To know less
A continuation of my exploration of uncertainty and a segue into knowledge
I’ve been reflecting on what I recently wrote about uncertainty, especially epistemic uncertainty and when I mentioned feeling like I knew less than before. I’ve had a realisation.
I initially thought I was experiencing more doubt about what I knew. But the truth is, that uncertainty was always there; I just hadn’t seen it. Through exploring, I’ve transformed unknown unknowns into known unknowns.
Types of Knowledge States
We can make sense of this by considering the knowledge states popularised by Donald Rumsfeld in a 2002 press briefing. This categorisation of knowledge states as known knowns, known unknowns, unknown knowns and unknown unknowns has deeper historical roots that link to epistemology, psychology, and risk assessment.
Johari Window (1955) – Psychological Perspective
Developed by psychologists Joseph Luft and Harrington Ingham, the Johari Window is a framework for self-awareness and communication, but equally applicable to knowledge expansion, it categorises individual knowledge into:
Open Area: known to self and to others (public self)
Blind Area: known by others, but not to self (unaware self)
Hidden Area: known to self, but not by others (private self)
Unknown Area: not known by self or others (potential self)
Risk Management & Decision Science (1960s–1970s)
Economist Frank Knight (1921) distinguished between measurable risks and unmeasurable uncertainties, anticipating the unknown unknowns concept.
NASA & Engineering (1990s)
NASA had already used similar terminology in systems engineering and risk assessment before Rumsfeld’s speech, classifying unknown unknowns as emergent risks. These issues cannot be anticipated based on existing knowledge.
Philosophical & Epistemological Roots
The idea that knowledge is structured around what we know, what we are aware we don’t know, and what we cannot yet conceive traces back to classical philosophy:
Socrates' paradox: “I know that I know nothing.” (Acknowledging known unknowns)
Immanuel Kant Discussed the limits of human cognition in Critique of Pure Reason.
Karl Popper developed the idea of the horizon of knowledge, where some gaps in understanding are detectable while others remain beyond our conceptual reach.
Recognising these transitions in my own thinking immediately triggered an old memory from when I was in Italy teaching innovation practices at Thales Alenia Space and diving into something called C-K Theory.
From Design Thinking to C-K Theory
During my time at Thales, I was deeply involved in the Design Centre (DC), an initiative focussed on design thinking and inspired by Stanford's d.School. We built d.School-style facilities and capabilities to drive innovation. It was founded by an inspiring trio of French and Belgian colleagues to whom I owe a great deal.
Over time, this evolved into a global network underpinning a broader design transformation across the Thales Group. You can read more about this here.
The Design Centre emphasised design thinking, human-centric innovation, and creative problem-solving. Yet, we existed in creative tension with our more structured innovation colleagues in the technical community, who favoured methods like Concept Development & Experimentation (CD&E), TRIZ and C-K Theory.
C-K Theory was often criticised for its formal and mathematical rigour. Initially, I saw it as a competing framework to design thinking, but I found the challenge of engaging with it intellectually stimulating, especially while teaching it to my Italian colleagues. Over time, I’ve seen these approaches as two sides of the same coin, with C-K Theory offering a theoretical basis for design.
This realisation led me to my insight about knowledge transitions.
C-K Theory: A Framework for Innovation
C-K Theory stands as a framework for understanding and facilitating innovation.
The theory is built upon two fundamental spaces:
Knowledge Space (K)
The domain of what we know, proven technologies, established principles, validated experiments. This is familiar territory, mapped and relatively predictable. It aligns with the known knowns.
Concept Space (C)
The domain of what if? where ideas are shaped before they become knowledge. Some are speculative, others emerge from weak signals of change. This space is where unknown unknowns transform into known unknowns.
The interplay between these spaces drives innovation, expressed through four fundamental operators:
C → K (Conjunction)
Turning a concept into knowledge by validating it (e.g. a prototype proves feasibility).
Example: Scientists hypothesise that gene editing can cure inherited diseases (Concept Space). Through CRISPR experiments, they demonstrate its feasibility in modifying human DNA safely, moving it into the Knowledge Space.
