#### POPULATION LOGISTIC MAP (WITH FEEDBACK)

##### Guy Lakeman

the maximum population is set to be one million, and the growth rate constant mu = 3. Nj: is the “number of items” in our current generation.

Delta Nj: is the “change in number of items” as we go from the present generation into the next generation. This is just the number of items born minus the number of items who have died.

mu: is the growth or birth rate parameter, similar to that in the exponential growth and decay model. However, as we extend our model it will no longer be the actual growth rate, but rather just a constant that tends to control the actual growth rate without being directly proportional to it.

F(Nj) = mu(1‐Nj/Nmax): is our model for the effective “growth rate”, a rate that decreases as the number of items approaches the maximum allowed by external factors such as food supply, disease or predation. (You can think of mu as the growth or birth rate in the absence of population pressure from other items.) We write this rate as F(Nj), which is a mathematical way of saying F is affected by the number of items, i.e., “F is a function of Nj”. It combines both growth and all the various environmental constraints on growth into a single function. This is a good approach to modeling; start with something that works (exponential growth) and then modify it incrementally, while still incorporating the working model.

Nj+1 = Nj + Delta Nj : This is a mathematical way to say, “The new number of items equals the old number of items plus the change in number of items”.

Nj/Nmax: is what fraction a population has reached of the maximum "carrying capacity" allowed by the external environment. We use this fraction to change the overall growth rate of the population. In the real world, as well as in our model, it is possible for a population to be greater than the maximum population (which is usually an average of many years), at least for a short period of time. This means that we can expect fluctuations in which Nj/Nmax is greater than 1.

This equation is a form of what is known as the logistic map or equation. It is a map because it "maps'' the population in one year into the population of the next year. It is "logistic'' in the military sense of supplying a population with its needs. It a nonlinear equation because it contains a term proportional to Nj^2 and not just Nj. The logistic map equation is also an example of discrete mathematics. It is discrete because the time variable j assumes just integer values, and consequently the variables Nj+1 and Nj do not change continuously into each other, as would a function N(t). In addition to the variables Nj and j, the equation also contains the two parameters mu, the growth rate, and Nmax, the maximum population. You can think of these as "constants'' whose values are determined from external sources and remain fixed as one year of items gets mapped into the next year. However, as part of viewing the computer as a laboratory in which to experiment, and as part of the scientific process, you should vary the parameters in order to explore how the model reacts to changes in them.

Environment MATHS Mathematics Chaos Fractals BIFURCATION Model Economics Finance TURBULENCE Population Growth DECAY STABILITY SUSTAINABLE Engineering Science Demographics Strategy

- 7 years 8 months ago

#### Climate System

##### Sam Poppleton

- 2 years 10 months ago

#### Post Office Simulation

##### Igor Benić

- 5 years 8 months ago

#### Parkrun 2

##### Robert Smith

- 3 years 9 months ago

#### Collaborative Model Development

##### Gene Bellinger

This is the model of the collaborative model development process which is being developed in a collaborative method to help understand what guidelines might be developed to aid in the model development.

- 1 year 10 months ago

#### Clone of Preliminary

##### Cameron and Michael

- 7 years 2 months ago

#### Koein en Reuzenvogels en blumentjens Dio 5V prey predator

##### Dionijs Burmeister

Prey Predator Abiotic Factors Dynamic Model Biology Population

- 5 years 3 months ago

#### MGMT 220 Assignment 3 - 44588917

##### Dylan Latham

Model Description:

This is a system dynamic model. This model is simulating the problem that is occurring in the town of Bourke NSW. It represents that as there is a lack of activities for the youth to participate in they take part in crime to satisfy their boredom. So, the model demonstrates what happens to the crime rates of the youth when more community investment is put in as well as what happens to crime when police presence is increased in Bourke. This simulation is displayed over 5 years monthly.

Assumptions:

<!- Community investment is distributed equally between all the activities.

2. Community investment affects participation rates and reduces the crime rates.

3. The number of police affects the caught rate of crimes

4. When investment increases crime decreases

5. When police presence increases crime rates decreases but the number of youth caught increases

6. The minimum amount in detention is 3 months so there is a 3-month delay – also detention released occur every 3 months and they are released in batches.

7. The amount being released corresponds to the amount caught

Interesting Results:

Police (5), Community Investment (0) – this is a based result showing crime is high and the caught rate is low with no police presence

Police (30), Community Investment (0) – it shows crime is decreasing and amount caught is increasing as more police are present

Police (45), Community Investment (0.2) – it displays that crime is decreasing and the other activities are becoming more popular and is satisfying the youths boredom as well. Boredom also decreases.

