An adaptation of the URBAN1 Model from Navid Ghaffarzadegan, John Lyneis and George P Richardson's How small system dynamics models can help the public policy process. System Dynamics Review 27: 22-44 (2011) Researchgate link  and eolss synopsis based on LA Alfeld and AK Graham's Introduction to Urban Dynamics 1976. Also p 195 (Dynamo Model Listing).

An element of Perspectives: The Foundation of Understanding and Insights for Effective Action. Register at http://www.systemswiki.org/

Description:
This model focuses on the youth population of the community of Bourke in Australia. It shows the crime development of the youth population depending on the relationships of the expenditure provided by the police and community, crime rate and release rate of Bourke, and the rate of atonement failure.

​Initial Assumptions:

Youth in community: 1000
Mischievous youths: 200
Youth involved in crime: 140
Youth in jail: 80

How the Model Works:

1. Youth in Community
The model starts from being a youth in the community with no real motive which is affected by the community development expenditure. There are two paths that the youth can take, becoming bored of the community and staying as a normal youth with no motive in the community. There is a 70% chance of the youth becoming bored of the community and becoming mischievous as the community did not satisfy the youths' boredom.

2. Mischievous Youth
After transitioning to a normal youth to a mischievous youth, there are two paths that the mischievous youth can take. The non criminal activity path which is when the mischievous youth is not bored from the community anymore and proceeds back into the community, this path is affected by the community development expenditure. The second path is becoming involved with criminal activity and thus becoming a criminal, which is affected by the 40% crime rate of Bourke.

3. Criminal
After becoming a criminal youth, there are two paths that the youth can go down. The first path involves not being caught by the police yet and are still wondering around in the community at a 20% chance. This path is affected by the atonement failure variable as the criminal youth, despite going back into the community, is still willing to be involved in crime. The second path is getting caught by the police and is jailed at an 80% chance. This path is affected by the police expenditure used to catch the criminal youth.

4. Jailed
After being jailed by the police, the criminal youth can be released back into the community after their sentence has been completed, they can either still be involved in crime or a mischievous youth. The first path is failing to atone for their crimes even after spending a period of time in jail, with a 20% chance of happening. This path is affected by the atonement failure variable. The second path is being released back in the community at a 90% chance as a mischievous youth as they have learned their lesson in jail and will cease any criminal activities for the time being. After a period of time the released mischievous youth can take two paths, being involved with criminal activity again at a 40% chance or atoning for their crimes and becoming a non criminal youth with no ill-intentions at a 70% chance.

Simulation:

Relationship of Number of Criminal Youths and the Number Non-Criminal Youths:
This time series compares the number of criminal youths, mischievous youths, and non criminal youths for each following half year for 10 years in Bourke.

Number of Jailed Criminal Youths and Criminal Youths who have yet to be caught:
This time series compares the number of jailed criminal youths and criminal youths who have yet to be caught for each following half year for 10 years in Bourke.

Youths who have Failed to Atone and who have Atoned:
This time series compares the number of youths who have failed to atone after being released from jail and the number of youths who have been released from jail and have atoned Youths for each following half year for 10 years in Bourke.

​THE SIMULATION OF COMMUNITY AND POLICE EFFECT ON YOUNG PEOPLE LIFESTYLE AT BOURKE TOWN – FOCUSING ON PEOPLE WITH NEGATIVE LIFESTYLE TO REDUCE THE CRIME CRATE

​Youth Crime rate in Bourke

Online Community to grow the use and effectiveness of Health Modelling using multiple methods

A collaborative class project with each participant creating an animal/plant sub-model​ to explore the greater population/community dynamics of the Yellowstone ecosystem.

Assumptions 

A collaborative class project with each participant creating an animal/plant sub-model​ to explore the greater population/community dynamics of the Yellowstone ecosystem. Changes to original: removal of non-connected variables to simplify view for middle school students. 

