Not-knowings, on demand

19/8/2025 ☼ not-knowing ☼ experience ☼ risk ☼ uncertainty

I’m back in Adelaide right now because I’m a Visiting Research Fellow this year at MOD., at the University of South Australia. At the end of my last visit in June, I spent three hours with 16 researchers from computer science, psychology, mathematics, statistics, art practice, and futures thinking. Our goal was to think collectively and across disciplines to push beyond the established framework of not-knowing — to see what new territory might emerge when diverse minds tackle the question of what we don’t know that we don’t know.

The workshop built on my synthesis of four types of non-risk not-knowing:

1. Not-knowing about actions (what we can do),
2. Not-knowing about outcomes (what could happen),
3. Not-knowing about causation (how actions cause outcomes), and
4. Not-knowing about subjective value (what outcomes are actually worth).

Four types of non-risk not-knowing and their sources.

But I designed the workshop specifically to try and break out of this framework in order to extend it and make it more useful.

What emerged was both validation of the original framework and something more interesting: Genuine extensions of our understanding of the territory of not-knowing, plus concrete mechanisms for experiencing different types of uncertainty firsthand. This is practical progress. When people viscerally understand the different types of unknowns they face, they’re less likely to confuse it with risk and more like to make better decisions for graceful adaptation and creative response.

New territories of not-knowing

The first part of the workshop asked participants to identify types of not-knowing that didn’t fit into my original four categories. Working in small groups, they surfaced seven additional territories.

Two felt genuinely novel. Epistemic not-knowing captures something fundamental about the limits of human perception—uncertainty about what exists beyond our ability to sense or detect. This isn’t about not knowing outcomes or causation within the bounds of what we can perceive; it’s about what lies beyond the edges of perception itself.

Intertemporal not-knowing addresses how “the past is another country” — the challenge of understanding lived experience and unrecorded events in earlier times. This goes beyond simple historical ignorance to capture something deeper about how context, meaning, and even basic categories shift across time. How do we understand medieval concepts of honor, or Victorian ideas about hysteria, or even what constituted “work” before the industrial revolution?

The other five types clustered around social and institutional dynamics. Self-reflective not-knowing emerged from recognizing social pressure to appear knowledgeable—the tension between being embedded in a system (where you’re expected to know) versus observing it externally. Historical not-knowing focused on the fallibility of memory, particularly in legal contexts where single-witness events like sexual assault create evidential challenges. Saying “I don’t know” about a detail makes testimony appear less reliable, even though that’s precisely how memory works.

Provenance not-knowing deals with uncertainty about the origins of objects, ideas, or information — increasingly relevant in our age of viral content and AI-generated material. Cultural not-knowing captures the hermeneutic circle of cross-cultural understanding. And action potential not-knowing addresses uncertainty about one’s own capabilities and possibilities.

These discoveries extend my original framework and reveal that an agent — a person who needs to act in the face of the unknown — confronts an even broader landscape of uncertainty than I had initially mapped.

From abstract concept to visceral experience

The second part of the workshop asked participants to come up with concrete examples that would make these abstract categories tangible. The most compelling centered on experiences where certainty and uncertainty coexist in counterintuitive ways.

Consider terminal illness. You know the outcome — death is certain. But you don’t know when it will occur or how the process will unfold. Participants observed that people often react more intensely to this uncertainty about timing and process than to the certain loss itself. This pattern extends beyond medical contexts to any “waiting experience” where the endpoint is known about with confidence but the precise path and timing remains opaque (i.e., what I’d call causal not-knowing). I see similar dynamics in how preparation for natural hazards is framed in some current work I’m doing with UNDP’s Crisis Bureau.

Another example emerged from academia: The evolving standards around AI-generated art. What counts as legitimate creative practice when artists use AI tools? Should AI assistance be disclosed in academic submissions? The example reveals values not-knowing in institutional settings—uncertainty about what should be valued rather than uncertainty about what is valued.

A third example involved co-design with procedural constraints, such as when different constituencies jointly allocate a shared and fixed amount of resources in a participatory budgeting exercise. When you create speculative objects using systems that determine process but not outcome—like designing under rules of procedural justice—you face genuine outcomes not-knowing despite having clear parameters for action.

Machines for experiencing not-knowing

The third and fourth parts of the workshop challenged participants to design mechanisms that would reliably generate specific types of not-knowing in isolation — and to propose actual instantiations of such mechanisms as experienceable devices or objects.

Four emerged as particularly promising.

