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Navigation to aspects of systems relevant to applying the methods to health care; adapted from John Barton's representation of a system slide
Systems Launchpad
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atmosphere earth system
Insight diagram
The model simulates the local environmental (specifically greenhouse gas emissions), economic, and resource impacts of transitioning from internal combustion engine vehicles (ICEVs) to electric vehicles (EVs) for personal ownership in New York City in the context of a sustainable program of new energy vehicles, which is the context of the current era. To be realistic, we combine delay and stochasticity in this model to simulate the real world. By understanding the model, one can gain insight into the importance of EV penetration for sustainable development.

Clone of Group 10 - Electrifying NYC: A System Dynamics Model of EV Adoption and Sustainability Impacts
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Attempts to model in the social dynamics of returning players
Clone of Streamer Social Media Virality 7 w Player loop
10 months ago
Insight diagram
Working model of Yellowstone dynamics created by students in Ecology 2015 at McDaniel College.​ This class collaboration is the first attempt by these students to develop a functioning model that includes competition, disease, predation, invasives and impacts of environmental variables on the major species over the last 30 years.
Although we are attempting to create a realistic model, we are not researchers and depend on varied data sources for coefficients.

Clone of Eco15 Yellowstone Model
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Evaluate Policy
Insight diagram
This is a clone of "Fast Fashion ISCI 360 Solutions Final submission" created by user "V B" which we are using as the foundation for an exercise in the DTU course 12100 "Quantitative sustainability".

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 to see which links were hidden from the model.

Part 2: Our solution for the issue surrounding "Fast Fashion" focuses on increasing individuals education about sustainability and how they can help reduce negative impacts on the environment by shopping less, recycling and donating. This effect of education on sustainability is seen in the "Online Shopping" equation where the impact of "Education on Sustainability" is increased by x1.5 which impacts the entire model. Furthermore, components of the feedback loop on the right are also influenced by increasing education on sustainability and thus, those values were altered accordingly. These values were chosen arbitrarily by taking into account that doubling any value is not realistic so the change should be between x1.0 and x2.0.
Clone of Clone of Fast Fashion ISCI 360 Solutions Final Edit
last week
Insight diagram
This is a clone of "Fast Fashion ISCI 360 Solutions Final submission" created by user "V B" which we are using as the foundation for an exercise in the DTU course 12100 "Quantitative sustainability".

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 to see which links were hidden from the model.

Part 2: Our solution for the issue surrounding "Fast Fashion" focuses on increasing individuals education about sustainability and how they can help reduce negative impacts on the environment by shopping less, recycling and donating. This effect of education on sustainability is seen in the "Online Shopping" equation where the impact of "Education on Sustainability" is increased by x1.5 which impacts the entire model. Furthermore, components of the feedback loop on the right are also influenced by increasing education on sustainability and thus, those values were altered accordingly. These values were chosen arbitrarily by taking into account that doubling any value is not realistic so the change should be between x1.0 and x2.0.
Clone of Clone of Fast Fashion ISCI 360 Solutions Final Edit
last week
Insight diagram

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.  

Clone of Clone of Social Media Virality
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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
Insight diagram
Full_Pot_Ex_A3
Insight diagram
This is a clone of "Fast Fashion ISCI 360 Solutions Final submission" created by user "V B" which we are using as the foundation for an exercise in the DTU course 12100 "Quantitative sustainability".

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 to see which links were hidden from the model.

Part 2: Our solution for the issue surrounding "Fast Fashion" focuses on increasing individuals education about sustainability and how they can help reduce negative impacts on the environment by shopping less, recycling and donating. This effect of education on sustainability is seen in the "Online Shopping" equation where the impact of "Education on Sustainability" is increased by x1.5 which impacts the entire model. Furthermore, components of the feedback loop on the right are also influenced by increasing education on sustainability and thus, those values were altered accordingly. These values were chosen arbitrarily by taking into account that doubling any value is not realistic so the change should be between x1.0 and x2.0.
Clone of Clone of Fast Fashion ISCI 360 Solutions Final Edit
Insight diagram
This is a clone of "Fast Fashion ISCI 360 Solutions Final submission" created by user "V B" which we are using as the foundation for an exercise in the DTU course 12100 "Quantitative sustainability".

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 to see which links were hidden from the model.