K → C (Disjunction)
Using existing knowledge to generate new conceptual possibilities (e.g. new materials inspiring novel applications).
Example: The discovery of graphene (Knowledge Space)—a single layer of carbon atoms—leads researchers to explore hypothetical applications in flexible electronics (Concept Space).
C → C (Expansion)
Elaborating the concept space through partitioning (refining an idea into sub-concepts) or inclusion (broadening a concept’s scope).
Example: The concept of autonomous vehicles (Concept Space) initially focused on self-driving cars. Researchers then explore variations: autonomous trucks, drones, ships, and flying taxis—expanding the Concept Space.
K → K (Expansion)
Expanding the knowledge space through experimentation, deduction, or integration of external discoveries.
Example: Newton’s laws of motion (Knowledge Space) were well established, but Einstein’s Theory of Relativity expanded our understanding by showing that Newtonian mechanics breaks down at high speeds and gravitational extremes.
Why This Matters
I find C-K Theory exciting because it illustrates the relationship and dynamics between concepts and knowledge. When we explore concepts outside our current understanding and seek novelty, we do something that can lead to breakthrough innovation.
We don’t just add to what we know; we fundamentally transform it. This is a knowledge transition.
This concept is directly relevant to my work in horizon scanning and foresight.
The foresight process encourages the continuous expansion of both ideas and knowledge. Weak signals are synthesised into plausible scenarios, expanding the Concept Space. As these scenarios are analysed and tested, some insights transition into the Knowledge Space, contributing to structured knowledge development, while others further expand the Concept Space—introducing new possibilities, revealing adjacent uncertainties, and reframing existing assumptions.
Exploring emerging possibilities in this way expands organisations’ capacity to anticipate disruptions, surface hidden opportunities, and challenge entrenched assumptions.
Scenario-building isn’t about mapping hypothetical futures; it’s about stretching the boundaries of what is considered possible, making uncertainty a tool rather than an obstacle.
When insights from foresight exercises transition into Knowledge Space, they become more than speculative reflections; they shape decision frameworks, investment strategies, and risk assessments. This process allows organisations to make more informed, resilient choices, not by predicting the future but by preparing for multiple versions of the future.
Foresight is not a passive exercise in trend detection. It is an active intervention in how knowledge evolves, ensuring that strategic choices are grounded in expanding future possibilities in addition to historical data and experience.
The Courage to Transform
This also resonates with personal and intellectual growth. Breakthrough innovation, whether in technology, organisations, or personal understanding, demands that we venture beyond certainty into unknowns that challenge and reshape our thinking.
The unknown doesn’t simply become known; we become different in the process of knowing.
This is why feeling like “I know less” signals growth.
We haven't lost knowledge; we’ve expanded our perspective.
We're not losing anything; we're growing and seeing things in a better, broader way.
What’s Next?
This C-K lens has triggered a cascade of thoughts and connections that set the stage for further explorations:
A deeper dive into the history of C-K Theory, its mathematical foundations and industry applications.
The relationship between C-K Theory, horizon scanning, and new perspectives on weak signals.
How the futures cone and foresight methodologies map onto C-K transitions.
Potential applications of C-K Theory in AI and automated foresight.
Deeper reading and exploration of the philosophical foundations of C-K Theory.
Exploration of these ideas and their relation to state space and Stuart Kauffman’s concept of the adjacent possible.
These perspective shifts have changed how I approach uncertainty in my own thinking. What once felt like a loss of certainty now feels like growth. Instead of trying to reduce uncertainty, I’ve learned to work with it, to see it as a space of possibility rather than a gap.
This mindset has shaped my work in foresight and deepened my curiosity and willingness to explore the unknown. Each time I’ve expanded my understanding, it has also developed me, but it has also felt very uncomfortable at times. Some things are still human.
I write this blog as a way to process my thinking and learning. The act of shaping this develops my understanding. I work with AI to research, test, and evolve my thinking and test and push these tools. While some of my thoughts may be speculative, I embrace that exploration and welcome your input. Lastly, I share these reflections, hoping they spark something interesting for you.