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Sorry. A Java 1.1-compatible browser is required for this applet.

Netscape Communicator 4.5 and Microsoft Internet Explorer 4.0 have been tested.

PK I$&§0…ēŚŚschellinghelp.html Schelling's segregation model

Quickstart

  1. Click the Add button to create an initial population (randomly placed)

  2. Click Start to run indefinitely or Step to run N times

PK I$&š0’ˆw w schellingintro.html Schelling's segregation model

Schelling's segregation model

Thomas Schelling, in 1971, showed that a small preference for one's neighbors to be of the same color could lead to total segregation. He used coins on graph paper to demonstrate his theory. Here's the high-tech equivalent. The rule this ALife model operates on is that for every colored cell, if greater than 33% of the adjacent cells are of a different color, the cell moves to another randomly selected cell.

This is my first experiment with writing computer programs to simulate interesting processes we come across in real life. There are a number of reasons why this kind of simulation is useful and I'll try to summarize what in my mind are the main ones:

  • Most modelling techniques are based on the notion that an equilibrium state is the norm, while cellular automata (CA) simulations do not have this bias.
  • Instead of trying to create a model that requires a full understanding of the highly complex outcomes of processes, CA allows us to understand the decision rules of a small number of individual actors.
  • Simulation models are very good at incorporating time and space, especially when tied to a geographic information system.
For more information on Schelling's segregation model, please see:
Schelling, Thomas C. 1971. "Dynamic Models of Segregation." Journal of Mathematical Sociology 1:143-186.


Krugman, Paul 1996. The Self-Organizing Economy Blackman, New York.


W.A.V. Clark 1991. "Residential Preferences and Neighborhood Racial Segregation: A Test of the Schelling Segregation Model." Demography 28:1.


For more information on artifial life models, please see Exploring Emergence and Sugarscape. PK Ś$&ŹūķffMETA-INF/MANIFEST.MFPK w$&yÜ00 ˜SchGod.classPK w$&Ž½¶1||ņSchPeople.classPK “$&› alife/PK $& æ alife/v1/PK w$&Pņś{ā ā ę alife/v1/God.classPK w$&’¢žå  ųalife/v1/Map.classPK w$&méG‹ ‹ <alife/v1/People.classPK w$&²W0Ŗ  ś)alife1.classPK I$&{E;¢¢&7schelling.htmlPK Ę$&N[a4ō8schellingapplet.htmlPK I$&§0…ēŚŚ7;schellinghelp.htmlPK I$&š0’ˆw w A=schellingintro.htmlPK éFPK x$&yÜ00 @¤SchGod.classUX~6c}6PK x$&Ž½¶1|| @¤jSchPeople.classUX~6c}6PK “$& @éA#alife/UX!~6†Q6PK $& @éAWalife/v1/UX!~6[R6PK x$&Pņś{ā ā  @¤Žalife/v1/God.classUX~6c}6PK x$&’¢žå   @¤°alife/v1/Map.classUX~6c}6PK x$&méG‹ ‹  @¤alife/v1/People.classUX~6c}6PK w$&²W0Ŗ  @¤Ņ$alife1.classUX~6b}6PK Ū$&O}—1JJ @¤2schelling.jarUX~6~6PK †h|PK Ę$&N[a4schellingapplet.html Schelling's segregation model

Sorry. A Java 1.1-compatible browser is required for this applet.

Netscape Communicator 4.5 and Microsoft Internet Explorer 4.0 have been tested.

PK I$&§0…ēŚŚschellinghelp.html Schelling's segregation model

Quickstart

  1. Click the Add button to create an initial population (randomly placed)

  2. Click Start to run indefinitely or Step to run N times

PK I$&š0’ˆw w schellingintro.html Schelling's segregation model

Schelling's segregation model

Thomas Schelling, in 1971, showed that a small preference for one's neighbors to be of the same color could lead to total segregation. He used coins on graph paper to demonstrate his theory. Here's the high-tech equivalent. The rule this ALife model operates on is that for every colored cell, if greater than 33% of the adjacent cells are of a different color, the cell moves to another randomly selected cell.

This is my first experiment with writing computer programs to simulate interesting processes we come across in real life. There are a number of reasons why this kind of simulation is useful and I'll try to summarize what in my mind are the main ones:

  • Most modelling techniques are based on the notion that an equilibrium state is the norm, while cellular automata (CA) simulations do not have this bias.
  • Instead of trying to create a model that requires a full understanding of the highly complex outcomes of processes, CA allows us to understand the decision rules of a small number of individual actors.
  • Simulation models are very good at incorporating time and space, especially when tied to a geographic information system.
For more information on Schelling's segregation model, please see:
Schelling, Thomas C. 1971. "Dynamic Models of Segregation." Journal of Mathematical Sociology 1:143-186.


Krugman, Paul 1996. The Self-Organizing Economy Blackman, New York.


W.A.V. Clark 1991. "Residential Preferences and Neighborhood Racial Segregation: A Test of the Schelling Segregation Model." Demography 28:1.


For more information on artifial life models, please see Exploring Emergence and Sugarscape. PK $&—nnMETA-INF/MANIFEST.MFPK $&yÜ00  SchGod.classPK $&Ž½¶1||śSchPeople.classPK “$&£ alife/PK $& Ē alife/v1/PK $&Y@„ &&ī alife/v1/God.classPK $&’¢žå  Dalife/v1/Map.classPK $&•0äöššˆalife/v1/People.classPK $&²W0Ŗ  «'alife1.classPK I$&{E;¢¢×4schelling.htmlPK Ü$&š‚*, „6schelling.zipPK Ę$&N[a4Ōµschellingapplet.htmlPK I$&§0…ēŚŚøschellinghelp.htmlPK I$&š0’ˆw w !ŗschellingintro.htmlPK[ÉĆ