This page contains a silly model.
| Quantity | Variable |
|---|---|
| Timestep | |
| Current population | |
| Knowledge | |
| Resources |
| Quantity | Parameter | Constraints |
|---|---|---|
| Initial population | ||
| No timesteps | ||
| Variability of interactions | ||
| Learning probability | ||
| Learning increment | ||
| Population growth | ||
| Fortune split rate |
For each index in to :
Uniformly randomly pick another index to engage in interaction. If compute
where is a uniformly distributed random variable.
Compute
and set the updated values
Possibly increase the participants’ knowledge through learning
Note that the possible knowledge increment is taken to be independent of the change in share of resources as it is generally possible to learn, and equally importantly not to learn, from either success or failure. (There is an argument about whether the amount learned, in the event something is learned, ought to scale in some way with the amount already known. For simplicity we start with the additive term here.)
At each time , after the changing of resources and gaining of experience calculation, randomly select individuals as “parents”. From each parent
Create a new descendant (with index ) with knowledge of .
Split the resources of each parent into kept by the parent and a resource value of is used to initialise the child.