Insight diagram
Model of Covid-19 outbreak in Burnie, Tasmania

This model was designed from SIR model(susceptible, infected, revovered) to find out the effect of covid-19 outbreak into economic outcomes via government policy.

Assumptions

The government policy is triggered when number of infected is more than ten.

The government policies will take negative effect into Covid-19 outbreaks and 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 change 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

Increase vaccination rate and testing rate will decrease the number amount of infected case and a little bit more negative effect to economic system. However economic system still need a long time to recover in both cases.
BMA708_Assignment 3_ndkvo_520272_COVID-19 outbreak and Burnie economy
Insight diagram

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
Insight diagram
Full Systems Model - Democratizing Internet
Insight diagram
System Zoo Z409 Fishery dynamics from Hartmut Bossel (2007) System Zoo 2 Simulation Models. Climate, Ecosystems, Resources

Fishing is a classic example for use of a renewable resource. Unless overfished, fish populations If is hardly by fishing, then the fish population will persist at a constant size corresponding to its specific ecological envi­ ronment If the stock is overfished, the juvenile generation becomes too small to fully replace the adult generation. If overfishing continues. the population cannot recover and will collapse in short time. Even if fish catch stops now/, it could take decades until the fish population recovers to its original size if it hasn't become extinct meanwhile. In many of the world overtlshing has led, and still leads, to the complete collapse of formerly huge tlsh populations: herring in the North Sea, codtlsh in the Northern Atlantic. tuna, whales to name only a few. With the collapse of fish stocks came the collapse of the t1shing industry in many regions. Employment and
incomes disappeared: whole regions (like Newfoundland) lost their economic base.​
REM 221 - Z409 Fishery dynamics
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Deforestation and Economic Development in an Underdeveloped County
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Economic model
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WIP Elements from macroeconomics, neoliberalism and commercial determinants of health frameworks to provide a background to the effects of the universal basic income on health and wellbeing for the first 1000 days. UBI diagram modified from Johnson2021 article Expanded in Insight 2
Employment and Welfare Interventions Effect on the first 1000 days 1
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Summary of UNEP ecosystems services CBA 2011 article by Wegner and Pascual
Value and cost benefit analysis
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Economical Factors of Science: C8
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An initial study of the economics of single use coffee pods.
Coffee Pods ISD Humanities - Anouk D 10.4
Insight diagram
This model blends insights from several research based sources to establish an initial capture or causal hypothesis.   The initial, presumed structures and influences captures the dominant research-reported threads, elements and dynamics as relate to radicalization and, conversely: legitimization & state stability.
Dynamics of Radicalization
Insight diagram
This is the summary of lecture ​1 of my Course about StartUps. It's an intro to the startup ecosystem and the different stakeholders that can interact with your new enterprise at different stages of its evolution and growth. -version 1 - for info or suggestions: bonato.pietroz@gmail.com
StartUp ecosystem
Insight diagram
Week 13.1 Lab Economic Model
Insight diagram
Economic Systems
Insight diagram
Economic model
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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
Insight diagram
Economic forecast model
Insight diagram
241004_economic growth model structure_SFD
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Laying out and testing before coupling to main model (which is Final Project)
Socio-Economic Factors
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Ocean/atmosphere/biosphere model tuned for interactive economics-based simulations from Y2k on.
Lab 13 Start
Insight diagram
Economic Model
Insight diagram

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