This ShowMe model uses:
- a multiple-instance submodel
- variables
- influence arrow
- the "Use values made in same time step" property of an influence arrow.
A repetitive influence chain is when a variable in one instance of a multiple-instance submodel the same variable in the next instance of the submodel.
For example:
You want to model the penetration of light through a series of layers (for example, through the layers in the canopy of a forest).
First solution
You make a model with 5 variables, each representing the amount of light that has penetrated through each of 5 layers. We'll call them light1, light2 etc. These are organised as an influence chain, since the amount of light at the top of any one layer is the amount that has penetrated through the layers above. Associated with each layer is a parameter, k, representing the proportion of light hitting the top of that layer which penetrates through to the bottom of the same layer. You add in a variable, top_light, representing the amount of light hitting the top of the top layer.
Your model diagram looks like this:
You enter appropriate equations:
light1 = k1*top_light
light2 = k2*light1
etc
Initialising the model shows that the amount of light is lower in each successive layer, as you would expect.
This approach is totally standard, and uses no special Simile features. However, it suffers from a major problem, in taht it does not scale up to a large number of layers: you would have to insert a large number of variables and enter their associated equations.
Second solution
So, you correctly arrive at the decision to have a multiple-instance submodel for the canopy layers (we'll call this submodel Layer). (You should always think in terms of having a multiple-instance submodel whenever you recognise a repetitive structure in the visual appearance of your model.). In fact, it is quite likely that you will already have this submodel in your model, since you will probably be wanting the dynamics (e.g. photosynthesis, carbon dynamics) of each canopy layer.
This submodel contains a variable (light) representing the amount of light reaching the bottom of each canopy layer.
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