Clone of IM-91683 from jacqui and vincy Summary of paper map produced by participants at the compelling case for prevention workshop 6 june 2017. 

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.  

 Goodwin cycle IM-2010 with debt and taxes added, modified from Steve Keen. THis can be extended by adding the Ponzi effect of borrowing for speculative investment.

A system diagram for the Mojave Desert including example socio-economic factors for an assignment at OSU- RNG 341.

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

Laying out and testing before coupling to main model (which is Final Project)

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

Ocean/atmosphere/biosphere model tuned for interactive economics-based simulations from Y2k on.

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

Overview of Part G Ch 27 to 30 of Mitchell Wray and Watts Textbook see IM-164967 for book overview

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

This model indicate indicates the modeling COVID-19 outbreaks and responses from government policies with the effect on the local economy. Model was occurred at Burnie, Tasmania. The model mainly contains three parts: COVID-19 pandemic outbreak, four differences government policies and what the impact on economy from those policies.

 

Assumptions:

(1) Various variables influence the model, which can result in varied outcomes. The following values are based on an estimate and may differ from actual values. Government initiatives are focused at reducing Covid-19 infections and, as a result, affecting (both positive and negative) economic growth.

 

(2) 42% of infected people will recovery. 10% of people who are infected will die and the rate relatively higher due to the much old people living in Burnie, Tasmania.

78% of cases get tested.

 

(3) Government policy will only be implemented when there are ten or more recorded cases. Four government policies have had influences on infection.  

 

(4) The rising number of instances will have a negative impact on Burnie's economic growth.

 

Insights:

1. As a result of the government's covid 19 rules, fewer people will be vulnerable. Less people going to be susceptible.

 

2. After the government policy intervention, there is a effectively reduce of infected people.

 

3. Overall, there is no big differences of economic performance from the graph, might due to the positive and negative effect of economy. And after two weeks, the economy maintained a level of development without much decline.

Aaron Smith Model / Simulation of the UK Air Transport System for : Modelling, Simulation and Visualisation. ELP076

WIP Summary of Mariana Mazzucato's 2018 book See also IM-901 MacroEc