วช
Ocean/atmosphere/biosphere model tuned for interactive economics-based simulations from Y2k on.
Lab 13 Model 3
WIP replication of Khalid Saeed's draft paper presented by the Economics chapter of the SD Society in Sept 2019 youtube video
Unlinking public finance and taxation in fiat currency
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
DRAFT IPA of Jim Gilmore Economic Policy
Simulation of MTBF with controls
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
Useful Life
Wearout
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
Overview:
Overall, this analysis showed a COVID-19 outbreak in Burnie, the government policies to curtail that, and some of the impacts it is having on the Burnie economy.
Variables
The simulation made use of the variables such as; Covid-19: (1): Infection rate. (2): Recovery rate. (3): Death rate. (4): Immunity loss rate etc.
Assumptions:
From the model, it is apparent that government health policies directly affect the economic output of Burnie. A better health policy has proven to have a better economic condition for Burnie and verse versa.
In the COVID-19 model, some variables are set at fixed rates, including the immunity loss rate, recovery rate, death rate, infection rate, and case impact rate, as this is normally influenced by the individual health conditions and social activities.
Moving forward, we decided to set the recovery rate to 0.7, which is a rate above the immunity loss rate of 0.5, so, the number of susceptible could be diminished over time.
Step 1: Try to set all value variables at their lowest point and then stimulate.
Outcome: the number of those Infected are– 135; Recovered – 218; Cases – 597; Death – 18,175; GDP – 10,879.
Step 2: Try to increase the variables of Health Policy, Quarantine, and Travel Restriction to 0.03, others keep the same as step 1, and simulate
Outcome: The number of those Infected – 166 (up); Recovered – 249 (up); Cases – 554 (down); Death – 18,077 (down); GDP – 824 (down).
With this analysis, it is obvious that the increase of health policy, quarantine, and travel restriction will assist in increase recovery rate, a decrease in confirmed cases, a reduction in death cases or fatality rate, but a decrease in Burnie GDP.
Step 3: Enlarge the Testing Rate to 0.4, variable, others, maintain the same as step 2, and simulate
Outcome: It can be seen that the number of Infected is down to – 152; those recovered down to – 243; overall cases up to – 1022; those that died down to–17,625; while the GDP remains – 824.
In this step, it is apparent that the increase of testing rate will assist to increase the confirmed cases.
Step 4: Try to change the GDP Growth Rate to 0.14, then Tourism Growth Rate to 0.02, others keep the same as step 3, and then simulate the model
Outcome: what happens is that the Infected number – 152 remains the same; Recovered rate– 243 the same; Number of Cases – 1022 (same); Death – 17,625 (same); but the GDP goes up to– 6,632.
This final step made it obvious that the increase of GDP growth rate and tourism growth rate will help to improve the overall GDP performance of Burnie's economy.
Clone of The Recent COVID-19 Outbreak in Burnie Tasmania - Buchi Okafor 546792
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
25
Description
This model attempts to show the complex interactions between the youth population of Bourke, the justice system, and community engagement programs. This model takes into consideration 3 key variables; the current socio-economic climate of Bourke, the number of police officers, and government funding allocated for community engagement programs. The models goal is to simulate the effects of the above specified variables on the youth population of Bourke.
Component Explanation
Total Youth Population - The total youth population of Bourke, set to an initial value of 100 to represent 100% of the youth population.
Stable Youth Population - Stable youth population is representing the proportion of youth in Bourke that have a stable upbringing and are not at risk of committing crime. Proportion can be altered by use of the socio-economic factors slider.
At Risk Youth Population - At risk youth population is representing the proportion of youth in Bourke that have an unstable upbringing and are at risk of committing crime. Proportion can be altered by use of the socio-economic factors slider.
Youth Crime - Is the amount of crime being committed by youth in Bourke.
Youth Detention - Is the amount of youth being detained for criminal activity.
Participants in Community Engagement Programs - The amount of the youth population that are being engaged to participate in community programs, such as football or netball clubs.
Youth Run Community Programs - Youth that have been engaged by community programs who go that step further and become more heavily involved in there club, such as coaching or youth leadership roles.
*NOTE* The goal of this stock is to attract at risk youth that are only involved as their friends are running it and take them out of that at risk population stock.
