A model is a simplified representation of reality used to understand how a system works and predict how it may respond to change. Simplification reduces accuracy because some components, interactions, variations, and uncertainties from the real system must be omitted or represented approximately.
This is a core SL and HL concept in Subtopic 1.2: Systems.
Models are necessary because environmental and societal systems are too complex to reproduce completely. A model selects important storages, flows, inputs, outputs, and relationships while excluding details considered less relevant to its purpose.
The process works as follows:
- A real system contains many interacting biotic, abiotic, social, economic, and political components.
- The modeller selects variables and assumptions to make the system manageable.
- Simplification makes patterns and cause-and-effect relationships easier to identify.
- However, excluded variables may affect the outcome, so predictions may differ from reality.
| Benefit of simplification | Resulting limitation |
|---|---|
| Makes a complex system easier to understand | Omits some real interactions |
| Allows predictions under different scenarios | Predictions depend on assumptions and data quality |
| Highlights major feedback loops | May overlook time lags or unexpected feedback |
| Supports environmental decision-making | May underrepresent stakeholder perspectives and unequal impacts |
For example, a model of the Aral Sea could represent river inflow, irrigation withdrawals, precipitation, and evaporation. It may successfully predict declining water storage, but its accuracy would be reduced if it omitted groundwater flows, changing agricultural policies, or differences in how water loss affects fishing communities and farmers.
A common misconception is that an inaccurate model is useless. Models are not expected to reproduce reality perfectly; their value depends on whether they are sufficiently accurate for their stated purpose and whether their assumptions and limitations are recognized.
Exam technique: For an explain question, link omission directly to reduced accuracy: simplification removes variables or interactions, so the model's output may not fully match the real system. For evaluate, give both a use and a limitation, apply a named example, and reach a supported judgement about the model's usefulness.
