MGMT220 | Insight Maker

This model depicts the yearly effects of the buyers and suppliers in the real-estate marketplace within the next 50 years. It gives an insight of how variables within the housing market interact with each other.

It shows a cycle of Residents (left-side). Once they become interested in buying a property and become home buyers, they are back to becoming homeowners. The Birth Rate has been added because household owners can breed and have their children to grow up becoming interested in buying a property. Once they become interest in buying, this instantly increases the Demand. As a result, the Interest Rate also affects the interest of home owners, has the higher the interest rate, the less likely they would want to buy.

Another cycle is the Properties cycle (right-side). Similar to the Birth Rate, there is the Construction Rate which gradually increases over time as new buildings are built and are entered in the market place. Once it is on sale, it gets sold and remains on the market.

Ultimately, the Price affects and is affected by the Supply and Demand. The higher the Price is, the less of the Demand as residents would rather buy a property at a cheaper rate. As a consequence, the higher the Price, the higher the Supply is as no one is there to buy property and the number of Supply is accumulate due to construction. Simultaneously, as the number of properties (Supply) available on the marketplace increases due to construction, the number of people interested in buying (Demand) decreases since there are more properties for them to buy. This is how the Supply, Demand and Price clash with each other.

The Inflation Rate has been added, as it can increase the number of properties since this gives a good opportunity for investors to add properties to the market.

During the simulation the graph, if the Interested in Buying (Demand) line intersects with the Properties on Sale (Supply), this is an equilibrium which means there is just enough properties for the amount of people (e.g 60 houses for 60 homebuyers). However, if the Supply is less than the Demand, than there is a shortage, meaning the construction rate should be increased.

Below are sliders which the users can adjust showing how each of the variable have an effect on each other.

Real-estate Simulation Model - Duc Nguyen 43280811

Duc Nguyen

5

Model Explanation

This simple model highlights key investment areas within the Bourke community that can influence the overall levels of crime.

The total population of Bourke is split into a percentage of adults and youth who have differing participation rates in community groups. Those who are engaged as a member of a community group most to a positive lifestyle state.

Those who do not wish to join or are not engaged in a community group are offered tertiary education. Similarly, those engaged in tertiary education move to a positive lifestyle state. Those who do not seek or engage in tertiary education are alienated from the community. They are at an 80% chance of committing crime. The other 20% voluntarily reengage with society. Once a crime is committed, the individual is either arrested or eludes arrest. The likelihood of arrest varies depending on policing expenditure. An individual who is arrested will be placed on trial and if found guilty will be placed in a correctional facility (either a juvenile detention centre of jail).

Assumptions

Community engagement participation rates vary depending on the individuals age, with youth more inclined to join a community group. Variance in expenditure is reflected in participation rates in a linear fashion.

Members of Bourke who are not engaged in community groups are targeted and presented with the opportunity to participate in tertiary education such as the teaching of trade-skills. Those targeted have the same likeliness to participate in tertiary education regardless of age.

Those who do not seek any for of community involvement are considered alienated and at a high risk of committing crime.

Sliders have been included to vary the total population size of Bourke, with ratios of Adult to Youth remaining the same. Expenditure can be varied depending on government distribution with the input in dollars.

It is assumed that all relationships are linear within this model. Individuals who are either in a positive lifestyle or correctional facility are in that state for six months before returning to be part of the population of Bourke.

Interesting Results

Even with no Policing Expenditure, there are times when there are no individuals in a correctional facility if Community Engagement Expenditure and Tertiary Skills Development Expenditure is maximised.

Clone of 44854730 - Justice Reinvestment Framework for Bourke

Josh Stevens

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

Clone of Assignment 3 - Ryan Salvaggio 43668070

Fons Wijnhoven

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

Clone of Assignment 3 - Ryan Salvaggio 43668070

Natty Gur

This model depicts the yearly effects of the buyers and suppliers in the real-estate marketplace within the next 50 years. It gives an insight of how variables within the housing market interact with each other.

