Measuring Intangibles
“On a scale from 1- 10, how close to the feeling of Infinity Are You?"
Reflections on the Unmeasurable and How to Make Sense of It
At a book presentation at the Hay-on-Wye Festival last year, biologist and fungi researcher Merlin Sheldrake shared a story: "I felt I had to study different kinds of hallucinogenic mushrooms, so I participated in a clinical study where I was administered LSD under close medical supervision. Throughout the experience I was approached every half hour by a researcher with a questionnaire, asking me this question: "On a scale from one to ten, how close are you to the feeling of infinity?"
We all laughed at the absurdity of trying to measure the unmeasurable. There was something funny and revealing about the clash between the enormity of the experience Sheldrake was describing, and the rigid, linear nature of a numerical scale trying to capture it. It’s the same joke Douglas Adams coined in the Hitchhiker's Guide to the Galaxy with the answer to Life, the Universe and All the Rest.
Ok, we get it. So why do we still see this same flawed logic - trying to express intangible qualities through numbers and linear scales, hoping to quantify what is fundamentally unquantifiable? This still triggers me after all these years.
Landscape Aesthetics: Measuring Beauty and Perception
When I studied nature conservation and environmental planning at Hanover University in the late 1980s and early 1990s, we grappled with this very problem. How do you assess something like the beauty of a landscape? For environmental impact assessments or to fulfil our assignment from Nature Conservation law to protect and enhance natural beauty, we needed to evaluate a baseline and the impact of projects on it.
The lecture I treasured most - one I attended every year even after passing the exam- was Landscape Aesthetics with Prof. H.-H. Wöbse. He led us deep into the rabbit hole of wrestling with the True, the Beautiful, and the Good - into philosophy, ethics, and aesthetics. We explored art, poetry, music, Goethe, Kant, Schiller, Aristotle and Caspar David Friedrich, all to grasp a fundamental truth: that we cannot truly fulfil our assignment without confronting the major design flaws in how we assess and measure intangibles.
These qualities are not inherent to the physical elements of a landscape but emerge from the interaction between the observer and the environment. They vary across individuals and cultures and depend on historical contexts and cultural values, further complicating efforts to create universal metrics.
Landscape beauty remains a highly complex phenomenon that cannot be exhaustively represented with checklists. The perception of beauty in nature is an expression of implicit knowledge that enables us to tap into it, though remains with the implicit to unlock its complex nature.
Human experience- how people feel about a landscape, animals, a neighborhood, how communities respond to social change, how culture shifts over time- is not linear. It is emergent. It is context-dependent. It is constantly adapting to interactions and feedback loops. The moment you try to isolate a variable and measure it in isolation, you’ve already stripped it of the conditions that made it meaningful in the first place.
The Persistence of 1 - 10 Linear Thinking
Still today, I regularly come across the same flawed attempts to assess intangible qualities with rigid, linear scales - Likert scales, forced categories, and pre-defined indicators. Some recent encounters:
- Landscape aesthetics & environmental assessments: where beauty, cultural significance, and emotional resonance are reduced to survey checkboxes. What is the value of hedgerows, of bird song? Of a species going extinct?
- Rewilding projects: where people are asked if they feel "more connected to nature" after the reintroduction of apex predators like wolves, lynxes, or vultures—as if such an experience can be distilled into a single number.
- Urban rehabilitation initiatives: where the sense of safety, social cohesion, and identity shifts are boxed into tidy numerical outputs.
- Impact investing and ESG evaluations: where human well-being, ethical governance, and environmental sustainability are scored and aggregated into a final percentage, as if complex social and ecological relationships can be summarized like financial returns.
- Politics and polarisation: most surveys remain at the level of opinion, which isn't helpful in teasing out "unarticulated needs". Also, most of them ask far too directly. In complexity, obliquity is a fundamental design principle. Indirect, non-hypthosesis questions are called for. If you want to know something about polarisation 1) don't mention polarisation 2) ask the emergent data, not the people.
Again and again, experts default to these rigid categories and linear scales, believing them to be the hallmark of scientific rigour. But it’s 42 all over again—only this time, the joke is lost.