Socio-Economic Factors - Takes into consideration the factors of government and economy to give a proportion of stable and at risk youth. Slider goes between 0 and 1, 0=100% at risk, 1= 100% stable.
Police Officers - The amount of police officers present in Bourke. Slider goes between 2 and 53, 2 assuming 1 officer is patrolling and 1 running the station. 53 as the average wage of a NSW police officer is $75000, so with $4000000 available 53 is the most that Bourke could pay.
Program Funding - The funding for community engagement programs. The more higher funding for a the programs the higher the level of youth engagement. Slider values between $25000 and $3850000 as it shares the same $4000000 that the police wages come out of.
*NOTE* program funding slider set step at $75000, which is the average annual salary of a police officer.
*NOTE* when program funding goes up 1 slider step the police officer slider step must go down and vice versa. In order to maintain the $4000000 available for use.
Interesting Results
- The populations follow a cyclic trend based on the 6 month detention youth offenders.
-With a smaller amount of police officers and a higher funding for community programs a high proportion of at risk youth get involved with community programs instead of crime.
- With a high amount of police officers and little funding for community programs. The stable youth population goes on a upward trend and the at risk youth population turn to crime and end up in detention.
Bourke Justice Reinvestment Model (Ryan Tucker 44648995)
This model illustrates the current practice and consequences of government spending. Following the
direction of the arrows from right to left the model shows the following sequence
based on current practice:
Government Spending at a certain point leads to spending in excess of tax receipts. This will automatically lead to the issue of treasuries in the belief that the excess spending must be financed by borrowing (although the government has the capacity to create money). This in turn will increase the national debt.
Consequences that follow from this practice:
1) That national debt increases whenever the government spends in excess of tax receipts.
2) That the government must pay interest on the debt issued, which in turn increases and reinforces the need for government spending.
3) That the interest paid on treasuries will increase private sector income.
There is an alternative view, supported by Modern Monetary Theory, of how government spending can proceed. Please see this Insight:
https://insightmaker.com/insight/19954
Government Spending (Current Practice)
Clusters of interacting methods for improving health services network design and delivery. Includes Forrester quotes on statistical vs SD methods and the Modeller's dilemma. Simplified version of IM-14982 combined with IM-17598 and IM-9773
Complex Decision Technologies
This is an evolving attempt to illustrate the interconnected nature of the economic assets of Roswell - Chaves County
RCC economic model 1.1
Using the reading assignment from El-Taliawi and Hartley on using a SSM for COVID-19 follow the steps for SSM to include: 1) Describe the Problem (unstructured). 2) Develop a Root Definition for the COVID-19 problem space by identifying the three elements: what, how, why. A System to do X, by (means of) Y, in order to achieve Z. X - What the system does Y - How it does it Z - Why is it being done (see slide 33 in the Systems Thinking Workshop reading)Download Systems Thinking Workshop reading) 3) Identify the Perspectives (CATWOE) 4) Develop a basic Systemigram / Rich Picture to tell the story. Submit your assignment as a Word document or PDF that addresses #1-4.
1) Problem Situation (Unstructured)
The COVID‑19 pandemic represents a complex, ill‑structured problem characterized by uncertainty, rapidly changing conditions, and conflicting stakeholder perspectives. As El‑Taliawi and Hartley emphasize, COVID‑19 is not merely a biomedical crisis but a socio‑technical system failure involving public health, governance, economics, social behavior, and global interdependence. There was no single agreed‑upon definition of “the problem.” For some actors, the problem was viral transmission and mortality; for others, it was economic collapse, civil liberties, misinformation, or institutional trust.
Key features of the unstructured problem include:
High uncertainty about the virus’s behavior, transmission, and long‑term effects.
Multiple stakeholders with competing values and priorities (health vs. economy, freedom vs. safety).
Nonlinear dynamics, where interventions (lockdowns, travel bans, vaccination campaigns) produced unintended consequences.
Fragmented governance, with responses varying across nations, states, and institutions.
Information overload and misinformation, complicating sense‑making and public compliance.
This ambiguity and plurality make COVID‑19 unsuitable for purely “hard” systems approaches and well suited for Soft Systems Methodology (SSM), which focuses on learning, interpretation, and accommodation rather than optimization.
