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Based on G.P. Cimellaro et al. Framework for analytical quantification of disaster resilience Engineering Structures 32 (2010) 3639–3649 paper

Facilities Disaster Resilience
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Business Cycle
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THE BROKEN LINK BETWEEN SUPPLY AND DEMAND CREATES TURBULENT CHAOTIC DESTRUCTION

The existing global capitalistic growth paradigm is totally flawed

Growth in supply and productivity is a summation of variables as is demand ... when the link between them is broken by catastrophic failure in a component the creation of unpredictable chaotic turbulence puts the controls ito a situation that will never return the system to its initial conditions as it is STIC system (Lorenz)

The chaotic turbulence is the result of the concept of infinite bigness this has been the destructive influence on all empires and now shown up by Feigenbaum numbers and Dunbar numbers for neural netwoirks

See Guy Lakeman Bubble Theory for more details on keeping systems within finite working containers (villages communities)

THE BROKEN LINK BETWEEN SUPPLY AND DEMAND CREATES CHAOTIC TURBULENCE (+controls)
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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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Economic Cost-Benefit Analysis- Roadkill Mitigation
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This page provides a structural analysis of POTUS Candidate Chris Christi's economic policy based on the information at: https://d70h9a36p82zs.cloudfront.net/Ccpres2016/base/assets/1-0-1/production/Chris-Christie-TheEconomy.pdf   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 Chris Christi economic policy
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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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BTC Previsions (Next 10 Years)
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Michael Marmot's Eur J Epidemiol Essay 2017 See also IM-62760  Social determinants of health from Michael Marmot's  ABC 2016 Boyer Lectures on Social Justice and the Health Gap
Social Justice, Epidemiology and Health Inequalities
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4H's Pioneer Seeds (Economical)
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From Walrave ISDC2014 paper Counteracting the success trap in publically owned corporations. Similar to the ordinary (efficiency focussed) and dynamic capabilities (explore) insight described by David Teece
See also evolution and brain control insight
Explore or Exploit
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Socio-Economic Factors
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Unfortunately, this model only produces the illusion of functioning, but I did manage to get it to give me the graph. However, because of the use of flows, if you change the time step to and the simulation length to anything other than the same numbers, you'll find the graph showing something that looks more exponential. This is due to the function referencing itself in regards to time, so inevitably each time consumption grows it changes the outcome on the other side of the equation. Still, this is a convincing mock up. I added a "45 degree" line so that one could conceivably see (and also change) the difference made by altering the level of autonomous consumption.
Keynesian Macroeconomics
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Ocean/atmosphere/biosphere model tuned for interactive economics-based simulations from Y2k on.
Lab 13 Start_Base model
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This model is developed to simulate how Burnie can deal with a new outbreak of COVID-19 considering health and economic outcomes. The time limit of the simulation is 100 days when a stable circumstance is reached. 

Stocks
There are four stocks involved in this model. Susceptible represents the number of people that potentially could be infected. Infected refers to the number of people infected at the moment. Recovered means the number of people that has been cured, but it could turn into susceptible given a specific period of time since the immunity does not seem everlasting. Death case refers to the total number of death since the beginning of outbreak. The sum of these four stocks add up to the initial population of the town.

Variables
There are four variables in grey colour that indicate rates or factors of infection, recovery, death or economic outcomes. They usually cannot be accurately identified until it happen, therefore they can be modified by the user to adjust for a better simulation outcome.

Immunity loss rate seems to be less relevant in this case because it is usually unsure and varies for individuals, therefore it is fixed in this model.

The most interesting variable in green represents the government policy, which in this situation should be shifting the financial resources to medical resources to control infection rate, reduce death rate and increase recovery rate. It is limited from 0 to 0.8 since a government cannot shift all of the resources. Bigger scale means more resources are shifted. The change of government policy will be well reflected in the economic outcome, users are encouraged to adjust it to see the change.

The economic outcome is the variable that roughly reflects the daily income of the whole town, which cannot be accurate but it does indicate the trend.

Assumptions:
The recovery of the infected won't happen until 30 days later since it is usually a long process. And the start of death will be delayed for 14 days considering the characteristic of the virus.
Economic outcome will be affected by the number of infected since the infected cannot normally perform financial activities.
Immunity effect is fixed at 30 days after recovery.