Design Flaws of Linear Measurement in Complex Intangibles
Measuring intangible qualities - perceptions, ideation, cultural identity, or social relational dynamics - through linear, quantitative methods presents fundamental design flaws. These qualities are not static, easily categorized, or reducible to numbers. Instead, they emerge from complex interactions between people, environments, and historical contexts. Yet, many conventional approaches rely on rigid measurement tools that fail to capture this complexity.
- Reducing Complexity and Subjectivity
One of the most significant issues is the reduction of complexity and subjectivity into fixed categories or numerical scores. Questionnaires using Likert scales, for instance, attempt to quantify context-dependent and highly personal experiences, averaging responses that should not be averaged. Social coherence, a strengthened sense of identity, or pride within a community are emergent properties that do not exist independently but arise from relational dynamics. Attempting to measure these through rigid, predefined metrics strips them of their depth, interrelational context, and meaning.
- Forced Positivity and Leading Questions:
Beyond oversimplification, survey designs often introduce bias by embedding assumptions about what should be measured, influencing responses before participants even answer. Direct questions with an implicit hypothesis—such as those assessing local attitudes toward rewilding—tend to elicit surface-level opinions rather than genuine insights. Survey designs frequently ignore the ambiguities that define human relationships with nature. For example, tensions exist between safety and wilderness, such as the fear of wolves, or between order and naturalness, as seen in attitudes toward beavers reshaping landscapes. Moreover, when funding from donor agencies is tied to specific "positive" outcomes, respondents may consciously or unconsciously provide the answers they believe are expected (a phenomenon known as gifting or gaming). This not only distorts data but also suppresses critical resistances, making it harder to address real concerns about conservation or rehabilitation efforts.
- The Challenge of Context-Specific and Longitudinal Measurement
Different landscapes, cultural contexts, and economic realities shape how intangible qualities are perceived and valued. A rewilding effort in a sparsely populated, already "wild" region of Europe is fundamentally different from one in an area with centuries of agricultural tradition. Similarly, impact investment projects in different regions operate within vastly different frameworks of land ownership, power distribution, economic values and decision-making structures. What constitutes 'poverty' or 'success' varies dramatically across cultures. Applying the same measurement tools across such diverse contexts leads to unreliable and biased conclusions. Tracking changes over time adds another layer of complexity. Measuring intangibles longitudinally requires adaptable and consistent methodologies that can respond to shifting conditions. A landscape’s aesthetic appeal changes with the seasons, with lighting, and with evolving social attitudes toward nature. The dynamic nature of these interactions makes static assessment models inadequate.
4. Scaling
Scaling qualitative research presents a different set of difficulties. While qualitative methods offer a richer, more nuanced understanding of intangible impacts, they are notoriously difficult to scale. Collecting in-depth narratives, ethnographic insights, or participatory assessments requires time, resources, and skilled facilitators. Unlike standardized surveys that can be deployed en masse, qualitative methods demand careful interpretation, local engagement, and ongoing analysis. Comparing results across diverse regions amplifies this challenge. Scaling up qualitative research without losing contextual depth often leads to oversimplification or forced categorization of responses. Human experience - how communities respond to social change, how culture shifts over time- is not linear. It is emergent. It is context-dependent. It is constantly adapting to interactions and feedback loops. The moment you try to isolate a variable and measure it in isolation, you’ve already stripped it of the conditions that made it meaningful in the first place.
A Call for Applied Complexity
In the case of landscape aesthetics, Prof. Wöbse, despite his efforts to develop integrative approaches and proxy criteria, concluded in 2002: "There is currently no generally accepted and widely applicable evaluation practice for the aesthetics of landscapes. Nor will there be one in the future if such practices rely primarily on scientific parameters and measurement methods. Due to its subjective nature, sensory perception challenges the formal-logical consistency of evaluation methods, which are based exclusively on quantitative and qualitative criteria."
