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Smithian growth model from Michael Joffe Fig. 3.7 p57 Ch3 Feedback Economics Book

Adam Smith's Growth through Division of labour

A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy. Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover

Assumptions

Govt policy reduces infection and economic growth in the same way.

Govt policy is trigger when reported COVID-19 case are 10 or less.

A greater number of COVID-19 cases has a negative effect on the economy. This is due to economic signalling that all is not well.

Interesting insights

Higher testing rates trigger more rapid government intervention, which reduces infectious cases. The impact on the economy, though, of higher detected cases is negative.

Burnie COVID-19 outbreak demo model version 2

Steven D'Alessandro

39

Implementation of a DSGE Model solved in a Macroeconomics class by Harald Uhlig (link), using Rational Expectations, in this case, the Hansens Real Business Cycle Model.

It shows the capacity of implementing Dynamic Stochastic General Equilibrium Model Analysis using System Dynamics.

Real Business Cycle Model (DSGE)

Fernando Fernandes Neto

Model Explanation:

This system dynamics model visualises the impact on investment into policing and community engagement resources on the crime rates within the youth population of Bourke, NSW.
The model also adds in the variable of funding for safe houses. With a high rate of domestic violence, unfavorable home conditions and other socio-economic factors, many youth roam the streets with no safe place to go, which may lead to negative behaviour patterns.

Assumptions

Youth Population: 700
Total youth population in 2016 for Bourke LGA was 646 (ages 10-29). (Census, 2016) Figures rounded to 700 for purposes of this model simulation.

Constants:
70% registration and engagement rates for Community funded programs
30% attendance rate for Safe Houses
50% crime conviction rate

Variables

Positive and Negative Influences

The model shows a number of key variables that lead youth to become more vunerable to commit a crime (such as alienation, coming from households with domestic violence, boredom and socio-economic disadvantages such as low income), as well as the variables that enhance the youth's likelihood to be a contributing member of the community (developing trusted relationships and connections with others, and having a sense of self worth, purpose and pride in the community). These factors (positive and negative) are aggregated to a single rate of 50% each for the purposes of the simulation, however each individual situation would be unique.

Police Funding / Resources

Police funding and resources means the number of active police officers attending to criminal activities, as well as prevention tactics and education programs to reduce negative behaviour. The slider can be moved to increase or decrease policing levels to view the impact on conviction rates. Current policing levels are approx 40 police to a population of under 3000 in Bourke.

Crime Rate

Youth crime rates in Australia were 3.33% (2016). Acknowledging Bourke crime rates are much higher than average, a crime rate of 40% is set initially for this model, but can be varied using the sliders.

Community Program Funding / Resources

Community Program Funding and Resources means money, facilities and people to develop and support the running of programs such as enhancing employability through mentorship and training, recreational sports and clubs, and volunteering opportunities to give back to the community. As engagement levels in the community programs increase, the levels of crime decrease. The slider can be moved to increase or decrease funding levels to view the impact on youth registrations into the community programs.

Observations

Ideally the simulations should show that an increase in police funding reduces crime rates over time, allowing for more youth committing crimes to be convicted and subsequently rehabilitated, therefore decreasing the overall levels of youth at risk.

A portion of those youth still at risk will move to the youth not at risk category through increased funding of safe houses (allowing a space for them to get out of the negative behaviour loop and away), whom them may consider registering into the community engagement programs. An increase in funding in community engagement programs will see more youth become more constructive members of the community, and that may in turn encourage youth at risk to seek out these programs as well by way of social and sub-cultural influences.

Justice & Community Support Investment and the Impacts on Bourke Youth Population

Sarah Vassallo

5

This Model was developed from the SEIR Model (Susceptible, Exposed, Infected, Recovered) and it predicts the COVID-19 outbreak in Burnie, Tasmania. This pandemic outbreak contributes to diverse rates including infection rate, death rates and recovery rate, government policies and its economic impacts.

Assumptions:

This model is driven by its determined rates, e.g., incubation rate, morality rate, test rate and immunity loss rate and its recovery rate.

Government policies are involved in fully vaccination rate, social distance, national border closure, travel, and business restriction which effect Burnie’s economy.

There are three economic entities dimensions in Burnie Island, we can tell that the pandemic has negative impact on Brick-and-Mortar enterprises and tourism business to some extent, whereas, e commercial business plays a crucial role to stimulate the regional economic activities during the COVID-19 period.

Interesting Insights:

The figure of susceptible changes significantly during the initial 3 weeks because of low recovery rate and high infection rate. On the other hand, the implementation and interventions of government policies is effective, because the number of patients who tested negative is increased and the majority of them release and go back home after medical follow-up.

Xueli Huang 501514, BMA708 Model of COVID-19 Outbreak in Burnie, Tasmania

XUELI HUANG

This model shows the operation of a simple economy. It demonstrates the effect of changes in the fractional rate of consumption (or the converse, the fractional rate of saving.) It also, unlike Models 2 & 3, shows the influence Savings has on the production rate.

