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.
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 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.
Description:
Model of Covid-19 outbreak in Burnie, Tasmania
This model was designed from the SIR model(susceptible, infected, recovered) to determine the effect of the covid-19 outbreak on economic outcomes via government policy.
Assumptions:
The government policy is triggered when the
number of infected is more than ten.
The government policies will take a negative effect on Covid-19 outbreaks and the financial system.
Parameters:
We set some fixed and adjusted variables.
Covid-19 outbreak's parameter
Fixed parameter: Background disease.
Adjusted parameters: Infection rate, recovery rate. Immunity loss rate can be changed from vaccination rate.
Government policy's parameters
Adjusted parameters: Testing rate(from 0.15 to 0.95), vaccination rate(from 0.3 to 1), travel ban(from 0 to 0.9), social distancing(from 0.1 to 0.8), Quarantine(from 0.1 to 0.9)
Economic's parameters
Fixed parameter: Tourism
Adjusted parameter: Economic growth rate(from 0.3 to 0.5)
Interesting insight
An increased vaccination rate and testing rate will decrease the number of infected cases and have a little more negative effect on the economic system. However, the financial system still needs a long time to recover in both cases.
- Labor Supply = 100
NOME E DESCRIÇÃO
LINK
TAMANHO
Prova1.Investimento Fixo e Tomada de Decisões Rápidas
https://canvas.instructure.com/courses/780776/files/folder/provahtml?preview=51184101
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Prova.2.Investimento método Lang
https://canvas.instructure.com/courses/780776/files/folder/provahtml?preview=5118414432 KB
Prova 3 Investimento Fixo método Chilton
ihttps://canvas.instructure.com/courses/780776/files/folder/provahtml?preview=51184169
33 KB
Prova4:Custo Fixo
https://canvas.instructure.com/courses/780776/files/folder/provahtml?preview=5118418932 KB
Prova 5:Custo de mao de obra
customaohtm custo de mao de obra
33 KB
Prova 6 Custo de mat,comb e enegia
CustoMat prima , energia
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Prova 7 Custo total
custo de operacional de producao33 KB
Prova 8 Ponto deEquilibrio
ponto de eqilibrioibrio
32 KB
Prova 9:Analise de lucro e beneficios
Fluxo de caixa33 KB
Theory of Structural Change for IAMO Research Group
The part-whole paradigm
Examples of research issues addressed here include the path dependence of farm structures, regime shifts in land-system change, as well as transitional processes in the evolution of farm structures and innovation systems. All these issues feature counter-intuitive systemic properties that could not have been predicted using standard agricultural economics tools. The key strength of the research group in regard to the part-whole paradigm is the internationally renowned expertise in the agent-based modelling of agricultural policy. (More on what happened here until now / is happening now)
The system-environment paradigm
This paradigm is represented by conceptual research drawing inspiration from Niklas Luhmann’s theory of “complexity-reducing” and “operationally closed” social systems. The attributes of complexity reduction and operational closure are shown to generate sustainability problems, conflicts, social dilemmas, ethical issues, and divergent mental models. The organizing idea explaining these phenomena is the complexity-sustainability trade-off, i.e., the tendency of the operationally closed systems to develop excessive internal complexity that overstrains the carrying capacity of the environment. Until now, the conceptual work along these lines has focused on developing the systems-theoretic principles of ecological degradation and highlighted the sustainability-enhancing role of nonprofit organizations and corporate social responsibility. Another overarching topic has been the analysis of connections between Luhmann’s social systems theory and the evolutionary economics approaches, such as those of Thorstein Veblen and Kenneth Boulding. <!--[if gte mso 9]> Normal 0 false false false DE X-NONE X-NONE <![endif]--><!--[if gte mso 9]> <![endif]--><!--[if gte mso 10]> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-ansi-language:DE;} <![endif]-->
The cotton industry expanded dramatically in Central America after WW2, increasing from 20,000 hectares to 463,000 in the late 1970s. This expansion was accompanied by a huge increase in industrial pesticide application which would eventually become the downfall of the industry.
The primary pest for cotton production, bol weevil, became increasingly resistant to chemical pesticides as they were applied each year. The application of pesticides also caused new pests to appear, such as leafworms, cotton aphids and whitefly, which in turn further fuelled increased application of pesticides.
The treadmill resulted in massive increases in pesticide applications: in the early years they were only applied a few times per season, but this application rose to up to 40 applications per season by the 1970s; accounting for over 50% of the costs of production in some regions.
The skyrocketing costs associated with increasing pesticide use were one of the key factors that led to the dramatic decline of the cotton industry in Central America: decreasing from its peak in the 1970s to less than 100,000 hectares in the 1990s. “In its wake, economic ruin and environmental devastation were left” as once thriving towns became ghost towns, and once fertile soils were wasted, eroded and abandoned (Lappe, 1998).
Sources: Douglas L. Murray (1994), Cultivating Crisis: The Human Cost of Pesticides in Latin America, pp35-41; Francis Moore Lappe et al (1998), World Hunger: 12 Myths, 2nd Edition, pp54-55.
https://srsroccoreport.com/the-blood-bath-continues-in-the-u-s-major-oil-industry/
