1. Introduction to Computing
for Complex Systems
(Lab Session 4)
daniel martin katz
illinois institute of technology
chicago kent college of law
@computationaldanielmartinkatz.com computationallegalstudies.com

2. Take a Look at
this Article
from the
Economist
(July 22, 2010)
Agent Based
Models and
Positive
Economic
Theory
Refer Back to
my slides about
equilibrium and
its discontents

4. Schelling Social Segregation Model

5. the full
model

7. Print This and Draw the
Connections for a Full Map
of the Schelling Code

8. Mapping of the
Schelling Code
(%-similar-wanted is a slider)

9. We Need to see
What “update-
varables” is
Actually Doing

12. Here we are going to “set” some of our
“turtles-own” variables

14. “Set” the Turtles-own Variable “similar-nearby”
to the count of “turtles-on” neighbors (8 of them)
but only those with color = my color

15. “Set” the Turtles-own Variable “other-nearby”
to the count of “turtles-on” neighbors (8 of them)
but only those with color = not my color

16. Take a look at what is happening here
The “happy?” condition is going to be important
involves an agent by agent comparison of the
spread between “similarity-wanted” & “similar-
nearby”

17. Now Lets Look at “Update-globals”
It involves “lets” and “sets”
uses the globals but also some of the
turtles-own variables

18. I will allow you to review this on your own
However, consider the syntax of “sum”

19. (1) Right Click
(ctrl + Click on Mac)
on the “percent-
similar” plot
(2) This will appear
and will allow for
various modifications
(color, interval, etc.)

24. We now consider the “to go”
portion of the code

25. lets reduce the “to go” procedures
the “to go” portion
of the code
above are the major new elements
remember conceptually the model relies
upon movement if a turtle is unhappy

26. Again, we have the “if”
the “to go” portion
of the code
The “to go” button with
stop when all turtles
are “happy?”
Here is how “Happy?” gets set:
model continues to tick until every agent is
above the “%-similar-wanted” as set on the
slider

27. create an agentset of “not happy?” turtles
the movement portion
of the code
For that agentset we run the “find-new-spot”
procedures
We know from the “to go” procedures that
this is going to continue to run until the
“if” condition is satisfied

28. the movement portion
of the code

29. the movement portion
of the code

30. the movement portion
of the code
Notice that it is going to re-run the “find-new-spot”
If the “if” condition is met
In other words, agents are going to move until
they find a open patch
then agent will occupy the center of open patch

32. Thinking about Extensions
to the Schelling Model
This is closer to a “representative agent” model
Agents are homogenous in their %similar-wanted
In reality, there is likely variance across agents
In other words, comparing across agents there
are differential preferences with respect to the
%similar-wanted

33. Thinking about Extensions
to the Schelling Model
spatial considerations
The 8 neighbors might not be how
individuals actually make their assessments
agents might make choices based upon a wider
assessment of the neighborhood
there might be different “prices” for different
patches (i.e. a simulated housing market)

34. Thinking about Extensions
to the Schelling Model
structural considerations
the model could encode certain barriers
to entry to particular neighborhoods
barriers could be highly asymmetric
(i.e. red turtles face no barriers and
green turtles face high barriers)

36. An Exercise
Start With the Default
Implementation of
Social Segregation
Imagine that you are
interested in
developing a certain
style of integration

37. Modify the Code as
Needed in Order to
Produce The Closest
Possible Model Run
to the Camouflage
Note: this involves 4
Groups not 2 Groups
Your Goal!
Homework Exercise

38. Send Me Your Best
Effort
daniel.martin.katz@gmail.com
I will announce the
Winner

40. Simple Birth Rates

41. Simple Birth Rates

42. Simple Birth Rates

43. Simple Birth Rates
take a few minutes and play around with the model
consider the questions offered above

44. Thinking Conceptually:
Simple Birth Rates
What Does the Turtle Movement Add to the Model?
Are Turtles Added to the Model?
and If So How?
Are Turtles Removed from the Model?
and If So How?

45. Simple Birth Rates:
Exploring the Code

46. Step 1: map the dependancies
Step 2: learn the syntax and
functionality for all
unknown primitives
Step 3: read each line of code and
determine what it doing
Simple Birth Rates
Step 4: sketch a procedures map
that follows the chronology
of your program
At this point it is more Important for you to go
though the models line by line on your own using
the above protocol

47. Simple
Birth
Rates

48. Experiment
Basic Setup
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Death
Plots
Reproduction
Movement

49. Simple Birth Rates
“To Setup” Procedures

50. Simple
Birth
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“To Go”
Procedures

51. Simple Birth Rates
Turtle Movement
Procedures

52. Simple Birth Rates
Please
Review
“ifelse”
How does
it work?

53. Simple Birth Rates
Take a Look
at the
Reproduction
Procedures

54. Simple Birth Rates
Death
Procedures
Plot
Procedures

56. Simple Birth Rates
Right Click (ctrl + Click on Mac) on the “run experiment” Button

57. Simple Birth Rates
forever
button
Notice
observer is
selected
Calls upon the
go-experiment
sub-procedures
Name of
our
Button

58. Simple Birth Rates
Take a look at
this for later in
the week
Automation is really
going to help us