speaking writing consulting book tool not-knowing

A not-knowing synthesis

2/8/2025 ☼ not-knowing ☼ uncertainty ☼ risk ☼ strategy

At base, I’ve been interested in the strategy implications of partial knowledge since 2008. Back then, I was investigating how people and organisations change when they explicitly embody the Knightian distinction between uncertainty and risk. That research turned into my first book, on how organisations structure themselves differently, innovate more effectively, and work better when they have an uncertainty mindset.

In writing that book, I realised that Knight’s framing of uncertainty is accurate but incomplete. So, since 2018, I’ve been developing a framework for thinking about what I currently call not-knowing. Not-knowing includes conventional ideas of risk and uncertainty, but also covers other forms of partial or ambiguous knowledge that conventional frameworks miss. Understanding these different types of non-risk not-knowing is critical for making better decisions in a world increasingly shaped by the unknown.

Not-knowing and mindsets

Not-knowing is any situation where our knowledge is partial, unclear, or fundamentally incomplete — where we don’t fully understand what is happening, what will happen, how things connect causally, or how valuable outcomes are.

Not-knowing is a more ramified concept than the more-familiar ideas of formal risk (where probabilities are known accurately and precisely) or Knightian uncertainty (where probabilities are unknown and unquantifiable for a wide range of possible reasons). We encounter many different types of not-knowing in daily practice.

Navigating not-knowing requires an appropriate mindset — a set of assumptions and mental habits that shape what we notice, how we interpret information, and how we decide to act.

Since words shape thought, the language we use to describe different types of not-knowing is crucial. Being precise about what kind of not-knowing we face helps us diagnose it correctly and thus respond effectively.

When a risk mindset is dysfunctional

Many (frankly, most) people and organisations default to what I call a risk mindset, which assumes that all unknowns are quantifiable risks with measurable probabilities. This mindset treats situations as if the full set of possible actions, outcomes, and their causal probabilities are known or knowable.

This approach creates problems. For example:

  1. It focuses attention on unknowns that fit neat risk models, while ignoring or downplaying other kinds of unknowns that are not quantifiable. During the early stages of the COVID-19 pandemic, many decision-makers relied on historical knowledge of pandemics and official data, missing early evidence of unusual features of the virus and the social dynamics that made it more dangerous.
  2. It encourages the hubris of classifying complex and uncertain situations, like some financial products before the 2008 crisis, as simply risky when in fact their causal relationships and potential outcomes were far less predictable than a risk mindset would assume. This mismatch contributed to major failures.
  3. It leads to overreliance on decision methods like cost-benefit analysis or expected value calculations, which require precise knowledge of possible actions, outcomes, probabilities, and valuations. These methods only work well when formal risk applies.

I’ve written elsewhere about how to think more clearly about risk. When we act on truly uncertain situations as if all the unknowns we face are formal risks, it causes a mindset mismatch. We end up applying inappropriate tools and mental models to uncertain situations, resulting in poor decisions and bad outcomes. Organisations tend to reinforce this because incentives reward neat, quantifiable analysis over messy-looking grapplings with uncertainty and ambiguity.

Language overloading and appropriation

Two related problems in language worsen the dysfunctional risk mindset:

  1. Overloading the word “risk” to describe many different types of not-knowing that aren’t actually formal risk. This leads people to apply risk management tools in contexts where those tools fail or cause harm. For example, calling everything “risk” during COVID-19 or the financial crisis encouraged false confidence in managing the unknown.
  2. Appropriating the word “uncertainty” by using it in ways that imply true Knightian uncertainty — fundamentally unquantifiable unknowns — but applying it instead to narrower partial knowledge situations like poor data quality or model opacity in machine learning. This appropriation creates a false sense of control by suggesting we understand or can manage what we really cannot.

Humans have a deep, visceral need for certainty, which is why not-knowing feels so hard. These language errors are coping mechanisms to mask the “terror of the unknown” by creating the illusion that what is unknown is controllable. This only works temporarily.