2) Root Definition (What–How–Why)A system to coordinate societal responses to the COVID‑19 pandemic (X), by integrating public health expertise, policy decision‑making, communication, and stakeholder engagement under conditions of uncertainty (Y), in order to reduce harm to human life and societal functioning while maintaining legitimacy, trust, and resilience (Z).
What (X) — Coordinating societal responses to COVID‑19.
How (Y) — Through adaptive governance, expert input, communication, and stakeholder engagement.
Why (Z) — To minimize health, social, and economic harm while sustaining trust and resilience.
This root definition reflects SSM’s emphasis on purposeful human activity systems, not technical control systems.
3) Perspectives (CATWOE Analysis)Customers — General public, vulnerable populations, healthcare workers, businesses, future generations affected by long‑term consequences.
Actors — Governments, public health agencies (e.g., WHO, CDC), healthcare providers, scientists, media organizations, community leaders.
Transformation — A society facing uncontrolled viral spread and systemic disruption is transformed into one that manages risk, adapts behavior, and mitigates impacts.
Worldview (Weltanschauung) — COVID‑19 is a complex, evolving crisis requiring learning, adaptation, and pluralistic decision‑making rather than linear control.
Owners — National governments, international bodies, and political authorities with the power to authorize or terminate interventions.
Environmental Constraints — Scientific uncertainty, political polarization, economic limitations, cultural norms, global interdependence, time pressure.
CATWOE highlights how different worldviews lead to different “valid” problem definitions, reinforcing the need for SSM.
4) Systemigram / Rich Picture (Narrative Description)The rich picture would depict COVID‑19 at the center as a disruptive force interacting with multiple subsystems. Public health institutions generate evolving scientific knowledge, feeding into government decision‑making. Governments issue policies (lockdowns, mandates, economic relief), which influence public behavior. Public response is shaped by trust, media narratives, misinformation, and social norms. Economic systems react to restrictions, feeding back political pressure on leaders. Healthcare systems experience strain, influencing policy urgency and public fear.
Feedback loops dominate the picture:
Policy decisions affect public behavior, which affects transmission rates.
Media narratives influence trust, which affects compliance.
Economic impacts influence political decisions, sometimes undermining health goals.
The systemigram emphasizes learning loops, delays, conflicts, and unintended consequences rather than linear cause‑effect chains.
If you want, I can translate this directly into a Word‑ready format, tighten it to a specific page limit, or sketch a labeled rich‑picture layout you can redraw by hand or digitally for submission.
translate this directly into a Word‑ready format 5 pages and sketch a labeled rich‑picture layout you can redraw by hand or digitally for submission.
Soft Systems Methodology (SSM) Analysis of the COVID‑19 Pandemic
Based on El‑Taliawi and Hartley
IntroductionThe COVID‑19 pandemic represents one of the most complex global crises of the modern era. Traditional “hard” systems approaches, which assume clearly defined problems and measurable objectives, proved insufficient for addressing the pandemic’s intertwined health, social, political, and economic dimensions. El‑Taliawi and Hartley argue that COVID‑19 is best understood as a messy, ill‑structured problem situation, making it well suited for analysis using Soft Systems Methodology (SSM).
SSM does not attempt to “solve” the problem in a technical sense. Instead, it provides a structured learning process to explore multiple perspectives, clarify assumptions, and identify purposeful human activity systems that can improve the situation. This document follows the core SSM steps required in the assignment: (1) describing the unstructured problem, (2) developing a root definition, (3) identifying perspectives using CATWOE, and (4) developing a systemigram / rich picture narrative.
1. Description of the Problem Situation (Unstructured)The COVID‑19 pandemic emerged rapidly and evolved unpredictably, creating a situation characterized by ambiguity, disagreement, and uncertainty. At the outset, there was no shared understanding of the nature or scale of the threat. Scientific knowledge about transmission, severity, and long‑term effects developed incrementally, often changing public guidance and policy decisions. This uncertainty undermined confidence and complicated coordinated action.
The problem situation extended far beyond public health. Governments faced competing pressures to protect lives, preserve economic stability, and maintain civil liberties. Healthcare systems experienced surges in demand, shortages of personnel and equipment, and moral distress among frontline workers. Businesses and workers faced closures, unemployment, and financial insecurity. Social isolation measures disrupted education, mental health, and community cohesion.
