Insight diagram
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

Clone of Final Midterm Student version of A More Realistic Model of Isle Royale: Predator Prey Interactions
Insight diagram
Simple box model for atmospheric and ocean carbon cycle, with surface and deep water, including DIC system, carbonate alkalinity, weathering, O2, and PO4 feedbacks.
Lab 11, coastal model
Insight diagram
Figure 4-4 Population
Insight diagram
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

Clone of Final Midterm Student version of A More Realistic Model of Isle Royale: Predator Prey Interactions
Insight diagram
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

Clone of Final Midterm Student version of A More Realistic Model of Isle Royale: Predator Prey Interactions
Insight diagram
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

Clone of Final Midterm Student version of A More Realistic Model of Isle Royale: Predator Prey Interactions
Insight diagram
Summary of the History of Pragmatism mostly based on Cheryl Misak's Books insight integrated with Cornelis de Waal's Introducing Pragmatism Peirce insight   See also Insight  Misak Peircean Truth and the end of Inquiry
History of Pragmatism
3 months ago
Insight diagram
This is a model which attempts to replicate a simple reinforcing loop described by Dennis Sherwood on page 75-87 of his book 'Seeing the forest for the trees - a manager's guide to applying systems thinking.

This is not a realistic model but I just wanted to reproduce it as practice of implementing causal loop models.

www.stantonattree.com
Clone of Seeing the forest for the trees example
Insight diagram
This model is the solution to the semiconductor problem as discussed in the class. The model exhibits goal seeking behavior, while centering around the 250 ppm defects introduced with equipment wear and tear, etc. 

The model matches real world data in terms of general trajectory, but not totally. There has to be modifications in terms of determining the real defect elimination rates, as opposed to ideal ones. That would make the model more accurate. However, for the purposes of understanding dynamics in goal seeking models, this is a good exercise.

If average defect elimination time is lesser, it leads to faster goal seeking. Any delay (>1 year) makes it impossible to seek the goal in realistic timelines.
Semiconductor Problem_ Vishwajit Vyas
Insight diagram
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

Clone of Final Midterm Student version of A More Realistic Model of Isle Royale: Predator Prey Interactions
Insight diagram
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

Clone of Final Midterm Student version of A More Realistic Model of Isle Royale: Predator Prey Interactions
Insight diagram
This model analyzes the growth and dynamics of Oshawa’s population using a logistic approach. Starting with an initial population of 170,000 and an increased carrying capacity of 180,000, it evaluates how the addition of new neighbourhoods, planned to accommodate an extra 10,000 residents over the next 10-15 years (or whatever time period) affects population changes. Key factors include the Oshawa Residents Death/Emigration Rate of 0.8% (realistic percent approximation), accounting for natural deaths and emigration, and the Oshawa Residents Birth/Immigration Rate of 2.4% (also a realistic percent approximation), reflecting new residents through births and immigration. The model tracks the net population change, providing insights into how Oshawa's population might grow or stabilize as it approaches its new carrying capacity!
Logistic Model of Oshawa's Population Growth with Increased Residential Carrying Capacity
Insight diagram
Electricity Grid Sim With Batteries
3 5 months ago
Insight diagram
Cloned from v6 on 11/21/25
Added variables to simulate various climatic changes and natural disasters.
Earth Climate Box Model v7
3 months ago
Insight diagram
Summary of Ray Pawson's Book The Science of Evaluation: A Realist Manifesto See also lse review
Clone of The Science of Evaluation
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.
Fast Fashion ISCI 360 Solutions Final Edit
11 months ago
Insight diagram
The Model

The model displayed depicts the interaction that the youth of Bourke has with the justice system and focuses on how factors like policing and community development affect the crime rate within this area. Bourke is a rural town that has a significant crime rate among youth. Local community members call for action to be taken in regards to this, meaning that steps must be taken to reduce the crime rate. This simple model explores how the amount of police and the investment of community development can have an effect on the town in regards to its issue of crime among youth.


Assumptions
  • Bourke's youth population is 1200, with 700 in town, 200 committing crimes and 300 already in jail
  • The amount of police, the expenditure on community development, and the domestic violence rate are the factors which have the potential to influence youth to commit crimes. The domestic violence rate is also influenced by the expenditure on community development.
  • Sporting clubs, interpersonal relationships between youth and police, and teaching trade skills all make up community expenditure
  • Activities relating to expenditure on community development run throughout the year, indicating that there is no delay where youth are not involved in these activities.
  • Every 6 months, only 60% of jailed youth are released. This may be for various factors such as committing crime in jail or being issued with lengthier sentences due to the severity of the crime(s) committed
  • 10% of youth who agree that domestic violence is an issue at home will commit crime
  • There is a delay of 1 month before youth go to jail for crime(s) committed. This model assumes that youth who have committed crime either return home (by decision or by not being caught) or go to jail. It also assumes that other punishments such as community service refer to returning back home.
  • The simulation takes place over a duration of 5 years (60 months)
  • Adults have little effect on the youth. Only where domestic violence is concerned do they play a factor within this model

How the Model Works

The model begins with the assumptions previously stated. Youth have the potential to commit a crime. 3 main variables influence this decision, including the amount of police, expenditure on community development, and domestic violence rate (which is influenced by the previous variable). These 3 variables are able to be adjusted using the relevant sliders with 0.5 indicating a low investment and 0.9 indicating a high investment. Police also have an influence on this decision. This variable is also able to be adjusted by a slider. Last of all, the domestic violence rate also contributes to this decision and this variable is negatively influenced by community development.

