Ambiguity is present in virtually all real-life situations and are those ‘situations in which we do not have sufficient information to quantify the stochastic nature of the problem. It is a lack of knowledge as to the ‘basic rules of the game’ where cause-and-effect are not understood and there is no precedent for making predictions as to what to expect
Ambiguity is often used, especially in the context of VUCA, to cover situations in situations that have:
Doubt about the nature of cause and effect
Little to no historical information to predict the outcome
Difficult to forecast or plan for
It is important to answer whether there are risks of lack of experience and predictability that might affect the situation, and interrogate our unknown unknowns.
People are ambiguity averse in that they prefer situations in which probabilities are perfectly known to situations in which they are unknown.
Walker et al. (2010) developed a taxonomy of “levels of uncertainty”, ranging from Level 1 to Level 4, which is useful in problem-solving:
Level 1uncertainties are defined as relatively minor – as representing “a clear enough future” set within a “single system model” whereby outcomes can be estimated with reasonable accuracy;
Level 2 uncertainties display “alternative futures” but, again, within a single system in which probability estimates can be applied with confidence.
Levels 3 and 4 uncertainties are described as representing “deep uncertainty”.
Level 3 uncertainties are described as “a multiplicity of plausible futures”, in which multiple systems interact, but in which we can identify “a known range of outcomes”
Level 4 uncertainties lead us to an “unknown future” in which we don’t understand the system: we know only that there is something, or are some things, that we know we don’t know.
This hierarchy can be useful to help us think carefully about whether the uncertainty behind a problem can be defined in terms of a Level 1 prediction, with parameters for variation. Or, can it be resolved as group of Level 2 possibilities with probability estimates for each? Can the issue only be understood as a set of different Level 3 futures, each with a clear set of defined outcomes, or only by means of a Level 4 statement to the effect that we know only that there is something crucial that we don’t yet know?
There is often no clear or unanimous view of whether a particular uncertainty is set at a specific level. Uncertainty should always be considered at the deepest proposed level, unless or until those that propose this level can be convinced by an evidence-based argument that it should be otherwise.
Walker, W.E., Marchau, V.A.W.J. and Swanson, D. (2010) “Addressing Deep Uncertainty using Adaptive Policies: Introduction to Section 2”, Technological Forecasting & Social Change, 77: 917–23.
An important part of innovation, risk management, change management, continuous improvement is overcoming the fear of the unknown. We humans are wired with an intense aversion to both risk and uncertainty. Research shows that both have separate neural reactions and that choices with ambiguous outcomes trigger a stronger fear response than do risky choices. Additional research shows that the risk itself isn’t so much the problem, but the uncertainty is: we are afraid primarily because we don’t know the outcome and less so because of the risk.
There are three types of uncertainty:
Aleatoric Uncertainty: The uncertainty of quantifiable probabilities.
Epistemic Uncertainty: The uncertainty of knowledge.
Knightian Uncertainty: The uncertainty of nonquantifiable risk.
I love the power of Karl Weick’s future-oriented sensemaking – thinking in the future perfect tense – for supplying us a framework to imagine the future as if it has already occurred. We do not spend enough time being forward-looking and shaping the interpretation of future events. But when you think about it quality is essentially all about using existing knowledge of the past to project a desired future.
This making sense of uncertainty – which should be a part of every manager’s daily routine – is another name for foresight. Foresight can be used as a discipline to help our organizations look into the future with the aim of understanding and analyzing possible future developments and challenges and supporting actors to actively shape the future.
Sensemaking is mostly used as a retrospective process – we look back at action that has already taken place, Weick himself acknowledged that people’s actions may be guided by future-oriented thoughts, he nevertheless asserted that the understanding that derives from sensemaking occurs only after the fact, foregrounding the retrospective quality of sensemaking even when imagining the future.
“When one imagines the steps in a history that will realize an outcome, then there is more likelihood that one or more of these steps will have been performed before and will evoke past experiences that are similar to the experience that is imagined in the future perfect tense.”
R.B. MacKay went further in a fascinating way by considering the role that counterfactual and prefactual processes play in future-oriented sensemaking processes. He finds that sensemaking processes can be prospective when they include prefactual “whatifs” about the past and the future. There is a whole line of thought stemming from this that looks at the meaning of the past as never static but always in a state of change.
A powerful tool in this reasoning, imagining and planning the future, is metaphor. Now I’m a huge fan of metaphor, though some may argue I make up horrible ones – I think my entire team is sick of the milk truck metaphor by now – but this underutilized tool can be incredibly powerful as we build stories of how it will be.
Think about phrases such as “had gone through”, “had been through” and “up to that point” as commonly used metaphors of emotional experiences as a physical movement or a journey from one point to another. And how much that set of journey metaphors shape much of our thinking about process improvement.
