GEOG3150 Semseter 2
Lecture 3
SOCIAL SIMULATION
AND AGENT-BASED
MODELLING
Dr Nick Malleson
Dr Alison Heppenstall
Recap: Last Week
Last week; first forays into the wonderful
world of programming.
Introduction to Netlogo
Practical
How did everyone get on with the practical?
Recap: Why learn to code?
New computing curriculum
for schools
Every child will learn to
code
Code is becoming the
“language of our world”
Computational thinking
Problem solving
See Year of Code
(http://yearofcode.org/)
“Computational thinking teaches you how to tackle large
problems by breaking them down into a sequence of
smaller, more manageable problems. It allows you to
tackle complex problems in efficient ways that operate at
huge scale. It involves creating models of the real world
with a suitable level of abstraction, and focus on the most
pertinent aspects. It helps you go from specific solutions
to general ones.”
Re-cap: Two
weeks ago…
Geocomputation
“The Art and Science of
Solving Complex Spatial
Problems with Computers.”
What is a model?
A simplification of reality.
Not a crystal ball
(Poster from GeoComputation
conference, 1999)
Some Readings
Papers – all offer excellent introductions to agent-based modelling
Crooks, A. and Heppenstall, A.J (2012) Introduction to Agent-based modelling. In
Heppenstall, A.J., Crooks, A.T., See, L.M. and Batty, M. (2012) Agent-based models of
Geographical Systems. Springer: Dordrecht.
Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation.
Journal of Simulation, 4(3), 151–162. doi:10.1057/jos.2010.3
Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating
human systems. Proceedings of the National Academy of Sciences, 99(90003), 7280–
7287. doi:10.1073/pnas.082080899
O’Sullivan & Haklay (2000), Agent-based models and individualism: is the world agentbased?, Environment and Planning A (32), 1409-25
Castle, C. J. E. and Crooks, A. T. (2006). Principles and concepts of agent-based
modelling for developing geospatial simulations. UCL Working Papers Series, Paper 110,
Centre For Advanced Spatial Analysis, University College London. Available online.
There is a long list of papers here: http://mass.leeds.ac.uk/2013/02/13/anexcellent-abm-paper/
Textbook
Heppenstall, A.J., Crooks, A.T., See, L.M. and Batty, M. (2012) Agent-based models of
Geographical Systems. Springer: Dordrecht.
Other resources
Prof. Bruce Edmonds is one of the big names in agentbased modelling. He has two videos that provide
excellent introductions to the methodology
A short one:
http://www.youtube.com/watch?v=JANTkSa4hmA
A longer version from a conference presentation:
http://www.youtube.com/watch?v=9nEPxb2J73w
Lecture 3
(Social) Simulation
A brief history
Uses of Simulation
Introduction to ABM
Seminar: GIS and GeoComputation
History of (Social) Simulation (1)
Simulation is a new idea – started 1960’s,
but didn’t take off until 1990’s.
Club of Rome (1974)
Simulations that predicted major environmental
catastrophe
Results fatally flawed as reliant on major
assumptions about many of the parameters
Early simulation attempts were predictive –
NOT focused on explaining (socioeconomic) processes.
History (2)
One simulation method that has survived
from the 1960s was microsimulation
(Orcutt, 1975)
Take a population of individuals and apply some
transition probability to them e.g. likelihood of
moving house or having a baby etc
This is still used today for examining impacts
of policy
E.g. What are the benefits to a population of
building a new hospital/school/business park…?
History (3)
No other simulation work until 1990’s and the
emergence of Artificial Intelligence
Cellular Automata and Agent-based modelling
Why? (Raw materials)
Computing power; data storage; data; technical knowhow
What else? Acceptance that we need new tools!
Aggregate versus individual
Scales of analysis
Interest in individual behaviour
DATA, DATA, DATA!!!
In 2015…
One of the largest and fastest expanding areas of research is...
Agent-based modelling
Barely 20 years since the first application
Now hundreds of papers written every year.
Why?
Multi-disciplinarity
computing power
data storage
Data
technical know-how
This is the simulation approach that you will be learning about
and building over the remainder of this course.
