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241004_economic growth model structure_SFD
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WIP Based on Steve Keen's Inaugural Kingston Lecture Youtube video slides models and data all at his blog
Is Capitalism Doomed to Crises
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Laying out and testing before coupling to main model (which is Final Project)
Socio-Economic Factors
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Urbanisation insight
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This page provides a structural analysis of POTUS Candidate Rand Paul's economic policy based on the information at:  https://www.randpaul.com/issue/spending-and-debt and also   https://www.randpaul.com/issue/taxes  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 Rand Paul Economic Policy
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Ecological economics
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Explanation
This model shows the COVID-19 outbreak in Burnie and how the government policy impacts the economy. The possible phases when the infectious disease spreads in Burnie can be labelled as Susceptible, Infection and Recovery, which are main factors in the model. It is concluded that the government policy can reduce the infectious disease and also the impact in the overall economy.

Assumption
The Government Healthy Policy will affect the decrease in the infection and economy growth rate at the same time.

The Government Health Policy is only triggered when there are more than 10 cases

The increase in number of COVID-19 cases can affect negatively towards the economic growth.

Interesting Insights:
The Government's vaccination promote will reduce the possibility of spreading the infectious disease. 

When vaccination rate increase, the dead, infected people and susceptible group will all decrease. This reveals that the crucial role in government's vaccination promote program.

When there is more than 10 confirmed cases, the government policies can effectively reduce the infections and the overall economic activities.


BMA708_Assignment 3_Joleen Tanjaya
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Assignment 3 – Complex Systems

 Ryan Salvaggio - 43668070

 

The Model

This model conceptualizes the effects on a real-estate market-model utilizing agent based modelling. This model utilizes basic economic principles of supply and demand.

The model bases itself on two Agents - one being ‘Customers’ of the real estate market model, whilst the other being the Real estate itself, coined 'Houses'.

Consumers (Demand)

The Agent population, ‘Consumers’ specifies the total amount of people whom can potentially become buyers within the market. This is limited to 30 for conceptual purposes. The Agent ‘Consumer’ exists in two states, either being an ‘Active Customer’ (Active) or an ‘Inactive Customer’ (Inactive).  The transition from Inactive to Active occurs upon the basis that the ‘Budget’ of the Consumer meets the desired price of the marketplace, this is specified through the variable ‘Budget’ defining the probability that this transition will occur – this is adjustable by the user indicating a highly resistive or by accepting the market. ‘Budget’s probability in a real life scenario would be based upon numerous factors however conceptually utilizing the slider can present many of these various situations.

Upon transitioning into an active state an ‘Active consumer’ will attempt to find the closest ‘For sale household’, this is represented and carried out through the ‘Enter’ action.  Upon finding a household the consumer and house will both return to their respected inactive state thus repeating the process.

Demand – ‘Count of active customers – demand’ is then calculated by a count of Consumers transitioned and currently in the Active state. A high demand would be indicative through a high ‘Budget’ responsiveness whilst a low demand would be indicative of a low ‘Budget’ responsiveness. The increase in Price and hence supply of household thus reduces demand and vise versa.  

House (Supply)

The Agent population, ‘Houses’ specifies the total amount of households that can potentially become for sale within the market. This is limited to 112 for conceptual purposes. The Agent ‘House’ exists in two states, either being ‘For Sale’ (Active) or ‘Not for Sale’ (Inactive).  The transition from Inactive to Active occurs upon the basis that the ‘Motivation to Sell’ of the House is satisfied, this satisfaction is specified by a set probability that this transition will occur – this is adjustable by the user indicating a highly responsive or restricted house market. ‘Motivation to sell’ probability in a real life scenario would be based upon numerous factors however conceptually utilizing the slider can present many of these various situations.

Upon transitioning into an active state a ‘For Sale’ house will wait for an ‘Active Customer’ ‘this is represented and carried out through the ‘Search’ action. Upon completion of the action both states become inactive and the process continues.