An appropriate mindset for not-knowing

Instead, I advocate for an appropriate mindset for not-knowing — one that explicitly distinguishes between different types of incomplete knowledge and uses clear, unambiguous language to describe them. This mindset allows us to:

  1. Perceive both risk and non-risk unknowns without undue bias for either variety.
  2. Reason explicitly about which type(s) of not-knowing apply in a given situation.
  3. Choose broad approaches and specific tools that are suited to the types of not-knowing that are present.

This mindset extends and clarifies what I’d previously defined as an uncertainty mindset by providing a more nuanced understanding of different types of not-knowing that aren’t risky.

Four types of not-knowing that aren’t risk

Through my research and practice, I’ve identified four main types of non-risk not-knowing:

  1. Not-knowing about actions: This arises from uncertainty about what actions or possibilities new and existing technologies offer. For example, when a company faces a new technology, it may be unclear what can actually be done with it or how to deploy it effectively. The broad approach here is to run many small, intentionally diverse experiments as a portfolio to explore the space of possible actions and discover new affordances.
  2. Not-knowing about outcomes: This involves uncertainty about what outcomes can be achieved or are even imaginable. Outcomes may be feasible but not yet imagined, or imaginable but not yet feasible. For known outcomes, experimentation through portfolios works well. For novel or unprecedented outcomes, structured imaginative techniques such as speculative fiction or scenario planning help expand what’s considered possible.
  3. Causal not-knowing: This concerns uncertainty about how actions lead to outcomes, including when multiple causes exist or causality is inconsistent. When causal relationships are resolvable, traditional hypothesis testing and structured experiments apply. When not resolvable, it may be more effective to try many varied small interventions and optimise for directional movement rather than precise control (“stacking the deck”).
  4. Not-knowing about values: This type deals with uncertainty about how much an outcome is worth—subjective and often contested judgments that can change over time or differ across stakeholders. Approaches include reasoning, argumentation, negotiation, and setting broad, flexible goals that can accommodate diverse values and changing norms.

These types interact dynamically, with changes in one often triggering shifts in others, creating a complex, evolving landscape of not-knowing.

Building a toolkit for not-knowing

To work effectively with not-knowing we need a toolkit structured around an appropriate mindset for not-knowing and broad approaches to action consistent with that mindset.

A toolkit for not-knowing would have four compartments:

  1. Diagnostic tools: To clarify what type of not-knowing is present and uncover hidden assumptions, preventing confusion between risk and other forms.
  2. Action tools: To implement broad strategies like portfolios of experiments, causal testing, negotiation of values, and formal risk measurement where applicable.
  3. Capacity-building tools: To increase individual and organisational comfort with uncertainty, through methods such as “desperation by design” and training to tolerate discomfort.
  4. Update tools: To sense when assumptions or environments change and lower barriers to adapting strategies, like routine reviews and staged resource commitments.

Using tools with clear intent is essential since the same tool can serve different purposes and produce different outcomes based on how it is used.

Opening the way

Thinking clearly about not-knowing opens a path toward innovation, curiosity, freedom, and practical effectiveness. It moves us beyond unrealistic certainty toward an adaptable realism, helping decision-makers navigate a world where incomplete knowledge and constant change are the norm.

For the last few years, I’ve been wrestling with the practical challenges of meaning-making in our increasingly AI-saturated world, developing frameworks for how humans can work effectively alongside these powerful tools while preserving the meaning-making work that is the irreplaceably human part of the reasoning we do. I’ve published this as a short series of essays on meaning-making as a valuable but overlooked lens for understanding and using AI tools

I’ve also been working on turning discomfort into something productive. idk is the first of these tools for productive discomfort.

And I’ve spent the last 15 years investigating how organisations can succeed in uncertain times. The Uncertainty Mindset is my book about how to design organisations that thrive in uncertainty and can clearly distinguish it from risk.