It shows a cycle of Residents (left-side). Once they become interested in buying a property and become home buyers, they are back to becoming homeowners. The Birth Rate has been added because household owners can breed and have their children to grow up becoming interested in buying a property. Once they become interest in buying, this instantly increases the Demand. As a result, the Interest Rate also affects the interest of home owners, has the higher the interest rate, the less likely they would want to buy.

Another cycle is the Properties cycle (right-side). Similar to the Birth Rate, there is the Construction Rate which gradually increases over time as new buildings are built and are entered in the market place. Once it is on sale, it gets sold and remains on the market.

Ultimately, the Price affects and is affected by the Supply and Demand. The higher the Price is, the less of the Demand as residents would rather buy a property at a cheaper rate. As a consequence, the higher the Price, the higher the Supply is as no one is there to buy property and the number of Supply is accumulate due to construction. Simultaneously, as the number of properties (Supply) available on the marketplace increases due to construction, the number of people interested in buying (Demand) decreases since there are more properties for them to buy. This is how the Supply, Demand and Price clash with each other.

The Inflation Rate has been added, as it can increase the number of properties since this gives a good opportunity for investors to add properties to the market.

During the simulation the graph, if the Interested in Buying (Demand) line intersects with the Properties on Sale (Supply), this is an equilibrium which means there is just enough properties for the amount of people (e.g 60 houses for 60 homebuyers). However, if the Supply is less than the Demand, than there is a shortage, meaning the construction rate should be increased.

Below are sliders which the users can adjust showing how each of the variable have an effect on each other.

Clone of Real-estate Simulation Model - Duc Nguyen 43280811

Jesse Behnke

This model depicts the yearly effects of the buyers and suppliers in the real-estate marketplace within the next 50 years. It gives an insight of how variables within the housing market interact with each other.

It shows a cycle of Residents (left-side). Once they become interested in buying a property and become home buyers, they are back to becoming homeowners. The Birth Rate has been added because household owners can breed and have their children to grow up becoming interested in buying a property. Once they become interest in buying, this instantly increases the Demand. As a result, the Interest Rate also affects the interest of home owners, has the higher the interest rate, the less likely they would want to buy.

Another cycle is the Properties cycle (right-side). Similar to the Birth Rate, there is the Construction Rate which gradually increases over time as new buildings are built and are entered in the market place. Once it is on sale, it gets sold and remains on the market.

Ultimately, the Price affects and is affected by the Supply and Demand. The higher the Price is, the less of the Demand as residents would rather buy a property at a cheaper rate. As a consequence, the higher the Price, the higher the Supply is as no one is there to buy property and the number of Supply is accumulate due to construction. Simultaneously, as the number of properties (Supply) available on the marketplace increases due to construction, the number of people interested in buying (Demand) decreases since there are more properties for them to buy. This is how the Supply, Demand and Price clash with each other.

The Inflation Rate has been added, as it can increase the number of properties since this gives a good opportunity for investors to add properties to the market.

During the simulation the graph, if the Interested in Buying (Demand) line intersects with the Properties on Sale (Supply), this is an equilibrium which means there is just enough properties for the amount of people (e.g 60 houses for 60 homebuyers). However, if the Supply is less than the Demand, than there is a shortage, meaning the construction rate should be increased.

Below are sliders which the users can adjust showing how each of the variable have an effect on each other.

Clone of Real-estate Simulation Model - Duc Nguyen 43280811

Hercules Bothma

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

Clone of Assignment 3 - Ryan Salvaggio 43668070

Harindu Sugathadasa

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

Clone of Assignment 3 - Ryan Salvaggio 43668070

pravir malik

EFFECTS OF POLICING AND COMMUNITY INVESTMENT ON ADOLESCENTS IN BOURKE

BACKGROUND
The model depicts the community of Bourke, analysing the implications of varying community investment and law enforcement expenditure on crime patterns. In particular, it focuses on youth crime.

The town's initially high crime rates is mostly attributable to its limited activities and remote location. Ultimately, the aim of this model is to show how a manipulation of variables can alter youth crime and other stocks.

ASSUMPTIONS OF THE MODEL

1. Bourke has a population of 3,000 - 1,200 (40%) which make up the youth population, and the remaining 1,800 (60%) is the adult population.

