| Thinking About Segregation and Integration
An Interactive Scientific Exploration Using Models
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A Basic Model
Understanding the Model
1. Locations in a two dimensional grid can be occupied by two differing
sorts of entities, one green and the other red.
2. Each location has eight neighbors (edge locations have neighbors on
the other side's edge) and each type of entity has a controllable preference
to be surrounded by neighbors like (or different from) themselves. The
preference is expressed by a wish to have some particular percentage of
the eight neighbors of any entity to be like (or different) from the entity
itself.
3. At each time step (iteration) of the model, entities check the relevant
percentage of their eight neighbors.
4. If the preference is greater than the preference value, the entities
remain where they are. If it is less than the preference value, they move
randomly to any open location.
5. The model continues running until no one moves on successive iterations.
The basic model will let you:
- start with either an integrated (random) or a segregated distribution
of reds and greens,
- control the population size (and hence density),
- control the strength of preference, and
- select the direction of preference (similar or different).
See what you discover!
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Some Tips for Your First Experiment
1. Select a number of entities using the top slider (1400 is a
good starting number). Half of them will be green and half red.
2. Click on "setup with random distribution" to get an integrated
starting point (you can try "setup with segregated distribution" later).
3. Set the "strength-of preference" using the second slider (50%
is a good starting point; it means the entities are comfortable and will
stay put if 50% or more of their neighbors are similar to them, or, to
put it differently, they will stay put unless more than 50 % of their
neighbors are different from them).
4. Leave "preference" as "prefers-similarity" (you can change
this later too).
5. Click "go" ... and watch what happens. The two little yellow
windows will show you changing values of the average number of similar
neighbors and the number of entities that want to move because they don't
have enough similar neighbors of the kind they prefer. The lower yellow
windows will give you a plot of how these values change over time. Try
it, keep an eye on the world and the values, and then, if you like, read
on
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Interpreting and Refining Your Results
So, did you see an integrated distribution turn into a segregated one? Were
you surprised? Schelling certainly was, and most people are when they
see the model for the first time. What it indicates is that a VERY mild preference
for having similar people around one is enough in this situation to result in
segregation. Notice that a 50% preference means that everyone would in fact
be content in an integrated environment, but because unhappy people move randomly,
a segregated environment results nonetheless.
Does that ALWAYS happen, or does it depend on the starting distribution? Try it again and see. As many times as you need to to get an answer that satisfies you.
What happens if you make the preference for similarity even less? Try a range
of values (be sure to do each several times) and plot the resulting percent
similar as a function of preference. Add some preference values above 50% and
plot those too. Is it a simple relationship? Are you surprised? Why is the relationship
the way it is?
Now, Try a "Different" Experiment
It looks like its surprisingly hard to get an integrated environment by reducing peoples' preferences to be around people like themselves. Frustrating perhaps, if one thinks integration is a good thing. Is there any OTHER way to get integration? Try "setup with a segregated distribution" and changing preference to "prefers difference" (click and hold on the red triangle and select appropriately from the drop-down menu). Is that interesting? Try varying "strength-of-preference" with those settings.
When you've got some feeling for how the model behaves for a given population
size/density, you can try varying that and see how it affects the conclusions
you might reach. And if all this makes you wonder about some other things that
might influence what happens, you can go on to the Advanced
Model.
This model was written by Ann Dixon and Paul Grobstein, with input from Doug Blank
and Ted Wong, and is a project of the Emergent Systems Working Group of the Center
for Science in Society at Bryn Mawr Colllege. The model was created and translated
into java applets using NetLogo software made available by the
Center
for Connected Learning, and is a modification of:
Wilensky, U. (1998). NetLogo Segregation model. http://ccl.northwestern.edu/netlogo/models/Segregation.
Center for Connected Learning and Computer-Based Modeling, Northwestern University,
Evanston, IL.
created with
NetLogo
view/download model file:
SerendipSegregation.nlogo
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- Last Modified:
Friday, 26-Oct-2007 20:01:46 EDT