Rather than forcing qualitative experiences into rigid numerical categories, we need methods that respect complexity, ambiguity, and emergence. Context-sensitive, participatory approaches- such as narrative-based research - can provide deeper insights into how people interact with and experience their environments. Instead of reducing lived experiences to scores on a scale, we should embrace more holistic, relational ways of understanding the intangible qualities that truly matter. The challenge ahead is not just to measure better, but to rethink what it means to understand.
Design Principles from Complexity Science
Though I now work more with human systems than with landscapes, Prof. Wöbse’s teachings have profoundly shaped my perspective. Over the years, I have sought approaches that embrace complexity and ambiguity rather than forcing everything into rigid, linear frameworks. Someone sent me a link to a podcast by Prof. Dave Snowden, and as I listened, a whole world opened up. Ideas that had been circling at the edges of my thinking suddenly clicked into place, offering a way to engage with complexity without forcing it into artificial structures.
He presented, amongst many other things, SenseMaker® as a different way of listening to what is actually going on in the presence. Instead of asking people to fit their experience into pre-defined categories, SenseMaker® invites them to tell a little story about something they have actually lived.
But here’s the key difference: they don’t just tell their story; they interpret it themselves using a structured framework of signifiers. Rather than an external expert deciding what a story means, meaning emerges from within the dataset itself.
Imagine you’re trying to understand how a community feels about a rewilding project. A typical survey might ask: On a scale from 1 to 5, how positive do you feel about the return of the Wolf? But what does that tell you? A "4" means nothing without context. Did they choose 4 because they’re excited but also worried about their livestock? Because they love the idea of wilderness but fear their children will be unsafe?
Now, imagine you ask something different: If a close friend were thinking about moving to your village, what would you tell them about how the landscape is changing? That question invites a story, not just a response. And when hundreds of people answer it, patterns begin to emerge- not because an expert imposed them, but because they arose organically from the way people actually experience their world.
Landscapes of “Small Noticings”
Hundreds, sometimes thousands, of small, personal stories- what we call micro-narratives - each offering a glimpse into lived experience. Instead of smoothing out the contradictions, it keeps them visible.
Instead of reducing diverse emotions to a meaningless average, it allows tensions to coexist, capturing the richness of perspectives in their full, unfiltered form. Over time, patterns appear. Patterns of perception, of meaning-making.
You might see that in one region, the return of wolves is talked about in the language of fear, while in another, it is discussed in terms of ecological renewal or pride. You might see that people with young children tell very different stories than older residents. You might see that those who live closest to rewilded areas use language that doesn’t match the assumptions of conservation planners at all.This approach does several things that conventional methods fail to do:
- It captures emergence. Because the data set consists of real, lived experiences, it reflects patterns as they develop, rather than imposing a predefined model onto reality.
- It avoids premature categorization. By using signifiers—such as triads that explore tensions between three perspectives—it allows complexity to be mapped rather than flattened into binary choices.
- It scales without losing meaning. Conventional qualitative research struggles with scale; you can’t conduct thousands of in-depth interviews without losing depth in the process. But because SenseMaker® structures interpretation within the collection process itself, it allows large-scale participation without sacrificing nuance.
This kind of pattern recognition isn’t about proving a point. It’s about seeing more clearly, about making better decisions—not because the data "tells us the truth," but because it helps us ask better questions, and it tells us about what is going on in the present - not in an ideal future state.
A Different Kind of Knowing
The real challenge isn’t just gathering data - it’s knowing what kind of data you need. In complex systems, the best way to understand change is to capture real experiences in real contexts and look for patterns as they emerge. Real understanding comes not from reducing the messy, shifting, contradictory nature of human experience into numbers, but from being willing to sit with the mess, to see the patterns beneath it, to ask different questions.
If we are serious about understanding complex systems, we need to stop trying to simplify them into false clarity. Instead, we need methods that embrace complexity—ones that allow us to see the system as it is, rather than as we wish it to be.
For the past nine years, I have been integrating this kind of complexity thinking and Snowden's methods into my work in various sectors: business, culture, natural environment and the regenerative movement, politics, decision-making, collective trauma healing, and building human sensor networks. With growing enthusiasm and widening impact.
The Storms to Come
