By Eugene le Roux, FSAIRAC, and Eamonn Ryan
Why every form of knowledge begins with a model.
When most people hear the word ‘model’, they think of a physical representation.
DC Studio | Magnific.com
One of the most useful observations in science, engineering and management is the statement: “If you cannot model something, you do not fully understand it.” While the phrase may sound simplistic, it points to a profound truth about how human beings make sense of the world.
Understanding is not merely the accumulation of facts. Rather, it is the ability to organise those facts into a coherent structure that explains how things work. In essence, understanding requires a model.
The completeness of our understanding is therefore closely linked to the completeness of the model we construct. The better the model, the deeper our insight. Conversely, gaps in our models often reveal gaps in our knowledge.
The different faces of modelling
When most people hear the word ‘model’, they think of a physical representation: a scale bridge, an architectural mock-up, or perhaps a prototype machine. These physical models have long played a role in helping people visualise complex systems.
However, physical models represent only one category. Two other forms are equally important and often more powerful: schematic models and analytical models.
Schematic models describe relationships, sequences and logical connections. They often take the form of block diagrams, flow charts or decision trees. Their purpose is not necessarily to calculate outcomes but to clarify processes and interactions.
Analytical models go a step further. They use mathematics to simulate reality and predict behaviour. Modern computer simulations of weather systems, financial markets, traffic flows and engineering structures all fall into this category.
Both schematic and analytical models share an important characteristic: they are invisible. Unlike physical models, they exist primarily as abstract representations within diagrams, equations or human thought.
Seeing what others cannot see
Consider an engineer standing beside a bridge. To the casual observer, the bridge is simply a physical structure made of steel and concrete. The engineer, however, sees something more. Hidden beneath the visible form are bending moment diagrams, stress distributions, load paths and safety factors.
These invisible structures do not physically exist in the bridge itself. They exist within the engineer’s mental and analytical models of how the bridge behaves.
In a sense, there is an invisible reality underlying the visible one. The same phenomenon occurs in many disciplines. A doctor looking at a patient sees physiological systems. An economist sees incentives and market dynamics. A software developer sees information flows and logical dependencies.
Expertise often involves the ability to perceive invisible structures that others do not recognise.
The mind as a modelling machine
This raises an intriguing question: when we say we understand something, are we not really saying that we possess a model of it? Every time we learn a new subject, we attempt to construct a mental framework that explains relationships, predicts outcomes and provides consistency. Without such a framework, information remains fragmented and difficult to apply.
Education itself can be viewed as the process of building increasingly sophisticated models. Students of physics learn models of motion and energy. Students of economics learn models of markets and incentives. Students of law develop models of legal reasoning and procedural logic.
Even subjects that appear largely descriptive rely heavily on modelling. The complexity of legislation, for example, often consists of interconnected conditions, exceptions and dependencies. Much of this complexity can be represented through schematic diagrams that reveal underlying logic.
The ability to model is therefore not confined to technical disciplines. It is a fundamental aspect of human cognition.
Why modelling drives progress
The history of civilisation can largely be viewed as the history of increasingly powerful models. Scientific breakthroughs occurred when people developed better models of nature. Industrial development accelerated when engineers created better models of machines and processes. Modern technology exists because of mathematical models that allow us to predict behaviour before anything is physically built.
Every advance in understanding begins by making the invisible visible. The remarkable success of engineering and science demonstrates the value of modelling as a tool for creating consistency, reducing uncertainty and improving decision-making.
Yet there remains an important question. If modelling has transformed our understanding of the physical world, can it do the same for the more complex and less predictable aspects of human society?
That challenge may define the next stage of human development.