Lecture 3
(Social) Simulation
A brief history
Uses of Simulation
Introduction to ABM
Seminar: GIS and GeoComputation
Uses of simulation
(from Gilbert and Troitzsch, 2005)
Understanding
Experimentation: Can we gain new insights and
understanding of the world?
Test existing theories.
Prediction
If we can accurately replicate the dynamics of
behaviour – we can predict what will happen in
the future (?)
However, the further ahead we predict, the less
accurate we become.
Uses of Simulation (2)
Substitute:
If we can simulate the expertise of a doctor
(expert systems), does this remove the need for
the human expert?
Training
Creation of programs/environments to train
experts e.g. virtual car and flight simulators
Uses of Simulation (3)
Discovery and Formalisation
discover new processes and knowledge about
the phenomenon we are simulating through
experimentation
Formalise this into new theories
Retire rich and smug.
Uses of Simulation (4)
Entertainment:
MASSIVE (LoTR)
http://www.youtube.com/watch?v=ixJiHx7jGx8
(esp. 3:10, 3:55)
Social Simulation –
Some definitions
Social science is the study of society and
the relationships of individuals in a society.
Social simulation is the application of
computational methods to study the
processes/issues in social science.
Why is social simulation
important to Geographers?
Tackling Societal Challenges (1)
Ageing population:
Can the NHS cope with an increase of age
related conditions? Where are the likely stress
points going to be?
Energy:
What policy can encourage home-owners to
take up more green technologies?
Tackling Societal Challenges (2)
Economics:
Can we simulate the UK economy and thus
experiment with different financial policies?
Crisis:
In the event of a large-scale incident
(epidemics); how do we respond? Where do
we deploy resources?
Lecture 3
(Social) Simulation
A brief history
Uses of Simulation
Introduction to ABM
Seminar: GIS and GeoComputation
Aggregate vs
Individual Level
‘Traditional’ modelling
methods work at an
aggregate level, from
the top-down
Ci = α + βxi + βyi + βzi
E.g. Regression,
spatial interaction
modelling, locationallocation, etc.
Aggregate vs. individual-level
Aggregate models work very well in some situations
Homogeneous individuals
Interactions not important
Very large systems (e.g. pressure-volume gas
relationship)
But they miss some important things:
Low-level dynamics, i.e. “smoothing out” (Batty, 2005)
Interactions and emergence (full lectures on these later)
Unsuitable for modelling complex systems
Aggregate vs. individual-level
Systems are driven by individuals
(cars, people, ants, trees, whatever)
Not controlled by god
Bottom-up modelling
An alternative approach to modelling
Rather than controlling from the top,
try to represent the individuals
Account for system behaviour
directly
Picture by Wayan Vota
(http://www.flickr.com/photos/dcmetroblogger/)
Agent-Based Modelling (ABM)
Autonomous, interacting agents
Represent individuals or groups
Situated in a virtual environment
Example: SimCity
https://www.youtube.com/watch?
v=vS0qURl_JJY
Photo attributed to James Cridland
Example: The “Playstation
Mountain”
https://www.youtube.com/watc
h?v=_1YV2sNRK4I
http://www.youtube.com/watch?v=W5pNPJAhsBI
Questions
When watching the MASSIVE video, think
about:
What do the agents represent?
What behaviours have been implemented?
How many agents can they model?
How have the agents’ brains been represented?
Example: MASSIVE
http://www.youtube.com/watch?v=W5pNPJAhsBI
http://www.lordoftherings.net/effects/index.html
What is an agent? (I)
No universal definition
But most people agree that agents should
exhibit some of the following criteria
Autonomy
Act independently, free from central control
Control its own state and make independent
decisions
What is an agent? (II)
Heterogeneity
Agents should not normally be identical
Groups of similar agents are formed from the
ground-up (e.g. by agents interacting with each
other)
Reactivity
Agents can sense their environment and
respond to changes
Responses should be goal-directed
What is an agent? (III)
Bounded rationality
Agents should not have full knowledge of the
world (this would be very unrealistic)
Environmental perception can be limited
Choices will not be perfectly rational – they can
make mistakes
Interactive
Agents can communicate with each other
Could be dependent on environment (e.g.
distance)
What is an agent? (IV)
Mobile
Often agents will be able to navigate a space.
