This simulation makes the negative effects of starting work too soon visible. You can play around with the parameters.    Find the full story behind this simulation  here .      If you have questions or feedback get in touch via  @swilluda
This simulation makes the negative effects of starting work too soon visible. You can play around with the parameters.

Find the full story behind this simulation here

If you have questions or feedback get in touch via @swilluda
 From Stephen Toulmin's Book The Uses of Argument Cambridge University Press 2003. See  wikipedia   Also Francis Miller Claim Hexagon  2025 web article

From Stephen Toulmin's Book The Uses of Argument Cambridge University Press 2003. See wikipedia  Also Francis Miller Claim Hexagon 2025 web article

  Model Explanation       

 The model to be simulate the possible
crime patterns among the youth population of Bourke, where levels of
alienation, policing and community engagement expenditure can be manipulated. Here
the youth in Bourke have a minimum percentage of the interested participated
on t

Model Explanation


The model to be simulate the possible crime patterns among the youth population of Bourke, where levels of alienation, policing and community engagement expenditure can be manipulated. Here the youth in Bourke have a minimum percentage of the interested participated on the community activities which government aims to improve their lifestyle and therefore they can specified on the reduce the rate of criminal activity. 

Assumption:

The assumption of the 2530 youth of the Bourke n the population susceptible to committing crime and simulations of criminal tendencies are only based on the factor presented, no external influences

Variable:

Alienation includes any factors that can increase the like hood of youth to commit crime such as exposure to domestic violence, household income, education level, and family background community engagement expenditure is the total monies budgeted into community activities to develop youths in and out of growth detention policing is the amount of police placed onto patrol in the town of Bourke to reinforce safety and that the law is abided.

Stocks: 

conviction rate is set to 60% A growth detention sentence for convicted criminals is set to 3 months the top 30% of the most server offenders are sent to rehabilitation for 3 months, to which they return to Bourke assuming in a better state and less likely to repeat a petty crime community activities are set to last 3 months to be calculating the align with the seasons: sporting club of the growth of community participants have 20% change of being disengaged as it may not align with their interests investments into policing are felt immediately & community engagement expenditure has a delay of 3 months. 

Finding the interest:

1. Alienation of the set maximum value is 0.2, policing and community engagement set to minimum shows a simulation where by all criminals are in town rather than being expedited and placed into growth detention even after a base value on the 500 youth placed into growth detention- this shouts that budget is required to control the overwhelming number of criminal youth as they overrun brouke.

2.  Set of community activity they can identified the 0.01 policing to max & alienation to max. The lack of social crime has caused much trouble among young people. The Police Immigration Police has not been deployed to the city of town, which has such a crime rate. Growth prevention can only last a long time, and all young people cannot be rehabilitated, so if they continue to commit crimes.

3. It plays an important role in considering the crime of young people. In order to keep the criminal activity minimal, the bulk of the budgets in police and social involvement among young people must be put at risk. Realistically, budget in a small town is an important factor, it may be engagement. 

4. To be set the police value 0.2, and engaged alienation expenditure value 0.04 of the community activities that can use of improve the youth in town of Bourke





 

 We start with an SEIR social virality model and adapt it to model social media adoption of Playcast Hosts.  *Note that this model does not attempt to model WOM emergent virality.  

We start with an SEIR social virality model and adapt it to model social media adoption of Playcast Hosts.  *Note that this model does not attempt to model WOM emergent virality.  

20 7 months ago
 ==edited by Prasiantoro Tusono and Rio Swarawan Putra==     Spring, 2020: in the midst of on-line courses, due to the pandemic of Covid-19.      With the onset of the Covid-19 coronavirus crisis, we focus on SIRD models, which might realistically model the course of the disease.     We start with a
==edited by Prasiantoro Tusono and Rio Swarawan Putra==

Spring, 2020: in the midst of on-line courses, due to the pandemic of Covid-19.

With the onset of the Covid-19 coronavirus crisis, we focus on SIRD models, which might realistically model the course of the disease.

We start with an SIR model, such as that featured in the MAA model featured in

Without mortality, with time measured in days, with infection rate 1/2, recovery rate 1/3, and initial infectious population I_0=1.27x10-4, we reproduce their figure

With a death rate of .005 (one two-hundredth of the infected per day), an infectivity rate of 0.5, and a recovery rate of .145 or so (takes about a week to recover), we get some pretty significant losses -- about 3.2% of the total population.

Resources:
 Assignment 3: Bourke Crime and Community Development​     This complex systems model depicts the impact of factors such as violence and community programs on the youth of Bourke. The time scale is in months and shows the next 6 years. The model aims to show how by altering expenditure in different
Assignment 3: Bourke Crime and Community Development​

This complex systems model depicts the impact of factors such as violence and community programs on the youth of Bourke. The time scale is in months and shows the next 6 years. The model aims to show how by altering expenditure in different areas, the town of Bourke can decrease crime and increase their population involvement in community programs. This model is intended to be dynamic to allow the user to change certain variables to see changes in impact

The town of Bourke has a population of 3634 people, 903 of which are classified as youth (being 0-24 inclusive) (ABS, 2016 census).
This population starts with all youths in three differing stocks:
- 703 in Youth
- 100 in Juvenile Detention
- 100 in Rehabilitation


Assumptions:
This model makes many assumptions that would not necessarily uphold in reality.

