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Simulation of how tiger population and anti poaching efforts effect the black market value of tiger organs.
Clone of Tiger Population and Black Market Value
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FORCED GROWTH GROWTH GOES INTO TURBULENT CHAOTIC DESTRUCTION 
 BEWARE pushing increased growth blows the system!
(governments are trying to push growth on already unstable systems !)

The existing global capitalistic growth paradigm is totally flawed

The chaotic turbulence is the result of the concept and flawed strategy 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)

FORCED GROWTH INTO TURBULENCE
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Josie Morgan Lab 2 Geog 166
Lab 2
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Dynamic simulation modelers are particularly interested in understanding and being able to distinguish between the behavior of stocks and flows that result from internal interactions and those that result from external forces acting on a system.  For some time modelers have been particularly interested in internal interactions that result in stable oscillations in the absence of any external forces acting on a system.  The model in this last scenario was independently developed by Alfred Lotka (1924) and Vito Volterra (1926).  Lotka was interested in understanding internal dynamics that might explain oscillations in moth and butterfly populations and the parasitoids that attack them.  Volterra was interested in explaining an increase in coastal populations of predatory fish and a decrease in their prey that was observed during World War I when human fishing pressures on the predator species declined.  Both discovered that a relatively simple model is capable of producing the cyclical behaviors they observed.  Since that time, several researchers have been able to reproduce the modeling dynamics in simple experimental systems consisting of only predators and prey.  It is now generally recognized that the model world that Lotka and Volterra produced is too simple to explain the complexity of most and predator-prey dynamics in nature.  And yet, the model significantly advanced our understanding of the critical role of feedback in predator-prey interactions and in feeding relationships that result in community dynamics.The Lotka–Volterra model makes a number of assumptions about the environment and evolution of the predator and prey populations:

1. The prey population finds ample food at all times.
2. The food supply of the predator population depends entirely on the size of the prey population.
3. The rate of change of population is proportional to its size.
4. During the process, the environment does not change in favour of one species and genetic adaptation is inconsequential.
5. Predators have limitless appetite.
As differential equations are used, the solution is deterministic and continuous. This, in turn, implies that the generations of both the predator and prey are continually overlapping.[23]

Prey
When multiplied out, the prey equation becomes
dx/dtαx - βxy
 The prey are assumed to have an unlimited food supply, and to reproduce exponentially unless subject to predation; this exponential growth is represented in the equation above by the term αx. The rate of predation upon the prey is assumed to be proportional to the rate at which the predators and the prey meet; this is represented above by βxy. If either x or y is zero then there can be no predation.

With these two terms the equation above can be interpreted as: the change in the prey's numbers is given by its own growth minus the rate at which it is preyed upon.

Predators

The predator equation becomes

dy/dt =  - 

In this equation, {\displaystyle \displaystyle \delta xy} represents the growth of the predator population. (Note the similarity to the predation rate; however, a different constant is used as the rate at which the predator population grows is not necessarily equal to the rate at which it consumes the prey). {\displaystyle \displaystyle \gamma y} represents the loss rate of the predators due to either natural death or emigration; it leads to an exponential decay in the absence of prey.

Hence the equation expresses the change in the predator population as growth fueled by the food supply, minus natural death.


Clone of Predator-Prey Model ("Lotka'Volterra")
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Modelagem do estado psicológico de uma população. Inicialmente, todos os indivíduos estão no estado "Calmo". Com o passar do tempo e com as interações mútuas, há o surgimento e progressivo aumento do total de indivíduos com raiva (estado "Raivoso"). Deste estado e, com o passar do tempo, os indivíduos podem evoluir mentalmente e atingirem o estado "Indiferente", nos quais eles se tornam indiferentes à qualquer interação. Outra possibilidade é o indivíduo se enriquecer e, assim, atingir a felicidade (estado "Feliz").
Estado psicológico de uma população (DS)
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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
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Simulation of how tiger population and anti poaching efforts effect the black market value of tiger organs.
Clone of Tiger Population and Black Market Value
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​Climate Sector Boundary Diagram By Guy Lakeman
 Climate, Weather, Ecology, Economics, Population, Welfare, Energy, Policy, CO2, Carbon Cycle, GHG (green house gasses, combined effects)

As general population is composed of 85% with an education level of a 12 grader or less (a 17 year old), a simple block of components concerning the health of the planet needs to be broken down into simple blocks.
Perhaps this picture will show the basics on which to vote for a sustained healthy future
Democracy is only as good as the ability of the voters to FULLY understand the implications of the policies on which they vote., both context and the various perspectives.   National voting of unqualified voters on specific policy issues is the sign of corrupt manipulation.

Climate Sector Boundary Diagram of Guy Lakeman
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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
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Flows between acute hospital and aged care for older people. See IM-1012 for a simpler version

Aged Care and Hospital Flows
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Shows the ecological impact of population.
Population Ecological Impact - Denmark
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This is a simulation of growth rate
New York's Population Growth with different functions
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A detailed insight map into the current population trends surrounding koalas and the different variables involved which will influence these trends in years to come.
UBD101- Koala Population
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This in-depth concept map portrays the factors influencing koala births and deaths in SEQ. It also shows that the eucalyptus tree population in SEQ is vital for the survival of the koala.
Clone of Koala Population SEQ
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InClassExercise--ExponentialGrowthRabbits
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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
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Simulation of how tiger population and anti poaching efforts effect the black market value of tiger organs.
Clone of Tiger Population and Black Market Value
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Een dynamisch model over een prooi predator relatie tussen verschillende populaties onder invloed van abiotische factoren.
Catastrophe, Abiotischefactoren Koein en Reuzenvogels en blumentjens Dio 5V prey predator
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A simple simulation used to observe the California Yellowtail population in San Diego
Clone of Yellowtail Population - San Diego
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This is a basic population estimator. Default values approximate recent data for the Czech Republic (Czechia).
Population Projection of Czech Republic
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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
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This is a first attempt to model I=PAT population growth. Impact on the renewables is equal to P*A*T. Footprint is limited by the amount of renewables left divided by the population. Death rate goes up if the Footprint goes down too far.
IPAT v.1.0
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First model with population 1000 and simulation for 20 years fixed.
First Model - JB
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The simulation integrates or sums (INTEG) the Nj population, with a change of Delta N in each generation, starting with an initial value of 5.
The equation for DeltaN is a version of 
Nj+1 = Nj  + mu (1- Nj / Nmax ) Nj
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.
Clone of POPULATION LOGISTIC MAP (WITH FEEDBACK)