This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.  We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale websi
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

  This model depicts the complex relationships between crime, number
of police, investment in community development programs and the youth
population of the small country town, Bourke.  

 In this system dynamics model, the user can observe how modifying
the spending on community development program

This model depicts the complex relationships between crime, number of police, investment in community development programs and the youth population of the small country town, Bourke. 

In this system dynamics model, the user can observe how modifying the spending on community development programs and changing the number of police in the town affects the crime rate and the engagement of youth. 

These variables can be altered using the sliders which are provided underneath the notes. The model runs for a period of 5 years. This was deemed the optimal time during which any generational changes could be observed.

The model is explained with more detail below, along with any assumptions and their appropriate reasoning.


Variables

Investment in Community Development Programs

It is assumed that the minimum that can be invested is $1000 and the maximum is $100 000.

Number of Police

It is assumed that the minimum number of police officers that can be present in Bourke is 10 and the maximum is 100.


Stocks and Flows

Bourke Population

The population of Bourke is set as 3000 as stated in the Justice Reinvestment document.

Boredom and lack of opportunity leads to

This flow is given the equation: (50000/[Investment in Community Development Programs])* 2. The greater the investment in community development programs, the lesser the number of youths who are bored.

Disengaged and Alienated Youth

Since there are not many activities for young adults (as stated in the Justice Reinvestment document), it is assumed that they are all currently disengaged and alienated. The disengaged and alienated youth population of Bourke is thus set as 1000 before the model is run.

Petty Crime

Since the youth crime rate for Bourke is quite high, it was assumed that 800 out of the 1000 youth would engage in petty crime. This is before any additions to the police force or increase in community development programs investment.

Commit

This flow is dependent on both the number of disengaged youth and the number of police. The more police that are present in Bourke, the more disengaged the youth become. This ensures that the level of petty crime committed is directly related to the number of police officers.

Convicted

This flow is given a constant rate of 7*[Number of Police] + (0.1*[Petty Crime]). This means that the greater the number of police officers present, the greater the number of convictions. It also means that at the highest number of police officers available (100), the highest the number of convictions is 700 + 10% of youths who commit a crime. Since the model assumes that there are 800 youths committing crime at the beginning of the models’ commencement, it realistically represents the police’s inability to catch ALL criminals.

Not Convicted

This flow has the equation ([Petty Crime]/[Number of Police])*2. Since the number of police is in the denominator, the lower the number, the higher the number of delinquents who are not convicted. This attempts to keep the model realistic. At the maximum level of 100 police officers, there will still remain some delinquents who escape conviction and this remains true to life.

Lesson Learnt

Since youth crime is so rife in Bourke, it is assumed that only 20% of offenders in the juvenile detention centre learn their lesson and never commit crime again. This was done to simplify the modelling.

Still Disenchanted

It is assumed that 80% of offenders do not learn their lesson after their time in the juvenile detention centre.

Feel Estranged

This flow is given the equation: [Number of Police]*5 + 50/([Investment in Community Development Programs]/1000).

Thus, the higher the number of police, the greater the number of youths who feel estranged. The greater the investment in community development programs, the lesser the number of youths who feel estranged.

Participate and engage in

This flow is dependent on the level of investment in community development programs. The greater the investment, the greater the participation. This is realistic as the more money is spent on such programs, the more interested that youths will be in participating.

Develop Inter-community relationships

It is estimated that the majority of youths who participate in community development programs will develop inter-community relationships. This model assumes that such programs will be largely successful in encouraging social harmony amongst the youths.

Relapse

However, youths participating in the community development programs may relapse and head back into the path of crime. However, this is assumed to only be a small minority (1/8 of those who participate).


Interesting Observations

1) Number of Police: 10 (minimum)

Investment in Community Development Programs: $1000 (minimum)

It is important to note that even the minimal amount of investment in community development programs is enough to cause the crime rate to decrease, to the point where, after 3 years,  there are more youths who are Reformed and Engaged than those involved in Petty Crime. However, the number of youths who are Reformed decreases after some time, indicating greater investment is needed. Somewhat surprisingly, the number of youths who are involved in the community development programs is at its highest, further suggesting the need for increased investment.

2) Number of Police: 100 (maximum)

Investment in Community Development Programs: $1000 (minimum)

Predictably, Petty Crime has drastically decreased, and in a much shorter time than when there were only 10 police officers. The number of youths who are Reformed and Engaged and those who are involved in the Community Development Programs has also increased, but they are not as high as in the previous observation, most likely due to increased alienation caused by the high police presence.

3) Number of Police: 10 (minimum)

Investment in Community Development Programs: $100 000(maximum)

Quite surprisingly, Petty Crime has decreased drastically, despite the low number of police officers present in Bourke. This shows that the large sums of money being invested in the Community Development Programs has created a social change within the town’s youth population with high numbers of youths participating in these programs and thus becoming Reformed and Engaged. Another interesting aspect is that while the number of youths participating in the programs reduces to zero at the end of the fifth year, the number of youths who are Reformed and Engaged is at an all time high.

4) Number of Police: 100 (maximum)

Investment in Community Development Programs: $100 000 (maximum)

While Petty Crime has decreased significantly, the number of youths who are Reformed and Engaged and those who participate in Community Development Programs is not as high as Scenario 3. Extremely large numbers of youths are also spending time in the Juvenile Detention Centre during the first 2 years of the 5-year model. While repeat offences are low, this may be more due to fear of police brutality and the prospects of harsher sentences than any conscious effort on the youth population’s part to be more harmonious members of society.

