What are ABMs and why are they so popular?
What are they being used for?
Pro’s and con’s
Predictive and dynamic modelling
Work we are involved in
'Traditional' modelling methods work at an aggregate level, from the top-down
E.g. Regression, spatial interaction modelling, location-allocation, etc.
Aggregate models work very well in some situations
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
Unsuitable for modelling complex systems
Systems are driven by individuals
(cars, people, ants, trees, whatever)
An alternative approach to modelling
Rather than controlling from the top, try to represent the individuals
Account for system behaviour directly
Autonomous, interacting agents
Represent individuals or groups
Situated in a virtual environment
One of the main attractions for ABM
"The whole is greater than the sum of its parts." (Aristotle?)
Simple rules → complex outcomes
E.g. who plans the air-conditioning in termite mounds?
Possible to prove the with simple computer programs
Conways 'Game of Life'
Key message: Complex structures can emerge from simple rules
Emergence is hard to anticipate, and cannot be deduced from solely analysis of an individual's behaviour
Individual-level modelling is focused on understanding how macro-level patterns emerge from micro-level through the process of simulation.
Modelling complexity, non-linearity, emergence
Natural description of a system
Bridge between verbal theories and mathematical models
Produces a history of the evolution of the system
Tendency towards minimal behavioural complexity
Computationally expensive (not amenable to optimisation)
Complicated agent decisions, lots of decisions, multiple model runs
Modelling "soft" human factors
Need detailed, high-resolution, individual-level data
Create an urban (or other) environment in a computer model.
Stock it with buildings, roads, houses, etc.
Create individuals to represent offenders, victims, guardians.
Give them backgrounds and drivers.
See what happens.
Exploring theory ('explanatory' models)
Simulation as a virtual laboratory.
Linking theory with crime patterns to test it.
Making predictions ('predictive' models)
Forecasting the impacts of social / environmental change.
Exploring aspects of current data patterns.
Extremely complex system:
Attributes of the environment (e.g. individual houses, pubs, etc.).
Personal characteristics of the potential offender and/or victim.
Features of the local community.
Physical layout of the neighbourhood.
Potential offender’s knowledge of the environment.
Traditional approaches often work at large scales, struggle to predict local effects
But cannot capture non-linear, complex systems.
Environmental Criminology theories emphasise importance of
Individual behaviour (offenders, victims, guardians)
Individual geographical awareness
Lots of research points to importance of micro-level environment
Brantinghams' environmental backcloth
Crime at places research (e.g. Eck and Weisburd, 1995; Weisburd and Amram, 2014; Andresen et al., 2016)
Choose a number between 1 and 4 (inclusive)
Were you able to chose a number at random?
Or did most people choose the number 3?
Small, but growing, literature
(apologies to the many who are missing!)
Birks et al. 2012, 2013;
Groff 2007a,b; Hayslett-McCall, 2008
Liu et al. (2005)
Me! (Malleson et. al ...)
A special issue of the Journal of Experimental Criminology entitled "Simulated Experiments in Criminology and Criminal Justice" (Groff and Mazerolle, 2008b)
Explanatory: exploring theory
Randomly generated abstract environments
Navigation nodes (proxy for transport network)
Potential targets (houses)
One type of agent: potential offenders
Behaviour is controlled by theoretical 'switches'
Rational choice perspective (decision to offend)
Routine activity theory (how they move and encounter targets)
Geometric theory of crime (how they learn about their environment)
Validation against stylized facts:
Spatial crime concentration (Nearest Neighbour Index)
Repeat victimisation (Gini coefficient)
Journey to crime curve (journey to crime curve)
All theories increase accuracy of the model
Rational choice had a lower influence than the others
Simple / abstract model:
Not directly applicable to practice
But simplicity allows authors to concentrate on theoretical mechanisms
Realistic backcloth might over-complicate model (Elffers and van Baal, 2008)
Predictive: exploring the real world
ABM to explore the impacts of real-world policies
Urban regeneration in Leeds
We need to better understand urban flows:
Crime – how many possible victims?
Pollution – who is being exposed? Where are the hotspots?
Economy – can we attract more people to our city centre?
Health - can we encourage more active travel?
Abundance of data about individuals and their environment
"Big data revolution" (Mayer-Schonberger and Cukier, 2013)
"Data deluge" (Kitchin, 2013a)
cities that "are increasingly composed of and monitored by pervasive and ubiquitous computing" (Kitchin, 2013a)
Large and growing literature
Creating rule sets
Representing behaviour accurately
Calibrating and validating
Getting the right amount of detail
New project: Data Assimilation for Agent-Based Models (dust)
5-year research project (€1.5M)
Funded by the European Research Council (Starting Grant)
Started in January
Main aim: create new methods for dynamically assimilating data into agent-based models.
Thoughts / comments
How would you model these behaviours?