modeling and simulating social systems with matlab · 2015-10-26 modelling and simulating social...
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![Page 1: Modeling and Simulating Social Systems with MATLAB · 2015-10-26 Modelling and Simulating Social Systems with Matlab 7 Simulation Models Weakness of (simulation) models? The choice](https://reader036.vdocument.in/reader036/viewer/2022062603/5f0b18ba7e708231d42ed4da/html5/thumbnails/1.jpg)
2015-10-26ETH Zürich
Modeling and Simulating Social Systemswith MATLAB
Lecture 5 – Cellular Automata
Olivia Woolley, Stefano Balietti, Lloyd Sanders
Computational Social Science (D GESS)
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2015-10-26 Modelling and Simulating Social Systems with Matlab 2
Strategies for Research Projects
“Data project”: Choose a dataset for which you have an interesting
question Come up with a research question that can be tested
with that data Implement an existing model form the scientific literature Run simulations that either use the dataset as input or
that reproduce certain aspects of the data
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2015-10-26 Modelling and Simulating Social Systems with Matlab 3
Strategies for Research Projects
“Paper project”: Choose a scientific paper (or several papers) on a
modeling and simulation approach that looks interesting Come up with a research question that builds upon the
paper but is different from their research questions Extend, simplify, modify, and/or combine existing models
for testing your research question Run simulations for your model and compare the results
to the ones for the original model
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2015-10-26 Modelling and Simulating Social Systems with Matlab 4
Repetition
Dynamical systems described by a set of differentialequations; numerical solutions can be calculatediteratively using Euler's method (examples: Lotka-Volterra and Kermack-McKendrick)
The values and ranges of parameters critically matter for the system dynamics (Example 2, epidemiologicalthreshold)
Time resolution in Euler's method must be sufficientlyhigh to capture “fast” system dynamics (Example 3)
MATLAB provides the commands ode23 and ode45 forsolving differential equations (Example 3)
4
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2015-10-26 Modelling and Simulating Social Systems with Matlab 55
Simulation Models
What do we mean when we speak of
(simulation) models?
Formalized (computational) representation of social
(or other kinds of) dynamics
Reduction of complexity, i.e. highly simplifying
assumptions
The goal is not to reproduce reality in general (only
very specific aspects of it)
Formal framework to test and evaluate causal
hypotheses against empirical data
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2015-10-26 Modelling and Simulating Social Systems with Matlab 66
Simulation Models
Strength of (simulation) models?
Computational laboratory: test how micro dynamics
lead to macro patters
Experiments with computer models are particularly
useful in the social sciences, where experiments in
the real world are typically not possible
Suitable where analytical models fail or where
dynamics are too complex for analytical models
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2015-10-26 Modelling and Simulating Social Systems with Matlab 77
Simulation Models
Weakness of (simulation) models? The choice of model parameters and implementation
details can have a strong influence on the simulation
outcome
We can only model aspects of a system, i.e. the
models are necessarily incomplete and reductionist
More complex models are generally not better (only
when simplification is not possible)
It can be hard to relate simulation results to a realistic
and relevant empirical question
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2015-10-26 Modelling and Simulating Social Systems with Matlab 88
Simulation Models
How to we deal with the known limitations?