Interesting Insights:
 In this model it is not hard to find that extreme government policy does not result in the best economic outcome, but the values in-between around 0.5 seems to reach the best financial outcome while the health issues are not compromised. That is why usually the government need to balance health and economic according to the circumstance. 
 

New outbreak of COVID-19 in Burnie
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Model Explanation:

This system dynamics model visualises the impact on investment into policing and community engagement resources on the crime rates within the youth population of Bourke, NSW. 
The model also adds in the variable of funding for safe houses. With a high rate of domestic violence, unfavorable home conditions and other socio-economic factors, many youth roam the streets with no safe place to go, which may lead to negative behaviour patterns.


Assumptions

Youth Population: 700
Total youth population in 2016 for Bourke LGA was 646 (ages 10-29). (Census, 2016) Figures rounded to 700 for purposes of this model simulation. 

Constants:
70% registration and engagement rates for Community funded programs
30% attendance rate for Safe Houses
50% crime conviction rate


Variables

Positive and Negative Influences

The model shows a number of key variables that lead youth to become more vunerable to commit a crime (such as alienation, coming from households with domestic violence, boredom and socio-economic disadvantages such as low income), as well as the variables that enhance the youth's likelihood to be a contributing member of the community (developing trusted relationships and connections with others, and having a sense of self worth, purpose and pride in the community). These factors (positive and negative) are aggregated to a single rate of 50% each for the purposes of the simulation, however each individual situation would be unique.  

Police Funding / Resources

Police funding and resources means the number of active police officers attending to criminal activities, as well as prevention tactics and education programs to reduce negative behaviour. The slider can be moved to increase or decrease policing levels to view the impact on conviction rates. Current policing levels are approx 40 police to a population of under 3000 in Bourke.

Crime Rate

Youth crime rates in Australia were 3.33% (2016). Acknowledging Bourke crime rates are much higher than average, a crime rate of 40% is set initially for this model, but can be varied using the sliders. 


Community Program Funding / Resources

Community Program Funding and Resources means money, facilities and people to develop and support the running of programs such as enhancing employability through mentorship and training, recreational sports and clubs, and volunteering opportunities to give back to the community. As engagement levels in the community programs increase, the levels of crime decrease. The slider can be moved to increase or decrease funding levels to view the impact on youth registrations into the community programs.

Observations

Ideally the simulations should show that an increase in police funding reduces crime rates over time, allowing for more youth committing crimes to be convicted and subsequently rehabilitated, therefore decreasing the overall levels of youth at risk.

A portion of those youth still at risk will move to the youth not at risk category through increased funding of safe houses (allowing a space for them to get out of the negative behaviour loop and away), whom them may consider registering into the community engagement programs. An increase in funding in community engagement programs will see more youth become more constructive members of the community, and that may in turn encourage youth at risk to seek out these programs as well by way of social and sub-cultural influences.

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Justice & Community Support Investment and the Impacts on Bourke Youth Population
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Circular equations WIP for Runy.

Added several versions of the model. Added a flow to make C increase. Added a factor to be able to change the value 0.5. Older version cloned at IM-46280
Clone of Circularity in Economic models
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This model simulates the economics of buying a home. It was created to compare buying a home against using investment returns to pay for rent.

Try cloning this insight, setting the parameter values for real-world scenarios, and then running sensitivity analysis (see tools) to determine the likely wealth outcomes. Compare buying a home to renting. Note that each run will keep the parameters the same while simulating market volatility.

version 1.8
Home buying simulation 1.8
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Prosperity Loop v1.0
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I propose we grow this sim model (or similar) over time to help ourselves better understand the opposing investment and austerity strategies now being advocated for the U.S. government. The hope is to build as simple a model as possible that subsumes the major underlying feedback loops that probably exist in the mental models of proponents of each of these positions. Starting this model was inspired by this Investment vs. Austerity discussion http://www.linkedin.com/groups/Investment-vs-Austerity-How-can-4582801.S.157876413

20120908a_InvestmentVsAusterity
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Economical 4H Insight
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My InsightPublic Opinion Toward Immigration Reform: The Role of Economic Motivations
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Vymazal Lukáš - seminární práce Finanční model
9 months ago