These models and simulations have been tagged “MGMT220”.
Assignment 3 – Complex Systems
Salvaggio - 43668070
conceptualizes the effects on a real-estate market-model utilizing agent based
modelling. This model utilizes basic economic principles of supply and
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'.
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
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.
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.
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
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).
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.
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.
- Your First ABM/Part II
EFFECTS OF POLICING AND COMMUNITY INVESTMENT ON ADOLESCENTS IN BOURKE
BACKGROUNDThe 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
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.
1. Youth population – percentage of youth residing
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.
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
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.
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)
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.