CritABCD
A.1 · 8 · A.1 Kinematics · mathematical models · Theory of Knowledge link

Models: “This is not a pipe”

x = vt is not the ball. A TOK look at what a model is, and how to test one against reality.

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Hook

In 1929 the painter René Magritte painted a smoking pipe, and wrote underneath it, in French: “This is not a pipe.” He was right. It is a painting of a pipe. What is a formula like x = vt a picture of?

Representation
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The image of a pipe is not a pipe

You cannot fill the painted pipe with tobacco. The image represents the pipe; it is not the pipe. Every model in physics works the same way: an equation, a graph or a simulation stands in for a real thing, and leaves most of it out.

The real thingA representation of it
a pipea painting of a pipe
a ball moving along a linethe equation x_f = x_i + vt
a whole car journeya position–time graph
the atoma drawing of electrons orbiting a nucleus
Check 1 of 3

The message of “This is not a pipe” for physics models is that…

Check 2 of 3

A model predicts x_f = 50 m, but the measured position is x_f* = 47 m. The right conclusion is…

Check 3 of 3

In the car example, the constant-acceleration model gets worse over time because it leaves out…

Apply

In 4–6 sentences: choose one model from this lesson (for example x = vt or x = ½at²). Say what it leaves out, one situation where it is useful, and one where it is misleading.

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Remember earlier lessons
From lesson A.1 · 7

On a v–t graph, the area is…

From lesson A.1 · 4

Y = vt assumes…

Summary card

Key points

  • • A model represents a real thing, but is not the thing: it leaves things out on purpose.
  • • A model’s prediction (x_f) and the measured result (x_f*) can differ; a useful model keeps the gap small where you use it.
  • • Know the range where a model works, and be careful outside it.

Formulas

  • x_f = x_i + vt
  • x_f = x_i + ut + ½at²

Key terms

Model
:
a simplified representation used to describe and predict something real
Assumption
:
something a model takes for granted, such as “no air resistance”

Common errors

  • • Treating a model as if it WERE reality.
  • • Throwing a model away because it is not perfect.
  • • Trusting a model outside the conditions it was built for.
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