Simple models with only few parameters
Test whether simulation results depend on details of
the implementation or model
Validate the model mechanism with observations and
causal plausibility
Use empirical data to evaluate the predictive power of
the simulation model
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2015-10-26 Modelling and Simulating Social Systems with Matlab 99
Cellular Automaton (plural: Automata)
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2015-10-26 Modelling and Simulating Social Systems with Matlab 1010
Cellular Automaton (plural: Automata)
A cellular automaton is a set of rules, defining
how the state of a cell in a grid is updated,
depending on the states of its neighboring cells
Cellular-automata simulations are discrete in
time and space
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2015-10-26 Modelling and Simulating Social Systems with Matlab 11
Cellular Automaton
The grid can have an arbitrary number of
dimensions:
1-dimensional cellularautomaton
2-dimensional cellularautomaton
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2015-10-26 Modelling and Simulating Social Systems with Matlab 12
Moore Neighborhood
The cells are interacting with each neighbor
cells, and the neighborhood can be defined in
different ways, e.g. the Moore neighborhood:
1st order Moore neighborhood 2nd order Moore neighborhood
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2015-10-26 Modelling and Simulating Social Systems with Matlab 13
Von-Neumann Neighborhood
The cells are interacting with each neighbor cells,
and the neighborhood can be defined in different
ways, e.g. the Von-Neumann neighborhood:
1st order Von-Neumann neighborhood
2nd order Von-Neumann neighborhood
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2015-10-26 Modelling and Simulating Social Systems with Matlab 14
Game of Life
Ni = Number of 1st order Moore neighbors to cell i
that are activated.
For each cell i:1. Deactivate active cell iff Ni <2 or Ni >3.2. Activate inactive cell iff Ni =3
i
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2015-10-26 Modelling and Simulating Social Systems with Matlab 1515
Game of Life
Ni = Number of 1st order Moore neighbors to cell i
that are activated.
For each cell i:1. Deactivate active cell if Ni <2 or Ni >3.2. Activate inactive cell if Ni =3
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2015-10-26 Modelling and Simulating Social Systems with Matlab 1616
Game of Life Want to see the Game of Life in action? Type:
life
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2015-10-26 Modelling and Simulating Social Systems with Matlab 17
Complex Patterns
17
“Gosper Glider Gun”:
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2015-10-26 Modelling and Simulating Social Systems with Matlab 18
Game of Life: Some Bits of History1970: Conway presents the Game of Life; discovery of the glider(speed c/4) and the standard spaceships (speed c/2); discovery of theGosper glider gun (first pattern found with indefinite growth)
1971: First garden of eden (pattern that has no parent pattern and canonly occur at generation 0) was discovered; first patter with quadraticgrowth (breeder)
1989: Discovery of the first spaceships with velocities c/3 and c/4
1996: Game of Life simulated in Game of Life
2000: A Turing machine was built in Game of Life
2004: Construction of the largest interesting pattern so far: caterpillarspaceship with speed 17c/45 consisting of 11,880,063 alive cells
2013: The first complete replicator (a pattern that produces an exactcopy of itself) was built
2014: Game of Life wiki contains more than 3000 interesting patterns
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2015-10-26 Modelling and Simulating Social Systems with Matlab 19
Surprising discoveries continue
“The loafer” is a very small spaceship
It is the slowest known orthogonal spaceship
(speed of c/7)
It was discovered only in 2013
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2015-10-26 Modelling and Simulating Social Systems with Matlab 20
Life in Life
20
The Game of Life can be used to simulate the
Game of Life:
http://www.youtube.com/watch?v=xP5-iIeKXE8
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2015-10-26 Modelling and Simulating Social Systems with Matlab 21
Life in Life
21
The Game of Life can be used to calculate
everything (if a Turing machine can):
http://www.youtube.com/watch?v=My8AsV7bA94
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2015-10-26 Modelling and Simulating Social Systems with Matlab 2222
1-dimensional Cellular Automata
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2015-10-26 Modelling and Simulating Social Systems with Matlab 2323
Cellular Automaton: Principles
1. Self Organization: Patterns appear from
random start patterns
2. Emergence: High-level phenomena can
appear such as gliders, glider guns, etc.