Multiple stakeholders framed the “problem” differently. For public health officials, the primary concern was reducing transmission and mortality. For political leaders, the challenge included maintaining legitimacy and public compliance. For citizens, the problem often centered on personal risk, economic survival, and trust in institutions. Media organizations and social platforms amplified both accurate information and misinformation, shaping public perception and behavior.
Covid-19 HW
7 months ago
From Jennifer Prah Ruger (2010) Health Capability Conceptualization and Operationalization Am J Public Health 100 p41-49 available from SSRN. Extends Insight 779 with Dahlgren and Whitehead's Sunrise Diagram of Social Determinants of Health added. See also wikipedia Capability Approach
Health Capability and Social Determinants
Assignment Four - Big Data & Marketing Analytics MKT563
Student No : 94040609
Trish Anderson
The Maranguka Justice Reinvestment (JR) project is a community led program that demonstrates how the redirection of government funding into community programs can address underlying issues which lead to crime in the community (KPMG, 2018). This interactive model shows how the redirection of funding from the Justice System into Community Programs improves the rate of year 12 graduates, reduces the number of incidents of domestic violence whilst reducing the number of days spent in custody. This model also shows how investment in Community Programs leads to positive economic impacts for the Community as well as cost savings for the Justice System over time.
One of the key findings of the Maranguka JP project found that redirecting funding from the Justice system into the Community has multiple benefits. This model begins with NSW government funding on a scale from 0 to 1000 which can be adjusted using the sliders based on available funding, sliding the scale to the right increases the available funding. The % Community Funding and % Justice System Funding variables can then be adjusted in the sliders to determine how much of the funding goes to the community as a percentage and how much of the available funding gets allocated to the justice system.
Investment into Community Programs is made available by the investments into the Bourke community. Community Leaders in the Bourke Community develop programs and each program has shown to have a positive impact on the number of students graduating year 12 in the community, the number of domestic violence incidents and the number of days spent in custody.
Variables
The current number of Year 12 graduates, the current number of domestic violence incidents and the current number of days in Custody are input into the sliders on the hand panel and the model simulation will show how these figures are impacted through investment in the community over time. These variables also contribute to the growth of Youth Development, Family Strength and Adult Empowerment in the community. These factors result in reinvestment opportunities, which have positive economic impacts on the community. Savings are also passed back to the justice system as underlying issues in the community are addressed over time.
The slider scale on the right hand panel uses six adjustable variables to model how the rate of investment in the community can impact the rate of positive impacts in the community and the rate of reinvestment opportunities that can be achieved. Running the simulation will show the larger the % of Community Funding, the faster the results can be seen over time and the greater the economic impact and justice system savings will be. The smaller the % Community Funding will show how impacts are still positive but occur over a longer period of time.
% Community Funding and % Justice System are a percentage of funding whereas NSW Government Funding is represented in dollars ($). NSW Government Funding is on a scale from 0 to 1000 but the assumed scale is $000’s, where 1,000 equals $1,000,000.
Parameter Settings
With community funding, the amount of Year 12 graduates increases by 31% (KPMG, 2018)
With community funding, the number of Domestic Violence incidents decreases by 23% (KPMG, 2018) (KPMG, 2018)
With community funding, the number of days spent in custody decreases by 42% KPMG, 2018)
Reinvestment opportunities contribute to 1/3 of economic impacts back the community where 2/3 of the reinvestment opportunities contribute to savings within the Justice System KPMG, 2018).
Assumptions
The rate of impact on year 12 graduating students, Domestic Violence Incidents and Days spent in custody changes at the same rate of % Community Funding available to the community.
References
Backing Bourke: How a radical new approach is saving young people from a life of crime. (Thompson, G). abc.net.au.
KPMG. (2018). Maranguka Justice Reinvestment Project Impact Assessment. Retrieved from https://www.justreinvest.org.au/wp-content/uploads/2018/11/Maranguka-Justice-Reinvestment-Project-KPMG-Impact-Assessment-FINAL-REPORT.pdf
Impacts of Community Funding and Reinvestment in Bourke
- ‹ previous
- ...
- 18
- 19
- 20