Once a youth has committed a crime they are either convicted and sent to jail or return back to town. The conviction rate is also influenced by the amount of police in town, as youth are more likely to get caught and thus jailed. Once again, the Police variable is able to be adjusted via the slider. This process takes a month.

From here, youth typically spend 6 months in jail. After this time period 60% are released while the remaining 40% remain in jail either due to lengthier sentences for more severe crimes or due to incidents within jail. The process then repeats.


Parameter Settings and Results
  • Initially there is a state of fluctuation within this model. It may be a good idea to ignore it and pay attention to how variables change over time from their initial state
  • Increasing the amount of police will raise the amount of people jailed and decrease crime
  • Increasing the community development variables from a minimal investment (i.e. set at 0.5) to a high investment (i.e. set at 0.9) will reduce both the crime rate and the conviction rate. It is worth noting that the community development variable also influences the domestic violence rate variable which also has an effect on the results
  • If only 2 of the 3 community development variables have a high investment then there is not much effect on the crime rate or jail rate. All 3 variables should be given the same level of investment to give us a desired outcome
  • The model does allow for a maximum of 40 police (as we do not want to spend more money on police than we already have in the past), as well as the maximum investment for community development. When choosing settings it may be necessary to ponder if it is financially realistic to maintain both a large number of police as well as investing heavily into community development
Justice Reinvestment In Bourke - 44560753
Insight diagram
This model demonstrates sustainable recycling and the effects it has on the environment as well as us. We modelled this using realistic statistics and estimates from gridwatch.ca and the Ontario Baseline and Waste & Recycling Report (2023). 

[Purple]: Metal demands on a region and the associated environmental and economic factors of production and recycling.

[Pink]: Demand of total residential household and business waste and energy demands on the system.

[Green]: Physical waste produced by human activity in the region.

[Teal]: The outflow of energy produced through waste recycling and its impact of energy production and demand in the region. The Durham-York Energy Center (DYEC) is a facility that combusts garbage into energy which is highlighted in teal, which accumulates with energy produced.

[Orange]: Total energy produced through all means of power generation including modelling of the impact that recycling waste has.

[Yellow]: Carbon emissions of energy generation from energy production methods. (Excluding Wind & Hydro)

Overall, this model examines and compares waste accumulation to energy production and the release of emissions.
Group Project
Insight diagram
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
Insight diagram
Defaults:
Conv Rate 0.11
Churn Rate 0.8
Recommendation Rate 0.05
Web Traffic Try 2
Insight diagram
This model tries to show the effect of car-sharing (CS) and its possible effect on reducing CO2 emission over a time period of 20 years. The main target of car-sharing is to reduce individual car ownership and the total number of cars on. In addition to that, with more fuel-efficient cars and the increased use of electric cars it could be an effective tool to reduce CO2 emissions.

We assumed that the total travel demand (yearly driven passenger units) shifts from private car ownership to CS services over a time of 20 years [1]. The possibility of buying a private car and a CS car is calculated by dividing the travel demand and the maximal travel demand possible, multiplied by the number of families without a private car respectively with the number of CS families. Private cars will abrade increasing the number of families without a car. However, in our model they will decide to join CS thereby increasing the number of CS families. By this the number of private cars will decrease while number of CS cars will increase. Gasoline CS cars will change over time into electric CS cars with a benefit for the CO2 emission due to lower lifecycle emission. Since CS cars are utilized more, they will abrade faster. However, this will overall result in more fuel efficient cars with another benefit for CO2 emissions.

Private Autos(100.000) Familien ohne private Autos(20.000) CS Familien(1.000) CS Autos - Benzin( 5.000) Bedarf private Autos(1.920.000.000 yearly passanger distance) Umstiegsrate(0.25) Kraftstoffeffizienz(0.15) CO2 emission of gasoline car(24 t CO2) [2] CO2 emission of electric car(19 t CO2)[2]

Thing to try (with influence on CO2 emission of CS cars):
Change the exchange-rate with which CS cars with gasoline motor are exchanged by electric motor  Change the fuel-efficiency of CS cars Both will influence the reduction of CO2 emission by CS cars

Extending the model:Demand for public transportation Would create a more realistic model since there are not only the options of having a private car or using CS.Public transportation could help reduce overall CO2 emissions.

Credits and references:
Cornelius Frank & Ameur GlouiaDepartment of Bioengineering SciencesWeihenstephan-Triesdorf University of Applied Science85354 Freising, Germanycornelius.frank@student.hswt.de / ameur.glouia@student.hswt.de

[1] Kawaguchi, T. (2019). Scenario Analysis of Car- and Ride-Sharing Services Based on Life Cycle Simulation. Procedia CIRP. 80: 328-333
[2] Low Carbon Vehicle Partnership. (2015). Lifecycle emissions from cars.


Effect of Car-sharing on CO2 Emission - Projectwork Dyn. Sim. SS19 HSWT - CorneliusFrankAmeurGlouia v2.0
Insight diagram
Final version of S&F diagram, with Renewable energy investment as an auxiliary variable.
Stock & Flow of Energy Infrastructure Investment, Atmospheric CO2 Accumulation, & Energy Supply & Demand
Insight diagram
atmosphere earth system
Insight diagram

The period from 900 AD until 1700 AD at Easter Island is an example of Overshoot and Collapse System Archetype. Around 900 AD, the island had a small population which grew over time alongside rampant consumption of island’s natural resources.  Natural resources depleted to such an extent that it could not sustain the high population resulting in a population crash amid wide spread starvation. Main events that occurred during this period are provided in the book Collapse: How Societies Choose to Fail or Survive by Jared Diamond.

The model attempts to retrace the history of Easter Island from the time people first settled on the island i.e. 900 AD until 1600 AD. 
Easter Island Overshoot and Collapse