Population | Insight Maker

Adapted from Hartmut Bossel's "System Zoo 3 Simulation Models, Economy, Society, Development."

Population model where the population is summarized in four age groups (children, parents, older people, old people). Used as a base population model for dealing with issues such as employment, care for the elderly, pensions dynamics, etc.

Clone of Z602 Population with four age groups

瀋壘

Exponential growth model for humans, based on birth rate and death rate, both a function of consumption.

Global ecosystem model with self-regenerating ecological capital, and ecological Impact (ecological footprint)

Nature and People, separate

Christopher Bystroff

Show prediction of birth and death rate over time, creating the elements of the demographic transition. This one is for Morocco.

Morocco Population Projection 2045

Mackenzie Emerson

Show relation of birth and death rate over time, creating the elements of the demographic transition. This one is for Sweden. You can clone this insight for other nations, just plug in the new crude birth and death rates and find the starting population in 1960.

Clone of Demographic Transition-Sweden

Emily Roach

Sakums Adazhiem

Evija Mirke

Shows the ecological impact of population.

Clone of Population Ecological Impact- Ecuador

Michelle Wieland

OVERSHOOT GROWTH GOES INTO TURBULENT CHAOTIC DESTRUCTION

The existing global capitalistic growth paradigm is totally flawed

The chaotic turbulence is the result of the concept of infinite bigness this has been the destructive influence on all empires and now shown up by Feigenbaum numbers and Dunbar numbers for neural netwoirks

See Guy Lakeman Bubble Theory for more details on keeping systems within finite limited size working capacity containers (villages communities)

Clone of OVERSHOOT GROWTH INTO TURBULENCE

Gary Leonard

This model incorporates several options in examining fisheries dynamics and fisheries employment. The two most important aspects are the choice between I)managing based on setting fixed quota versus setting fixed effort , and ii) using the 'scientific advice' for quota setting versus allowing 'political influence' on quota setting (the assumption here is that you have good estimates of recruitment and stock assessments that form the basis of 'scientific advice' and then 'political influnce' that desires increased quota beyond the scientific advice).

Clone of Clone of Fixed Quota versus Fixed Effort

travis smith

Adapted from Hartmut Bossel's "System Zoo 3 Simulation Models, Economy, Society, Development."

Population model where the population is summarized in four age groups (children, parents, older people, old people). Used as a base population model for dealing with issues such as employment, care for the elderly, pensions dynamics, etc.

Clone of Clone of Z602 Population with four age groups

Bechara Assouad

Exploring the conditions of permanent coexistence, rather than gradual disappearance of disadvantaged competitors. Z506 p32-35 System Zoo 3 by Hartmut Bossel.

Clone of Competition for Resources

Dennis Bao

The Logistic Map is a polynomial mapping (equivalently, recurrence relation) of degree 2, often cited as an archetypal example of how complex, chaotic behaviour can arise from very simple non-linear dynamical equations. The map was popularized in a seminal 1976 paper by the biologist Robert May, in part as a discrete-time demographic model analogous to the logistic equation first created by Pierre François Verhulst.

Mathematically, the logistic map is written

where:

is a number between zero and one, and represents the ratio of existing population to the maximum possible population at year n, and hence x0 represents the initial ratio of population to max. population (at year 0)r is a positive number, and represents a combined rate for reproduction and starvation. To generate a bifurcation diagram, set 'r base' to 2 and 'r ramp' to 1
To demonstrate sensitivity to initial conditions, try two runs with 'r base' set to 3 and 'Initial X' of 0.5 and 0.501, then look at first ~20 time steps

Clone of Clone of The Logistic Map

Joshua Messick

Simulation of MTBF with controls

F(t) = 1 - e ^ -λt

Where

• F(t) is the probability of failure

• λ is the failure rate in 1/time unit (1/h, for example)

• t is the observed service life (h, for example)

The inverse curve is the trust time
On the right the increase in failures brings its inverse which is loss of trust and move into suspicion and lack of confidence.
This can be seen in strategic social applications with those who put economy before providing the priorities of the basic living infrastructures for all.

