Test model 5:2 fast PJK
From Stephen Toulmin's Book The Uses of Argument Cambridge University Press 2003. See wikipedia Also Francis Miller Claim Hexagon 2025 web article
Toulmin and Miller Argument Models
The model takes into account clothing production and textile waste on a global scale while incorporating Vancouver's own "Fast Fashion" issue into the model.
Please refer to the notes for each variable and stock for more information and to see which links were hidden from the model.
Fast Fashion ISCI 360 Solutions Final Submission
4
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.
Simplified and changed Z504 Market and Price - System Zoo 3
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.
Clone of Bourke Justice Reinvestment - Nicholas Hayward 44553625
The simulation starts with 10 persons sick on 1. March 2020. It will pause every 14 days to adjust parameters.
Corona in Austria
Multilevel context mechanisms and outcomes for hospital infection control
Hospital Infection Factors Levels
Simulation d'un MRU d'un corps qui avance avec une régulation de vitesse réaliste.
serie 08 ex4 Une regulation de vitesse plus realiste V1
==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
https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model
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:
- http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
- https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model
Coronavirus: A Simple SIR (Susceptible, Infected, Recovered) with death - based on Andrew E Long
Clusters of interacting methods for improving health services network design and delivery. Includes Forrester quotes on statistical vs SD methods and the Modeller's dilemma. Simplified version of IM-14982 combined with IM-17598 and IM-9773
Complex Decision Technologies
To provide a brief overview of the description of this model, here is a table of contents of sorts:
- The Program - Overview
- The Model Itself - Macro Scale
- Principal Inputs
- Principal Outputs
- Inputs & Outputs - Brief Explanation
- The Details - How This All Works
- Viewing Data Outputs
The Program - Overview:
The model as seen revolves around the main variable components featuring "program" in their name and identified by their green color. The individual household starts at the "Building Envelope" program, which involves changes and modifications made to the actual building envelope of the house to make it more energy efficient, such as modifications to the insulation, windows, doors, etc. Then, the individual household will begin to progress through the behavioral component of the energy reduction program, starting with Climate Control. This portion concerns thermostat set points, the use of windows and fans to modulate temperature, and a behavioral adjustment in how to tolerate different levels of hot/cold temperature in the household. From here, the household moves through each room in the house, implementing energy reduction practices as appropriate. Because this program is designed to be modular and applicable to wide varieties of homes across the country, these rooms have been broken up into some standard categories that should apply to most households. These categories include the kitchen, the washroom, the main room/living room/"den", any bathrooms, and any bedrooms. Each category has its own set of energy reduction practices that can all be applied from a behavioral standpoint; clicking on each individual "program" will show a brief description of what these practices are in the notes section. Once the individual household has progressed through all of these areas, making the appropriate adjustments in each, they have more or less effectively "completed" the program. In reality, areas may continue to pop up where adjustments can be made to reduce energy consumption, so even though the program has been "completed" the members of the household will be continually working to maintain the new efficiency standard they have achieved with the end goal of cultivating a permanent, sustainable lifestyle.
The Model Itself - Macro Scale:
The above is all essentially a description of how the household energy reduction program operates; the model is obviously tied to this, however it also includes an energy component that takes into account energy savings not only from a single house but all houses in a single community. How this all works will be discussed more in detail below, but first some basics will be gone over.
Principal inputs:
- energy capable of being saved in each portion of the program through behavioral changes (e.g. total possible energy reductions compared with initial baseline use prior to starting the program are X kWh/year and Y CCF/year)
- % of progress that needs to be made on meeting the reduction goal prior to moving onto the next program (e.g. for a total possible energy reduction of X compared to the initial energy use prior to starting the program, the participant must have reduced 90% of that total energy prior to moving on to the next program)
- time each program is projected to take (e.g. 4 weeks, 5 weeks, etc.)
- households in the community
- time (i.e. how long to run the model for, e.g. 52 weeks, 104 weeks, etc.)
Principal Outputs:
- amount of kWh of electricity saved by a household over the given period of time since starting the program (based on a kWh/yr basis)
- amount of CCF of gas saved by a household over the given period of time since starting the program (based on a CCF/yr basis)
- amount of gas and amount of electricity saved by the community the given period of time since starting the program (based on a per year basis)
- a plot of the progress made on each program for a specific period of time (e.g. which program is the household in, and what is their progress on the rest of the program they have already completed)
Inputs & Outputs - Brief Explanation:
For this model, the only inputs that could vary significantly from community to community are the specific number of households as well as the time the program has been in operation. Obviously the power that each household is capable of reducing can vary from household to household, however we are mainly concerned with the average energy reduction when looking at the community scale as there will always be outliers, which is why average numbers are used. Of the outputs produced, the kWh and CCF savings can be translated to lbs of CO2 saved, as well as other useful energy savings metrics that can better explain the impact of CE4A to the normal person than trying to explain the details behind what 1 kilo-watt hour is. Additionally, for a specific area's utility rate, the number of kWh/CCF saved overtime can yield data about how much money the specific household has saved since starting the program. This last statistic would be more helpful if the program were operating by strictly giving all the savings from energy reduction back to the homeowner; as this isn't exactly how CE4A handles this component, the model would have to be modified to more accurately depict the total savings going back to the homeowner/company revenue based on energy savings over time.