- Only the youth of the town are committing crimes.
- All convicted youths spend 6 months in juvenile detention.
- All convicted youths must go to rehabilitation after juvenile detention and spend 2 months there.
- The risk rate impacts upon every youth committing a crime and is a  broad term covering effects such as abuse.
- No gaol effect, youths do not return to town with a tendency to re- commit a crime.
- No further external factors than those given.
- There cannot be zero expenditure in any of the fields.


The stocks:
Each stock depicts a different action or place that an individual youth may find themselves in. 
These stocks include:
- Youth (the youths living in Bourke, where youths are if they are not committing crimes or in community programs)
- Petty Crime (crimes committed by the youths of Bourke such as stealing)
- Juvenile Detention (where convicted youths go)
- Rehabilitation
- Community Programs


The variables:
- Community Expenditure (parameter 0.1-0.4)
- Law Enforcement Expenditure (parameter 0.1-0.6)
- Rehabilitation Expenditure (parameter 0.1-0.4)
- Risk Rate (not adjustable but alters with Law Enforcement Expenditure)

Sliders on each of the expenditure variables have been provided. These variables indicate the percentage of the criminal minimising budget for Bourke.
Note that to be realistic, one should make the three differing sliders be equal to 1, in order to show 100% of expenditure

Base Parameter Settings:
- Law Enforcement Expenditure = 0.5
- Community Expenditure = 0.25
- Rehabilitation Expenditure = 0.25

Interesting Parameter Settings:
- When Law Enforcement is at 0.45 and Community and Rehabilitation at 0.3 and 0.25 (in either order) then convicted and not-convicted values are the same. If Law Enforcement expenditure goes any lower then the number of convicted youths is less than those not-convicted and vice versa if the expenditure is increased.
- When Law Enforcement is at 0.2 and Community and Rehabilitation at 0.4 each then the increase in community programs and decrease in crime and thus detention occurs in a shorter and more rapid time frame. This shows that crime can be minimised in this model almost entirely through community initiatives.
- Alternatively, when Law Enforcement is at 0.6 and Community and Rehabilitation at 0.2 each then the increase in community programs and decrease in crime occurs over a longer time period with more incremental change.



Population Source:

 This map is a WIP derived from the MIT D-memo 4641 presentation by Nelson Repenning 1996 and the paper "Nobody Ever Gets Credit for Fixing Problems that Never Happened: Creating and Sustaining Process Improvement" by Nelson P. Repenning and John D Sterman.  http://bit.ly/jCXGKL  See  Insight 9781  

This map is a WIP derived from the MIT D-memo 4641 presentation by Nelson Repenning 1996 and the paper "Nobody Ever Gets Credit for Fixing Problems that Never Happened: Creating and Sustaining Process Improvement" by Nelson P. Repenning and John D Sterman. http://bit.ly/jCXGKL See Insight 9781 for a simulation of this model. This map adds additional features mentioned in the article to the bare bones simulation in IM-9781

5 10 months ago
Assignment 3 MGMT220 **Scroll down for adjustable sliders**    What is this model?   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
Assignment 3 MGMT220
**Scroll down for adjustable sliders**

What is this model?
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 version 8B of the   CAPABILITY DEMONSTRATION   model. A net Benefit ROI has been added. The Compare results feature allows comparison of alternative intervention portfolios.  Note that the net causal interactions have been effectively captured in a very scoped and/or simplified forma
This version 8B of the CAPABILITY DEMONSTRATION model. A net Benefit ROI has been added. The Compare results feature allows comparison of alternative intervention portfolios.  Note that the net causal interactions have been effectively captured in a very scoped and/or simplified format.  Relative magnitudes and durations of impact remain in need of further data & adjustment (calibration). In the interests of maintaining steady progress and respecting budget & time constraints, significant simplifying assumptions have been made: assumptions that mitigate both completeness & accuracy of the outputs.  This model meets the criteria for a Capability demonstration model, but should not be taken as complete or realistic in terms of specific magnitudes of effect or sufficient build out of causal dynamics.  Rather, the model demonstrates the interplay of a minimum set of causal forces on a net student progress construct -- as informed and extrapolated from the non-causal research literature.
Provided further interest and funding, this  basic capability model may further developed and built out to: higher provenance levels -- coupled with increased factorization, rigorous causal inclusion and improved parameterization.
2f. [thought question] Is it possible for r maxrmax to be positive and yet for the total regional abundance to exhibit a persistent declining trend? Explain your reasoning, using at least one biologically realistic example. You can use the agent-based metapopulation model in InsightMaker to help tes
2f. [thought question] Is it possible for r maxrmax to be positive and yet for the total regional abundance to exhibit a persistent declining trend? Explain your reasoning, using at least one biologically realistic example. You can use the agent-based metapopulation model in InsightMaker to help test your ideas, but this is not required.
Based on the Market and Price simulation model in System Zoo 3, Z504. In this model the profit calculations were not realistic. They were based on the per unit profit, which does not take items not sold into account. Also the model was not very clear on profit since it was included in the total prod
Based on the Market and Price simulation model in System Zoo 3, Z504. In this model the profit calculations were not realistic. They were based on the per unit profit, which does not take items not sold into account. Also the model was not very clear on profit since it was included in the total production costs and consequently in the unit costs and subsequently profit was calculated by subtracting unit costs of the market price. Thus profit had a double layer which does not make the model better accessible. I have tried to remedy both in this simplified version.
Attempts to model in the social dynamics of  Pavilion host aquisition
Attempts to model in the social dynamics of  Pavilion host aquisition
 Rich picture version of Tanner's Clinical Judgment Model, with the addition of clinical reasoning cycle concepts from T Levett-Jones et al Nurse Education Today 30 (2010) 515-520

