Preview - Evolution Of Populations

Using models to learn science

Scientists use scientific modeling approaches to construct knowledge about the world. In this section, we explore the ideas behind scientific models.



Please answer the questions below.

It only became widely accepted knowledge that all matter in the world is made up of tiny elementary particles in the early 19th century.

Let's look at the the picture below. What do you think this image is a model of?

Some of you probably said it's a model of an atom. Others might say it's a model of a 'Neon atom', because it has 10 electrons. In fact, since we don't know the number of protons, it could be an ion of a different element!

The point is that these representations in a model allow us to think about natural phenomena (like atoms containing electrons) that are associated with the model in certain way. Can you think of what this particular model could be useful for?

Now, let's look at a computational model of a forest. Imagine that you have a drone with a camera that is hovering above a forest. In other words, this model shows a top-down view of a forest. Each green patch you see represents a tree. A red patch represents a burning tree. 

Play with the model and make some observations.

To run the model, make sure that you press 'setup' before you press 'go'.

What do you think a researcher or scientist could use this model for?

Make sure to change the density of trees in the model and observe the spread of the fire.

In this model, trees are called agents because their behaviors are programed into the model using a set of rules. 

An example of one such rule is a tree cannot move. Another is when a tree is on fire it turns red. All trees follow the same set of rules.

How might you write a rule that a tree could follow that describes how they catch on fire?

Based on your exploration of the model, can you guess how the density of trees affects the spread of the fire in the forest?

This 'fire model' is a computational model, used to study how the interactions between the agents (trees) allows us to observe and understand emergent patterns like the spread of fire in the forest. Because it is a computational model, we can easily change parameters/variables such as the density of trees and then study how that change affects the spread of fire in the forest. Although this is just a model, we can use that knowledge to make predictions regarding the spread of fire in a real forest.

However, this model does not include all the factors that affect the spread of fire in a real forest. Brainstorm several other factors that might affect the spread of a real forest fire that could be added to this model?


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