Maybe the first Agent-Based Model. T.C. Schelling, “Dynamic Models of Segregation”,

*Journal of Mathematical Sociology***1**, 143–186 (1971),*Micromotives and Macrobehavior*(WW Norton, 1978).No computers. Schelling worked the model on a checkerboard.

https://ees4760.jgilligan.org/models/class_24/segregation.nlogo

- Turtles represent households.
- Two colors of turtles: red and blue
- Turtles have one state-variable:
*happy?*(true or false)

- There is a global variable
*%-similar-wanted*and a turtle is*happy?*if at least this fraction of its neighbors have the same color as its own. - At each tick, unhappy turtles move to a random empty patch.
- When all turtles are
*happy?*, the model stops.

Vary *%-similar-wanted* and the *density* of turtles on the patches.

- Try extreme values of parameters:
- Set
*density*and*%-similar-wanted*to different combinations near maximum, minimum, and in the middle. - What do you see?

- Set

- Set
*density*to 75% and set*%-similar-wanted*to 95% - Press
**setup**and then press**go**- What happens?

- Now, with
**go**still pushed, slowly reduce*%-similar-wanted*.- Now what happens?

- Using Behaviorspace, create a new experiment to vary
*%-similar-wanted*- Set _
**time limit**to 1000 - Set
to 75*density* - Measure
*percent-similar*

- Set _
- What do you see?
- Try adjusting both
and _*%-similar-wanted***density**

Add the following to the procedure

`to update-turtles`

, after`set happy?`

`ifelse happy? [ set shape "square" ] [ set shape "square-x" ]`

- Repeat the exercise of:
- set
*density*= 75% and*%-similar-wanted*= 95%, - press _
**setup**and*go* - gradually reduce
*%-similar-wanted*

- set

- Is it easier to see the emerging patterns now?

- When you’re at an interesting value for one parameter (e.g.,
*%-similar-wanted*= 75%), vary other paremters (*density*).

- Use several
*currencies*to evaluate models- Statistical analysis of spatial patterns and time-series
- Analyze agent properties: Are they unimodal or multimodal (e.g., are turtles divided into distinct groups of rich/poor, healthy/sick, etc., or distributed continuously around one dominant value of state variables?)
- Stability: Does system return quickly to steady state after it’s disturbed?

- Simplify models:
- Make all patches the same
- Make all turtles the same
- Reduce places where you use stochasticity
- Use fewer turtles and patches

- Explore unrealistic scenarios
- See book for heuristics for statistical analysis of model output…