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System Zoo Z409 Fishery dynamics from Hartmut Bossel (2007) System Zoo 2 Simulation Models. Climate, Ecosystems, Resources

Fishing is a classic example for use of a renewable resource. Unless overfished, fish populations If is hardly by fishing, then the fish population will persist at a constant size corresponding to its specific ecological envi ronment If the stock is overfished, the juvenile generation becomes too small to fully replace the adult generation. If overfishing continues. the population cannot recover and will collapse in short time. Even if fish catch stops now/, it could take decades until the fish population recovers to its original size if it hasn't become extinct meanwhile. In many of the world overtlshing has led, and still leads, to the complete collapse of formerly huge tlsh populations: herring in the North Sea, codtlsh in the Northern Atlantic. tuna, whales to name only a few. With the collapse of fish stocks came the collapse of the t1shing industry in many regions. Employment and

incomes disappeared: whole regions (like Newfoundland) lost their economic base.

REM 221 - Z409 Fishery dynamics

Steve Conrad

29

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

CHANGE - Angela's version

Angela Bearth

Unfortunately, this model only produces the illusion of functioning, but I did manage to get it to give me the graph. However, because of the use of flows, if you change the time step to and the simulation length to anything other than the same numbers, you'll find the graph showing something that looks more exponential. This is due to the function referencing itself in regards to time, so inevitably each time consumption grows it changes the outcome on the other side of the equation. Still, this is a convincing mock up. I added a "45 degree" line so that one could conceivably see (and also change) the difference made by altering the level of autonomous consumption.

Keynesian Macroeconomics

Nicholas Lowe

4

Michael Marmot's Eur J Epidemiol Essay 2017 See also IM-62760 Social determinants of health from Michael Marmot's ABC 2016 Boyer Lectures on Social Justice and the Health Gap

Social Justice, Epidemiology and Health Inequalities

Geoff McDonnell

Socio-economic factors (kaya)

Olivia Barber

This model is comparing healthy and sick residents in Burnie, Tasmania after the Covid-19 Outbreak in 2020. It will also show how the Burnie economy is effected by the disease, how the Government Health Policies are implemented and how they are enforced.

This model is based on the SIR, Susceptible, Infection, Recovery (or Removed) These are the three possible states related to the members of the Burnie population when a contagious decease spreads.

The Government/Government Health Policy, played a big part in the successful decrease in Covid-19 infections. The Government enforced the following.
- No travel (interstate or international)
- Isolation within the residents homes
- Social distancing by 1.5m
- Quarantine
- Non essential companies to be temporarily closed
- Limitations on public gatherings
- And limits on time and kilometers aloud to travel from ones home within a local community

This resulted in lower reported infection rates of Covid-19 and higher recovery rates.

In my opinion: When the first case was reported the Government could have been even faster to enforce these rules to decrease the fatality rates further for the Burnie, population.

Assumption: Government policies were only triggered when 10 cases were recorded.
Also, more cases that had been recorded effected the economic growth during this time.

Interesting Findings: In the simulation it shows as the death rates increases towards the end of the week, the rate of testing goes down. You would think that the government would have enforced a higher testing rate over the duration of this time to decrease the number of infections, exposed which would increase the recovery rates faster and more efficiently.

Figures have been determined by the population of Burnie being 19,380 at the time of assignment.

Complex Systems How Burnie Tasmania dealt with Covid-19 Outbreak BMA708

Kate Elizabeth Craddock

I propose we grow this sim model (or similar) over time to help ourselves better understand the opposing investment and austerity strategies now being advocated for the U.S. government. The hope is to build as simple a model as possible that subsumes the major underlying feedback loops that probably exist in the mental models of proponents of each of these positions. Starting this model was inspired by this Investment vs. Austerity discussion http://www.linkedin.com/groups/Investment-vs-Austerity-How-can-4582801.S.157876413

20120908a_InvestmentVsAusterity

Paul Newton

Model-SIM from Chapter 3 of Wynn Godley and Marc Lavoie's Monetary Economics, adapted for an open economy. The model is stock-flow consistent with only government money--no bills or bonds. No central bank and interest rates do not change. Government spends buying output from the production sector. The production sector is passive turning over all revenue over to households. Households save out of income and spend partially spend out of wealth. Imports and exports pass through the production sector illustrating the idea that consumer households buy from domestic businesses that which they have imported. The model also tracks the sectoral balance flows and changes in equity. Sectoral flows and equity balances match each other dollar for dollar to satisfy the sectoral balances accounting identity (Household Saving - Consumption) + (Business Saving - Expenditure) + (Taxes - Government Spending) - (Exports - Imports) = 0. Since business investment occurs internally to the Business Sector,

SFC_Model-SIM_open_economy

Aaron Medlin

5 months ago

An initial study of the economics of single use coffee pods.

Coffee Pods ISD Humanities - Anouk D 10.4

Anouk de Jong

Economic Model

Maria Sol Perez Aguirreburualde

Model of Covid-19 outbreak in Burnie, Tasmania

This model was designed from SIR model(susceptible, infected, revovered) to find out the effect of covid-19 outbreak into economic outcomes via government policy.

Assumptions

The government policy is triggered when number of infected is more than ten.

The government policies will take negative effect into Covid-19 outbreaks and financial system

Parameters

We set some fixed and adjusted variables.

Covid-19 outbreak's parameter

Fixed parameters: Infection rate, Background disease, recovery rate.

Adjusted parameter: Immunity loss rate can be change from vaccination rate.

Government policy's parameters

Adjusted parameters: Testing rate(from 0.15 to 0.95), vaccination rate(from 0.3 to 1), travel ban(from 0 to 0.9), social distancing(from 0.1 to 0.8), Quarantine(from 0.1 to 0.9)

Economic's parameters

Fixed parameter: Tourism

Adjusted parameter: Economic growth rate(from 0.3 to 0.5)

Interesting insight

Increase vaccination rate and testing rate will decrease the number amount of infected case and a little bit more negative effect to economic system. However economic system still need a long time to recover in both cases.

BMA708_Assignment 3_ndkvo_520272_COVID-19 outbreak and Burnie economy

KV

Marine Tourism Task III - Great Blue Hole

Emma Malte Larsen

A framework exploring flood risk management in a developing city, under the old paradigm of control - characterised by narrowly defined goals and an over-reliance on hard-engineered structural solutions.

Urban flood risk (control paradigm)

Michael Meadows

13

This insight includes a Limits to Success archetype. (Bubbles colored with a darker blue)

Economical Factor

Bruce Ree

Economical Factors of Science: C8

Varsha Vasudevan

Week 13.1 Lab Economic Model

Rachel Christensen

Map of SD work on Samuelson's 1939 model of the business cycle. See also D-memo D-2311-2 Gilbert Low 1976 and IM-165713. An alernative to the Ch 26 Macroeconomics textbook exposition. From Gil Low's Multiplier Accelerator Model of Business Cycles, Ch 4 of Elements of the System Dynamics Method Book edited by Jorgen Randers 1976 (MIT Press) and 1980 (Productivity Press)

Samuelson multiplier accelerator model

Geoff McDonnell