2. Simulation value for community investment is 40%.

3. Simulation value for police officers is 450.

4. The reconviction rate (70%) is assumed to be higher than the conviction rate (60%). This is because we assume that law enforcement will be imposed more strictly to those who have already committed a crime at least once.

5. The ‘Engaged’ rate is assumed to be 80%. Given the lesser presence of youth (1,200) in comparison to adults (1,800), it is more likely that the youth population will be required to engage in ‘Community Engagement Programs’ such as sporting clubs or trade-skills.

6. The ‘Improved Youth’ rate is 80%. This is assumed to be high given the nature of the target study. In short, the youth population will be easier to improve, as opposed to if we were analysing adults.

7. It is assumed that, if convicted, juvenile detention time is six months due to the ‘petty’ nature of youth crimes.

STOCKS

1. Youth population – percentage of youth residing in Bourke.

2. Youth crime – number of people out of the youth population who have committed an offence.

3. Juvenile detention – consequence of committing a crime for youth.

4. Community engagement program – a government expenditure reform that involves providing support to the disadvantaged.

VARIABLES

1. Community investment – effectiveness of the community engagement program implemented by government, (initial simulation value 40, or 40%). This has been applied on a linear basis to flows applicable to ‘Community Engagement Programs’.

2. Police officers – number between 1-1800 out of the adult population who are police officers (initial simulation value 450, or 25%). This value is linked with ‘Law Enforcement’, to which Law Enforcement is applied to Conviction Rates. Law Enforcement is assumed to increase at a decreasing rate due to strong beliefs that there are decreasing marginal benefits in increasing the absolute number of police officers (according to the Law of Diminishing Returns).

HOW TO GET INTERESTING RESULTS

1. At 50% community investment, disengagement is higher than improved youth throughout the study time period. This is interesting because there is a significantly higher amount of ‘Improved Youth’ (80%) compared to ‘Disengaged’ (20%). For that reason, it is surprising that there are more disengaged than improved adolescents in Bourke. However, at 100% community investment, ‘Improved Youth’ outweighs ‘Disengaged’ adolescents.

2. At 50% Police capacity (900 Police Officers), the conviction and reconviction rates are higher than adolescents not being convicted. ‘Convicted’ and ‘Not Convicted’ tend to move more closely together, on an absolute basis, compared to ‘Reconvicted’ and ‘Not Reconvicted’.

Olivia Miu (44909209)

Justice Reinvestment in Bourke - 44909209

Olivia Miu

This model depicts the yearly effects of the buyers and suppliers in the real-estate marketplace within the next 50 years. It gives an insight of how variables within the housing market interact with each other.

It shows a cycle of Residents (left-side). Once they become interested in buying a property and become home buyers, they are back to becoming homeowners. The Birth Rate has been added because household owners can breed and have their children to grow up becoming interested in buying a property. Once they become interest in buying, this instantly increases the Demand. As a result, the Interest Rate also affects the interest of home owners, has the higher the interest rate, the less likely they would want to buy.

Another cycle is the Properties cycle (right-side). Similar to the Birth Rate, there is the Construction Rate which gradually increases over time as new buildings are built and are entered in the market place. Once it is on sale, it gets sold and remains on the market.

Ultimately, the Price affects and is affected by the Supply and Demand. The higher the Price is, the less of the Demand as residents would rather buy a property at a cheaper rate. As a consequence, the higher the Price, the higher the Supply is as no one is there to buy property and the number of Supply is accumulate due to construction. Simultaneously, as the number of properties (Supply) available on the marketplace increases due to construction, the number of people interested in buying (Demand) decreases since there are more properties for them to buy. This is how the Supply, Demand and Price clash with each other.

The Inflation Rate has been added, as it can increase the number of properties since this gives a good opportunity for investors to add properties to the market.

During the simulation the graph, if the Interested in Buying (Demand) line intersects with the Properties on Sale (Supply), this is an equilibrium which means there is just enough properties for the amount of people (e.g 60 houses for 60 homebuyers). However, if the Supply is less than the Demand, than there is a shortage, meaning the construction rate should be increased.

