Programming for
Geographical Information Analysis:
Advanced Skills
Lecture 9: Modelling I: Agents and Netlogo
Dr Andy Evans
With additions from Dr Nick Malleson
Modelling
Cellular Automata
Agent-based models
Netlogo
What are models?
Hypothetical or Theoretical statement of how a system
works, ignoring “accidental” influences.
Physical recreation of the same using scaled objects and
applying the same processes.
Computer recreation of the same using virtual objects and
applying virtual processes.
Why model?
Prediction:
Future
Past (backcasting)
Testing our knowledge is complete.
Emergence (how simple processes give us complicated
patterns)
Holding our knowledge in a framework.
Virtual model types
Analytical models
Mathematical equation.
Logical
Prolog models; fuzzy logic; etc.
Iterative models
Finite element models; agent-based models
Statistical models
Regression equations; Bayesian nets; etc.
Verisimilitude
Abstract models:
Simplified objects
Subset of processes
Thought experiments
Verify that core processes are appropriate
Alternative is to try and model everything accurately.
Is this possible in systems that don’t have closure?
Model scale
Aggregate models
Regression
Disaggregate models
Range from area processing to individual-based
Individual elements
Finite-element models
Agent-based models
Individual level modelling
Aggregate representation was good when it all had to be
done in our heads.
Now we have power for individual representation, i.e. as we
understand the world.
We struggle to understand emergence – how simple rules
of individuals end up in complicated system behaviour.
Emergence
May seem subjective (how to do recognise “complicated”, why
worry about other scales?)
But subjective aggregate elements have a big effect (e.g.
inflation rates affect interest rates)
Systems of small scale elements that combine to make large
scale systems in unclear ways are called complex.
Individual-level models are a useful way of exploring
complexity.
Modelling
Identify interesting patterns
Build a model of elements you think interact and the
processes / decide on variables
Verify model
Optimise/Calibrate the model
Validate the model/Visualisation
Sensitivity testing
Model exploration and prediction
Prediction validation
This lecture/practical
Individual based modelling.
Relatively abstract models
Next few lectures
More complexity/detail.
Modelling
Cellular Automata
Agent-based models
Netlogo
Cellular Automata (CA)
Can be used for searching/simulation but much much more.
Essentially a grid of cells which may be different states
dependant on the conditions around them.
Each cell has a rule base determining how it reacts.
For example,
if a cell has less than two neighbours it dies,
if it has more than two it reproduces and the
neighbouring cells come alive.
Can be used for searches, modelling crowds, ecosystems etc.
One dimensional CA
Simplest CA: a line of cells.
Each cell has a number of states it can be in, depending on the
state of its neighbours and itself at the last time step.
Usually drawn as a line of cells, with each line below each
other being another time step.
Simple rules can generate complex emergent behaviour.
Emergence
From the simple rule: if both neighbours the same as you, go
white else go black.
One dimensional CA
Steven Wolfram classified CAs into…
Class 1 always end in the same stable state.
Class 2 settle into repeat patterns.
Class 3 chaotic / white noise patterns.
Class 4 complex patterns of near repetition
with movement.
Shows the wide range of natural patterns that
could develop from simple rules.
One dimensional CA uses
While they show that complex patterns
can emerge from simple rules, this isn’t
necessarily happening.
However, we can use them to see if our
simple rules develop complex patterns,
and how resistant they are to
perturbations to the starting conditions.
E.g. crowd flow, street development,
river networks.
The Schelling Model
Thomas Schelling (Harvard) looked at racial segregation.
Found that even if people were willing to live in areas
made up of 50% other skin colours, segregation would
develop.
1D CA. Cells either 1 or 0 (“black” or “white”).
If neighbourhood > 50% of another colour, move to nearest
area this isn’t true.
Two + dimensional CA
Real power in geography comes from having two dimensions.
Grid of cells that interact.
Complex behaviour emerges that includes movement like a
multi-cellular organism.
John von Neumann (1966) came up with the idea.
John Conway (late 60’s) “Game of Life” simplified it and gave it
purpose.
The Game of Life
Game of Life
If dead, and alive neighbours = 3 →
Alive
If alive, and alive neighbours < 2 →
Die of loneliness
If alive and alive neighbours > 3 →
Die of overcrowding
Otherwise, stays as it is.
Two dimensional Game of Life CA
In two dimensions, over time, some CA cell
combinations form moving or periodically repeating
multi-cellular organisms.
Classic two dimensional CA uses
Modelling forest fires.
Modelling animal and plant
systems.
Finding routes through
mazes / landscapes etc.
Other uses
SLEUTH Model of Keith Clarke (Santa Barbara).
Uses CA to investigate urban growth.
Uses current urban areas, slope, transport, landuse,
exclusions.
Grows cities.
Used to study “what if?” scenarios.
Essentially a whole new way of looking at urban form.
Gigalopolis: http://www.ncgia.ucsb.edu/projects/gig/
The lessons of CA
Perfect fidelity (representation) isn’t necessary to model the
world.
Simple rules can lead to realistic and complex behaviour.
These can be quite robust – missing information can turn out
to be irrelevant to generating patterns, and they can often
survive changes.
However, representing the world as fixed and movement by
state-changes seems wrong. Why not move the objects?
Intelligent Agents
In part evolved from CAs.
Essentially free moving cells or programs that intelligently
interact with each other and their environment.
One of the most important was Robert Axelrod’s 1980
cooperation competition.
Axelrod’s competition
Imagine you’re an agent.
If you cooperate with another agent, you get a moderate
reward.
Alternatively, if they’re suckered into cooperating with you,
and you stab them in the back, you get a massive reward.
However, if you both stab each other in the back, you get
very little.