Supply – ‘Count of houses for sale –supply’ is then calculated by a count of Houses ‘For Sale’ that are currently in the active state. Ultimately a high Motivation to sell would sharply increase supply, whilst a low motivation would have the adverse effects.  

Movement Speed

Movement speed – describes the base movement rate of Consumers. This variable describes the transition into the ‘Inactive’ state of a consumer, ultimately when a household is found and purchased. Movement speed affects both demand and supply in the sense that the transitioning of stages is quickened and more responsive. (Indicated by a more rigid demand and supply curve).

Market Price

In economics Price is a linear function (straight line) of the proportion of houses for sale (positive slope), and also a linear function of the proportion of buyers (negative slope).Therefore , the variable ‘Market Price’ is calculated by 10 * the portion of ‘House’ in the active state (which is the supply) over the portion of ‘Consumers’ in the active state (which is the demand) Ultimately this presents the economic principles  that as Supply is directly related to Price and demand is inversely related to Price.

Note

Each simulation (with the same settings) will present a different and unique simulation. I have set a Random Boolean to the active component that randomizes the amount of Customers or houses that begin in their active state. The probability is only 0.008 but is useful in describing the effects on the market from various position’s and seeing unique models.  

References

https://www.youtube.com/watch?v=ynuoZQbqeUg - Your First ABM/Part II

https://insightmaker.com/insight/35714/Foraging-Model

Assignment 3 - Ryan Salvaggio 43668070
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Implementação do modelo Handy.

Referência:

Motesharrei, S.; Rivas, J.; Kalnay, E. "Human and nature dynamics (HANDY): Modelling inequality and use of resources in the collapse or sustainability of societies". Ecological Economics 101 (2014) 90-102

http://www.sciencedirect.com/science/article/pii/S0921800914000615
HANDY
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Social determinants of health are economic and social conditions that influence the health of people and communities. These conditions are shaped by the amount of money, power, and resources that people have, all of which are influenced by policy choices. Social determinants of health affect factors that are related to health outcomes. Factors related to health outcomes include:
  • How a person develops during the first few years of life (early childhood development)
  • How much education a persons obtains
  • Being able to get and keep a job
  • What kind of work a person does
  • Having food or being able to get food (food security)
  • Having access to health services and the quality of those services
  • Housing status
  • How much money a person earns
  • Discrimination and social support
Determinates of a healthy population
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Overview of Part G Ch 27 to 30 of Mitchell Wray and Watts Textbook see IM-164967 for book overview
History of Macroeconomic Thought
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The key issue here (still an incomplete model) is that insight maker doesnt like the agent folder and the flows. I've solved the original problem where the flows couldn't go across agent boundaries (I did this simply by moving the stocks and their respective flows all OUTSIDE of the folder), but now the main issue is that the flows outside of the folder (particularly [Internal Factor Flow] don't pick up the links from the variables inside of the folder. A key variable, susceptability, has to connect to the previously mentioned flow. The problem is that it lies in the agent folder which means that the flow can't recognize the link. I even tried using a ghost of the Susceptability variable and placed it outside of the folder but that didn't work. 
Fixing_GSGS_GREECE_GERMANY_MIGRATION_DRAFT
27 3 months ago
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Launchpad about reorganisation based on Bogdanov's Tektology general theory of organization, perceptual control theory, personal history and current concerns, linked to the modern (or historical) organization of biology and political economy. See a Bogdanov overview insight
Personalised Reorganization Bogdanov Biology and Political Economy
3 months ago
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WIP Summary of Mariana Mazzucato's 2018 book See also IM-901 MacroEc
The Value of Everything
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Full Systems Model - Democratizing Internet
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Fig 4. The Casual Loop Diagram of the Socio-Political and Economic Subsystems
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EcoCinco_Deforestation_Land Changes
8 months ago
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Ocean/atmosphere/biosphere model tuned for interactive economics-based simulations from Y2k on.
This Scenario hits Affluence (1% decrease per annum) to increase decarbonization of energy. Additionally, decrease in affluence is increased by temperature increases damaging the global economy
Final Project 4 W/ Socio-Economic Factors - Investment+Temperature Degradation
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Cornerstore Economic Model
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Aaron Smith Model / Simulation of the UK Air Transport System for : Modelling, Simulation and Visualisation. ELP076
Air Transport System Model
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Social pressures create {Youth Alienation}, leading to youth developing bad behaviours and committing crimes. This attracts {Police Enforcement} who will, in turn, engage the {Community Leadership} where they introduce programs that are designed to assist youth to prevent re-offending through the development of {Community Clubs}, which then contributes to {Community Development}.

