The map is not the territory

It’s easy to be seduced by the power and precision of digital models, writes Mark Bessoudo. But they can only ever offer an approximation of reality

August 2019

Words by Mark Bessoudo

“I have rivers but no water; forests but no trees; cities but no buildings. What am I?”

It took a few seconds to realize that the question being asked of me was actually a riddle.

I was in Hong Kong, dining at a cha chaan teng. Literally translated as “tea restaurant,” these humble, retro diners have been serving no-nonsense comfort food since the 1950s. To the uninitiated or unprepared, eating at a cha chaan teng can be a jarring experience. You are made to share an impossibly small table with complete strangers. Servers can be a bit too brisk, too impersonal. Upon entering, one immediately feels the pressure to quickly sit, order, eat, pay and leave. Servers have no time to engage customers in idle chit-chat, let alone issue existential challenges. The fact that this server was bending these unwritten rules to ask me a riddle — so calmly and so nicely — was a surprise.

"Models do not replace skill or knowledge, they augment and inform it. Knowing their limitations is essential"

After I admitted defeat, he revealed the answer: a map.

Of course! Maps show rivers, forests and cities, but they don’t contain any water, trees or buildings.

I thought more about it as I sipped on the famous milk tea that gives these establishments their name. I realized that it was more than just a clever riddle — it reveals something more profound about our perception of, and engagement with, the wider world.

“The map is not the territory” is a phrase coined by the Polish-American philosopher and engineer Alfred Korzybski. He used it to convey the fact that people often confuse models of reality with reality itself.

According to Korzybski, models stand to represent things, but they are not identical to those things. Even at their best, models require interpretation. They are imperfect because they are, by definition, an abstraction of some larger complexity. Furthermore, we often misunderstand their limitations, preferring an incorrect model to no model at all. It’s human nature.

This has practical implications. For technical professionals, it can be all too easy to become captivated by the quantitative precision or predictive power of models. Consultants are paid to provide clients with a level of assurance that their models and methods will produce accurate results. Engineers are trained to use tools that seek to change the world using “first principles” — the mathematical models and physical laws that govern the universe.

But the models we use — from building information modelling and energy performance modelling to life-cycle assessment (LCA) and even old-fashioned Excel spreadsheets — should never be thought of as the end goal of our work. They are a means to an end. Models do not replace skill or knowledge, they augment and inform it. Knowing their limitations and the context within which they operate is essential.

Take, for example, an LCA of a building. It can show the relative order-of-magnitude differences between design options — comparing the life-cycle carbon impacts of concrete versus steel, say — but it cannot offer precision on the real-world carbon emissions of any individual building material. As Jason McLennan, architect and founder of the Living Building Challenge (the most advanced measure of sustainability in the built environment) puts it, “Decimal points in the spreadsheet do not translate to real-world precision … Outputs are never truth. They merely approximate a potential truth.”

From The Possible, issue 05

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To be clear, we need models, maps and tools. They are critical for careful analysis, forecasting and planning. They help us navigate a terrain of uncertainty across a landscape of potential outcomes. And, paradoxically, tools can help in overcoming some of their own shortcomings: measuring a building’s actual energy performance, for example, can help to minimize the latent uncertainty in the predictive energy model used to design it.

These issues extend beyond technical domains. To understand the complexity of reality, our minds create their own kind of mental maps: ideas, beliefs, shortcuts. They, too, can be misguided. My idea of how a cha chaan teng server should behave, while based on knowledge and previous experience, didn’t quite map onto reality. Not this server. Not this time.

Soon after finishing my tea I headed to the airport. On the plane later that evening, I glanced at the rivers, forests and cities glowing on the screen in front of me: the map. I turned to look out the window at the water, trees and buildings below me: the territory. The two are interconnected — but worlds apart.

Mark Bessoudo is research manager at WSP in London. He is also founder of

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