This applies to policies and strategic decisions as well as physical equipment.
A) Equipment wears out through friction and preventive maintenance can increase the useful lifetime,
B) Policies/working practices/guidelines have to be updated to reflect changes in the external environment and eventually be replaced when for instance a population rises too large (constitutional changes are required to keep pace with evolution, e.g. the concepts of the ancient Greeks, 3000 years ago, who based their thoughts on a small population cannot be applied in 2013 except where populations can be contained into productive working communities with balanced profit and loss centers to ensure sustainability)

Early Life

If we follow the slope from the leftmost start to where it begins to flatten out this can be considered the first period. The first period is characterized by a decreasing failure rate. It is what occurs during the “early life” of a population of units. The weaker units fail leaving a population that is more rigorous.

Useful Life

The next period is the flat bottom portion of the graph. It is called the “useful life” period. Failures occur more in a random sequence during this time. It is difficult to predict which failure mode will occur, but the rate of failures is predictable. Notice the constant slope.

Wearout

The third period begins at the point where the slope begins to increase and extends to the rightmost end of the graph. This is what happens when units become old and begin to fail at an increasing rate. It is called the “wearout” period.

Clone of Clone of BATHTUB MEAN TIME BETWEEN FAILURE (MTBF) RISK

Ivan Stamenkovic

Simulation of MTBF with controls

F(t) = 1 - e ^ -λt

Where

• F(t) is the probability of failure

• λ is the failure rate in 1/time unit (1/h, for example)

• t is the observed service life (h, for example)

The inverse curve is the trust time
On the right the increase in failures brings its inverse which is loss of trust and move into suspicion and lack of confidence.
This can be seen in strategic social applications with those who put economy before providing the priorities of the basic living infrastructures for all.

This applies to policies and strategic decisions as well as physical equipment.
A) Equipment wears out through friction and preventive maintenance can increase the useful lifetime,
B) Policies/working practices/guidelines have to be updated to reflect changes in the external environment and eventually be replaced when for instance a population rises too large (constitutional changes are required to keep pace with evolution, e.g. the concepts of the ancient Greeks, 3000 years ago, who based their thoughts on a small population cannot be applied in 2013 except where populations can be contained into productive working communities with balanced profit and loss centers to ensure sustainability)

Early Life

If we follow the slope from the leftmost start to where it begins to flatten out this can be considered the first period. The first period is characterized by a decreasing failure rate. It is what occurs during the “early life” of a population of units. The weaker units fail leaving a population that is more rigorous.

Useful Life

The next period is the flat bottom portion of the graph. It is called the “useful life” period. Failures occur more in a random sequence during this time. It is difficult to predict which failure mode will occur, but the rate of failures is predictable. Notice the constant slope.

Wearout

The third period begins at the point where the slope begins to increase and extends to the rightmost end of the graph. This is what happens when units become old and begin to fail at an increasing rate. It is called the “wearout” period.

Clone of BATHTUB MEAN TIME BETWEEN FAILURE (MTBF) RISK

atif

Predator-prey models are the building masses of the bio-and environments as bio masses are become out of their asset masses. Species contend, advance and scatter essentially to look for assets to support their battle for their very presence. This model is designed to represent the moose and wolf population on Isle Royal. The variables include moose population, wolf population, moose birth rate, wolf birth rate, moose death proportionality constant, and wolf death proportionality constant. This model was adapted from https://insightmaker.com/insight/3A0dqQnXXh8zxWJtkwwAH9/Lotka-Volterra-Model-Prey-Predator-Simulation.

Looking at Lotka-Volterra Model:

The well known Italian mathematician Vito Volterra proposed a differential condition model to clarify the watched increment in predator fish in the Adriatic Sea during World War I. Simultaneously in the United States, the conditions contemplated by Volterra were determined freely by Alfred Lotka (1925) to portray a theoretical synthetic response wherein the concoction fixations waver. The Lotka-Volterra model is the least complex model of predator-prey communications. It depends on direct per capita development rates, which are composed as f=b−py and g=rx−d.

A detailed explanation of the parameters:

The parameter b is the development rate of species x (the prey) without communication with species y (the predators). Prey numbers are reduced by these collaborations: The per capita development rate diminishes (here directly) with expanding y, conceivably getting to be negative.
The parameter p estimates the effect of predation on x˙/x.
The parameter d is the death rate of species y without connection with species x.
The term rx means the net rate of development of the predator population in light of the size of the prey population.