Below are sliders which the users can adjust showing how each of the variable have an effect on each other.

Clone of Real-estate Simulation Model - Duc Nguyen 43280811

shriddhi Gopali

Bourke's Justice Reinvestment Options

Bourke consists of a community of 3000 people; 1000 being adults, and 2000 being youth.

This model presents the Youth and Adults from the Bourke and the estimated arrest rates for whether Bourke’s Justice Reinvestment program chooses to increase punishment for crime, or invest in prevention programs to help the community better themselves and avoid trouble. The overall aim is to reduce imprisonment.

Variables such as ‘Arrest rate for Increased Punishment for Youth’ and ‘Arrest rate for Increased Punishment for Adults’ are integrated to show the arrest rates over time when there is an increase in punishment and more policing. Variables such as ‘Arrest rate for Prevention for Youth’ and ‘Arrest rate for Prevention for Adults’ are integrated to show the arrest rates over time when preventative measures have been put in place. All variables are given an estimated rate, though the rate is not fixed and viewers are able to adjust the rates using the appropriate slider bars, as limited information has been provided at this given time in relation to the specified rates.

The slider bars for the youth have been given a range of -2000 to 2000 as the population of youth is 2000 and those are the limits. Same applies to the slider bars specified for the adults, though here the limit is from -1000 to 1000.

By setting parameter settings to a value lower than 0, you will start to see a decline in arrests. By setting the parameter over 0 you will see an increase.

Stocks have been colour coded to represent their lines shown in the simulated graph.

Victoria Jakubowski 43300936

Victoria Jakubowski

This model depicts the yearly effects of the buyers and suppliers in the real-estate marketplace within the next 50 years. It gives an insight of how variables within the housing market interact with each other.

It shows a cycle of Residents (left-side). Once they become interested in buying a property and become home buyers, they are back to becoming homeowners. The Birth Rate has been added because household owners can breed and have their children to grow up becoming interested in buying a property. Once they become interest in buying, this instantly increases the Demand. As a result, the Interest Rate also affects the interest of home owners, has the higher the interest rate, the less likely they would want to buy.

Another cycle is the Properties cycle (right-side). Similar to the Birth Rate, there is the Construction Rate which gradually increases over time as new buildings are built and are entered in the market place. Once it is on sale, it gets sold and remains on the market.

Ultimately, the Price affects and is affected by the Supply and Demand. The higher the Price is, the less of the Demand as residents would rather buy a property at a cheaper rate. As a consequence, the higher the Price, the higher the Supply is as no one is there to buy property and the number of Supply is accumulate due to construction. Simultaneously, as the number of properties (Supply) available on the marketplace increases due to construction, the number of people interested in buying (Demand) decreases since there are more properties for them to buy. This is how the Supply, Demand and Price clash with each other.

The Inflation Rate has been added, as it can increase the number of properties since this gives a good opportunity for investors to add properties to the market.

During the simulation the graph, if the Interested in Buying (Demand) line intersects with the Properties on Sale (Supply), this is an equilibrium which means there is just enough properties for the amount of people (e.g 60 houses for 60 homebuyers). However, if the Supply is less than the Demand, than there is a shortage, meaning the construction rate should be increased.

Below are sliders which the users can adjust showing how each of the variable have an effect on each other.

Clone of Real-estate Simulation Model - Duc Nguyen 43280811

PT

Model Explanation

This simple model highlights key investment areas within the Bourke community that can influence the overall levels of crime.

The total population of Bourke is split into a percentage of adults and youth who have differing participation rates in community groups. Those who are engaged as a member of a community group most to a positive lifestyle state.

Those who do not wish to join or are not engaged in a community group are offered tertiary education. Similarly, those engaged in tertiary education move to a positive lifestyle state. Those who do not seek or engage in tertiary education are alienated from the community. They are at an 80% chance of committing crime. The other 20% voluntarily reengage with society. Once a crime is committed, the individual is either arrested or eludes arrest. The likelihood of arrest varies depending on policing expenditure. An individual who is arrested will be placed on trial and if found guilty will be placed in a correctional facility (either a juvenile detention centre of jail).