This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.  We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale websi
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

 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 model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.  We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale websi
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.  We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale websi
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.  We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale websi
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

  About
the Model  

 This
model is designed to simulate the youth population in Bourke, specifically
focusing on the number of criminals and incarcerated dependent on a few key
variables. 

 Within the model, a young person living in Bourke can be classified as being in any of five states:  Young C

About the Model

This model is designed to simulate the youth population in Bourke, specifically focusing on the number of criminals and incarcerated dependent on a few key variables.

Within the model, a young person living in Bourke can be classified as being in any of five states:

Young Community Member: The portion of the youth population that is not committing crime and will not commit crime in the future. Essentially the well behaved youths. A percentage of these youths will become alienated and at risk.

Alienated and At Risk Youths: The youths of Bourke that are on the path of becoming criminals, this could be caused by disruptive home lives, alcohol and drug problems, and peer pressure, among other things.

Criminal: The youths of Bourke who are committing crimes. Of these criminals a percentage will be caught and convicted and become imprisoned, while the remainder will either go back to being at risk and commit more crimes, or change their behaviour and go back to being a behaving community member.

Imprisoned: The youths of Bourke who are currently serving time in a juvenile detention centre. Half of the imprisoned are released every period at a delay of 6 months.

Released: Those youths that have been released from a detention centre. All released youths either rehabilitate and go back to being a community member or are likely to re-offend and become an alienated and at risk youth.

The variables used in the model are:

Police- This determines the police expenditure in Bourke, which relates to the number of police officers, the investment in surveillance methods and investment in criminal investigations. The level of expenditure effects how many youths are becoming criminals and how many are being caught. An increase in police expenditure causes an increase in imprisoned youths and a decrease in criminals.

Community Engagement Programs- The level of investment in community engagement programs that are targeted to keep youths in Bourke from becoming criminals. The programs include sporting facilities and clubs, educational seminars, mentoring programs and driving lessons. Increasing the expenditure in community engagement programs causes more young community members and less criminals and at risk youths.

Community Service Programs- The level of investment in community service programs that are provided for youths released from juvenile detention to help them rehabilitate and reintegrate back into the community. An increase in community service expenditure leads to more released prisoners going back into the community, rather than continuing to be at risk. Since community service programs are giving back to the community, the model also shows that an increase in expenditure causes a decrease in the amount of at risk youths.

All three of these variables are adjustable. The number of variables has been kept at three in order to ensure the simulation runs smoothly at all times without complicated outputs, limitations have also been set on how the variables can be adjusted as the simulation does not act the same out of these boundaries.

Key Assumptions:

The model does not account for the youths’ memory or learning.

There is no differentiation in the type of criminals and the sentences they serve. Realistically, not all crimes would justify juvenile detention and some crimes would actually have a longer than six-month sentence.

The constants within in the calculations of the model have been chosen arbitrarily and should be adjusted based on actual Bourke population data if this model were to be a realistic representation of Bourke’s population.

The model assumes that there are no other factors affecting youth crime and imprisonment in Bourke.

There are 1500 youths in Bourke. At the beginning of the simulation:

Young Community Member = 700

Alienated and At Risk Youth = 300

Criminal = 300

Imprisoned = 200

Noteworthy observations:

Raising Police expenditure has a very minimal effect on the number of at risk youths. This can be clearly seen by raising Police expenditure to the maximum of twenty and leaving the other two variables at a minimum. The number of Alienated and at Risk Youths is significantly higher than the other states.

Leaving Police expenditure at the minimum of one and increasing community development programs and community service programs to their maximum values shows that, in this model, crime can be decreased to nearly zero through community initiatives alone.

Leaving all the variables at the minimum position results in a relatively large amount of crime, a very low amount of imprisoned youth, and a very large proportion of the population alienated and at risk.

An ideal and more realistic simulation can be found by using the settings: Police = 12, Community Engagement Programs = 14, Community Service Programs = 10. This results in a large proportion of the population being young community members and relatively low amounts of criminals and imprisoned.



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%. 

This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.  We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale websi
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.  We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale websi
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.  We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale websi
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.  We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale websi
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

This model illustrates the key processes that influence the water level within Lake Okeechobee.        References:     Southwest Florida Water Management District. (2020). Lake Okeechobee. Retrieved from https://apps.sfwmd.gov/sitestatus/     United States Geological Survey. (2020). USGS Water-Year
This model illustrates the key processes that influence the water level within Lake Okeechobee.


References:

Southwest Florida Water Management District. (2020). Lake Okeechobee. Retrieved from https://apps.sfwmd.gov/sitestatus/

United States Geological Survey. (2020). USGS Water-Year Summary for Site USGS 02276400. Retrieved from https://nwis.waterdata.usgs.gov/nwis/wys_rpt?dv_ts_ids=210619&wys_water_yr=2019&site_no=02276400&agency_cd=USGS&adr_water_years=2006%2C2007%2C2008%2C2009%2C2010%2C2011%2C2012%2C2013%2C2014%2C2015%2C2016%2C2017%2C2018%2C2019&referred_module=

Winchester, J. (2020, October 10). Water releases from Lake Okeechobee to begin next week. Retrieved from https://www.winknews.com/2020/10/09/water-releases-from-lake-okeechobee-to-begin-next-week/


Created By:

Roger Al-Bahou
Carlos Alvarez
Christina Burgess
Devin Hanley
Daniel Harper
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.  We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale websi
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

           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.
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.  We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale websi
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W