Reference:

http://www.scholarpedia.org/article/Predator-prey_model

https://insightmaker.com/insight/3A0dqQnXXh8zxWJtkwwAH9/Lotka-Volterra-Model-Prey-Predator-Simulation

Lotka-Volterra Model: Moose-Wolf Simulation

Emily Blackaby

This model incorporates several options in examining fisheries dynamics and fisheries employment. The two most important aspects are the choice between I)managing based on setting fixed quota versus setting fixed effort , and ii) using the 'scientific advice' for quota setting versus allowing 'political influence' on quota setting (the assumption here is that you have good estimates of recruitment and stock assessments that form the basis of 'scientific advice' and then 'political influnce' that desires increased quota beyond the scientific advice).

Clone of Fixed Quota versus Fixed Effort

Sianna

The Logistic Map is a polynomial mapping (equivalently, recurrence relation) of degree 2, often cited as an archetypal example of how complex, chaotic behaviour can arise from very simple non-linear dynamical equations. The map was popularized in a seminal 1976 paper by the biologist Robert May, in part as a discrete-time demographic model analogous to the logistic equation first created by Pierre François Verhulst.

Mathematically, the logistic map is written

where:

is a number between zero and one, and represents the ratio of existing population to the maximum possible population at year n, and hence x0 represents the initial ratio of population to max. population (at year 0)r is a positive number, and represents a combined rate for reproduction and starvation. To generate a bifurcation diagram, set 'r base' to 2 and 'r ramp' to 1
To demonstrate sensitivity to initial conditions, try two runs with 'r base' set to 3 and 'Initial X' of 0.5 and 0.501, then look at first ~20 time steps

Clone of Clone of The Logistic Map

Troy Gamboa

The Logistic Map is a polynomial mapping (equivalently, recurrence relation) of degree 2, often cited as an archetypal example of how complex, chaotic behaviour can arise from very simple non-linear dynamical equations. The map was popularized in a seminal 1976 paper by the biologist Robert May, in part as a discrete-time demographic model analogous to the logistic equation first created by Pierre François Verhulst.

Mathematically, the logistic map is written

where:

is a number between zero and one, and represents the ratio of existing population to the maximum possible population at year n, and hence x0 represents the initial ratio of population to max. population (at year 0)r is a positive number, and represents a combined rate for reproduction and starvation. To generate a bifurcation diagram, set 'r base' to 2 and 'r ramp' to 1
To demonstrate sensitivity to initial conditions, try two runs with 'r base' set to 3 and 'Initial X' of 0.5 and 0.501, then look at first ~20 time steps

Clone of The Logistic Map

Nicole M Radziwill

17

The Logistic Map is a polynomial mapping (equivalently, recurrence relation) of degree 2, often cited as an archetypal example of how complex, chaotic behaviour can arise from very simple non-linear dynamical equations. The map was popularized in a seminal 1976 paper by the biologist Robert May, in part as a discrete-time demographic model analogous to the logistic equation first created by Pierre François Verhulst.

Mathematically, the logistic map is written

where:

is a number between zero and one, and represents the ratio of existing population to the maximum possible population at year n, and hence x0 represents the initial ratio of population to max. population (at year 0)r is a positive number, and represents a combined rate for reproduction and starvation. To generate a bifurcation diagram, set 'r base' to 2 and 'r ramp' to 1
To demonstrate sensitivity to initial conditions, try two runs with 'r base' set to 3 and 'Initial X' of 0.5 and 0.501, then look at first ~20 time steps

Clone of Clone of The Logistic Map

Brian J Elliott

This is a basic BIDE (birth, immigration, death, emigration) model. Not all parts are implemented, however Birth and Death are.

First homework insight

ella ah

Clone of Lake Sturgeon Population Model

Ashley Hendricks

6

Unconstrained exponential model to simulate population growth. There are three scenarios (Main, Big Population, High Growth) and four charts (Population, Growth, Population & Growth, Population vs Growth)

Population Growth

Pandega Abyan Zumarsyah

Adapted from Hartmut Bossel's "System Zoo 3 Simulation Models, Economy, Society, Development."

Population model where the population is summarized in four age groups (children, parents, older people, old people). Used as a base population model for dealing with issues such as employment, care for the elderly, pensions dynamics, etc.

Clone of Z602 Population with four age groups

Nirmal Chandra

Simulation of Population

CVD prevention model

Gbenga Olatunde

EIGENE MODIFIKATIONEN

Clone of Miniwelt nach Bossel

Thomas Neher

Population Models