Assumptions

Community engagement participation rates vary depending on the individuals age, with youth more inclined to join a community group. Variance in expenditure is reflected in participation rates in a linear fashion.

Members of Bourke who are not engaged in community groups are targeted and presented with the opportunity to participate in tertiary education such as the teaching of trade-skills. Those targeted have the same likeliness to participate in tertiary education regardless of age.

Those who do not seek any for of community involvement are considered alienated and at a high risk of committing crime.

Sliders have been included to vary the total population size of Bourke, with ratios of Adult to Youth remaining the same. Expenditure can be varied depending on government distribution with the input in dollars.

It is assumed that all relationships are linear within this model. Individuals who are either in a positive lifestyle or correctional facility are in that state for six months before returning to be part of the population of Bourke.

Interesting Results

Even with no Policing Expenditure, there are times when there are no individuals in a correctional facility if Community Engagement Expenditure and Tertiary Skills Development Expenditure is maximised.

44854730 - Justice Reinvestment Framework Model for Bourke

Josh Stevens

assignment 3

44051476_Bourke Model

Nicholas Goh Kayshin

Description of the Model:

This model represents the youth community of the town of Bourke, where boredom and lack of motivation is a key issue in the community that has lead to an increase in crime.

The state government has decided to spend money in the town, and the model represents the effects of the level of funding as well as the effects of how the funding is used.

The model can be useful for the state government to decide how much they should spend, and whether it should be spent on policing or sporting clubs and trade schools, while also accounting for the effectiveness of the program and deciding how strict the juvenile detention center should be.

The Model in Detail:

Good Children:

The model assumes that all the children (Based on an estimated 1000 children) are good at the beginning. They very gradually become bored regardless of the level of social sports clubs, as not all children may be interesting in sports.

The funding towards sports clubs and the effectiveness of the programs determine the rate of boredom among the good children.

Bored Mischievous Children:

As children get bored, they will cause mischief and varying degrees of crime.

The level of funding for sports and the effectiveness of the program will affect the level of social engagement that will pull children away from causing mischief, and become good children again.

At the same time the level of funding for policing will affect how many of the trouble making youths get caught and placed in juvenile detention.

Juvenile Detention:

Once the children are in Juvenile detention, they must serve their time to be released.

The release rate reflects how well behaving inmates are released, and the strictness of the sentences applied to youths.

Once they are released, they are still considered bored and causing mischief until they reengaged with the community through social activities funded by the state government such as playing sports.

Adjustments to the Model:

Government Funding:

The state government has decided to spend in Bourke to reduce the level of crime thought to be caused by boredom in teens.

The slider can be adjusted from 0.1 to 100 to reflect the level of funding that the town should receive, where 100 is the maximum and 0.1 is very little funding.

Funding Ratio:

The funding Ratio can slide from 0.1 to 0.9 (left to right). When the slider is at 0.9, 90% the funding goes to policing which is at the right of the diagram and 10% goes to sports clubs.

At 0.1, 90% of the funding goes to Sports clubs which is at the left of the diagram, and only 10% will go to more policing.

Release Rate:

As part of solving the criminal mindset of youths in Bourke, the state government may decide to also be more lenient towards good behaving youths in detention centers by letting them out earlier to reduce the negative influence of other detainees, or simply shortening the time spent in general.

A higher release rate allows more youths to be released meaning a more lenient approach can be modelled.

Effectiveness of Program:

The state government may choose to run various programs with various levels of effectiveness before resorting to diverting spending on policing.

The state government can model the consequences of ineffective programs as well as the benefits of a well run program.

An effectiveness of 1 is maximum effectiveness, while an effectiveness of 0 will result in no effect as result of spending.

Initial Values:

Good Children: 1000

Bored Mischievous Children: 0

Juvenile Detention: 0

Government funding: 100

Funding Ratio: 0.75

Release Rate: 0.1

Effectiveness of Program: 1

42103053 - Riki Gleeson

Riki Gleeson

The story board runs through the premise of the project with the approach I took

Clone of 42845270: Nathaniel Vala_ Assignment 3

Natty Gur

The story board runs through the premise of the project with the approach I took

42845270: Nathaniel Vala_ Assignment 3

Nathaniel Vala

Model Explanation

This simple model highlights key investment areas within the Bourke community that can influence the overall levels of crime.