HOW A NEW COMMUNITY ENGAGEMENT INITATIVE MAY IMPACT YOUTH CRIME IN THE TOWN OF BOURKE, NSW

MKT563 Assessment 4:  Kari Steele 

 

Aim of Simulation: 

Bourke is a town in which Youth are involved in high rates of criminal behaviour (Thompson, 2016).  This simulation focuses on how implementation of a community engagement initiative may impact crime patterns of youths in Bourke.   The specific aim is to assess whether the town should initiate a program such as the Big Brothers Big Sisters Community-Based Mentoring (CBM) (Blueprints for Healthy Youth Development, 2018) program to reduce crime and antisocial behaviour (National Institute of Justice, n.d).  Big Brothers Big Sisters is a community mentoring program which matches a volunteer adult mentor to an at-risk child or adolescent to delay or reduce antisocial behaviours; improve academic success, attitudes and behaviours, peer and family relationships; strength self-concept; and provide social and cultural enrichment (Blueprints for Healthy Youth Development, 2018). 

 

Model Explanation:

An InsightMaker model is used to simulate the influence of Big Brothers Big Sisters Initiative on Criminal Behaviour (leading to 60% juvenile detention rates) with variables including participation rate and also drug and alcohol use.

Assumptions:

1/ ‘Youth’ are defined, for statistical purposes, as those persons between the ages of 15 and 24 (United Nations Department of Economic and Social Affairs, n.d).

2/ Youth population (15 – 24 years) makes up 14.1% of the total population of LGA Bourke which according to the most up-to-date freely available Census data (2008) is 3091 (Australian Bureau of Statistics, 2010).  Therefore, youth population has been calculated as 435 individuals.

3/ Big Brothers Big Sisters Program is assumed to impact LGA Bourke in a similar manner that has been shown in previous studies (Tierney, Grossman, and Resch, 2000) where initiative showed mentored youths in the program were 46% significantly less likely to initiate drug use and 27 percent less likely to initiate alcohol use, compared to control.  They were 32 less likely to have struct someone during the previous 12 months.  Compared to control group, the mentored youths earned higher grades, skipped fewer classes and fewer days of school and felt more competent about doing their schoolwork (non-significant).  Research also found that mentored youths, compared with control counterparts, displayed significantly better relationships with parents.  Emotional support among peers was higher than controls. 

Initial Values:

Youth Population = 435

Criminal Behaviour = 100

40% of youth population who commit a crime are non-convicted

60% of youth population who commit a crime are convicted

20% of youth involved in the Big Brothers Big Sisters Initiative are non-engaged

80% of youth involved in the Big Brothers Big Sisters Initiative are engaged

Variables:

The variables include ‘Participation Rate’ and ‘Drug and Alcohol Usage’.  These variables can be adjusted as these levels may be able to be impacted by other initiatives which the community can assess for introduction; these variables may also change in terms of rate over time.

Interesting Parameters

As can be seen by increasing the rate of participation to 90% we can see juvenile detention rate decreases with engagement (even with the 20% non-engagement of youths involved in program).  By moving the slider to 10% participation however you can see the criminal behaviour increase.   

Conclusion:

From the simulation, we can clearly see that the community of Bourke would benefit in terms of the Big Brothers Big Sisters Initiative decreasing criminal behaviour in youths (15 – 24 years of age) over a 5-year timeframe.  Further investigation regarding expenditure and logistics to implement such a program is warranted based on the simulation of impact.

 

References:

Australian Bureau of Statistics.  (2010).  Census Data for Bourke LGA.  Retrieved from www.abs.gov.au/AUSSTATS/abs@.nsf/Previousproducts/LGA11150Population/People12002-2006?opendocument&tabname=Summary&prodno=LGA11150&issue=2002-2006

 

Blueprints for Healthy Youth Development.  (2018).  Big Brothers Big Sisters of America Blueprints Program Rating: Promising, viewed 26 May 2018, <www.blueprintsprograms.com/evaluation-abstract/big-brothers-big-sisters-of-america>

 

National Institute of Justice.  (n.d.).  Program Profile: Big Brothers Big Sisters (BBBS) Community-Based Mentoring (CBM) Program, viewed 26th May 2018, <https://www.crimesolutions.gov/ProgramDetails.aspx?ID=112>

 

Tierney, J.P., Grossman, J.B., and Resch, N.L. (2000). Making a Difference: An Impact Study of Big Brothers/Big Sisters. Philadelphia, Pa.: Public/Private Ventures.
http://ppv.issuelab.org/resource/making_a_difference_an_impact_study_of_big_brothersbig_sisters_re_issue_of_1995_study

