Regulation of resource allocation to production in response to inventory adequacy and delivery delay. A non-price-mediated resource allocation system. From Sterman JD Business Dynamics p172 Fig 5-27

Artificial Economics Model based on Multi-Avatar Agents following the papers: "An economic experiment to investigate Firms Financial decisions" and "Towards a Multi-Avatar Macroeconomic System"

 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.

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

Image Used;

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

An initial study of the economics of single use coffee pods.

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. 

Assignment Four - Big Data & Marketing Analytics MKT563

Student No : 94040609

Trish Anderson

The Maranguka Justice Reinvestment (JR) project is a community led program that demonstrates how the redirection of government funding into community programs can address underlying issues which lead to crime in the community (KPMG, 2018). This interactive model shows how the redirection of funding from the Justice System into Community Programs improves the rate of year 12 graduates, reduces the number of incidents of domestic violence whilst reducing the number of days spent in custody. This model also shows how investment in Community Programs leads to positive economic impacts for the Community as well as cost savings for the Justice System over time.

 

One of the key findings of the Maranguka JP project found that redirecting funding from the Justice system into the Community has multiple benefits. This model begins with NSW government funding on a scale from 0 to 1000 which can be adjusted using the sliders based on available funding, sliding the scale to the right increases the available funding. The % Community Funding and % Justice System Funding variables can then be adjusted in the sliders to determine how much of the funding goes to the community as a percentage and how much of the available funding gets allocated to the justice system.  

 

Investment into Community Programs is made available by the investments into the Bourke community.  Community Leaders in the Bourke Community develop programs and each program has shown to have a positive impact on the number of students graduating year 12 in the community, the number of domestic violence incidents and the number of days spent in custody.

 

Variables

The current number of Year 12 graduates, the current number of domestic violence incidents and the current number of days in Custody are input into the sliders on the hand panel and the model simulation will show how these figures are impacted through investment in the community over time. These variables also contribute to the growth of Youth Development, Family Strength and Adult Empowerment in the community. These factors result in reinvestment opportunities, which have positive economic impacts on the community. Savings are also passed back to the justice system as underlying issues in the community are addressed over time.

 

The slider scale on the right hand panel uses six adjustable variables to model how the rate of investment in the community can impact the rate of positive impacts in the community and the rate of reinvestment opportunities that can be achieved. Running the simulation will show the larger the % of Community Funding, the faster the results can be seen over time and the greater the economic impact and justice system savings will be. The smaller the % Community Funding will show how impacts are still positive but occur over a longer period of time.

 

% Community Funding and % Justice System are a percentage of funding whereas NSW Government Funding is represented in dollars ($). NSW Government Funding is on a scale from 0 to 1000 but the assumed scale is $000’s, where 1,000 equals $1,000,000.

 

 

Parameter Settings

With community funding, the amount of Year 12 graduates increases by 31% (KPMG, 2018)

With community funding, the number of Domestic Violence incidents decreases by 23% (KPMG, 2018) (KPMG, 2018)

With community funding, the number of days spent in custody decreases by 42% KPMG, 2018)

Reinvestment opportunities contribute to 1/3 of economic impacts back the community where 2/3 of the reinvestment opportunities contribute to savings within the Justice System KPMG, 2018).

 

Assumptions

The rate of impact on year 12 graduating students, Domestic Violence Incidents and Days spent in custody changes at the same rate of % Community Funding available to the community.

 

References

 

Backing Bourke: How a radical new approach is saving young people from a life of crime. (Thompson, G). abc.net.au.

KPMG. (2018). Maranguka Justice Reinvestment Project Impact Assessment. Retrieved from https://www.justreinvest.org.au/wp-content/uploads/2018/11/Maranguka-Justice-Reinvestment-Project-KPMG-Impact-Assessment-FINAL-REPORT.pdf

Summary and Causal loop diagram of Paulo Freire's 1968 book . See also Youtube video esp Chomsky, Freire 1998 Cultural Action for Freedom publication and Zhong Yaying ubc 2018 thesis

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

A systems model of the relationships amongst economic situation, health situations and Covid-19 in Burnie, Tasmania.

An initial study of the economics of single use coffee pods.

Summary of US apa2017 report pdf link