The Details - How This All Works:
Program Progression per Program:
The progress of the individual household through the home energy reduction program is essentially dictated by the progress through each individual program within. Progress through these individual programs is dictated by an inverse tangent curve that models behavioral change. The curve essentially outputs the % of progress the individual household has made, going from a value of 0 to 100.
- Why an inverse tangent curve? - the shape of the curve includes an initial portion in which changes made are significantly large, followed by a portion in which the rate of change decreases as the easily made changes are completed over time. Compared with curves of similar shape, the important part about the inverse tangent curve is that it has a horizontal asymptote that the curve will only get close to, but never actually reach over time. This is representative of the concept that individuals will always have to work to maintain energy reduction practices until they become habit, as well as the reality that new challenges in the field of energy reduction can and will arise over time as people and technology changes.
***
Important to note: the inverse tangent function has been written to operate on a basis of weeks and % (in terms of a whole number XX.YY, not 0.XXYY). If the time scale is to be adjusted, say from weeks to months, then the entire tangent function must be rewritten to reflect this. Additionally, the function outputs values going from 0 to 100. This is a key reason why the function would need to be rewritten, as this would be drastically changed if different time units were used.
***
Inputs for each program include the progress % that the household needs to reach to advance on to the next program, as well as the time (in weeks) it should take them to reach this % threshold. Given the above explanation for how the inverse tangent curve works, the % progress and time threshold values should be chosen based on how much change is realistically possible within that time range (e.g. if it is realistic for an individual to complete 95% of possible changes within a 3 week period and form the habits to maintain those changes, then those values are well-suited for that program. However, if some programs have components that will take a long time to adapt to, then a longer period of time should be picked or a lower progress threshold, ideally the former.
Program Progression from Program to Program:
Each program following the first includes if-then statements related to the progress threshold of the previous program; once that program reaches that threshold, then the code the programs were written on will start the next program and reset its specific time scale to start at time=0 instead of time=current time in order to allow for flexibility in changing time thresholds without rewriting the entire inverse tangent function every time. In this way, changing progress thresholds not only affects the rate of progress of the current program but the start time of all others after it as well.
Energy Reduction & Values:
The energy reduction numbers used in this model are all based on roughly what types of energy would be used in each room and how much is possible to be reduced. These numbers will all total up to the total projected energy reduction per household in terms of CCF/kWh, but the individual breakdown per room type as found in this model is entirely arbitrary and was chosen according to what made the most sense based on knowledge of what energy is used in which room and roughly how much with regards to the savings measures for the room type. These values are also on a per-week basis, so the small size is understandable in that context (originally on a yearly basis, then divided by 52 to get weeks to make this work with the model)
Original Use & Baseline Use:
Although this model does not utilize this and instead operates on a total savings possible basis, the initial energy usage of a particular household can be put into the "___.CCFOriginalUse" and "___.CCFBaseline" variables (note that CCF is interchangeable with KWH here) to get the total amount of possible savings based on real data. Currently, baseline use is set to 0 for each program with original use equivalent to the total amount of energy capable of being reduced per week for that room type. These numbers were derived from an estimate on the total energy reduction possible in terms of kWh and CCF, which was then broken down into each room and the type of energy capable of being reduced in each (see above section for more on this).
Note that "TotalKWH/CCFSavings" is for each individual household, whereas "NeighborhoodKWH/CCFSavings" is for the entire neighborhood composed of the amount of houses stored in the variable "#Households."
Viewing Data Outputs:
- Viewing current program progress at time X:
- use the plot option to while selecting "BuildingEnvelope.Program", "ClimateControl.Program", "Kitchen.Program," etc., to see the progress curves for each program over time.
- Viewing savings data:
- use the data table option to view the kWh/CCF savings over time for the household, the community, or both, changing the time column to display most recent time first; this will give the total savings in each of those areas for that entire time period.
Home Energy Reduction Progression
Details:
<!--[if !supportLists]-->- <!--[endif]-->This model shows the effect of ‘reinvestment program ‘or the expenditure on policing and community development affects the cycles of petty-crime and youth detention, and domestic violence and jail.
More details:
<!--[if !supportLists]-->- <!--[endif]--> Bourke is a town of 3000 people in the North West of New South Wales, about 750Km from Sydney. See the map: https://goo.gl/maps/VgNqgMNzJ7H2. It’s nowhere and there’s not much to do there if you’re young. So, a lot of kids get into mischief, and a lot of adult’s drink. Sometimes they’re violent.
