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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)
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This paper aims at describing a case where system dynamics modeling was used to evaluate the effects of information and material supply lead-time variation on sales contributions margins and operating cash conversion cycle of a commodity export business.  An empirical dynamic model, loaded with econometric theory of price effect on competitive demand, was used to describe the input data.  The model simulation outputs proved themselves relevant in analyzing the complex interconnections of multiple variables affecting  the profitability in a commercial routine, supporting the decision process among sales managers.

SDR Case study System dynamic modelling
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WIP Dynamic map from Steve Keen's Minsky at 100 Lecture video and slides and later Emergent Macroeconomics papers
Minsky Instability from Macrodefinitions Keen
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A Model for COVID-19 outbreak
AT3
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Just need to add sensitivity testing and storytelling, as well as make simulation displays for each thing we want to show

Also, everything in the agent folder cannot be displayed as its own primitive, so you can't see their individual outputs. We need to make agent states that simply have the [primitive title] as their code.
GSGS4610_Greece_Germany_Migration_FinalModel
7 last week
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An initial study of the economics of single use coffee pods.
3 variables-- ORIGINAL Coffee Pods ISD Humanities v 1.02
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Ocean/atmosphere/biosphere model tuned for interactive economics-based simulations from Y2k on.
Lauren Final Project
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BAM economic model
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Simple model of the global economy, the global carbon cycle, and planetary energy balance.

The planetary energy balance model is a two-box model, with shallow and deep ocean heat reservoirs. The carbon cycle model is a 4-box model, with the atmosphere, shallow ocean, deep ocean, and terrestrial carbon. 

The economic model is based on the Kaya identity, which decomposes CO2 emissions into population, GDP/capita, energy intensity of GDP, and carbon intensity of energy. It allows for temperature-related climate damages to both GDP and the growth rate of GDP.

This model was originally created by Bob Kopp (Rutgers University) in support of the SESYNC Climate Learning Project.
Clone of Simple Climate-Carbon-Economic Model
Insight diagram

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

Introduction

The 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
2 months ago
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This model aims to show that how Tasmania government's Covid-19 policy can address the spread of the pandemic and in what way these policy can damage the economy.

This model assumes that if the COVID-19 cases are more than 10, the government will take action such as quarantine and lockdown at the area. These policy can indirectly affect the local economy in many different way. At the same time, strict policy may be essential for combating Covid-19.

From the simulation of the model, we can clearly see that the economy of Burine will be steady increase when government successfully reduces the COVID-19 cased and make it spreading slower.

Interesting finding: In this pandemic, the testing rate and the recovery rate are important to stop Covid-19 spreading. Once the cases of Covid-19 less than 10, the government might stop intervention and the economy of Burnie will back to normal.

Model of Covid-19 outbreaks at Burnie (Yingchao Yang,503757)
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Part 2 of Lab 1
Van Dusen_Energy Economics and Fossil Fuel
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Scott Page's Aggregation diagram from Complexity and Sociology 2015 article see also IM-9115 and SA IM-1163
Macro micro dynamics
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Government and economic systems, and their effect on climate change
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Nghi Son Economic Zone Pollutant Loads
4 months ago
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This model is based off Meadows economic capital with reinforcing growth loop constrained by a renewable resource model.
Tourism Simulator
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DeWitz_Energy Economics and Fossil Fuel
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Environmental, social, and economic strategy integration for better business ideas
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Bt resistance systems map
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Do you like travelling? Find out how we can help make it more sustainable!

Images used:
Suitcase: 
http://vignette2.wikia.nocookie.net/animaljam/images/a/ab/Travel-suitcase_(1).png/revision/latest?cb=20140907211051
Fiji: http://cdn.newsapi.com.au/image/v1/91c1263a2a357b3673af8ff8362c0c8d?width=1024
Hotels:
https://taj.tajhotels.com/content/dam/luxury/hotels/taj-palace-delhi/images/master2/16x7/38849817-H1-Exterior_1-16x7.jpg
Poverty:
http://www.montana.edu/extensionecon/images/povertywordcloud.jpg
Leverage Point
https://image.slidesharecdn.com/genderinintegratedsystemsresearchbycynthiamcdougallseniorscientistgenderequitythemeleader-150311041710-conversion-gate01/95/gender-in-integrated-systems-research-by-cynthia-mcdougall-senior-scientist-gender-equity-theme-leader-25-638.jpg?cb=1426065542

Sources:
https://www.statista.com/statistics/270422/forecast-for-2020-arrivals-of-foreign-tourists-worldwide/

http://www.sustainabletourism.net

Week 10 Lecture- Dr. David Tindall Lecture pdf

https://www.adb.org/countries/fiji/poverty

Economic Impact of Tourism on Fiji's Economy: Empirical Evidence from Computable General Equilibrium Model by Paresh Kumar Narayan

https://www.wttc.org/-/media/files/reports/economic%20impact%20research/countries%202015/fiji2015.pdf

http://sustainabletravel.org/our-work/regional-alliances/pacific/hotel-sustainable-resources-pacific/

http://reusegraywater.com/about-us/

Rubin, K.E., The Valuation of Hotels and Motels for Assessment Purposes, 1984

https://en.wikipedia.org/wiki/Leverage-point_modeling

Meadows, D.H., & Wright, D. (2009). Thinking in systems: a primer, London: Earthscan.






Sustainable Tourism (Final)
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integratrated solar  energy  economics  for  northeast brazil depend   consultant engs
Solar Energy - Efficiency economic s
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Concept map for Market revenue
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Model of Covid-19 outbreak in Burnie, Tasmania

Balancing Health and Economy factor
Vaccination rate will help to recovered more people and decrease the immunity loss rate.


Additionally. The lack of food during the covid-19 pandemic still an obstacle for economic development.

In someway, Health balancing in every people will help to shut down covid-19 and help economic development even grow up faster.


Model of Covid-19 outbreak in Burnie, Tasmania
Insight diagram
Economic Loop D7
8 months ago