Introduction:

Explanation of the Model

​Climate Sector Boundary Diagram By Guy Lakeman

This page provides a structural analysis of POTUS Candidate Jim Gilmore's economic policy based on the information at: http://www.gilmoreforamerica.com/jims-growth-code/  The method used is Integrative Propositional Analysis (IPA) available: ​ http://scipolicy.org/uploads/3/4/6/9/3469675/wallis_white_paper_-_the_ipa_answer_2014.12.11.pdf

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

• This model examines how sustainable consumerism is from social, economic, and environmental aspects.  

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.

This simple model describes wealth accumulation. The value in income is described by the following simple equation:

The recent moratorium on deep-sea drilling will reduce the supply of oil. But the world-wide trend is an ever increasing demand for it. This simple CLD  tries to illustrate the dampening effect on demand and on economic activity of diminishing oil supplies and of rising prices: oil prices  affect virtually all products and especially agricultural production. As it becomes more and more difficult to extract oil, prices must rise. At the moment the global recession counteracts this effect, but the recession will not last forever. Is it too early to speak of Peak Oil?

The complex model reflects the COVID-19 outbreak in Burnie, Tasmania. The model explains how the COVID-19 outbreak will influence the government policies and economic impacts. The infected population will be based on how many susceptible, infected, and recovered individuals in Burnie. It influences the probability of infected population meeting with susceptible individuals.

The fatality rate will be influenced by the elderly population and pre-existing medical conditions. Even though individuals can recover from COVID-19 disease, some of them will have immunity loss and become part of the susceptible individuals, or they will be diagnosed with long term illnesses (mental and physical). Thus, these variables influence the number of confirmed cases in Burnie and the implementation of government policies.

The government policies depend on the confirmed COVID-19 cases. The government policies include business restrictions, lock down, vaccination and testing rate. These variables have negative impacts on the infection of COVID-19 disease. However, these policies have some negative effects on commercial industry and positive effects on e-commerce and medical industry. These businesses growth rate can influence the economic growth of Burnie with the economic

Most of the variables are adjustable with the slider provided below. They can be adjusted from 0 to 1, which illustrates the percentages associated with the specific variables. They can also be adjusted to three decimal points, i.e., from 0.1 to 0.001.

Assumptions

- The maximum population of Burnie is 20000.
- The maximum number of infected individuals is 100.
- Government policies are triggered when the COVID-19 cases reach 10 or above.
- The government policies include business restrictions, lock down, vaccination and testing rates only. Other policies are not being considered under this model.
- The vaccination policy implemented by the government is compulsory.
- The testing rate is set by the government. The slider should not be changed unless the testing rate is adjusted by the government.
- The fatality rate is influenced by the elderly population and pre-existing medical conditions only. Other factors are not being considered under this model.
- People who recovered from COVID-19 disease will definitely suffer form immunity loss or any other long term illnesses.
- Long term illnesses include mental illnesses and physical illnesses only. Other illnesses are not being considered under this model.
- Economic activities are provided with an assumption value of 1000.
- The higher the number of COVID-19 cases, the more negative impact they have on the economy of Burnie. 

Interesting Insights

A higher recovery rate can decrease the number of COVID-19 cases as well as the probability of infected population meeting with susceptible persons, but it takes longer for the economy to recover compared to a lower recovery rate. A higher recovery rate can generate a larger number of people diagnosed with long term illnesses.

Testing rate triggers multiple variables, such as government policies, positive cases, susceptible and infected individuals. A lower testing rate can decrease the COVID-19 confirmed cases, but it can increase the number of susceptible people. And a higher testing rate can trigger the implementation of government policies, thus decreasing the infection rate. As the testing rate has a strong correlation with the government policies, it can also influence the economy of Burnie. 

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. 

First Basic Macro economic model

This model shows the structure and operation of a simple economy. It can represent economic systems at different levels of abstraction (e.g. a single good, a group of goods, multiple groups, & an "economy.")

In summary, lower rates of consumption (based on production) result in higher rates of production and consumption in the long-run. Rates of consumption over 100% of production will diminish the savings stock and eventually cause rates of production and consumption to fall.

This model shows the operation of an extremely simple economy. The system produces and consumes each item (or good) at a fixed rate.

When production exceeds consumption, consumer goods accumulate in stocks. Trading may occur between actors in this system. That will not, however, affect the quantities of the stocks of goods. It only affects ownership (not a concern of this model.)

Eastern oyster growth model calibrated for Long Island Sound
Developed and implemented by Joao G. Ferreira and Camille Saurel; growth data from Eva Galimany, Gary Wickfors, and Julie Rose; driver data from Julie Rose and Suzanne Bricker; Culture practice from the REServ team and Tessa Getchis. This model is a workbench for combining ecological and economic components for REServ. Economic component added by Trina Wellman.

This is a one box model for an idealized farm with one million oysters seeded (one hectare @ a stocking density of 100 oysters per square meter)

1. Run WinShell individual growth model for one year with Long Island Sound growth drivers;

2. Determine the scope for growth (in dry tissue weight per day) for oysters centered on the five weight classes)
 
3. Apply a classic population dynamics equation:

dn(s,t)/dt = -d[n(s,t)g(s,t)]/ds - u(s)n(s,t)

s: Weight (g)
t: Time
n: Number of individuals of weight s
g: Scope for growth (g day-1)
u: Mortality rate (day-1)

4. Set mortality at 30% per year, slider allows scenarios from 30% to 80% per year

5. Determine harvestable biomass, i.e. weight class 5, 40-50 g (roughly three inches length)

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

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

Clone of Wagdy Samir Macroeconomics work in progress IM-901 Additions and deletions based on Robert Skidelsky's description of Keynes general THeory from his Biography Vol2 p 549 -571

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

This model shows the operation of a simple economy with two modifications made to Model 2 -- 1) feedback from production rate to consumption rate and 2) the use of a fractional rate input for calculating consumption rate. 

In summary, lower fractional rates of consumption (based on production) result in higher levels of Savings.

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