 

Thompson, G. (2016) Backing Bourke: How a radical new approach is saving young people from a life of crime.  Retrieved from < www.abc.net.au/news/2016-09-19/four-corners-bourkes-experiment-in-justice-reinvestment/7855114>

 

United Nations Department of Economic and Social Affairs (UNDESA).  (n.d.).  Definition of Youth, viewed 24th May 2018, www.un.org/esa/socdev/documents/youth/fact-sheets/youth-definition.pdf

​

​The Model

This model presents correlation between young people who finished University and young people who dropped University. Also, this model shows relationships among youth alienation, crime rate, police expenses, dysfunctional family and percentage of youth sent to jail and youth who were justified, re-educated and found job through employment exchange.

The Cred System is an alternative to traditional currency that increases community resiliency and reduces participant's dependence on traditional dollars. This model is a basic description of the Cred System, involving four people and two loops.

This insight illustrates the relationship between the funding of policing and community development initiatives, to reduce crime rates in Bourke, NSW.

Assumptions 

This model is an advanced form of the community growth model. It has three stages and different transitions between the stages. I hope that it behaves more flexible than the Community Growth Model

Assignment 3 MGMT220
**Scroll down for adjustable sliders**

This model is designed as a simplified field of inputs and outputs for the proposed future justice reinvestment in the north-western NSW town of Bourke. This town is quite small with a total population of around 3,000 people but a worryingly high rate of criminal  activity, antisocial behaviour and a generally low sense of community engagement. To plan for a better future this model has been created to map future patterns and changes given certain levels of community investment and policing which can me modified by users, including you!

Key Assumptions & Things to Note:
-Model interactions and consequences only focused on the effects of youth not adults.
-Total youth population assumed to be 1,500 out of the total 3,000 people in Bourke
-Model moves in monthly increments
-Model duration is 5 years (60 Months) as this seems like a realistic time frame for such a project plan to span over
-Engagement return modification allows between 0 and 6 months return to allow insight into the positive effects a shorter engagement time can have on the community
-Police Investment allows adjustment of police force units between 15 and 50
-Community Investment allows an investment of between 0 and 100 to provide a full spectrum of the town with or without investment

Model Prerequisite Understandings:
The model commences with 400 people engaging in criminal activity, and a further 300 people already in juvenile detention to provide a more realistic start point.

Model Analysis:
The most important message this model shows is that there is no one sided solution for everything. Without community investment, regardless of how many police you have the town is still going to be full of bored people committing crimes - just more will be caught and convicted.

On the flip side a town with no police and only community investment may have a low rate of people in juvenile detention and a high number of people in sports teams - but criminal activity may still be higher than optimal due to a low chance of getting caught.

You can see these results for yourselves simply by adjusting the variable sliders on the bottom right of the page to suit your investment interests. Relevant boundaries have been set to give only useful and meaningful information. Furthermore an engagement return tool has been added to show the effects of a slow or fast engagement pickup time ranging from 0 to 6 months. You will note that things change a lot quicker with a shorter engagement return time.

An interesting thing to note is how evenly 3 of the 4 key data fields in the first simulation display (with the outlier being sports team enrolment) when police investment is set to maximum and community investment is set to the minimum - we see essentially an even split between the 3 possibilities: In town, In Juvenile Detention or engaging in Criminal Activity. a 2:1 split of "bad" to "good" things happening. This shows with certainty that just adding policing with no positive reward or outlet for good behaviour results in a flattened cycle of boredom, criminal activity and conviction.

In this model it also seems that Bourke does require a fairly even but high matching of Police and Community Investment. For example setting the policing at 20 and the community engagement higher at say 50 results in indeed a high intake and output of town to sports team memberships however crime rates do still maintain a steady high dictating a more even match between policing and community investment like 40 and 60 to the former and latter to "eradicate" crime. (Of course this will never be 0 in the real world but it is a positive indicator here)

This model illustrates the simulation of a range of variables which have an effect within the country town of Bourke. Bourke is known for its high crime right from youths to adults. Through adjustments made by the user, this model will show the result of such variations. 

Assessment 4.

This model highlights the interdependent relationship between:

A base level model to outline the impact of a mesh network on a community