The competition invited people to put in agents with different
behaviours to see who won after hundreds of rounds.
Consistently it was “Tit-for-tat”. Starts off cooperating but
punishes backstabbers.
This competition was later run with intelligent 2D CAs.
Jay Forrester (1970) “Urban Dynamics”
SIMCity
Describes cities as complex interacting dynamic systems.
1985 - 1987 Will Wright takes Forrester’s principals and
builds them into a “bottom up” system of CA-like
components “SIMCity”.
The SIMs
Latest version “The SIMs” moved to direct autonomous humanoid
intelligent agents.
Similar agent based games include Populus (1989) and Civilization
(1991). Intelligent agents are also used in games like Half-Life (1999).
The agents adapt to player strategies.
Other Basic Competitive
Agent Environments
SugarScape.
RoboCode.
Coding an Agent
Agent Class
Has an update method called each iteration, eg. move(),
trade().
Has a position.
Has a list of all other agents and can get their position.
Can communicate with other agents if necessary.
Environment Class
Has environmental conditions.
Calls the agents to update.
Agents might, for example, trade with their nearest
neighbours.
Petrol Price Modelling: can we determine
the rules used by petrol stations to set
their prices by guessing them from the
patterns?
Landscape fragmentation and aphid
infection.
Itzhak Benenson’s model of Tel-Aviv
migration under ethnic influxes.
Kai Nagel’s team at ETH in Switzerland.
Model ~1million people in Zurich.
Examples
Flocks: agent based
swarms
Utilise the emergent character of agents
working together to produce groups
that follow each other
Like herds of animals, flocks of birds, or
schools of fish.
Flocks
Invented by Craig Reynolds (1986).
Each agent has the following rules…
Avoid colliding with your neighbour most of all.
Fly in the average direction of your neighbours.
Try to move into the centre of the flock (to prevent being
eaten / to go for optimal solutions).
Flocks - how they act
Massive
http://www.lordoftherings.net/effects/simulator.html
Prologue > Armies > Massive Simulator
Uses of Flocking
Agents
Simulating how crowds move.
Popular Post-Hillsborough.
Notting Hill Carnival
Controlling resilient robot networks.
ABM Frameworks
What are they?
Pieces of software to help people build ABMs
Wide range of tools
Pre-written functions
Entire graphical environment
Somewhere in the middle
Why use them?
For non-programmers:
Graphical “point-and-click” model development.
Easier than having to learn a programming language.
For programmers:
No need to write ‘external’ functionality (e.g. drawing
graphs, scheduling events, creating displays).
Can concentrate on model logic.
Save time (?)
Other Advantages
Create an Applet (Netlogo).
Talk to other programs, e.g. R, Weka, MATLAB.
Output information (to databases, flat files, reports etc).
many more…
Commonly Used Platforms
Netlogo: http://ccl.northwestern.edu/netlogo/
Repast: http://repast.sourceforge.net/
MASON: http://cs.gmu.edu/~eclab/projects/mason/
Ascape: http://ascape.sourceforge.net/
ABLE: http://www.research.ibm.com/able/
Agent Analyst: http://www.spatial.redlands.edu/agentanalyst/
Introduction to Netlogo
Based on “StarLogo”
Popular teaching tool
Aimed at school children, very easy to learn and use
Implemented in Java
Cross-platform (models will run on any computer which
runs Java).
Uses its own scripting language –Java is hidden
Excellent documentation
http://ccl.northwestern.edu/netlogo/docs/
NetLogo basics
Two windows: Interface and Procedures
Interface contains graphical elements
Procedures are user-defined functions
NetLogo Basics: The Interface
Lots of items can be added to view or control the simulation
These are linked to model code
Sliders: set a variable value
Buttons: call a procedure
Monitors: output the value of a variable
Plots: create a graph of variable(s)
NetLogo Basics: Turtles and Patches
Turtles are the agents
Move around
Make decisions
Interact with other turtles
Interact with patches
Patches are the grid cells
Cannot move, but can interact with other patches, and
with turtles
NetLogo Basics: buttons
Example: ‘setup’ button and code
NetLogo Basics: Built-in Commands
Set the colour of all patches:
ask patches [ set pcolor yellow ]
Set the name of turtle 12:
ask turtle 12 [ set name “bob”]
0.01 probability that each turtle will give birth
ask turtles [
if random 100 <1 [ set give-birth? True]
]
Change colour of houses surrounding person 12
ask turtle 12 [
ask neighbors4 [ set pcolor blue ]
]
NetLogo Basics: Commands
Commands make complicated functions easy:
NetLogo command:
Set the colour of the houses surrounding person 12:
ask person 12 [
ask neighbors4 with [ ptype= "house" ] [ set pcolor blue ]
]
Java command:
for (int i=0; i<people.size(); i++) {
if (people.get(i).getID() == 12 ) {
for (int j=0; j<houses.size(); j++) {
if ( <house next to person> ) {
house.setColour(blue);
}
}
}
}
//
//
//
//
//
Loop over all people
Find person 12
Loop over all houses
Work out house/person position
Set the house colour
What’s Netlogo good for?
Rapid model design.
Abstract thought experiments.
Verification testing on abstract landscapes.
Further info
Steven Wolfram “A new kind of science”
A major project applying CAs to Life, the
Universe and Everything.
MultiAgent Systems and Simulation
http://mass.leeds.ac.uk/
Craig Reynold’s website.
http://www.red3d.com/cwr/boids/
Further info
Flake (2000) The Computational Beauty of
Nature: Computer Explorations of Fractals,
Chaos, Complex Systems, and Adaptation.
Wooldridge (2009) An Introduction to
MultiAgent Systems.
Next Lecture
Modelling II: The modelling process
Practical
CA in Excel
Netlogo