Learning / adaption
Agents should be able to adapt future
decisions, based on past experiences
Appeal of ABM (I)
Most ‘natural’ way of thinking about social
systems
Individual actions drive the system
Modelling emergence
“A phenomenon is emergent when it can only be described
and characterised using terms and measurements that are
inappropriate or impossible to apply to the component
units”
- Gilbert (2004) page 3.
Appeal of ABM (II)
Can include physical
space / social
processes
Designed at abstract level: easy to change scale
Appeal of ABM
(III)
Bridge between verbal
theories and
mathematical models
Precise/quantitative
description of theory
Dynamic history of
system
Disadvantages of ABM (I)
Known unknowns
We don’t know exactly what someone will do.
So we guess
E.g. There is a 30% change of attending this morning’s lecture,
and 70% chance of staying in bed
Models that use randomness
like this are probabilistic
The need to run many times to
ensure robust results
E.g. Wolf-Sheep model (results
were always different)
Disadvantages of ABM (II)
Computationally expensive.
Complicated agent decisions
Lots of decisions!
Multiple model runs (robustness)
Modelling “soft” human factors
Benefit that we can include complex psychology
But this is really hard!
Potential to over-complicate
Need to think carefully about what to include
A Third Way of Doing Science
Deduction
Induction
Third way:
“Like deduction, [simulation] starts with a set of explicit assumptions. But unlike
deduction, it does not prove theorems. Instead, a simulation generates data that can be
analyzed inductively. Unlike typical induction, however, the simulated data comes from a
rigorously specified set of rules rather than direct measurement of the real world”
- Axelrod (1997, p24).
Axelrod, R. (1997). Advancing the art of simulation in the social sciences. In Conte, R., Hegsel-mann, R., and Terna, P., editors, Simulating Social Phenomena, pages
21–40. Springer-Verlag, Berlin.
Diagrams from: http://www.socialresearchmethods.net/kb/dedind.php (that site also has a fantastic concise comparison of the two methods)
Applications
Urban Simulation
How people move around cities
Shopping centres, Art Galleries, evacuation
Crime Simulation
Spread of Disease
Spread of Early Humans from Africa
Full lecture on applications later ..
Lecture 3
(Social) Simulation
A brief history
Uses of Simulation
Introduction to ABM
Seminar: GIS and GeoComputation
Important:
Activity Next week
We’re going outside!
Wear warm cloths and
sensible shoes
Photo attributed to Tony Alter (CC-BY-2.0)
School of Geography
FACULTY OF ENVIRONMENT
Masters Degrees
MA Activism & Social Change
MA Social & Cultural Geography
MSc River Basin Dynamics & Management with GIS
MSc Geographical Information Systems (GIS)
MSc GIS via Online Distance Learning
MA/MSc by Research
www.geog.leeds.ac.uk/study/masters
PhD
www.geog.leeds.ac.uk/study/phd
Alumni Fee Bursary
You may be eligible for a 10% alumni tuition fee bursary
www.leeds.ac.uk/info/20021/postgraduate/1923/alumni_bursary
Seminar 1 – GIS and Geocomputation
Questions
Seminar: Compare and
contrast Geocomputation methods
with GIS.
Reading
Gilbert, Nigel and
Klaus G. Troitzsch
(2005) Simulation for
the Social Scientist.
Open University Press
Epstein, J.M., (2009)
Modelling to contain
pandemics. Nature
460, 687-687.
What models of systems have you already produced
in this course, and others?
Gilbert and Troitzsch say that, when creating a
model of a model of a target system, "we hope that
conclusions drawn about the model will also apply to
the target because the two are sufficiently similar" (p
15) . When you have created models in the past,
how have you verified that the two are sufficiently
similar?
The authors note that because social systems are
dynamic, models should be dynamic as well (p 15).
What do they mean by dynamic in this context? Are
you familiar with any dynamic models?
How do analytical methods differ to using simulation
as a means of understanding how a model develops
over time?
What do the authors mean by "explanatory" and
"predictive" models?
What are the stages of simulation-based research (p
18). How do these compare to the non-simulation
(e.g. GIS) research that you are accustomed with?
How is the 14th centuary principle of Occam's razor
relevant to the design of computer models today?
(Hint - see 'Designing a Model' on page 19).