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)

Questions & ContactDan Birks

Justice Modelling @ GriffithGriffith University

d.birks@griffith.edu.au