1. Causal not-knowing: Interdependent action driven by lossy communication and asymmetric information. The underlying mechanism is that an interdependent group must produce a joint outcome; the outcome definition is known to only one member of the group, while the other members of the group are only able to take action under direction from the member who knows the outcome definition. This mechanism is exemplified by a collaborative Etch-a-Sketch machine used by 2-5 players, each controlling dials that influence a shared drawing apparatus. One player knows what image they’re trying to create and must describe it to others, but no one can predict how their own dial movements will affect the collective outcome. The interconnections between dials are deliberately obscured. Workshop participants estimated that the game might be playable for 4-6 minutes per session, reliably generates causal not-knowing, noted it would be “lots of fun,” and emphasized its potential for both physical gallery spaces and online implementation.
2. Not-knowing about subjective value: Individual elicitation of relative value followed by joint disclosure within the group. This mechanism is exemplified by a participatory budgeting game with revelation. Participants receive identical budgets and individually allocate resources across different options in order to achieve some kind of objective (such as building a model city). Only after making private decisions do they reconvene to reveal their final allocations. The mechanism works for groups from 5 to 100, reliably exposing hidden value systems and generating uncertainty about what others consider worthwhile. The same mechanism drives my Boris workshop for developing more effective shared strategic goals by articulating tradeoffs.
3. Not-knowing about outcomes and causation: Applying unpredictably selected transformations on user actions. This mechanism is exemplified by the camera of not-knowing, which applies an unpredictably selected but stable set of filters or transformations to any image generated by the camera’s user. This could be implemented as RAW image processing guided by randomized parameters seeded persistently per user/session. Users have control over their input actions — what they choose to shoot, how they choose to light and frame it — but the transformation process is opaque to them; all they see is the post-transformation image. This mechanism generates not-knowing about outcomes with high reliability, can be used by individuals or small groups, and could plausibly be deployed as a web application accepting RAW uploads (making the experience available to anyone with a moderately sophisticated smartphone).¹
4. Not-knowing about valuation: Valuation by selection before context for valuation is provided, and revaluation by composition. Exemplified by the DJ of uncertainty, where a player selects 25 songs from a provided pool of songs, after which they receive the context in which they must create an 8-song playlist: a garden party, a funeral, a wedding reception. The mechanism highlights how the value of a thing (in this case a song) is strongly context-dependent, and provides a context for the player to generate new and unexpected value from an existing set of resources through selection and ordering.

Experiencing not-knowing

Viscerally experiencing different types of not-knowing is vital for organisations which deal in not-knowing which isn’t risky.

Most people (including leaders and managers) have some exposure to decisionmaking under formal risk. We have tools and ways of thinking, like cost-benefit analyses or expected value calculations, that help us decide how to act in genuinely risky situations: When we don’t know exactly what will happen but we know almost everything about what we don’t know.

But most of the important unknowns we face are now both truly uncertain and risky. To succeed, organisations need leaders, managers, and employees who can distinguish between risk and other types of not-knowing, and make appropriate decisions.

This is especially true for organisations whose core work and business model involves non-risk types of not-knowing. Some examples of these organisations include insurance companies, hedge funds and other capital allocators, deep tech companies, pharmaceuticals companies, and higher education institutions.

Learning to respond correctly to uncertainty that isn’t risky requires personal and visceral experience of different types of not-knowing, not just a theory-based explanation. You can read about different types of not-knowing, but understanding them viscerally requires confronting them directly.

The mechanisms and prototype ideas emerging from this workshop are a first step to creating a more sophisticated culture around uncertainty. The uncertain camera shows you — literally — what happens when optimization requires acting without knowing the causal pathway between the actions you take and the outcome that results. A participatory budgeting game reveals how value judgments that feel obvious and indisputable in private become uncertain and up for debate and negotiation when exposed to social consideration.

Experience matters because uncertainty isn’t only a cognitive challenge — it’s an emotional and social one. We default to treating unknowns as quantifiable risks partly because risk feels manageable in ways that genuine uncertainty doesn’t. Learning to work productively with uncertainty requires building comfort with discomfort, developing what I’ve previously called an uncertainty mindset through practice rather than theory.

Next

This workshop surfaced some possibilities for prototyping rather than finished experiences. It’ll be harder to build an actual collaborative Etch-a-Sketch or a working camera of not-knowing than what a workshop imagines. But the core insight stands: Different types of not-knowing can be instantiated through designed experience. This opens possibilities for training, research, and public engagement around uncertainty that go beyond academic discussion.

The workshop didn’t just extend theory; it created practical tools for learning what uncertainty feels like in controlled environments. For organisations that operate at the edges of the known — from universities researching emerging phenomena, to companies navigating unprecedented change or building novel technologies, to multilaterals working in rapidly evolving geostrategic contexts — this represents a new approach to uncertainty training. Instead of trying to reduce all unknowns to manageable risks, we can build capacity for working productively with different types of not-knowings.

The question now is: Which organisations are ready to take this seriously? If your organisation regularly confronts genuine uncertainty (not just risk), these mechanisms could help your people develop the mindset and skills for thriving rather than merely surviving in uncertain environments.

This isn’t about embracing chaos or abandoning planning. It’s about building organisational capacity to distinguish between different types of unknowns and respond appropriately to each. When people understand what kind of not-knowing they’re facing, they can choose appropriate approaches and tools that actually match the situation rather than defaulting to risk management techniques that create false confidence in uncertain environments.

The mechanisms that emerged out of the Adelaide workshop offer concrete starting points for anyone interested in moving beyond theory toward practical experience of uncertainty. What remains is building these prototypes into robust tools and finding the organisations that are ready to use them.

If your organisation regularly confronts genuine uncertainty (not just risk) and you’re interested in commissioning bespoke experiences of not-knowing for training, research, or public engagement, reach out at me@vaughntan.org.

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1. There’s an uncanny correspondence between the camera of not-knowing and Alessandro Vasapolli’s work to, in his words, “develop custom-built photographic apparatuses and systems designed explicitly to access otherwise hidden aspects of reality, enabling the camera to cease imitating human sight and instead become a ‘non-human observer’. The authenticity of the resulting images lies in their origin: they are not the outcome of a subjective manipulation, but emerge from a perceptual process embedded in the camera itself, firmly anchored in direct and objective interaction with the world.”↩︎