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.
Theory of Structural Change for IAMO Research Group
The part-whole paradigm
Examples of research issues addressed here include the path dependence of farm structures, regime shifts in land-system change, as well as transitional processes in the evolution of farm structures and innovation systems. All these issues feature counter-intuitive systemic properties that could not have been predicted using standard agricultural economics tools. The key strength of the research group in regard to the part-whole paradigm is the internationally renowned expertise in the agent-based modelling of agricultural policy. (More on what happened here until now / is happening now)
The system-environment paradigm
This paradigm is represented by conceptual research drawing inspiration from Niklas Luhmann’s theory of “complexity-reducing” and “operationally closed” social systems. The attributes of complexity reduction and operational closure are shown to generate sustainability problems, conflicts, social dilemmas, ethical issues, and divergent mental models. The organizing idea explaining these phenomena is the complexity-sustainability trade-off, i.e., the tendency of the operationally closed systems to develop excessive internal complexity that overstrains the carrying capacity of the environment. Until now, the conceptual work along these lines has focused on developing the systems-theoretic principles of ecological degradation and highlighted the sustainability-enhancing role of nonprofit organizations and corporate social responsibility. Another overarching topic has been the analysis of connections between Luhmann’s social systems theory and the evolutionary economics approaches, such as those of Thorstein Veblen and Kenneth Boulding. <!--[if gte mso 9]> Normal 0 false false false DE X-NONE X-NONE <![endif]--><!--[if gte mso 9]> <![endif]--><!--[if gte mso 10]> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-ansi-language:DE;} <![endif]-->
Modern industrial civilisation has created massive interdependencies which define it and without which it could not function. We all depend on industrial farming to produce the food we eat, we depend on gasoline being available at the gas station, on the availability of electricity and even on the bread supplied by the local baker. Naturally, we tend to support the institutions that supply the amenities and goods to which we have become accustomed: if we get our food from the local supermarket, it is likely that we would be opposed to it’s closure. This means that the economic system that relies on continuous growth enjoys implicit societal support and that nothing short of environmental disaster or a shortage of essential raw materials will impede it’s growing indefinitely. It is not hard to work out the consequences of this situation!
There is plenty of empiric evidence to show that this has happened time and time again. For instance, a report from UNCTAD (United Nations Conference on Trade and Development) found that between 1990 and 2000 in all the cases examined where cutbacks in public spending and tax increases were used, the fiscal situation did not only not improve but worsened. Despite such repeated evidence, unfortunately calls for austerity measures continue to be heard.
Description
Model of Covid-19 outbreak in Burnie, Tasmania
This model was designed from the SIR model(susceptible, infected, recovered) to determine the effect of the covid-19 outbreak on economic outcomes via government policy.
Assumptions
The government policy is triggered when the number of infected is more than ten.
The government policies will take a negative effect on Covid-19 outbreaks and the financial system.
Parameters
We set some fixed and adjusted variables.
Covid-19 outbreak's parameter
Fixed parameters: Infection rate, Background disease, recovery rate.
Adjusted parameter: Immunity loss rate can be changed from vaccination rate.
Government policy's parameters
Adjusted parameters: Testing rate(from 0.15 to 0.95), vaccination rate(from 0.3 to 1), travel ban(from 0 to 0.9), social distancing(from 0.1 to 0.8), Quarantine(from 0.1 to 0.9)
Economic's parameters
Fixed parameter: Tourism
Adjusted parameter: Economic growth rate(from 0.3 to 0.5)
Interesting insight
An increased vaccination rate and testing rate will decrease the number of infected cases and have a little more negative effect on the economic system. However, the financial system still needs a long time to recover in both cases.
- Labor Supply = 100
- ‹ previous
- ...
- 18
- 19
- 20