Part 2: Our solution for the issue surrounding "Fast Fashion" focuses on increasing individuals education about sustainability and how they can help reduce negative impacts on the environment by shopping less, recycling and donating. This effect of education on sustainability is seen in the "Online Shopping" equation where the impact of "Education on Sustainability" is increased by x1.5 which impacts the entire model. Furthermore, components of the feedback loop on the right are also influenced by increasing education on sustainability and thus, those values were altered accordingly. These values were chosen arbitrarily by taking into account that doubling any value is not realistic so the change should be between x1.0 and x2.0.
Clone of Clone of Fast Fashion ISCI 360 Solutions Final Edit
5 months ago
Insight diagram
cscn | on coordinating a conference
3 months ago
Insight diagram
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:
  1. http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
  2. https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model
Clone of Coronavirus: A Simple SIR (Susceptible, Infected, Recovered) with death
Insight diagram
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:
  1. http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
  2. https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model
Clone of Coronavirus: A Simple SIR (Susceptible, Infected, Recovered) with death
Insight diagram

MODEL EXPLANATION:

This model simulates 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 interest to participate in community activities in which the government aims to improve their lifestyle and therefore reduce the rate of criminal activity. ASSUMPTIONS:There are 1500 youths of Bourke in the population susceptible to committing crime and simulations of criminal tendencies are only based the factors presented, no external influences.
VARIABLES:“Alienation” includes any factors that can increase the likelihood of youths 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 Juvenile detention‘Policing’ is the amount of police placed onto patrol in the town of Bourke to reinforce safety and that the law is abided by. STOCKS:Conviction rate is set to 60%A juvenile detention sentence for convicted criminals is set to 3 monthsThe top 30% of the most severe offenders are sent to rehabilitation for 3 months, to which they return to Bourke, assumingly in a better state and less likely to repeat a petty crimeCommunity activities are set to last for 3 months to align with the seasons: these could be sporting clubs or youth groupsCommunity participants have a 20% chance of being disengaged as it may not align with their interestsInvestments into policing are felt immediately& community engagement expenditure has a delay of 3 months
INTERESTING FINDS:1.    Alienation set to max (0.2), policing and community engagement set to minimum shows a simulation whereby all criminals are in town rather than being expedited and placed into juvenile detention, even after a base value of 200 youths placed into juvenile detention – this shows that budget is required to control the overwhelming number of criminal youths as they overrun Bourke2.    Set community activity to 0.01, policing to max & Alienation to max. A lack of community activity can produce high disengagement amongst youths regardless of police enforcement to the town of Bourke that has a high criminal rate. Juvenile detention only lasts for so long and not all youths can be rehabilitated, so they are released back into Bourke with chances of re-committing crime. 3.    Alienation plays a major role in affecting youths to consider committing crime. To keep criminal activity to a minimum, ideally the maximum rates of budget in policing and community engagement within youths highly at risk of committing crime should be pushed. Realistically, budget is a sensitive case within a small town and may not be practical. 4. Set policing to 0.25, community engagement to 0.2 & alienation to 0.04. Moderate expenditure to community activities and policing can produce high engagement rates and improved youths in the town of Bourke.



Clone of MGT563 (11605457) - Crime, Policing & Community Development in Bourke
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A student thought of this model.  It is based on the water flow data and projections on pages 68 and 69 of LTG30.  
It models all the available freshwater in rivers and dams in one year, which is represented as a stock. 
The water flowing in is all the world's runoff, and the water flowing out is in two categories: Water used by people, and water not used by people.
This model uses a converter which allowed us to enter the UN projection of water use in 2050.  Click on the converter to see how it works.
Rivers flow example
Insight diagram
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:
  1. http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
  2. https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model
Clone of Coronavirus: A Simple SIR (Susceptible, Infected, Recovered) with death
Insight diagram
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:
  1. http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
  2. https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model
Clone of Coronavirus: A Simple SIR (Susceptible, Infected, Recovered) with death
Insight diagram
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.
Clone of Version 6A: Calibrated Student-Home-Teachers-Classroom
Insight diagram
This is a clone of "Fast Fashion ISCI 360 Solutions Final submission" created by user "V B" which we are using as the foundation for an exercise in the DTU course 12100 "Quantitative sustainability".

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 to see which links were hidden from the model.

Part 2: Our solution for the issue surrounding "Fast Fashion" focuses on increasing individuals education about sustainability and how they can help reduce negative impacts on the environment by shopping less, recycling and donating. This effect of education on sustainability is seen in the "Online Shopping" equation where the impact of "Education on Sustainability" is increased by x1.5 which impacts the entire model. Furthermore, components of the feedback loop on the right are also influenced by increasing education on sustainability and thus, those values were altered accordingly. These values were chosen arbitrarily by taking into account that doubling any value is not realistic so the change should be between x1.0 and x2.0.
Clone of Clone of Fast Fashion ISCI 360 Solutions Final Edit
Insight diagram
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:
  1. http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
  2. https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model
Clone of Coronavirus: A Simple SIR (Susceptible, Infected, Recovered) with death
Insight diagram
Untitled Insight