In summary, lower rates of consumption (based on production) result in higher rates of both production and consumption in the long-run.

Simple Economy: Model 4

Jim Berger

4

This model demonstrate how the exisitng tested COVID cases effects economic recovery via goverment intervenes.

Assumption:Goverment intervenes positively contribute on transmission, patients recovery, and death elimination. When existing cases equal or lower than 10 cases, economic growth will be soaring with helps of influencial elements.

Interesting points: even though there are certain amount of unknow cases, enhancing social restriction and increasing test rate ould still reduce amount of cases

Complex Model to Simulate How COVID Outbreak Influence Economic Recovery in Burnie

Youjia Xiao

Explanation:

Explanation:

This model presents the COVID-19 outbreak in Burnie and how the government reacts to it. Moreover, the model also illustrates how the economy in Burnie is impacted by the pandemic. The possible stages of residents when the infectious disease spreads in Burnie can be concluded as Susceptible, Infection and Recovery, which are used as the main data in this model. However, the improvement of decreasing of reported infection rates of this infectious disease and increasing of recovery rates are contributed by the implementation of the Government Health Policy.

Assumption

The decrease of both infection rate and economic growth are all influenced by the Government Health Policy simultaneously. The Government Health Policy is only triggered when there are 10 cases reported. However, the increase in reporting COVID-19 cases affects economic growth negatively.

Interesting Insights:

There are two interesting insights that have been revealed from the simulation. First, the death rate continuously increased even though the infection rate goes down. However, the increase in testing rates contributed to the stability of the death rate towards the end of the week. Moreover, higher testing rates also trigger faster government intervention, which can reduce infectious cases. Second, as the Government Health Policy limited the chance of going out and shopping, the economic growth is negative due to the higher cases.

BMA708, Assessment 3: Complex system, Burnie Covid-19 outbreak

Hanyong Li

Climate Sector Boundary Diagram By Guy Lakeman

Climate, Weather, Ecology, Economics, Population, Welfare, Energy, Policy, CO2, Carbon Cycle, GHG (green house gasses, combined effects)

As general population is composed of 85% with an education level of a 12 grader or less (a 17 year old), a simple block of components concerning the health of the planet needs to be broken down into simple blocks.

Perhaps this picture will show the basics on which to vote for a sustained healthy future

Democracy is only as good as the ability of the voters to FULLY understand the implications of the policies on which they vote., both context and the various perspectives. National voting of unqualified voters on specific policy issues is the sign of corrupt manipulation.

Climate Sector Boundary Diagram of Guy Lakeman

Guy Lakeman

3

Biodiversity -> Climate Change

Victoria Yanco

Ocean/atmosphere/biosphere model tuned for interactive economics-based simulations from Y2k on.

Q2 Final Project w/ socio-economic

Claire Murley

Bicycle ridership

Nemanya Sedoglavich

Fig 4. The Casual Loop Diagram of the Socio-Political and Economic Subsystems

Rony Zhu

Economic contibution

Nienke

not a mathematical model. just a general one

impact of globalization

Venkatesan S

Cornerstore Economic Model

Michelle Molskow

IASAM

Artis

DaceA

Multiscale view of Combined PH and Economic Views IM 70763 in preparation for integrating with Prevention Investment Framework (private) IM

Multiscale Zoomable Prevention Model View

Geoff McDonnell

Summary WIP of Thomas Palley's 2012 Book

From Financial Crisis to Stagnation

Geoff McDonnell

Irving Fisher's Debt Deflation Theory from Michael Joffe Fig. 3.4 p54 Ch3 Feedback Economics Book with Private Credit Inflation boom added to the bust cycles

Irving Fisher's Debt Deflation Theory

Geoff McDonnell

Goodwin cycle IM-2010 with debt and taxes added, modified from Steve Keen. THis can be extended by adding the Ponzi effect of borrowing for speculative investment.

Minsky Financial Instability Model

John Vorhauer

3

• This model examines how sustainable consumerism is from social, economic, and environmental aspects. The question in focus is "How will our second-hand clothing donations affect communities in developing countries, specifically Kenya?"

5 Stock Variables:

• U.S. Consumers

• Multinational Corporations

• Overseas Factories

• Kenya

Highlight Findings:

To sum up, there are 4 major problems associated to donations:

• 1. Source of problem is the consumer: Cheap deals attract hundreds of millions in revenue for fast fashion, and contribute to 100,000 tonnes of clothing to Kenya annually.

• 2. Rapid consumerism leads to over-utilization of slowly-renewable resources, such as water.

• 3. Nearly 96% of textiles jobs are eradicated by the massive inflow of clothing donations to Kenya.

• 4. The offshoring of textiles jobs enrages U.S. blue-collar workers, leading to the rise of protectionism.

Environmental, social, and economic sustainability aspects of textiles donations

Christina Cheung

GSGS_GREECE_GERMANY_MIGRATION_DRAFT

Dennis Hadjimichael

29 5 months ago

Economic Capital

Simon Baynes