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Current and proposed Structure of CCP and related Models expanding on the details provided in the Project Completion plan IM-101760
Structure of the CCP Models
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Social determinants of health are economic and social conditions that influence the health of people and communities. These conditions are shaped by the amount of money, power, and resources that people have, all of which are influenced by policy choices. Social determinants of health affect factors that are related to health outcomes. Factors related to health outcomes include:
  • How a person develops during the first few years of life (early childhood development)
  • How much education a persons obtains
  • Being able to get and keep a job
  • What kind of work a person does
  • Having food or being able to get food (food security)
  • Having access to health services and the quality of those services
  • Housing status
  • How much money a person earns
  • Discrimination and social support
Determinates of a healthy population
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Economic Effect
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ESI6550 Group 6 (Model 2)
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Template to start Assignment 1.  Click "Clone Insight" above to make a copy for yourself.
ISCI 360 Assignment 1
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There is an old saying that says that 'opportunity makes thieves'.  But there is  also a research paper entitled 'Opportunity makes the Thief: Practical Theory of Crime Prevention (1998)' that provides evidence supporting this common observation. The paper argues that  opportunity is a “root cause” of crime. This therefore also applies to the behaviour of corporations.

 

The Causal Loop Diagram on the left indicates that the number of crimes will  increase rapidly as the opportunities to commit them increases.  This suggests that the introduction of a negative feedback loop aimed at diminishing  opportunities for committing crimes is an appropriate measure to improve the situation. A number of remedial mesures  tailored to specific situations are contained in the report. A generally effective means must be the use of law and regulations that were also mentioned in the report.



Opportunity makes Thieves
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WIP Summary of MIchael Hudson's Book Killing the Host: How Financial Parasites and Debt destroy the Global Economy 
Killing the Host
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This model shows the simulation of COVID-19 outbreaks when it hit Burnie, Tasmania. This model will show how government intervention will impact the total number in COVID 19 cases and the overall economic activity.

 

Assumptions

1.   The current Burnie population in 19550. Therefore, the susceptible population is equal to the current Burnie population.

2.       Since Burnie is just a regional city, the virus infection rate is 25% as 5000 people in Burnie went into quarantine during the outbreak last year.

3.       50% of people who are infected will recover.

4.       20% of people who are infected will die because Burnie population average is old.

5.       Government intervention and policy will reduce the Infection

6.       COVID-19 is only countable as a case if the infected people have been tested, and the percentage of testing depends on how many infected people have been tested.

7.       Following a recovery, there is a chance that people could lose their immunity, and also the immunity loss rate measures this.

8.       Government intervention will reduce the infection rate by 15%.

9.       Lockdown will cause tourism industry to shut down and affect the overall economic activity.

10.   Lockdown is one of the most effective way to prevent infection.

11.   Strict health protocol also contributes to reduce the infection.

12.   Vaccination will not make people fully immune to the virus. However, vaccinated people will reduce the immunity loss percentage.

13.   Economic growth rate percentage is based on year 2020.

Findings

1.       COVID-19 could be significantly reduced in number and the spread of the vaccine could make a significant impact on the epidemic.

2.       Economic activity will drop during the first phase of government intervention, However, it will steadily increase overtime

3.       Less people going to be susceptible as government imposed covid 19 rules.

BMA708 Michael Sunjaya Jurenang ID:547923
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This model indicate indicates the modeling COVID-19 outbreaks and responses from government policies with the effect on the local economy. Model was occurred at Burnie, Tasmania. The model mainly contains three parts: COVID-19 pandemic outbreak, four differences government policies and what the impact on economy from those policies.

 

Assumptions:

(1) Various variables influence the model, which can result in varied outcomes. The following values are based on an estimate and may differ from actual values. Government initiatives are focused at reducing Covid-19 infections and, as a result, affecting (both positive and negative) economic growth.

 

(2) 42% of infected people will recovery. 10% of people who are infected will die and the rate relatively higher due to the much old people living in Burnie, Tasmania.

78% of cases get tested.

 

(3) Government policy will only be implemented when there are ten or more recorded cases. Four government policies have had influences on infection.  

 

(4) The rising number of instances will have a negative impact on Burnie's economic growth.

 

Insights:

1. As a result of the government's covid 19 rules, fewer people will be vulnerable. Less people going to be susceptible.

 

2. After the government policy intervention, there is a effectively reduce of infected people.

 

3. Overall, there is no big differences of economic performance from the graph, might due to the positive and negative effect of economy. And after two weeks, the economy maintained a level of development without much decline.

BMA708 Yanglin Hu
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Peak oil will occur when it is too expensive to bring oil to the surface and not when reserves reach their limit. Companies must make a profit to be able to extract oil and stay in the oil business.  However, that endeavour is becoming more and more difficult because of diminishing returns. They have to dig ever deeper to get to the oil  at ever increasing costs, and the oil they find deep down is of a lesser quality.  We have now reached a point where the price needed by oil companies to make a profit and stay in business is far higher than the price  the market can bear. That price is probably about $ 100 per barrel - and rising every year! A market price o $ 100 will almost certainly cause a sharp recession and cause the price of oil to fall back beyond the point of profitability. For example, the combined profit of ExxonMobile, Chevron and Conocophillips fell from 80.4 billion in 2011 to only 3.7 billon in 2016 - see URL below. What the market can bear depends on the spending power of the mass of non-elite workers. The CLD shows the negative feedback loops that prevent oil prices to rise above the level of  affordability. If non-elite workers cannot afford the goods and services offered,  then there will be no demand for them and by extension for oil.  In this situation the market price will not the cover the cost that oil companies need to extract oil. Oil supplies will decline and so will economic activity!

https://srsroccoreport.com/the-blood-bath-continues-in-the-u-s-major-oil-industry/

THE PRICE TRAP AND PEAK OIL
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Farming_small vs large
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Social pressures create {Youth Alienation}, leading to youth developing bad behaviours and committing crimes. This attracts {Police Enforcement} who will, in turn, engage the {Community Leadership} where they introduce programs that are designed to assist youth to prevent re-offending through the development of {Community Clubs}, which then contributes to {Community Development}.

