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THE 2018 MODEL (BY GUY LAKEMAN) EMPHASIZES THE PEAK IN POLLUTION BEING CREATED BY OVERPOPULATION.
WITH THE CARRYING CAPACITY OF ARABLE LAND NOW BEING 1.5 TIMES OVER A SUSTAINABLE FUTURE (PASSED IN 1990) AND NOW INCREASING IN LOSS OF HUMAN SUSTAINABILITY DUE TO SEA RISE AND EXTREME GLOBAL WATER RELOCATION IN WEATHER CHANGES IN FLOODS AND DROUGHTS AND EXTENDED TROPICAL AND HORSE LATTITUDE CYCLONE ACTIVITY AROUND HADLEY CELLS

The World3 model is a detailed simulation of human population growth from 1900 into the future. It includes many environmental and demographic factors.

THIS MODEL BY GUY LAKEMAN, FROM METRICS OBTAINED USING A MORE COMPREHENSIVE VENSIM SOFTWARE MODEL, SHOWS CURRENT CONDITIONS CREATED BY THE LATEST WEATHER EXTREMES AND LOSS OF ARABLE LAND BY THE ALBEDO EFECT MELTING THE POLAR CAPS TOGETHER WITH NORTHERN JETSTREAM SHIFT NORTHWARDS, AND A NECESSITY TO ACT BEFORE THERE IS HUGE SUFFERING.

BY SETTING THE NEW ECOLOGICAL POLICIES TO 2015 WE CAN SEE THAT SOME POPULATIONS CAN BE SAVED BUT CITIES WILL SUFFER MOST.

CURRENT MARKET SATURATION PLATEAU OF SOLID PRODUCTS AND BEHAVIORAL SINK FACTORS ARE ALSO ADDED

Use the sliders to experiment with the initial amount of non-renewable resources to see how these affect the simulation. Does increasing the amount of non-renewable resources (which could occur through the development of better exploration technologies) improve our future? Also, experiment with the start date of a low birth-rate, environmentally focused policy.

2018 OVERPOPULATION LEADS TO POLLUTION based on Weather & Climate Extreme Loss of Arable Land and Ocean Fertility by Guy Lakeman - The World3+ Model: Forecaster

Guy Lakeman

19

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

Guy Lakeman

8

Economics

Mohammed Hossain

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

Economical Factor

Bruce Ree

clone economics price product

Pagandai V Pannir Selvam

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

Lab 13 Base Model

Jessica LeDuc

6

Reinforcing Economic Inequality

Niccolas Albiz

Additional Research:

1. DuPont Renewably Sourced Materials Report - I learned how DuPont uses separation, fermentation and chemistry to create high performance crops.

No Author, No Date, Retrieved from: http://www2.dupont.com/Renewably_Sourced_Materials/en_US/assets/DuPont_Renewably_Sourced.pdf

2. The Science of Hybrid Crops - This article explains the history of hybrid crops.

Reinhart, K. (2003) Living History - Science of Hybrid Crops. Retrieved from: http://www.livinghistoryfarm.org/farminginthe30s/crops_03.html

Spencer Beane Final Assignment

Spencer Beane

About the Model

This model is a dynamic model which explains the relationship between the police of the government and the economy situation in Burnie Tasmania after the outbreak of Corona Virus.

This model is based on SIR model, which explains the dynamic reflection between the people who were susceptible, infected,deaths and recovered.

Assumptions

This model assumes that when the Covid-19 positive is equal or bigger than 10, the government policy can be triggered. This model assumes that the shopping rate in retail shops and the dining rates in the restaurants can only be influenced by the government policy.

Interesting Insights

The government police can have negative influence on the infection process, as it reduced the possibility of people get infected in the public environments. The government policy has a negative effect on shopping rate in retail shops and the dining rate in the restaurants.

However, the government policy would cause negative influence on economy. As people can not shopping as normal they did, and they can not dinning in the restaurants. The retail selling growth rate and restaurant revenue growth rate would be reduced, and the economic situation would go worse.