{Police Enforcement} collaborates with {Educational Institutions} to boost retention, which translates to socio-economic progress through {Community Development}. On the other hand, criminals are detained and put through the {Court} system, where the offenders are removed from the community through {Imprisonment}. This results in a stable and safe environment, which aid support for {Community Development).

The role of {Community Leadership} in the system, particularly at the grassroots will result is huge savings in the economy, aiding economic growth. The {Community Leadership} collaborates with the {Employment & Justice Agencies}, translating into socio-economic progress {Community Development}

The Community Development Model

This model provides an understanding into the relationships and links between a range of variable units and fixed units, and how {Community Development} is supported.

As {Youth Alienation} rate increases,  the {Crime} rate increases (both variables) demands police enforcement. {Police Enforcement} is a fixed variable as increase in police force is fixed over a period of time. 

To increase efficiency, engages or collaborate with:

•{Community Leadership} (fixed and variable) – is fixed for a certain period, and becomes variable as youth criminal activities increases

•{Court} (variable) – as youth criminal activities increase, the court resources reman fixed. It then removes some offenders from the community and imprison them, creating peace and stability in the community 

•{Educational Institutions} (variables) – as student retention increases, more institutions are needed.  

Variables that are linked to the {Community Leadership} which include;
•{Youth Sports Clubs}
•{Employment & Justice Agencies}
•{Economic Preservation; and}
•{Educational Institutions}

Contribute/support {Community Development}. These are variables, as more youths are referred or engaged.

The {Community Leadership} and {Police Enforcement} collaborate, support and design community programs to reduce youth criminal activities, which could potentially reduce the justice system expenditure.

The relationship between these fixed and variable units create a sustainable Community Development.

The Community Development Model
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An initial study of the economics of single use coffee pods.
Copy of humanities economy thing
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This model shows the simulation of COVID-19 outbreaks when it hit Burnie, Tasmania. This model will show how government intervention will impact the total number in COVID 19 cases and the overall economic activity.

 

Assumptions

1.   The current Burnie population in 19550. Therefore, the susceptible population is equal to the current Burnie population.

2.       Since Burnie is just a regional city, the virus infection rate is 25% as 5000 people in Burnie went into quarantine during the outbreak last year.

3.       50% of people who are infected will recover.

4.       20% of people who are infected will die because Burnie population average is old.

5.       Government intervention and policy will reduce the Infection

6.       COVID-19 is only countable as a case if the infected people have been tested, and the percentage of testing depends on how many infected people have been tested.

7.       Following a recovery, there is a chance that people could lose their immunity, and also the immunity loss rate measures this.

8.       Government intervention will reduce the infection rate by 15%.

9.       Lockdown will cause tourism industry to shut down and affect the overall economic activity.

10.   Lockdown is one of the most effective way to prevent infection.

11.   Strict health protocol also contributes to reduce the infection.

12.   Vaccination will not make people fully immune to the virus. However, vaccinated people will reduce the immunity loss percentage.

13.   Economic growth rate percentage is based on year 2020.

Findings

1.       COVID-19 could be significantly reduced in number and the spread of the vaccine could make a significant impact on the epidemic.

2.       Economic activity will drop during the first phase of government intervention, However, it will steadily increase overtime

3.       Less people going to be susceptible as government imposed covid 19 rules.

BMA708 Michael Sunjaya Jurenang ID:547923
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ESI6550 Group 6 (Model 2)