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Synthesis over Analysis: Using Multi-Agent Simulations to Examine the Interactions of Crime Dan Birks Justice Griffith Griffith University.

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Presentation on theme: "Synthesis over Analysis: Using Multi-Agent Simulations to Examine the Interactions of Crime Dan Birks Justice Griffith Griffith University."— Presentation transcript:

1 Synthesis over Analysis: Using Multi-Agent Simulations to Examine the Interactions of Crime Dan Birks Justice Griffith Griffith University Justice Modelling Workshop – July 2008

2 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

3 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.

4  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?

5 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

6 TheoryFormalism Agent behaviourAgent behaviour Environment dataEnvironment dataModel Compare with real data Test Refine Simulation Methodology Theory – formalism – test - refine

7 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. Victimisation / (x,y,t) Suitably Motivated Offender Suitable Target Absence / Presence of Capable Guardian

8 Prevention/Detection Occurs if(is_present(x,y,t,offender) & is_present(x,y,t,target) & is_present(x,y,t,guardian) Prevention/Detection Occurs if(is_present(x,y,t,offender) & is_present(x,y,t,target) & is_present(x,y,t,guardian) Crime occurs if(is_present(x,y,t,offender) & is_present(x,y,t,target) & not(is_present(x,y,t,guardian)) Crime occurs if(is_present(x,y,t,offender) & is_present(x,y,t,target) & not(is_present(x,y,t,guardian)) Cops & Robbers From 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.

9 Cops & Robbers – The Simulation implementation in NetLogo Person (Potential Target) Cop (Capable Guardian) Robber (Motivated Offender)

10 Multi Agent Testbed for Volume Crime Activity

11 Routine Activity Theory (Felson 1979) Crime(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 Areas Activity Space WorkRecreationHome 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)

12 Multi-Agent Testbed & Theories of Crime Aim: Victim - Offender - Location  To develop a multi-agent test-bed which enables the examination of Victim - 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 Areas Activity Space WorkRecreationHome

13 Multi-Agent Testbed for Volume Crime Activity Combining 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.

14 Crime occurs when Perception of Opportunity > threshold(propensity,readiness) Where Perception of opportunity = Awareness_of_opportunity * Target Density * capability_to_exploit(Opportunity(Target))AND Offender_percieved_reward(Opportunity) > Percieved_Risk(Opportunity) * Effort(Opportunity)

15 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

16  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

17 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).

18 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

19 An Example Environment Residential Property Commercial Property

20 BUILDING OCCUPANCY – T1 BUILDING OCCUPANCY – T2 RESIDENTIAL HOUSING DENSITY How might we formalise the environment TRANSPORT NETWORK

21 An Example Environment

22  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

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24 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.

25 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

26 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)

27 Questions & Contact Dan Birks Justice Griffith Griffith University


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