Corona virus outbreak in Burnie Tasmania (Xuexiao Zhang 538712)

Xuexiao Zhang

One of the Archetypes that I see is the way out. The Pioneer Seeds bought will directly keep affecting DuPont's Profit until they start buying Ghanaian corn seeds. My Behavior over Time graphs keep disappearing so, I will just explain them instead. DuPont's Profit will keep going up because so many people like buying the pioneer seeds. Soil Erosion will also go up because there will be more plants growing. Farmers profit will go down because more and more farmers are buying the Pioneer seeds. Affordability of Pioneer Seeds is also going down because of there popularity. The amount of GMO labels is also going down because DuPont feels like it will risk there business. The amount of Pioneer Seeds bought is going up because more and more people keep buying them. The amount of Farming Jobs are going up because more and more people think they will get profit in this business and then it starts to stabilize and goes down a little because they realize farming isn't that good. The amount of Pioneer Seeds is affecting how big the harvest is so, the Harvest is going up. The Amount of Ghanaian Seeds bought is going down because there are more and more Pioneer seeds being bought. Dependency on DuPont is increasing because they need there Pioneer Seeds.

Also my story mode isn't working so sorry about that, but my dad couldn't figure it out either.

Pioneer Seed vs. Ghanian Seed Economical Approach

Winter Hayle-Blackburn

Description:

This is a system dynamics model of COVID-19 outbreak in Burnie which shows the process of infections and how government responses, impact on the local economy.

First part is outbreak model, we can know that when people is infected, there are two situations. One is that he recovers from treatment, but even if he recovered, the immunity loss rate increase, makes him to become infected again. The other situation is death. In this outbreak, the government's health policies (ban on non-essential trips, closure of non-essential retailers, limits on public gatherings and quarantine ) help to reduce the spread of the COVID-19 new cases. Moreover, government legislation is dependent on number of COVID-19 cases and testing rates.

Second part: the model of Govt legislation and economic impact. Gov policy can help to reduce infection rate and local economy at same way. The increase of number of COVID-19 cases has a negative impact on local Tourism industry and economic growth rate. On the other hand, Govt legislation also can be change when reported COVID-19 case are less or equal to 10.

Model of COVID-19 outbreak in Burnie(Yafei Shi 489576)

Yafei Shi

Comprehensive model to predict water system operations given hydrologic, economic, and managerial parameters/data

HW 7 Water Supply and Demand

Matt Grehm

Summary of Hermans Scale dynamics of grassroots innovations through parallel pathways of transformative change Ecological Economics 2016 article (paywalled) This is applied to health in a subsequent insight

Multiscale innovation dynamics

Geoff McDonnell

This page provides a structural analysis of POTUS Candidate Martin O'Malley based on the information at: https://martinomalley.com/vision/ 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

DRAFT IPA of Martin O'Malley Economic Policy

Steve Wallis

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

The government has reduced both the epidemic and economic development by controlling immigration.

Yuhao c, BMA708_Marketing insights into Big Data.

Yuhao Chen

Semirara island Casual Loop Diagram

Paul Gabriel Fajardo

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.

BMA708 COVID-19 Outbreak in Burnie, Tasmania

ching tung li

4

This page provides a structural analysis of POTUS Candidate Marco Rubio's economic policy based on the information at: https://marcorubio.com/issues/debt/ 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

DRAFT IPA of Marco Rubio economic policy

Steve Wallis

In CIV4 a City's Health stock is a flourishing yet complex condition which acts as a City attribute intended to limit growth. Positive and negative feedback impacts City Health flow and derives from many sources: city's site, buildings, wonders, civics, resources, population, etc.

What drives this flow? When a cities unhealthiness is greater than its health limit, then for each point of population above the health limit consumes 3 food apiece rather than the standard 2 pieces. This further depletes city resources leading to starvation. In addition the city’s health also affects the rate at which it accumulates food.

For example: too much growth causes overpopulation which leads to an unhealthy city. This unhealthy city leads to less food accumulation thereby resulting in starvation which further compounds the cities unhealthiness.

Civilization IV: City Health Modeling

Jason Blackstone

This page provides a structural analysis of POTUS Candidate Lindsey Graham's economic policy based on the information at: http://www.lindseygraham.com/issue/restore-fiscal-discipline/ http://www.lindseygraham.com/issue/ease-tax-and-regulatory-burdens/ http://www.lindseygraham.com/issue/achieve-energy-independence/ http://www.lindseygraham.com/issue/reform-entitlements/ 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

DRAFT IPA of Lindsey Graham Economic Policy

Steve Wallis

Economical Factors of Science: C8

Varsha Vasudevan

Textile and confection industry

112233443322

This high-level simulation model presented by Jay Forrester in his book World Dynamics, simulates socio-economic-environmental world system. The world Model was created in a time where pollution and other negative effects of industrialization and economic growth started to become recognized in 1970. For this exam purpose, we have rebuilt the model to do some experiments and analyze the results.

World Model2

Ali Akhlaqi

Island economic system

Bertrand Nzomigni