Police (65), Community Investment (0.4) - crime and boredom have reduced dramatically due to an increase in investment. Also, the caught rate is becoming more frequent. Also sporting and tafe activities are becoming more prevalent.

Police (100), Community Investment (0.5) – Max police and community investment, shows crime, boredom and amount caught have diminished. Sport and tafe have increase rapidly.

<!- Variables involved:

<!- Community investment – is an adjusted variable as it displays the increase in investment in the community showing a maximum of 50% and a minimum of 5%. This variable can be adjusted with the community investment slider.

<!- Caught variable – it determines the rate of being caught by dividing the amount of police by 100 to get a percentage. This is fixed, but is adjusted by the police stock. This variable can be adjusted with the police slider.

Educated Rate - is a fixed variable with a if statement saying once crime is lower than 100 people more people are leaving tafe educated.This is to show that the rate changes once crime decreases.

Leaving rate - is a variable that is fixed with a if statement saying once crime reaches less than a 100 it reduces the amount leaving. This is to show that the rate changes once crime decreases.

Stocks:

Tafe - Trade skills, Hospitality, vet, personal training

Sport - AFL, Rugby, Netball, Volleyball, Soccer, Cricket

Boredom - in the community walking around the streets, at home doing nothing and looking for trouble.

Youth - population of youth

Crime - Stealing, breaking and entering, drinking under age, taking illegal substances, assault and destroying property.

Caught - gets caught by police.

Police - on duty to take of the community.

Detention - jail/juvenile detention is the punishment for the crimes.

Va

- 2 years 10 months ago

#### Abiotische factoren aanwezig of Koein en Reuzenvogels en blumentjens Dio 5V prey predator

##### Dionijs Burmeister

Prey Predator Abiotic Factors Dynamic Model Biology Population

- 5 years 3 months ago

#### Catastrophe, Abiotischefactoren Koein en Reuzenvogels en blumentjens Dio 5V prey predator

##### Dionijs Burmeister

Prey Predator Abiotic Factors Dynamic Model Biology Population

- 5 years 3 months ago

#### Yuba River Reservoir Model, EL

##### EVAN LESTER

- 6 months 2 weeks ago

#### Final Post Office Simulation

##### Igor Benić

- 5 years 8 months ago

#### Climate

##### Hubert Thompsom-Wakama Tokoni

- 2 years 10 months ago

#### Preliminary

##### Cameron and Michael

- 7 years 3 months ago

#### MOSI Pošta

##### Igor Benić

- 5 years 8 months ago

#### Modelatie marijuana

##### Timo

- 7 years 6 months ago

#### Model: Install Monitors in Coffee Corners for better information/communication

##### Gaby Uhricek

What would be the short-term impact if Monitors would be installed in Coffee Corners

- 4 years 5 months ago

#### Vlcek

##### Sfelar

- 6 years 9 months ago

#### Assignment 2

##### Ismail Khan

- 1 year 9 months ago

#### kalai

##### kalaiarasi

- 6 years 9 months ago

#### Clone of POPULATION LOGISTIC MAP (WITH FEEDBACK)

##### Shrishail

the maximum population is set to be one million, and the growth rate constant mu = 3. Nj: is the “number of items” in our current generation.

Delta Nj: is the “change in number of items” as we go from the present generation into the next generation. This is just the number of items born minus the number of items who have died.

mu: is the growth or birth rate parameter, similar to that in the exponential growth and decay model. However, as we extend our model it will no longer be the actual growth rate, but rather just a constant that tends to control the actual growth rate without being directly proportional to it.

F(Nj) = mu(1‐Nj/Nmax): is our model for the effective “growth rate”, a rate that decreases as the number of items approaches the maximum allowed by external factors such as food supply, disease or predation. (You can think of mu as the growth or birth rate in the absence of population pressure from other items.) We write this rate as F(Nj), which is a mathematical way of saying F is affected by the number of items, i.e., “F is a function of Nj”. It combines both growth and all the various environmental constraints on growth into a single function. This is a good approach to modeling; start with something that works (exponential growth) and then modify it incrementally, while still incorporating the working model.

Nj+1 = Nj + Delta Nj : This is a mathematical way to say, “The new number of items equals the old number of items plus the change in number of items”.

