The economy is a self-organizing system that needs continuous growth and a constant inflow of energy and materials in order to maintain itself. Absence of growth will make the system fragile, and economic contraction could lead very quickly to its collapse. These are characteristics of dissipative systems that apply to the free market economy. Another characteristic is that economic activity will unavoidably lead to the generation of waste heat, greenhouse gases and waste materials that the system must expel into its environment, making the system unviable in the present context of global warming and increasing oil prices.
The simplified graphic representation of the economy shows how it is basically profits that generate the funds for the resources needed to guarantee that the system can continue to grow. Loans do not fulfil this function, since loans must be repaid from profit and credit institutions will be reluctant to extend loans if they fear their profits are endangered by the inability of creditors to generate enough income to meet interest payments. So the system depends on private companies and blind market forces. However, society can no longer rely on a system that is blindly guided by the profit motive and that is to a large degree responsible for much of the environmental problems that now afflict us. The system cannot continue in its present self-reinforcing growth mode. Governments can and must step in to fulfil their responsibility and fundamentally reform a system that has become harmful and that is driven exclusively by profit.
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.
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
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
Este modelo es una copia de "Goodwin Business Cycle". Quité al menos una variable y aproximé la relación discreta entre el nivel de empleo y el crecimiento anual del salario con una función basada en la tangente hiperbólica.
Goodwin cycle IM-2010 with debt and taxes added, modified from Steve Keen's illustration of Hyman Minsky's Financial Instability Hypothesis "stability begets instability". This can be extended by adding the Ponzi effect of borrowing for speculative investment.
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.
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.
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