Entire careers have been built on questioning the heavy use of sport or war metaphors in business thought and how it shapes us. I don’t even watch sports and I find myself constantly using it as short hand.
To make sense of the future find a plausible answer to the question ‘what is the story?’, this brings a balance between thinking and acting, and allows us to see the future more clearly.
Cornelissen, J.P. (2012), “Sensemaking under pressure: the influence of professional roles and social accountability on the creation of sense”, Organization Science, Vol. 23 No. 1, pp. 118-137, doi: 10. 1287/orsc.1100.0640.
Luscher, L.S. and Lewis, M.W. (2008), “Organizational change and managerial sensemaking: working through paradox”, Academy of Management Journal, Vol. 51 No. 2, pp. 221-240, doi: 10.2307/20159506.
MacKay, R.B. (2009), “Strategic foresight: counterfactual and prospective sensemaking in enacted environments”, in Costanzo, L.A. and MacKay, R.B. (Eds), Handbook of Research on Strategy and Foresight, Edward Elgar, Cheltenham, pp. 90-112, doi: 10.4337/9781848447271.00011
Tapinos, E. and Pyper, N. (2018), “Forward looking analysis: investigating how individuals “do” foresight and make sense of the future”, Technological Forecasting and Social Change, Vol. 126 No. 1, pp. 292-302, doi: 10.1016/j.techfore.2017.04.025.
Weick, K.E. (1979), The Social Psychology of Organizing, McGraw-Hill, New York, NY.
Weick, K.E. (1995), Sensemaking in Organizations, Sage, Thousand Oaks, CA.
Our goal should always be to reduce ignorance. Many unknown unknowns are just things no one has bothered to find out. What we need to do is ensure our processes and systems are constructed so that they recognize unknowns.
There are six factors that need to be explored to find the unknown unknowns.
Complexity: A complex process/system/project contains many interacting elements that increase the variety of its possible behaviors and results. Complexity increases with the number, variety, and lack of robustness of the elements of the process, system or project.
Complicatedness: A complicated process/system/project involves many points of failure, the ease of finding necessary elements and identifying cause-and-effect relationships; and the experts/participants aptitudes and experiences.
Dynamism: The volatility or the propensity of elements and relationships to change.
Equivocality: Knowledge management is a critical enabler of product and project life cycle management. If the information is not crisp and specific, then the people who receive it will be equivocal and won’t be able to make firm decisions. Although imprecise information itself can be a known unknown, equivocality increases both complexity and complicatedness.
Perceptive barriers: Mindlessness. This factor includes a lot of our biases, including an over-reliance on past experiences and traditions, the inability to detect weak signals and ignoring input that is inconvenient or unappealing.
Organizational pathologies: Organizations have problems, culture can have weaknesses. These structural weaknesses allow unknown unknowns to remain hidden.
The way to address these six factors is to evaluate and challenge by using the following approaches:
Interviews with stakeholders, subject matter experts and other participants can be effective tools for uncovering lurking problems and issues. Interviewers need to be careful not to be too enthusiastic about the projects they’re examining and not asking “yes or no” questions. The best interviews probe deep and wide.
Build Knowledge by Decomposing the System/Process/Project
Standard root cause analysis tools apply here, break it down and interrogate all the subs.
Identifying the goals, context, activities and cause-effect relationships
Examining the complexity and uncertainty of each element to identify the major risks (known unknowns) that needed managing and the knowledge gaps that pointed to areas of potential unknown unknowns.
Construct several different future outlooks and test them out (mock exercises are great). This approach accepts uncertainty, tries to understand it and builds it into the your knowledge base and reasoning. Rather than being predictions, scenarios are coherent and credible alternative futures built on dynamic events and conditions that are subject to change.
Communicate Frequently and Effectively
Regularly and systematically reviewing decision-making and communication processes, including the assumptions that are factored into the processes, and seeking to remove information asymmetries, can help to anticipate and uncover known unknowns. Management Review is part of this, but not the only component. Effective and frequent communication is essential for adaptability and agility. However, this doesn’t necessarily mean communicating large volumes of information, which can cause information overload. Rather, the key is knowing how to reach the right people at the right times. Some important aspects include:
Candor: Timely and honest communication of missteps, anomalies and missing competencies. Offer incentives for candor to show people that there are advantages to owning up to errors or mistakes in time for management to take action. It is imperative to eliminate any perverse incentives that induce people to ignore emerging risks.
Cultivate an Alert Culture: A core part of a quality culture should be an alert culture made up of people who strive to illuminate rather than hide potential problems. Alertness is built by: 1) emphasizing systems thinking; 2) seek to include and build a wide range of experiential expertise — intuitions, subtle understandings and finely honed reflexes gained through years of intimate interaction with a particular natural, social or technological system; and 3) learn from surprising outcomes.
By working to evaluate and challenge, to truly understand our systems and processes, our risk management activities will be more effective and truly serve to make our systems resilient.