Nj/Nmax: is what fraction a population has reached of the maximum "carrying capacity" allowed by the external environment. We use this fraction to change the overall growth rate of the population. In the real world, as well as in our model, it is possible for a population to be greater than the maximum population (which is usually an average of many years), at least for a short period of time. This means that we can expect fluctuations in which Nj/Nmax is greater than 1.

This equation is a form of what is known as the logistic map or equation. It is a map because it "maps'' the population in one year into the population of the next year. It is "logistic'' in the military sense of supplying a population with its needs. It a nonlinear equation because it contains a term proportional to Nj^2 and not just Nj. The logistic map equation is also an example of discrete mathematics. It is discrete because the time variable j assumes just integer values, and consequently the variables Nj+1 and Nj do not change continuously into each other, as would a function N(t). In addition to the variables Nj and j, the equation also contains the two parameters mu, the growth rate, and Nmax, the maximum population. You can think of these as "constants'' whose values are determined from external sources and remain fixed as one year of items gets mapped into the next year. However, as part of viewing the computer as a laboratory in which to experiment, and as part of the scientific process, you should vary the parameters in order to explore how the model reacts to changes in them.

Environment MATHS Mathematics Chaos Fractals BIFURCATION Model Economics Finance TURBULENCE Population Growth DECAY STABILITY SUSTAINABLE Engineering Science Demographics Strategy

- 7 years 5 months ago

#### Clone of POPULATION LOGISTIC MAP (WITH FEEDBACK)

##### Arash

the maximum population is set to be one million, and the growth rate constant mu = 3. Nj: is the “number of items” in our current generation.

Delta Nj: is the “change in number of items” as we go from the present generation into the next generation. This is just the number of items born minus the number of items who have died.

mu: is the growth or birth rate parameter, similar to that in the exponential growth and decay model. However, as we extend our model it will no longer be the actual growth rate, but rather just a constant that tends to control the actual growth rate without being directly proportional to it.

F(Nj) = mu(1‐Nj/Nmax): is our model for the effective “growth rate”, a rate that decreases as the number of items approaches the maximum allowed by external factors such as food supply, disease or predation. (You can think of mu as the growth or birth rate in the absence of population pressure from other items.) We write this rate as F(Nj), which is a mathematical way of saying F is affected by the number of items, i.e., “F is a function of Nj”. It combines both growth and all the various environmental constraints on growth into a single function. This is a good approach to modeling; start with something that works (exponential growth) and then modify it incrementally, while still incorporating the working model.

Nj+1 = Nj + Delta Nj : This is a mathematical way to say, “The new number of items equals the old number of items plus the change in number of items”.

Nj/Nmax: is what fraction a population has reached of the maximum "carrying capacity" allowed by the external environment. We use this fraction to change the overall growth rate of the population. In the real world, as well as in our model, it is possible for a population to be greater than the maximum population (which is usually an average of many years), at least for a short period of time. This means that we can expect fluctuations in which Nj/Nmax is greater than 1.

This equation is a form of what is known as the logistic map or equation. It is a map because it "maps'' the population in one year into the population of the next year. It is "logistic'' in the military sense of supplying a population with its needs. It a nonlinear equation because it contains a term proportional to Nj^2 and not just Nj. The logistic map equation is also an example of discrete mathematics. It is discrete because the time variable j assumes just integer values, and consequently the variables Nj+1 and Nj do not change continuously into each other, as would a function N(t). In addition to the variables Nj and j, the equation also contains the two parameters mu, the growth rate, and Nmax, the maximum population. You can think of these as "constants'' whose values are determined from external sources and remain fixed as one year of items gets mapped into the next year. However, as part of viewing the computer as a laboratory in which to experiment, and as part of the scientific process, you should vary the parameters in order to explore how the model reacts to changes in them.

Environment MATHS Mathematics Chaos Fractals BIFURCATION Model Economics Finance TURBULENCE Population Growth DECAY STABILITY SUSTAINABLE Engineering Science Demographics Strategy

- 7 years 6 months ago

#### Clone of POPULATION LOGISTIC MAP (WITH FEEDBACK)

##### Shrishail

the maximum population is set to be one million, and the growth rate constant mu = 3. Nj: is the “number of items” in our current generation.

Delta Nj: is the “change in number of items” as we go from the present generation into the next generation. This is just the number of items born minus the number of items who have died.

mu: is the growth or birth rate parameter, similar to that in the exponential growth and decay model. However, as we extend our model it will no longer be the actual growth rate, but rather just a constant that tends to control the actual growth rate without being directly proportional to it.

