Find Insights | Insight Maker

I have tried to capture the unemployment benefits budget in a causal loop diagram. You can make this as extensive as you want, but I have tried to focus on how unemployment benefits are financed and on the main determinants of expenditures and income. I was not (yet) able to 'close te loop' - to build the diagram up from feedback cycles.
The diagram is in Dutch.

Causal loop diagram of unemployment benefits

Henny van Dongen

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

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.

The environmental, social, and economic sustainability aspects of textiles donations

Christina Cheung

Study of the self-and all the rest society

Economic Insight

Bala Subramanian

11 months ago

Book Summary of The Great Transformation by Karl Polanyi see Wikipedia . See also more Karl Polanyi ideas IM-181325

The Great Transformation

Geoff McDonnell

8

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

Adam Smith's Growth through Division of labour

Geoff McDonnell

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

Q2 Final Project w/ socio-economic

Claire Murley

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

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

This Scenario has Affluence decreasing due to Anthropogenic climate change

Final Project 3 W/ Socio-Economic Factors - Temperature Degradation

Geoffrey Alan Chapman

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

From Jay Forrester 1988 killian lectures youtube video describing system dynamics at MIT. For Concepts See IM-185226. For more detailed biography See Jay Forrester memorial webpage For MIT HIstory see IM-184930

System Dynamics Applications

Geoff McDonnell

Sandbox for testing InsightMaker features using pipeline Construction & ROW land conversion as a driver of changes in ecosystem service value.

ESV Test

Spencer Phillips

Simple epidemiological model for Burnie, Tasmania

SIR: Susceptible to infection - Infected - Recovery, Government responses and Economic impacts

Government policy is activated when there are 10 or fewer reported cases of COVID-19. The more people tested, the fewer people became infected. So the government's policy is to reduce infections by increasing the number of people tested and starting early. At the same time, it has slowed the economic growth (which, according to the model, will stop for next 52 weeks).

Model of Covid-19 Outbreak in Burnie, Tasmania (Yue Xiang 512994)

Yue Xiang

5

During the 'big recession' many governments have deliberately repressed salaries, usually via structural reforms, in order to gain competitivity. However, repression of salaries increases inequality, social discontent and often has counterintuitive effects. Salaries are a cost for companies, but they are also the basis for the demand for the goods and services they offer: people with little income cannot afford them. Scientific studies have shown repeatedly that economic growth generated via salary increases does not endanger the creation of employment, but rather reinforces it. In most countries, the 'positive effect of salary increases' eclipses any possible negative effects on export competitivity and even any detrimental effect on investment. A good example of such a study is the work of ONARAN and OBST on Wage-led Growth in the EU15 Member States (2016). This positive dynamic has been highlighted in the model by prominent arrows. The policy implications for governments are clear!

Dynamik Linking Wage Increases to Higher Growth and Profits

Hanns-Jürgen Hodann

Economic contibution

Nienke

WIP Clone of Conceptualizing Capitalism Insight to summarise Thorstein Veblen's writings on the Nature of Capital and other Institutional economics concepts

Veblen Nature of Capital

Geoff McDonnell

4

References:

http://hcrs.co/wp-content/uploads/Hay%20Egg%20Loss%20Folder/
Dahlberg%201979%20A%20Review%20of%20Surviva
l%20Rates%20of.pdf

http://www.bbc.com/news/science-environment-36716579

https://adventure.howstuffworks.com/outdoor-activities/fishing/fish-conservation/fish-populations/fishing-quota1.htm

https://healthresearchfunding.org/pros-cons-fish-farming/

http://www.academyforchange.org/wp-content/uploads/2012/08/Fishery-Causal-Loop-Diagram-2011.9.pdf

http://www.dfo-mpo.gc.ca/Library/329545.pdf

http://worldoceanreview.com/en/wor-2/fisheries/illegal-fishing/

http://www.dfo-mpo.gc.ca/stats/commercial/cfs/2012/section4-eng.htm

http://fishcount.org.uk/fish-count-estimates

https://www.worldatlas.com/articles/how-many-fish-are-there-in-the-ocean.html

http://www.umich.edu/~csfound/545/1998/narayans/chap07.htm

https://www.tandfonline.com/doi/abs/10.1080/09669580802276000

http://www.dfo-mpo.gc.ca/oceans/mpa-zpm/index-eng.html

https://www.newstatesman.com/politics/2015/07/selling-sea-how-our-fish-lost-their-freedom-market-forces

ISCI360_Group12Solution

Caitlin Low

3

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

An attempt to combine ideas from Joe Stiglitz's Book The Price of Inequality, Peter Turchin's book Secular Cycles and Khalil Saeed and Oleg Pavlov's Dynastic Cycles SD model paper

Inequality Crisis and Secular Cycles

Geoff McDonnell

3

I made this model to simulate how a companies revenue will change depending on the lifetime of the appliances it manufactures, in combination with the ratio of repair costs and price. It also shows the accumulation of e-waste.

Simulatie apparaten

Henny van Dongen

This model simulates the economics of buying a home. It was created to compare buying a home against using investment returns to pay for rent. According to Micheal Finke, house prices typically run 20x monthly rental rates.

Try cloning this insight, setting the parameter values for real-world scenarios, and then running sensitivity analysis (see tools) to determine the likely wealth outcomes. Compare buying a home to renting. Note that each run will keep the parameters the same while simulating market volatility.

version 2.0

Home buying simulation 2.0

Larry Lee

7 months ago

IASAM

Artis

DaceA

Model 2

Laura Anne Geronimo

Insight Stage 4 Dress Rehearsal Economy and Fossil Fuels

Victor Wong

3