synthesis over analysis: using multi-agent simulations to examine the interactions of crime dan...
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Synthesis over Analysis: Synthesis over Analysis: Using Multi-Agent Simulations to Using Multi-Agent Simulations to
Examine the Interactions of CrimeExamine the Interactions of Crime
Dan BirksJustice Modelling @ Griffith
Griffith University
Justice Modelling Workshop – July 2008
Overview What are multi-agent simulations?
Multi-agent simulations & crime analysis
A simple example – Cops & Robbers
Advanced example - MAS of Volume
Crime
Some initial results
Potential Applications
What are Agent-based Simulations? Aim to model complex systems.
Offer a “bottom-up” approach which concentrates on the study & replication of micro-level interactions which produce the macro level outputs we observe.
“Thought experiments” or “Intuition pumps” allow us to examine the ramifications of our theoretical assumptions.
Allow us to attempt to bridge the gap between theory and observed phenomena.
Definition from artificial intelligence: Autonomous program situated within some simulation environment.
Agents perceive, reason and act
In order to do so agents have: Internal representations
(memory or state) Method for modifying internal
representations (perceptions) Methods for modifying environment
(behaviours)
What is an Agent?
Agent-based Modelling & Crime Analysis
Large proportion of conventional crime analysis is “top-down” involving examination of crime or crime patterns at the macro level.
Large proportion of theory is positioned at the micro level.
A gap exists between observed macro level crime patterns and the micro level mechanisms theories hypothesise about.
ABM: tool to test the ramifications of theoretical assumptions by creating a population of virtual offenders, guardians and targets and bestowing upon them behaviours defined by our theories.
We can then examine the emergent properties of our simulation and evaluate whether our theories are causally sufficient to explain the macro level phenomena observed in the real world
TesTestt
RefineRefine
Simulation MethodologySimulation MethodologyTheory – formalism – test - refineTheory – formalism – test - refine
Simulation MethodologySimulation MethodologyTheory – formalism – test - refineTheory – formalism – test - refine
A simple example: victimisation & detection
Cops & Robbers Imagine we want to examine the
following theories of victimisation & detection:
– A victimisation occurs when an offender comes into the same location as a potential target in the absence of a capable guardian.
– A detection/prevention occurs when an offender, potential target and guardian all come together at the same point in space and time.
Prevention/Detection OccursPrevention/Detection Occursif(is_present(x,y,t,offender) & if(is_present(x,y,t,offender) & is_present(x,y,t,target) & is_present(x,y,t,target) & is_present(x,y,t,guardian) is_present(x,y,t,guardian)
Crime occursCrime occursif(is_present(x,y,t,offender) & if(is_present(x,y,t,offender) & is_present(x,y,t,target) & is_present(x,y,t,target) & not(is_present(x,y,t,guardian)) not(is_present(x,y,t,guardian))
Cops & RobbersFrom thought experiment to simulation
A crime occurs when an offender comes into the same location as a potential target in the absence of a capable guardian.
A detection/prevention occurs when an offender, potential target and guardian all come together at the same point in space and time.
Cops & Robbers – The Simulationimplementation in NetLogo
Person(Potential Target)
Cop(Capable Guardian)
Robber(Motivated Offender)
Multi Agent Testbed for Volume Crime Multi Agent Testbed for Volume Crime ActivityActivity
Routine Activity Theory
(Felson 1979)
CrimeCrime(space,time)(space,time)
Absence of Capable Guardian
Motivated Offender
Suitable
Target
Rational Choice Theory(Clarke and Cornish 1985)
“sees criminal behaviour not as a result of psychologically and socially determined dispositions to offend, but as the outcome of the offender’s broadly rational choices and decisions”
Some background theory…
Target AreasTarget Areas
Activity SpaceActivity Space
WorkWork
RecreationRecreation
HomeHome
• Offender search patterns and personal activity space
• Home to work to recreation – nodes and paths, and mental maps
• Looking for opportunities (which are non-uniformly distributed)
• Templates or schemas for successful offending developed
• Crimes in areas where offenders activity spaces overlap with target areas
Crime Pattern Theory(Brantingham and
Brantingham 1993)
Multi-Agent Testbed & Theories of Crime
Aim: To develop a multi-agent test-bed
which enables the examination of Victim - Offender - LocationVictim - Offender - Location interactions
Offender Data– Offender RAT Nodes, Propensity,
etc.– Offender awareness space – Offender Behaviour
(bounded rationality)• Schemas for offending by type
Geographical ‘back cloth’ data– Simulate location & environment
• Geo-demographic Data• Transport network
Investigate “Victim – Offender - Location” interactions
Target AreasTarget Areas
Activity SpaceActivity Space
WorkWork
RecreationRecreation
HomeHome
Multi-Agent Testbed for Volume Crime ActivityCombining the three “Opportunity” theories we might say that
When an individual of a certain criminal disposition, going
about his/her routine activities, comes into the location of a
suitable target, which provides an opportunity for gain and
which he/she is aware of and capable of exploiting, and when
he/she perceives the reward to be sufficient to expend the
effort required and endure the risks involved, he/she will
commit the offence at the current point in space and time.