F(Nj) = mu(1‐Nj/Nmax): is our model for the effective “growth rate”, a rate that decreases as the number of items approaches the maximum allowed by external factors such as food supply, disease or predation. (You can think of mu as the growth or birth rate in the absence of population pressure from other items.) We write this rate as F(Nj), which is a mathematical way of saying F is affected by the number of items, i.e., “F is a function of Nj”. It combines both growth and all the various environmental constraints on growth into a single function. This is a good approach to modeling; start with something that works (exponential growth) and then modify it incrementally, while still incorporating the working model.

Nj+1 = Nj + Delta Nj : This is a mathematical way to say, “The new number of items equals the old number of items plus the change in number of items”.

Nj/Nmax: is what fraction a population has reached of the maximum "carrying capacity" allowed by the external environment. We use this fraction to change the overall growth rate of the population. In the real world, as well as in our model, it is possible for a population to be greater than the maximum population (which is usually an average of many years), at least for a short period of time. This means that we can expect fluctuations in which Nj/Nmax is greater than 1.

This equation is a form of what is known as the logistic map or equation. It is a map because it "maps'' the population in one year into the population of the next year. It is "logistic'' in the military sense of supplying a population with its needs. It a nonlinear equation because it contains a term proportional to Nj^2 and not just Nj. The logistic map equation is also an example of discrete mathematics. It is discrete because the time variable j assumes just integer values, and consequently the variables Nj+1 and Nj do not change continuously into each other, as would a function N(t). In addition to the variables Nj and j, the equation also contains the two parameters mu, the growth rate, and Nmax, the maximum population. You can think of these as "constants'' whose values are determined from external sources and remain fixed as one year of items gets mapped into the next year. However, as part of viewing the computer as a laboratory in which to experiment, and as part of the scientific process, you should vary the parameters in order to explore how the model reacts to changes in them.

- 7 years 5 months ago

#### Clone of POPULATION LOGISTIC MAP (WITH FEEDBACK)

##### Ana Cristina Beja

the maximum population is set to be one million, and the growth rate constant mu = 3. Nj: is the “number of items” in our current generation.

Delta Nj: is the “change in number of items” as we go from the present generation into the next generation. This is just the number of items born minus the number of items who have died.

mu: is the growth or birth rate parameter, similar to that in the exponential growth and decay model. However, as we extend our model it will no longer be the actual growth rate, but rather just a constant that tends to control the actual growth rate without being directly proportional to it.

F(Nj) = mu(1‐Nj/Nmax): is our model for the effective “growth rate”, a rate that decreases as the number of items approaches the maximum allowed by external factors such as food supply, disease or predation. (You can think of mu as the growth or birth rate in the absence of population pressure from other items.) We write this rate as F(Nj), which is a mathematical way of saying F is affected by the number of items, i.e., “F is a function of Nj”. It combines both growth and all the various environmental constraints on growth into a single function. This is a good approach to modeling; start with something that works (exponential growth) and then modify it incrementally, while still incorporating the working model.

Nj+1 = Nj + Delta Nj : This is a mathematical way to say, “The new number of items equals the old number of items plus the change in number of items”.

Nj/Nmax: is what fraction a population has reached of the maximum "carrying capacity" allowed by the external environment. We use this fraction to change the overall growth rate of the population. In the real world, as well as in our model, it is possible for a population to be greater than the maximum population (which is usually an average of many years), at least for a short period of time. This means that we can expect fluctuations in which Nj/Nmax is greater than 1.

This equation is a form of what is known as the logistic map or equation. It is a map because it "maps'' the population in one year into the population of the next year. It is "logistic'' in the military sense of supplying a population with its needs. It a nonlinear equation because it contains a term proportional to Nj^2 and not just Nj. The logistic map equation is also an example of discrete mathematics. It is discrete because the time variable j assumes just integer values, and consequently the variables Nj+1 and Nj do not change continuously into each other, as would a function N(t). In addition to the variables Nj and j, the equation also contains the two parameters mu, the growth rate, and Nmax, the maximum population. You can think of these as "constants'' whose values are determined from external sources and remain fixed as one year of items gets mapped into the next year. However, as part of viewing the computer as a laboratory in which to experiment, and as part of the scientific process, you should vary the parameters in order to explore how the model reacts to changes in them.

- 4 years 7 months ago