3. Complexity: Simple rules produce complex
phenomena
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2015-10-26 Modelling and Simulating Social Systems with Matlab 2424
Highway Simulation
Simple example of a 1-dimensional cellular
automaton
Rules for each car at cell i:
1. Stay: If the cell directly to the right is occupied.
2. Move: Otherwise, move one step to the right, with
probability p
Move to the next cell, withthe probability p
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2015-10-26 Modelling and Simulating Social Systems with Matlab 2525
Highway Simulation
We have prepared some files for the highway
simulations: draw_car.m : Draws a car, with the function
draw_car(x0, y0, w, h)
simulate_cars.m: Runs the simulation, with the
function
simulate_cars(moveProb, inFlow,
withGraphics)
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2015-10-26 Modelling and Simulating Social Systems with Matlab 2626
Highway Simulation
Running the simulation is done like this:
simulate_cars(0.9, 0.2, true)
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2015-10-26 Modelling and Simulating Social Systems with Matlab 2727
Kermack-McKendrick Model
In lecture 4, we introduced the Kermack-
McKendrick model, used for simulating disease
spreading
We will now implement the model again, but this
time instead of using differential equations we
use the approach of cellular automata
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2015-10-26 Modelling and Simulating Social Systems with Matlab 28
Kermack-McKendrick model
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2015-10-26 Modelling and Simulating Social Systems with Matlab 2929
Kermack-McKendrick Model
The Kermack-McKendrick model is specified as:
S: Susceptible persons
I: Infected persons
R: Removed (immune)
persons
β: Infection rate
γ: Immunity rate
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2015-10-26 Modelling and Simulating Social Systems with Matlab 3030
Kermack-McKendrick Model
The Kermack-McKendrick model is specified as:
S: Susceptible persons
I: Infected persons
R: Removed (immune)
persons
β: Infection rate
γ: Immunity rate )(
)( )()(
)()(
tIdt
dR
tItStIdt
dI
tStIdt
dS
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2015-10-26 Modelling and Simulating Social Systems with Matlab 3131
Kermack-McKendrick Model
The Kermack-McKendrick model is specified as:
S: Susceptible persons
I: Infected persons
R: Removed (immune)
persons
β: Infection rate
γ: Immunity rate )(
)( )()(
)()(
tIdt
dR
tItStIdt
dI
tStIdt
dS
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2015-10-26 Modelling and Simulating Social Systems with Matlab 3232
Kermack-McKendrick Model
The Kermack-McKendrick model is specified as:
S: Susceptible persons
I: Infected persons
R: Removed (immune)
persons
β: Infection rate
γ: Immunity rate
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2015-10-26 Modelling and Simulating Social Systems with Matlab 3333
Kermack-McKendrick Model
The Kermack-McKendrick model is specified as:
For the MATLAB implementation, we need to
decode the states {S, I, R}={0, 1, 2} in a matrix x.
S S S S S S S
I I I S S S S
I I I I S S S
R I I I S S S
R I I I S S S
I I I S S S S
I S S S S S S
0 0 0 0 0 0 0
1 1 1 0 0 0 0
1 1 1 1 0 0 0
2 1 1 1 0 0 0
2 1 1 1 0 0 0
1 1 1 0 0 0 0
1 0 0 0 0 0 0
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2015-10-26 Modelling and Simulating Social Systems with Matlab 3434
Kermack-McKendrick Model
The Kermack-McKendrick model is specified as:
We now define a 2-dimensional cellular-
automaton, by defining a grid (matrix) x, where
each of the cells is in one of the states: 0: Susceptible
1: Infected
2: Recovered
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2015-10-26 Modelling and Simulating Social Systems with Matlab 3535
Kermack-McKendrick Model
Define microscopic rules for Kermack-
McKendrick model:
In every time step, the cells can change states
according to:
A Susceptible individual can be infected by an
Infected neighbor with probability β, i.e. State 0 → 1,
with probability β.
An individual can recover from an infection with
probability γ, i.e. State 1 → 2, with probability γ.
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2015-10-26 Modelling and Simulating Social Systems with Matlab 3636
Cellular-Automaton Implementation
Implementation of a 2-dimensional cellular
automaton in MATLAB can be done like this:
The iteration over the cells can be done either
sequentially, in parallel, or randomly
Iterate the time variable, tIterate over all cells, i=1..N, j=1..N
Iterate over all neighbors, k=1..M
End k-iterationEnd i-iteration
End t-iteration
…
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2015-10-26 Modelling and Simulating Social Systems with Matlab 3737
Cellular-automaton implementation
Sequential update:
...