The total population of Bourke is split into a percentage of adults and youth who have differing participation rates in community groups. Those who are engaged as a member of a community group most to a positive lifestyle state.

Those who do not wish to join or are not engaged in a community group are offered tertiary education. Similarly, those engaged in tertiary education move to a positive lifestyle state. Those who do not seek or engage in tertiary education are alienated from the community. They are at an 80% chance of committing crime. The other 20% voluntarily reengage with society. Once a crime is committed, the individual is either arrested or eludes arrest. The likelihood of arrest varies depending on policing expenditure. An individual who is arrested will be placed on trial and if found guilty will be placed in a correctional facility (either a juvenile detention centre of jail).

Assumptions

Community engagement participation rates vary depending on the individuals age, with youth more inclined to join a community group. Variance in expenditure is reflected in participation rates in a linear fashion.

Members of Bourke who are not engaged in community groups are targeted and presented with the opportunity to participate in tertiary education such as the teaching of trade-skills. Those targeted have the same likeliness to participate in tertiary education regardless of age.

Those who do not seek any for of community involvement are considered alienated and at a high risk of committing crime.

Sliders have been included to vary the total population size of Bourke, with ratios of Adult to Youth remaining the same. Expenditure can be varied depending on government distribution with the input in dollars.

It is assumed that all relationships are linear within this model. Individuals who are either in a positive lifestyle or correctional facility are in that state for six months before returning to be part of the population of Bourke.

Interesting Results

Even with no Policing Expenditure, there are times when there are no individuals in a correctional facility if Community Engagement Expenditure and Tertiary Skills Development Expenditure is maximised.

44854730 - Justice Reinvestment Framework for Bourke

Josh Stevens

Background Information

Bourke is a town of 3000 people in the North West of New South Wales, about 750Km from Sydney.

The state government is implementing a new measure to prevent further crime committed by installing community programs such as sporting clubs, and classes to develop relationships among police and the community.

Many youth were going from home to juvenile detention and back, and many adults were on a similar roundabout between the community and jail.

Community development programs hopefully will reduce the levels of domestic violence among adults, and petty crime among the town’s youth.

Model Explanation

Firstly, you notice Adult and Youth population is separated and assumed that 50% of adults will commit crime whereas 20% in youth. A certain percentage is given for the number that is guilty and not guilty. Guilty, will receive certain punishment according to their age category and after their sentence is served, they go back to town.

Policing Expenditures shows how many officers are needed to reduce the amount of crime. Officers are called when a crime/mischief is committed, whether they're caught or not and found guilty or not.

Stocks

Adult Population: Adults in Bourke

Youth Population: Teenagers in Bourke

Crime: Domestic Violence/Homicide

Mischief: Petty Crimes

Jail: If Guilty, adults are sent to jail

Juvenile Detention: If guilty, youth are sent to detention

Community Programs: Sporting clubs, developing interpersonal relationships among police and “at risk” households, and teaching trade-skills

Assumptions

Adult Population: 2100

Youth Population: 900

Adult Crimes: 50%

Youth Mischief: 20%

Goal

The aim of this model is to illustrate the affects of implementing change i.e. policing/government aid within a community

Trends

The increase of Government Aid and Policing Expenditures decreases the crime performed in the community.

We can see a positive outcome from this and can take into account the affects of proper execution by the state government

44763514 Assignment 3 - Justice Reinvestment in Bourke

Kevin Chu

Brief Description of this Model

This model is design to stimulate the community in Bourke reflecting the involvement of police and community engagement to reduce alienation behavior, crime being committed which would lead to jail. With only 3000 members in the community, Bourke tops the charts of youth crime rates and domestic violence amongst adults which has accumulated cost of millions of dollars. A new approach has been propose to relocate of spending away from policing and justice system into community engagement which this model tries to demonstrate. Investment in communities represents investing in community worker.

