This page was automatically generated by NetLogo 2.1.0.
Questions, problems? Contact feedback@ccl.northwestern.edu.
The applet requires Java 1.4.1 or higher to run. It will not run on
Windows 95 or Mac OS 8 or 9. Mac users must have OS X 10.2.6 or higher and use
a browser that supports Java 1.4 applets (Safari works, IE does not).
On other operating systems, you may obtain the latest Java plugin from
Sun's Java site.
created with
NetLogo
Neighborhood Morbidity Model
By Michael Makowsky
George Mason University
Center for Public Choice, 2004
The Story
Within the world constructed the observer (you) has the ability to create a population of agents that will be born, grow up, choose a career, reproduce, and eventually die. The major areas of concern are the expected and experience mortality rates, the population breakdown of careers chosen (especially number of criminals), the growth/decay rate of the population in large, and number/percentage of agents that are incarcerated.
Getting Started
Starting in the upper left-hand corner of the interface, you are presented with a the option to SETUP and GO. When you are ready to begin, you simply hit SETUP to populate the board and GO to initiate the simulation. The simulation may be paused (and subsequently restarted) at anytime by hitting GO.
Before you populate the board there are a series of sliders which are in fact parameters the dictate setup and control the underlying reality of the model. All sliders are in turquoise.
The upper left sliders are all setup related (and are labeled accordingly). Below them are the probability sliders that control the percentages that dictate the algorithms of the model. Finally, on the right side are the career path related parameters that control what careers can be pursued, the cost of education, and the incomes that agents can aspire to.
How the Game Plays Out
Agents are born and up until age 16 they simply move around. At age 16 they choose their career based on an expected utility comparison.
Exp_Utility_Professional = -4 * Education_Cost +
(Expected_Life - 22 - Expected_Jail)* Pro_Income
Exp_Utility_Labor = ((Expected_Life - 18 - Expected_Jail) * Labor_Income )
Exp_Utility_Criminal = ((Expected_Life - 16 - Expected_Jail) * Crime_Income)
The keys to the equations are the amount of time that the agent expects to spend in jail over his lifetime and the age at which he expects to die. These expectations are determined by the 8 neighboring patches that surround him. Each patch keeps a record of the average number of years spent in jail and the average age of death of past occupants. In sum, the agent's neighborhood is his source of information in forming his expectations.
In simple economic terms, career selection is represented by agents
choosing a parameter set for a production function of utility that maximizes
their lifetime utility over the time horizon they anticipate. In this context,
time represents a resource whose relative scarcity or abundance is estimated by
the agent. Rationality is thus bounded as agents employ a simple heuristic, with
limited information, in choosing which parameter set (career) maximizes their
lifetime utility production.
The objective function in question, Lifetime Utility (Ui), is calculated for
agent i using:
ADDITIONAL INFORMATION
N-REGIONS allows the user to break the board into 2, 3, or 4 sections. No matter the number of regions each agent carries a region_id tag that identifies where in the standard cartestian plane he exists at any moment. For example, even if there are only two regions, an agent in the lower left corner would have a region_id = 3.
SHOCKS are critical to running experiments on the model. Regional shocks and total poplation shocks can be applied, where the SHOCK_SIZE slider will control the absolute number of agents who will die, chosen
randomly within the region indicated. Region numbers are assigned counter clockwise starting from the upper right. For example, if N-REGIONS = 3, then the large region on top is 1, the lower left is 2, and the lower right is region 3.
There exists along the bottom a variety of graphs and monitors that are self-explanatory.
CONCLUSION
You have been fully indoctrinated. Now go experiment with the model and write a bunch of papers.
CREDITS
Copyright Michael Makowsky, George Mason University, 2004. All rights reserved