- Drag-drop weighting and scoring to help users keep these tasks simple. By matching the inherent (1 significant figure) precision of weights and scores, we focus users on precise definitions, not delusionally precise estimates.
- Rationale capture to support scoring estimates. The mental effort to briefly state why an alternative is estimated to be “Great” or “Poor” against a factor encourages users to think more deeply about the alternative’s definition (its structure and resulting behaviors). This can help them detect ambiguity.
Ultimately, tools can only do so much in this area. Avoiding ambiguity requires a combination of skill, honest intentions and some thinking discipline.
Avoid ambiguity and better manage uncertainty by leveraging the powerful capabilities within the Decision Driven® Solutions Framework (DDSF). Start your free trial of DDSF by contacting the Decision Driven® Solutions team at email@example.com or firstname.lastname@example.org.