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2015-10-26 Modelling and Simulating Social Systems with Matlab 38
Cellular-automaton implementation
Parallel update (use a copy of the grid):
... ...
data from last iteration: working copy:
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2015-10-26 Modelling and Simulating Social Systems with Matlab 3939
Cellular-automaton implementation
Random update:
...
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2015-10-26 Modelling and Simulating Social Systems with Matlab 4040
Cellular-automaton implementation
Attention: Simulation results can be very sensitive to the type of
update used
Random update is usually preferable for social
simulations (but also not always the best solution)
Just be aware of this potential complication and
check your results for dependency on the
updating scheme!
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2015-10-26 Modelling and Simulating Social Systems with Matlab 4141
Boundary Conditions
The boundary conditions can be: Periodic: The grid is wrapped, so that what exits on
one side reappears at the other side of the grid.
Fixed: Agents are not influenced by what happens at
the other side of a border.
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2015-10-26 Modelling and Simulating Social Systems with Matlab 4242
Boundary Conditions
Periodic boundaries are usually preferable:
Fixed boundaries Periodic boundaries
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2015-10-26 Modelling and Simulating Social Systems with Matlab 4343
MATLAB Implementation of the Kermack-McKendrick Model
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2015-10-26 Modelling and Simulating Social Systems with Matlab 4444
MATLAB implementation
Set parameter values
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2015-10-26 Modelling and Simulating Social Systems with Matlab 4545
MATLAB implementation
Define grid, x
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2015-10-26 Modelling and Simulating Social Systems with Matlab 4646
MATLAB implementation
Define neighborhood
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2015-10-26 Modelling and Simulating Social Systems with Matlab 4747
MATLAB implementation
Main loop. Iterate thetime variable, t
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2015-10-26 Modelling and Simulating Social Systems with Matlab 4848
MATLAB implementation
Iterate over all cells,i=1..N, j=1..N
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2015-10-26 Modelling and Simulating Social Systems with Matlab 4949
MATLAB implementation
For each cell i, j: Iterateover the neighbors
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2015-10-26 Modelling and Simulating Social Systems with Matlab 5050
MATLAB implementation
The model, i.e. updatingrule goes here.
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2015-10-26 Modelling and Simulating Social Systems with Matlab 5151
Aborting Execution
When running large computations or animations, the
execution can be aborted by pressing Ctrl+C in the main
window:
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References
Wolfram, Stephen, A New Kind of Science. Wolfram
Media, Inc., May 14, 2002.
http://www.conwaylife.com/wiki/
Martin Gardner. The fantastic combinations of John
Conway's new solitaire game "life". Scientific American
223 (October 1970): 120-123.
Schelling, Thomas C. (1971). Dynamic Models of
Segregation. Journal of Mathematical Sociology 1:143-
186.
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2015-10-26 Modelling and Simulating Social Systems with Matlab 53
Exercise 1
Get draw_car.m and simulate_cars.m from
www.soms.ethz.ch/matlab or from
https://github.com/msssm/lecture_files
Investigate how the flow (moving vehicles per
time step) depends on the density (occupancy
0%..100%) in the simulator. This relation is
called the fundamental diagram in transportation
engineering.
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2015-10-26 Modelling and Simulating Social Systems with Matlab 54
Exercise 1b
Generate a video of an interesting case in your
traffic simulation.
We have uploaded an example file:
simulate_cars_video.m
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2015-10-26 Modelling and Simulating Social Systems with Matlab 55
Exercise 2
Download the file disease.m which is an
implementation of the Kermack-McKendrick
model as a cellular automaton
Plot the relative fractions
of the states S, I, R, as
a function of time, and
see if the curves look
the same as for the
implementation last class
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2015-10-26 Modelling and Simulating Social Systems with Matlab 56
Exercise 3
Modify the model Kermack-McKendrick model in
the following ways: Change from the 1st order Moore neighborhood to a
2nd and 3rd order Moore neighborhood.
Make it possible for
Removed individuals
to change back to
Susceptible
What changes?