Crime occurs when Crime occurs when
Perception of Opportunity > threshold(propensity,readiness)
Where Perception of opportunity =
Awareness_of_opportunity
*
Target Density * capability_to_exploit(Opportunity(Target))
ANDAND
Offender_percieved_reward(Opportunity) >
Percieved_Risk(Opportunity) * Effort(Opportunity)
Offender Agent Specification Characteristics
Propensity / Lambda (Dynamic) Readiness / Current desire (Dynamic) Awareness Space (Dynamic)
Agent Behaviours Navigation
Agents dynamically navigate the street network building up awareness space. Choice of route based on simple heuristics / greedy algorithms Identify the shortest/most direct routes Awareness of route
The choice to offend Awareness of location and opportunities Perception of Opportunity/Risk/Reward
Static targets
Relatively good geographic data
Matches well with geodemographic data– Acorn (A classification of residential
neighbourhoods)– IMD (Index of multiple deprivation)
Existing observable & potentially replicable phenomena such as Repeat Victimisation and Near Repeats
Good existing metrics which allow quantitative comparison of simulation & real-world output, i.e. Knox / Mantel
An example offending schema: Domestic Burglary
Offending schema: Domestic Burglary
Crime-specific prerequisites: A domestic property
Crime-specific risk factors: Occupancy of property; presence of deterrent
measures (alarms etc); pedestrian footfall in the vicinity.
Crime-specific reward factors: Affluence of area; likelihood of property containing
„CRAVED‟ goods.
Crime-specific effort: Security of property in question (e.g., door locks);
layout of property (e.g., back alleys).
Simulation Environment Formalism Sufficiently detailed to allow behaviours to
draw upon enough data to encapsulate theories from environmental criminology
Geo-descriptive data Housing Density Transport network nodes, paths Road Capacity
Geo-demographic Data Pedestrian Footfall Deprivation Indicators Household makeup
An Example Environment
Residential Property
Commercial Property
BUILDING OCCUPANCY – T1BUILDING OCCUPANCY – T1
BUILDING OCCUPANCY – T2
BUILDING OCCUPANCY – T2 RESIDENTIAL HOUSING DENSITY
RESIDENTIAL HOUSING DENSITY
How might we formalise the environment
TRANSPORT NETWORKTRANSPORT NETWORK
An Example Environment
Real Environmental Data Road Network Residential Address Points
Hypothetical Offender Data X number of agents created Agents randomly allocated 4-6 routine
activity nodes Agents randomly allocated journeys
(e.g. home work; work leisure) Agents randomly allocated propensity rates
Simulation Demo Initial Conditions
Initial results: Emergent properties
Remember our aim: Simulate the interactions of victim-offender-location - if our simulation of behaviour is accurate, then realistic crime patterns should emerge.
Initial results show the presence of Repeat and Near-repeat victimisation. Simulation crimes produce similar profiles to that of actual residential burglary data.
Potential Applications Academic Applications
Examine, test & refine current criminological theory
Practitioner Focused Applications Intervention prototyping Evolving optimal deployment of resources Offender routine activity profiling
Educational Applications Visualisation of theory
Relevant Publications:Birks, D.J., Donkin, S., Wellsmith, M. (2007) Synthesis over Analysis: Towards an ontology for volume crime simulation. In John Eck & Lin Liu (Eds.), Crime Analysis Systems: Using Computer Simulations and Geographic Systems. Idea Group PLC
Groff,L., Birks, D.J., (2008) Simulating Crime Prevention Strategies: A Look at the Possibilities. Policing - A journal of Policy and Practice
Townsley,M., Birks, D.J., (In Press) Building Better Crime Simulations: Systematic replication and the introduction of incremental complexity. Journal of Experimental Criminology “Simulated Experiments in Criminology and Criminal Justice”
Birks, D.J., Eck, J., (Forthcoming) Neighbourhood Differences in Crime May Be the Result of Individual Connectivity (not all that other stuff you were taught)