Rich picture version of Tanner's Clinical Judgment Model, with the addition of clinical reasoning cycle concepts from T Levett-Jones et al Nurse Education Today 30 (2010) 515-520

           This version of the   CAPABILITY DEMONSTRATION   model has been further calibrated (additional calibration phases will occur as better standardized data becomes available).  Note that the net causal interactions have been effectively captured in a very scoped and/or simplified format.  Re
This version of the CAPABILITY DEMONSTRATION model has been further calibrated (additional calibration phases will occur as better standardized data becomes available).  Note that the net causal interactions have been effectively captured in a very scoped and/or simplified format.  Relative magnitudes and durations of impact remain in need of further data & adjustment (calibration). In the interests of maintaining steady progress and respecting budget & time constraints, significant simplifying assumptions have been made: assumptions that mitigate both completeness & accuracy of the outputs.  This model meets the criteria for a Capability demonstration model, but should not be taken as complete or realistic in terms of specific magnitudes of effect or sufficient build out of causal dynamics.  Rather, the model demonstrates the interplay of a minimum set of causal forces on a net student progress construct -- as informed and extrapolated from the non-causal research literature.
Provided further interest and funding, this  basic capability model may further de-abstracted and built out to: higher provenance levels -- coupled with increased factorization, rigorous causal inclusion and improved parameterization.
The complex
systems model ‘Engagement vs Police Expenditure for Justice Reinvestment in
Bourke, NSW’ evaluates the effectiveness of allocating government funding to
either community engagement activities or law enforcement. In this model, it is
possible for the user to designate resources from a sca
The complex systems model ‘Engagement vs Police Expenditure for Justice Reinvestment in Bourke, NSW’ evaluates the effectiveness of allocating government funding to either community engagement activities or law enforcement. In this model, it is possible for the user to designate resources from a scale of 20-100 and to also modify the crime rate for both adults and youth. Below, there are detailed notes that describe the reasoning and assumptions that justify the logic applied to this model. Similar notes can be found when stocks, flows and variables is clicked under the field ‘notes’.

Portions

Government statistics from the Australian Bureau of Statistics (ABS) show that Bourke Shire Regional Council has approximately 3000 residents, made up of 65-63% adults and 35-37% youths.

Crime Rate

Police variable is in the denominator to create a hyperbolic trend. The aim was to achieve a lower crime rate if police expenditure was increased, thus also a higher crime rate if police expenditure was decreased. The figure in the numerator can be changed with the ‘maximum crime rate’ variable which represents the asymptotic crime rate percentage. Where police = 100 the selected crime rate is maximised.

Avoiding Gaol

Originally the formula incorporated the police as a variable, where the total amount of convicted crimes was subtracted from the total amount of crimes committed. However, the constant flow of crimes from repeat offender/a created an unrealistic fluctuation in the simulation. I settled for a constant avoidance rate of 25%. This assumes that an adult or youth committing a crime for the first time is just as likely to avoid conviction as a repeat offender.

Conviction

​It is difficult to predict in a mathematical model how many adults or youths are convicted of crimes they commit. I determined a reasonable guess of maximum 75% conviction rate when Police = 100. In this formula, decreasing police spending equates into decreased conviction rate, which is considered a realistic representation.

Released

​It is assumed that the average sentence for a youth is approximately 6 months detention. For an adult, it will be assumed that the average sentence is 12 months gaol. The discrepancy is due to a few basic considerations that include 1. Adults are more often involved in serious crime which carries a longer sentence 2. youths are convicted with shorter sentences for the same crime, in the hopes that they will have a higher probability of full rehabilitation. 

Engagement

​Rate of adult/youth engagement was estimated to be a linear relation. The maximum rate of engagement, assuming expenditure = 100, is set to 80%. This rate of engagement is a reasonable guess with consideration that there will also exist adults who refused to engage in the community and end up in crime, and adults or youth that refuse to engage in the community or crime. 

Boredom

Engagement Expenditure variable is in the denominator to create a hyperbolic trend. The aim was to achieve a lower boredom rate with a higher engagement expenditure, and thus a higher boredom rate with a lower engagement expenditure. The figure in the numerator of 25 represents the asymptotic boredom rate percentage, where if engagement expenditure = 100 the adult/youth boredom rate is maximised at 25%.