Assumptions

Community workers are 80% successful in engaging of community.
Total elimination of pretty crime is not possible.

Initial Values - Members of Bourke Community

Home: 1000

Alienation: 120

Crime: 80

Jail: 200

Community: 500

Local Sport Clubs and Training Course: 100

Police

How this model works

The essences of this model is to dissolve the cycle of disengage community members from feeling alienated and being influence by antisocial activities, in which would likely lead to breaking the law and end up in jail. This model seeks to break the cycle by investing in policing and community workers running of community activities. The police involvement reduces crime rates and antisocial behavior. Engagement by community workers are also able to reduce antisocial.

A couple of program have reportedly been implement including of Operation Solidarity, were police officer follow up of victim's and perpetrator of domestic violence. Broadly represented by consultation/ rehabilitation in the model to include other services provided for alienated and previous offenders to resolve of issues and ease of reconnecting with community.

Antisocial activities is experience at home, local sports clubs and course training and community engagement. Such activities involve in abuse of drug and alcohol, and the effects of unemployment and boredom. Such activities is countered with engagement in community and local sports clubs and course, consultation / rehabilitation.

After consultation / rehabilitation, community members are reconnected with local sports club and from there to community engagement. Some would take time to reflect of their issues at Home, in which they would follow up consultation sessions or engage with community. However, like everyone at home, there is a possibility of expose to alienation and anti-social activities.

Community Engagement represents positive activities and connecting with the community. Idea for adults. The Local sports clubs and course training represents community activities for youth. Such activities have been implement such as the Muranguka Justice reinvestment Project were driving lessons and pre-school activities are offered for disadvantage kids.

Interesting Settings.

As assume not all criminal activities are prevented, therefore, jail would still contain a member of the public. However, graphs would indicate long-term that jails are rather empty.

Police : 99

Community Worker: 90

Justice Reinvestment Framework for Bourke

Siofilisi Latu Sikalu

Assignment 3 depicts how the demand of buyers, the supply of sellers and the price all affect each other. This Insight Maker model indicates a replicate of a live real estate market.

When the demand to purchase a home is high and the supply to sell is low, people selling houses are able to charge higher prices due to lessen competition within the market. This also means that the market has reached a slow point within the year.

When the demand price is considerably higher than the supply price, this demonstrates that people are willing to spend more to buy the home than how much the sellers are selling it for. Therefore, it displays high levels of demand/supply with higher prices, followed by low levels of demand/supply and lower prices due to sellers selling the homes when demands are high. This will result in supply being low as there will be no properties left to sell.

Brandon Virakorn 43238254 Assignment 3 MGMT220

Brandon Sean Virakorn

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

Ryan Salvaggio

4

Kindly view story for a more in-depth simulation.

Background

The crime rates for the town of Bourke have been high, with youth committing petty crimes, going in and out of juvenile detention and adults committing domestic violence and ending up in jail, setting as bad examples for the youth community. Hence, the state government has decided to invest in community activities and police enforcement in hopes of being down the town's crime rate.

This storyline will guide you through a simple model representing the town of Bourke and it shows the impact that community investment and police have on the town's youth and adult crime rates.

Key Assumptions:

1. There are 1,000 youth in Bourke. Out of these 1,000 youth, 250 are already committing petty crimes and 250 are already in detention.

2. There are 2,000 adults in Bourke. Out of these 2,000 adults, 400 are committing domestic violence and 600 are already in jail.

3. Convicted youth/adults stay in detention/jail for 6 months

4. 50 police and 50 community investment represents the maximum amount of funds

5. For every 10 adults who commit domestic violence, 1 youth is influenced to commit petty crimes.

Conclusion/Findings:

The best rate of investment for the town of Bourke is to have 40 police and 40 community investment because that is the scenario when the crime rates and jail/detention rates of both adults and youth are kept at minimum.

(By Andrea Jaelyn Ho 44184727)

Bourke Town Investment Model (44184727, Andrea Jaelyn Ho)

Andrea Jaelyn

MGMT220 Models