ICPSR - Complex Systems Models in the Social Sciences - Lab Session 4 - Professor Daniel Martin Katz
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This document discusses an introduction to agent-based modeling and computational social science using NetLogo. It begins with an overview of Thomas Schelling's social segregation model and examines the code line-by-line. Extensions to the basic model are proposed, including varying preferences across agents and incorporating spatial considerations. The document then shifts to exploring a simple birth-death model, mapping the code dependencies and procedures. Participants are instructed to modify the basic segregation model to produce a specific integrated output.
ICPSR - Complex Systems Models in the Social Sciences - Lab Session 4 - Professor Daniel Martin Katz
- 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
- 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
- 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
- 48. Experiment Basic Setup Simple Birth Rates Death Plots Reproduction Movement
- 49. Simple Birth Rates “To Setup” 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
- 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