s ocial s imulation and a gent -b ased m odelling dr nick malleson dr alison heppenstall geog3150...
TRANSCRIPT
SOCIAL SIMULATION AND
AGENT-BASED MODELLING
Dr Nick Malleson
Dr Alison Heppenstall
GEOG3150 Semseter 2
Lecture 3
Recap: Last Week
Last week; first forays into the wonderful world of programming.
Introduction to Netlogo
PracticalHow 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 thinkingProblem 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 ReadingsPapers – 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 agent-
based?, 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/an-excellent-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 resourcesProf. Bruce Edmonds is one of the big names in agent-
based 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 (socio-economic) 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 know-how
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)
UnderstandingExperimentation: Can we gain new insights and understanding of the world?
Test existing theories.
PredictionIf 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?
TrainingCreation of programs/environments to train experts e.g. virtual car and flight simulators
Uses of Simulation (3)
Discovery and Formalisationdiscover 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
E.g. Regression, spatial interaction modelling, location-allocation, etc.
Aggregate vs. individual-level
Aggregate models work very well in some situationsHomogeneous 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 modellingAn 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: SimCityhttps://www.youtube.com/watch?v=vS0qURl_JJY
Photo attributed to James Cridland
Example: The “Playstation Mountain”
https://www.youtube.com/watch?v=_1YV2sNRK4I
Questions
What do the agents represent?
What behaviours have been implemented?
How many agents can they model?
How have the agents’ brains been represented?
When watching the MASSIVE video, think about:
http://www.youtube.com/watch?v=W5pNPJAhsBI
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
AutonomyAct independently, free from central control
Control its own state and make independent decisions
What is an agent? (II)
HeterogeneityAgents should not normally be identical
Groups of similar agents are formed from the ground-up (e.g. by agents interacting with each other)
ReactivityAgents can sense their environment and respond to changes
Responses should be goal-directed
What is an agent? (III)
Bounded rationalityAgents 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
InteractiveAgents can communicate with each other
Could be dependent on environment (e.g. distance)
What is an agent? (IV)
MobileOften agents will be able to navigate a space.
Learning / adaptionAgents 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)
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)
Known unknowns
We don’t know exactly what someone will do.
So we guessE.g. There is a 30% change of attending this morning’s lecture, and 70% chance of staying in bed
Disadvantages of ABM (II)
Computationally expensive.Complicated agent decisions
Lots of decisions!
Multiple model runs (robustness)
Modelling “soft” human factorsBenefit that we can include complex psychology
But this is really hard!
Potential to over-complicateNeed to think carefully about what to include
A Third Way of Doing Science
Deduction Induction
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)
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).
Applications
Urban SimulationHow 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)
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 bursarywww.leeds.ac.uk/info/20021/postgraduate/1923/alumni_bursary
School of GeographyFACULTY OF ENVIRONMENT
Seminar 1 – GIS and Geocomputation
Seminar: Compare and contrast Geo-computation 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.
QuestionsWhat 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).