a cellular automata approach to population modeling alexa m. silverman
TRANSCRIPT
A Cellular Automata Approach to Population Modeling
Alexa M. Silverman
Purpose To use cellular automata to model
population growth and change To observe the effects of temperature
on the behavior of populations To determine whether a cellular-based
model of population is valid and realistically predicts the behavior of individuals in a population
Cellular Automata
A cellular automaton is a ‘cell’ on a grid which determines its state (‘live’ or ‘dead’) based on the states of neighboring cells.
2D cellular automata consider all eight neighboring cells.
2D cells with live neighbor counts
Agent-Based Modeling
• Uses individual ‘agents’ with simple instructions to observe emergent behavior
• Based on interactions between agents• More variable results than mathematical models
Agent-Based ModelingSome well known models, in NetLogo:
DaisyworldLovelock & Watson
Rabbits Grass Weeds (Predator/Prey)U. Wilensky
AltruismMitteldorf & Wilson
Life
“Life” notation is written s/b, where numbers on the right side of the slash represent neighbor counts needed to survive and numbers on the left side of the slash represent neighbor counts needed for cell birth.
“34 Life” (34/34) “Conway’s Game of Life” (23/3)
CA Modeling
The field of cellular automata modeling is still relatively new, but several models have been created.
“Rumor Mill” models spread of a rumor
“Urban Suite – Cells”models growth of cities
14/3 Population Model
For this model, the rule 14/3 was chosen because it causes cells to grow and move in a pattern resembling the spread of a species (starts localized and becomes more widespread).
14/3 automata suggest two types of individuals: “antisocial” (survives with 1 neighbor) and “social” (survives with 4 neighbors).
Code and Testing
This program was created in NetLogo because the NetLogo graphics window allows for ‘real time’ view of population growth and change. NetLogo code is object-based, which is ideal for a model where each cell must “know” its state, and the states of its eight surrounding cells.
In test runs, cell population and temperature are graphed. Cell ‘birth rate’ (likelihood of a ‘dead’ cell to come to life) varies quadratically with temperature and temperature varies linearly with changes in population. A highly variable population, therefore, will cause frequent changes in temperature.
Code and Testing
Population varies as 100 - .25(temperature - 21)^2
Temperature varies as current temp + (1/20)(pop. - last pop.)
made using equationgrapher.com
NetLogo Interface
Walls
The “make-wall” button allows the user to draw “walls” around segments of the population. The cells cannot cross these walls.
Code Sample
Methods to ‘birth’ and ‘kill’ cells
Methods to check if a cell will ‘survive’ or be ‘born’ the next turn
Results
High initial density causes quick decline in populations, isolation of groups
Results
Ideal initial density seems to be around 55%
Results
• With default settings for temperature (21 degrees Celsius) and population density (55%), population and temperature eventually drop off.
• Does this model global warming?
ResultsThe nature of cellular automata causes each test run to be slightly different even with the same initial parameters. Here are the results of five test runs with default settings:
1 9 17 25 33 41 49 57 65 73 81 89 97 105
113
121
129
137
145
153
161
169
177
185
193
201
209
217
225
233
241
249
257
265
273
281
289
297
305
313
321
329
337
345
0
500
1000
1500
2000
2500
Java Program
• Will extend capabilities of model due to Java’s object-oriented nature
• Difficulties of Java programming with CA
• Extensions—virus spread modeling?
Java Program
CellDriver
CellPanel JPanel
extends
Cell
Timer
Grid
ActionListener
Graphics
BufferedImage
class hierarchy
Sources and Background
Individual-Based Artificial Ecosystems for Design and Optimization by Srinivasa Shivakar Vulli and Sanjeev Agarwal
Using a Genetic Algorithm to Evolve Behavior in Multi-Dimensional Cellular Automata by R. Brukelaar and Th. Back
A Hybrid Agent-Cellular Space Modeling Approach for Fire Spread and Suprression Simulation by Xiolin Hu, Alexandre Muzy, Lewis Ntaimo
Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology by Grimm, Revilla, et al.
Seeing Around Corners by Jonathan Rauch A Brief History of Cellular Automata by Palash Sarkar
Image Sources on the Web
http://www.anl.govhttp://www.kevlindev.comhttp://ccl.northwestern.edu/netlogo/http://psoup.math.wisc.edu/mcell/http://i.cnn.net