{Police Enforcement} collaborates with {Educational Institutions} to boost retention, which translates to socio-economic progress through {Community Development}. On the other hand, criminals are detained and put through the {Court} system, where the offenders are removed from the community through {Imprisonment}. This results in a stable and safe environment, which aid support for {Community Development).

The role of {Community Leadership} in the system, particularly at the grassroots will result is huge savings in the economy, aiding economic growth. The {Community Leadership} collaborates with the {Employment & Justice Agencies}, translating into socio-economic progress {Community Development}

The Community Development Model

This model provides an understanding into the relationships and links between a range of variable units and fixed units, and how {Community Development} is supported.

As {Youth Alienation} rate increases,  the {Crime} rate increases (both variables) demands police enforcement. {Police Enforcement} is a fixed variable as increase in police force is fixed over a period of time. 

To increase efficiency, engages or collaborate with:

•{Community Leadership} (fixed and variable) – is fixed for a certain period, and becomes variable as youth criminal activities increases

•{Court} (variable) – as youth criminal activities increase, the court resources reman fixed. It then removes some offenders from the community and imprison them, creating peace and stability in the community 

•{Educational Institutions} (variables) – as student retention increases, more institutions are needed.  

Variables that are linked to the {Community Leadership} which include;
•{Youth Sports Clubs}
•{Employment & Justice Agencies}
•{Economic Preservation; and}
•{Educational Institutions}

Contribute/support {Community Development}. These are variables, as more youths are referred or engaged.

The {Community Leadership} and {Police Enforcement} collaborate, support and design community programs to reduce youth criminal activities, which could potentially reduce the justice system expenditure.

The relationship between these fixed and variable units create a sustainable Community Development.

The Community Development Model
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Fig 17.15 p700 Causal structure of commercial real estate markets of Case Study from John Sterman's 2000 Business Dynamics Book 
Boom and bust in Commercial Real Estate
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Simulation of MTBF with controls

F(t) = 1 - e ^ -λt 
Where  
• F(t) is the probability of failure  
• λ is the failure rate in 1/time unit (1/h, for example) 
• t is the observed service life (h, for example)

The inverse curve is the trust time
On the right the increase in failures brings its inverse which is loss of trust and move into suspicion and lack of confidence.
This can be seen in strategic social applications with those who put economy before providing the priorities of the basic living infrastructures for all.

This applies to policies and strategic decisions as well as physical equipment.
A) Equipment wears out through friction and preventive maintenance can increase the useful lifetime, 
B) Policies/working practices/guidelines have to be updated to reflect changes in the external environment and eventually be replaced when for instance a population rises too large (constitutional changes are required to keep pace with evolution, e.g. the concepts of the ancient Greeks, 3000 years ago, who based their thoughts on a small population cannot be applied in 2013 except where populations can be contained into productive working communities with balanced profit and loss centers to ensure sustainability)

Early Life
If we follow the slope from the leftmost start to where it begins to flatten out this can be considered the first period. The first period is characterized by a decreasing failure rate. It is what occurs during the “early life” of a population of units. The weaker units fail leaving a population that is more rigorous.

Useful Life
The next period is the flat bottom portion of the graph. It is called the “useful life” period. Failures occur more in a random sequence during this time. It is difficult to predict which failure mode will occur, but the rate of failures is predictable. Notice the constant slope.  

Wearout
The third period begins at the point where the slope begins to increase and extends to the rightmost end of the graph. This is what happens when units become old and begin to fail at an increasing rate. It is called the “wearout” period. 
BATHTUB MEAN TIME BETWEEN FAILURE (MTBF) RISK
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This model was proposed in a regulatory framework in Brazil. Its main idea is the obtainment of a dynamic control model to avoid the related parties issues on regulated public services over contract extensions. As the terminal condition of these contract extensions is NPV=0, the firms would have an incentive to contract related parties to inflate costs, and diminish their profits, in order to request a larger time extension. So, this system creates a stable "shadow" based on the 5 years before these extensions, where the company did not have such incentives.
Cost Efficiency Capture Model
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Transportation Model
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An initial study of the economics of single use coffee pods.
K Collins Coffee Pods ISD Humanities v 1.02
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Overview of Part G Ch 27 to 30 of Mitchell Wray and Watts Textbook see IM-164967 for book overview
History of Macroeconomic Thought
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lab 13 Social and economic
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Team 3 Environmental Sustainability Rich Picture
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วช
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A possible "Rule" element to represent the relationship between the rate of illegal immigration into the United States and identifiable key variable factors could be:

"If the levels of identifiable key variable factors such as economic opportunity, political stability, and security in countries of origin and the effectiveness of border security measures and immigration policies in the United States are favorable, then the rate of illegal immigration into the United States is likely to be lower. Conversely, if these key variable factors are unfavorable, then the rate of illegal immigration into the United States is likely to be higher."

This rule suggests that the rate of illegal immigration into the United States is influenced by a combination of push factors in countries of origin and pull factors in the United States, such as economic opportunity, political stability, security, and the effectiveness of border security measures and immigration policies. By considering these key factors, policymakers can develop more effective strategies to manage and reduce the rate of illegal immigration into the United States.

Number of illegal immigrants in US Model