Population Health techniques

Agent-Based Models and Simulations. Introduction to Agent-Based models and
simulations with NetLogo software.
◦ 2 Notes are attached.
◦ Attached filled that has a tool to model agent behavior
◦ Described as one of the Population Health techniques, many ABM resources
can be found in this website (Links to an external site.)
.
◦ Access the NetLogo website (Links to an external site.)
for:
▪ Information about the software
▪ Download the software: select version 6.0.4 (or an earlier version).
▪ In the downloaded NetLogo 6.0.4 folder, open the NetLogo User
Manual. Study ALL sections before “Transition Guide”.
▪ When choose an NetLogo application to run, make sure to select the
standard version, not 3D or Hubnet, etc.
▪ You can also access the user manual (Web or Printable) , tutorials,
sample programs, etc. from the NetLogo home page.
PROJECT:
1. Run NetLogo application. In File menu, use the Models Library option to find IABM
Textbook -> chapter 3 -> Segregation Extensions. There is a simulation called Segregation
Simple. Double click on this model to run it.
2. In the Segregation Simple model, use the Info tab to understand what this model is and
how to use it. For this model, answer the following questions and save your answer in a Word
file:
◦ What happens when you run the model with the default settings? Run it a few
times. What do you notice? (2 points)
◦ Keep the population number the same, but change the %-similar-wanted value
to run the model at various conditions. At what % -similar-wanted does the
model not converge any more? In other words, the model vacillates, and does
not stop (or takes a very long time to stop). (1 point)
◦ Now that you find the %-similar-wanted at which the model does not
converge, does it make a difference if you keep the %similar-wanted but
change the population number? do the vacillating patterns persist? (2 points)
3. In the NetLogo -> File menu -> Models Library -> IABM Textbook -> chapter 3 ->
Segregation Extensions, there are three Segregation Simple Extensions. For Extension 1 and
Extension 2, use the Info tab to understand what this model is and how to use it. Then answer
the following questions for each extension using the following format and save your answer
in the same Word file used in Step 2 above:
◦ Segregation Simple Extension #
◦ What happens when you run the model with the default settings? Run it a few
times. What do you notice? (2 points)
◦ Keep the population number the same, but change the %-similar-wanted value
to run the model at various conditions. At what % -similar-wanted does the
model not converge any more? In other words, the model vacillates, and does
not stop (or takes a very long time to stop). (1 point)
◦ Now that you find the %-similar-wanted at which the model does not
converge, does it make a difference if you keep the %similar-wanted but
change the population number? do the vacillating patterns persist? (2 points)
At the end of step 3, explain how the individual preference for %-similar-wanted in
Extension 2 affects the system’s ability to reach convergence. (5 points) hint: study the code
in the Code tab, understand how individual turtle’s threshold of %-similar-wanted is
calculated based on the general %-similar-wanted and how it affects the happiness of
individual turtle.
4. In the NetLogo -> File menu -> Models Library -> IABM Textbook -> chapter 3 ->
Segregation Extensions, there are three Segregation Simple Extensions. For Extension 3, use
the Info tab to understand what this model is and how to use it. Then answer the following
questions for each extension using the following format and save your answer in the same
Word file used in Step 3 above:
◦ Segregation Simple Extension #
◦ What happens when you run the model with the default settings? Run it a few
times. What do you notice? (2 points)
◦ Keep the population number and the %-different-wanted the same, but change
the %-similar-wanted value to run the model at various conditions. At what %
-similar-wanted does the model not converge any more? In other words, the
model vacillates, and does not stop (or takes a very long time to stop). (1
point)
◦ Now that you find the %-similar-wanted at which the model does not
converge, can you change the %-different-wanted to make the system
converge again? If yes, what’s the %-different-wanted to use? (2 points)
At the end of step 4, explain how the two factors %-similar-wanted and %-different-wanted
in Extension 3 affects the system’s ability to reach convergence. (5 points)
5. Build a NetLogo simulation as described in the Mystery Pattern
(Links to an external site.)
tutorial.
◦ Complete Tasks 1-4 (15 points). Save the model as HK_Pattern.nlogo