To build a model we must first strip out all the complexities of the real world that we are not interested in. The real world is a very complicated and messy place, and if we try to capture all of its complexity in our model then the model itself will be complicated to the point of being useless.

We exclude and include things in a model and simplify the real world by making assumptions. For example we might assume that demand is linear, and that many things such as the weather, people’s incomes, unemployment, etc, remain constant. Once again, the point of all these assumptions is to make the model simple enough that we can understand it and focus our attention on what we really want to understand. Most of the skill of model building is in choosing appropriate assumptions.

For this reason, a model should not be judged solely by its assumptions (although highly dubious assumptions are not a good thing). Rather we should focus on the model’s ability to teach us something, and its ability to explain the economics of something in a plausible way.

To summarise, a model does:

• Highlight the incentives or tradeoffs that are relevant in a particular economic situation.
• Generate predictions about the behaviour of economic agents in response to controlled changes in conditions.
• Help us to design strategies or policies to maximise profits or economic welfare.

A model does not:

• Explain the entire world.
• Capture all possible interactions between economic agents or markets, or all realistic features.
• Generate predictions beyond what it was designed to do.