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Ocean/atmosphere/biosphere model tuned for interactive economics-based simulations from Y2k on.
Final Project - Working
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Overview of Ch 25 of Mitchell Wray and Watts Textbook see IM-164967 for book overview
The Role of Investment in Profit Generation
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System Thinking and Modelling of Biophysical, Socio-economic and Cultural components of Barangay Iwahig - Judy Ann Simil
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THE NEW SCIENCE OF PLEASURE Daniel L. McFadden NBER Working Paper 18687

From Extended Neoclassical to Behavioral Choice Models
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CLD exposition of Goodwin01 from Steve Keen's August 2019 course on Introduction to Economic Dynamics and Minsky software See video and powerpoint slides. Based on IM-2011 Minsky FIH and IM-168865 MacroEconomics CLDs. SeeIM-172005 for Simulation

Goodwin cycle Minsky Keen August 2019
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This is a toy model of fractional reserve banking.

In the first period, there is foreign spending using domestic currency. This spending creates offshore banking reserves.  The offshore bank then lends to the domestic bank. In consequence, the banking sector captures all of yield on government debt.  
Banking Leverage
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COVID Systemigram
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lab 13 Social and economic
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Ijssel Delta Final
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Model description:
This model is designed to simulate the outbreak of Covid-19 in Burnie in Tasmania, death cases, the governmental responses and Burnie local economy. 

More importantly, the impact of governmental responses to both Covid-19 infection and to local economy, the impact of death cases to local economy are illustrated. 

The model is based on SIR (Susceptible, Infected and recovered) model. 

Variables:
The simulation takes into account the following variables: 

Variables related to Covid-19: (1): Infection rate. (2): Recovery rate. (3): Death rate. (4): Immunity loss rate. 

Variables related to Governmental policies: (1): Vaccination mandate. (2): Travel restriction to Burnie. (3): Economic support. (4): Gathering restriction.

Variables related to economic growth: Economic growth rate. 

Adjustable variables are listed in the part below, together with the adjusting range.

Assumptions:
(1): Governmental policies are aimed to control(reduce) Covid-19 infections and affect (both reduce and increase) economic growth accordingly.

(2) Governmental policy will only be applied when reported cases are 10 or more. 

(3) The increasing cases will negatively influence Burnie economic growth.

Enlightening insights:
(1) Vaccination mandate, when changing from 80% to 100%, doesn't seem to affect the number of death cases.

(2) Governmental policies are effectively control the growing death cases and limit it to 195. 

Burnie Tasmania Covid - 19 outbreak simulation Model by Yankang Huang 541 277
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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
8 months ago
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This is a toy model of an investment market.

Households follow a simple ratio to invest in bonds or equities.  In part, the investment decision is stochastic, such that stock market returns are volatile, with equities more volatile than bonds and with a higher yield. As such, the system shows increasing volatility as the investment bubble grows.


Investment Markets
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New Power Book summary about platform participation by Jeremy Heimans and Henry Timms 2018 book and 2014 hbr article See also videos TED talk 2014 and Talk at Google 2018
New Power Book
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Summary of Ch1 of Mitchell Wray and Watts Textbook see IM-164967 for overview
Macroeconomics Introduction
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Economic capital (Fig. 37)
2 months ago
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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
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BTC Previsions (Next 10 Years)
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This model is comparing healthy and sick residents in Burnie, Tasmania after the Covid-19 Outbreak in 2020. It will also show how the Burnie economy is effected by the disease, how the Government Health Policies are implemented and how they are enforced.

This model is based on the SIR, Susceptible, Infection, Recovery (or Removed) These are the three possible states related to the members of the Burnie population when a contagious decease spreads.

The Government/Government Health Policy, played a big part in the successful decrease in Covid-19 infections. The Government enforced the following.
- No travel (interstate or international)
- Isolation within the residents homes
- Social distancing by 1.5m
- Quarantine
- Non essential companies to be temporarily closed
- Limitations on public gatherings
- And limits on time and kilometers aloud to travel from ones home within a local community

This resulted in lower reported infection rates of Covid-19 and higher recovery rates.

In my opinion:
When the first case was reported the Government could have been even faster to enforce these rules to decrease the fatality rates further for the Burnie, population.  

Assumption: Government policies were only triggered when 10 cases were recorded.
Also, more cases that had been recorded effected the economic growth during this time.

Interesting Findings: In the simulation it shows as the death rates increases towards the end of the week, the rate of testing goes down. You would think that the government would have enforced a higher testing rate over the duration of this time to decrease the number of infections, exposed which would increase the recovery rates faster and more efficiently.  

Figures have been determined by the population of Burnie being 19,380 at the time of assignment.

Complex Systems How Burnie Tasmania dealt with Covid-19 Outbreak BMA708
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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
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Ocean/atmosphere/biosphere model tuned for interactive economics-based simulations from Y2k on.
Final Project with KAYA Q1
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This causal loop diagram illustrates the interconnected factors affecting the economic empowerment of Congolese refugee women in Rwanda, with economic dependency as the central problem reinforced by limited access to vocational training, employment opportunities, and financial services. The diagram shows two key reinforcing loops: one where vocational training leads to employment and income generation, which reduces dependency and improves access to further training (R1), and another where income generation builds self-confidence and skills recognition, leading to better employment opportunities (R2), while language barriers and cultural constraints act as inhibiting factors throughout the system.

Causal Loop Diagram: Economic Empowerment of Congolese refugee women in Rwanda.
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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
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Fig 4. The Casual Loop Diagram of the Socio-Political and Economic Subsystems