<!--[if !supportLists]-->- <!--[endif]-->http://www.justreinvest.org.au/justice-reinvestment-in-bourke/
Assumption:
<!--[if !supportLists]-->· <!--[endif]-->Bourke Funding consist of Law enforcement funding and Community Development funding only
<!--[if !supportLists]-->o <!--[endif]-->Bourke budget only has $400,000
<!--[if !supportLists]-->· <!--[endif]-->Juvenile detention stay last for 6 months
<!--[if !supportLists]-->· <!--[endif]-->There is only 2 options as a Youth, commit petty crime or engage in Youth development programs
<!--[if !supportLists]-->· <!--[endif]-->1 unit of Police, Juvenile and Educational program HR and Equipment is = 0.25
<!--[if !supportLists]-->o <!--[endif]-->1 unit increase results in an 0.25 effectiveness increase
<!--[if !supportLists]-->· <!--[endif]-->Sport clubs, educational programs and social programs are comprised into Youth Development Program as 1 stock.
<!--[if !supportLists]-->· <!--[endif]-->Juvenile support relies on encouraging youth who are in detention centers to join youth development programs, if not they will reoffend.
Stocks:
<!--[if !supportLists]-->o <!--[endif]-->Home
<!--[if !supportLists]-->o <!--[endif]-->Youth Development program
<!--[if !supportLists]-->o <!--[endif]-->Discharged
<!--[if !supportLists]-->o <!--[endif]-->Juvenile detention center
<!--[if !supportLists]-->o <!--[endif]-->Petty Crime
Variable:
<!--[if !supportLists]-->· <!--[endif]-->Reinvestment Allocation – ranges from 0 – 1 , law enforcement investment allocation is 1 – reinvestment allocation. Slide the slider through 0 to 1 to change the reinvestment allocation by 10% l
<!--[if !supportLists]-->· <!--[endif]-->Bourke funding budget is fixed to make it seem more realistic (imagine employing a whole army of teachers or police, it wouldn’t make sense)
<!--[if !supportLists]-->· <!--[endif]-->Youth Population varies , from 1000 to 10,000 for realism along with its time period (4 years). Slider the the slider to increase or decrease the population by 1,000s
Juvenile support effectiveness rate, Youth development program effectiveness rate, conviction rate, Police HR/ equipment, Juvenile Support HR/ equipment, Youth Development program HR/ equipment
Interrelationship and reinforcing loops
<!--[if !supportLists]-->· <!--[endif]-->The youth population starts as as Neutral (Home) then leans towards alienation and connectedness
<!--[if !supportLists]-->· <!--[endif]-->Alienation Reinforcing Loop - Alienation has Conviction rate as a factor as conviction rate increase Alienation increase. This is because as youths get arrested, meaning they’ll have to stay in Detention centers, their friends are more likely to follow on due to them getting ‘bored’.
<!--[if !supportLists]-->· <!--[endif]-->Connectedness Reinforcing Loop - The opposite exist with Connectedness, as educational program effectiveness increase so as Connectedness. This follows onto the same assumption that youth will always follow peer pressure. The more friends they have in the program, the more likely they will join aswell.
Analysis:
<!--[if !supportLists]-->1. <!--[endif]-->Which loop is the youth in?
<!--[if !supportLists]-->· <!--[endif]-->Once the allocation slider is used with its minimum or maximum value, the loop at which majority of the youth population is ‘stuck in’ becomes obvious. E.g. Once allocation = 1, the entire youth is stuck between educational program and their home, showing the effectiveness of community development funding. On the other hand, once allocation = 0, the entire youth loops around from doing Petty Crimes, spending their time in Juvenile detention centers, then getting discharged to only commit petty crimes again.
<!--[if !supportLists]-->2. <!--[endif]-->Alienation vs. Connectedness
<!--[if !supportLists]-->· <!--[endif]-->Set the allocation slider on 0.8, The massive difference between the youth of population feeling connected with their community and youth being alienated can be seen. The increase in Reinvestment, the increase in connectedness. Try the extremes as well, 100% reinvestment funding results in 0 Alienation rate.
<!--[if !supportLists]-->3. <!--[endif]--> What is the Youth Engaged in ? Educational Programs or Petty Crime ?
<!--[if !supportLists]-->· <!--[endif]-->Leaving the slider on 0.8, it can be seen that the there are more youth engaged into educational programs than petty crime. This shows that reinvestment and petty crime has a negative relationship .
<!--[if !supportLists]-->4. <!--[endif]-->More police = safer ?
<!--[if !supportLists]-->- <!--[endif]-->Set the slider on 0.1 , it can be seen that Conviction which has police as a factor is positively correlated to Crime. This means that an increase in conviction rate is equivalent to more youth being alienated and committing crime. Therefore, more police less safer.
Have fun!
44911017_Lorenzo_Casaol_MGMT220
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
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
Clone of Final Midterm Student version of A More Realistic Model of Isle Royale: Predator Prey Interactions
