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Summary of Ch 22 of Mitchell Wray and Watts Textbook see IM-164967 for book overview

Fiscal Space and Fiscal Sustainability

Geoff McDonnell

EK map - Motivation

Benna McInally

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:

Clone of Coronavirus: A Simple SIR (Susceptible, Infected, Recovered) with death

Juan Carlos Morales Melo

9 months ago

Homework 7 Sandmann

Sandmann

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.

Version 6A NULL PRACTICE Student-Home-Teachers-Classroom

Robert L. Brown

8

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

Allison Zembrodt

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

Magnus Gram

This is the third in a series of models that explore the dynamics of infectious diseases. This model looks at the impact of two types of suppression policies.

Press the simulate button to run the model with no policy. Then explore what happens when you set up a lockdown and quarantining policy by changing the settings below. First explore changing the start date with a policy duration of 60 days.

SIRD Epidemic Model with Suppression Policies

Bob Hawkins

7

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:

http://www.censusdata.abs.gov.au/census_services/getproduct/census/2016/quickstat/LGA11150?opendocument

Bourke Community (C.Woods, 44593961)

Caitlin Woods

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 #9: Modern Marine 2-box Carbon Cycle

Ashley Morris

3

Model of the autonomi network economics

autonomi

Christian

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

Ted Lindsay

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:

SD India

Алина Чеботарь

9 months ago

Clone of Streamer Social Media Virality

Alexander St. John

8 months ago

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

Alex V

7 months ago

Logic Model

Mary Quandt

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

Abdurehman Ahmad

Adaptive Capacity Model

Eric K

yesterday

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.

Version 6B: Calibrated Student-Home-Teachers-Classroom

Robert L. Brown

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

Ethan Homesley

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:

SD USA

Abdramanova Akmaral

8 months ago

This model simulates the electricity supply and demand dynamics for the Napanee community in Ontario, with a focus on the impact of micro-generated solar electricity from households and businesses.

Units Used in the Model:

Electricity Supply: Measured in MWh/h (megawatt-hours per hour), representing the total amount of electricity available from both external sources and local solar generation.
Electricity Demand: Measured in MWh/h, covering both household and business consumption.
Micro-Generated Solar Electricity: Solar power generated by households and businesses, measured in MWh/h.

Key Components:

Electricity Supply: The total electricity provided by both external sources (the grid) and solar generation.
Electricity Demand: The total community demand, which includes household and business consumption and varies over a 24-hour period based on usage patterns.
Micro-Generated Solar: Solar power generated by both households and businesses. This power reduces the demand for grid electricity and follows a sinusoidal pattern, peaking during the day and dropping to zero at night.
Total Demand: The remaining electricity that the grid needs to supply after accounting for micro-generated solar power.

Napanee Community Electricity Model