The simple  graph shows two feedback loops that interact to make climate change and its consequences worse, leading to an unexpected (& inescapable?) dilemma. Presently,  there are 413 ppm of CO2 gasses in the atmosphere. Even without any further emission of greenhouse gasses, this high level of CO2 in the atmosphere will ensure constantly worsening climatic consequences because of delays that operate in the climate system. The dilemma is caused by relentlessly worsening of extreme weather events, droughts, forest fires etc., the need for draconian measures to deal with the situation and the opposition to the measures, described by the feedback loop B2.This opposition is rooted in human nature, the psychological defence mechanisms that cause us to repress or even deny unpalatable truths that threaten our basic assumptions and the way we understand life. Together,  the loops B1 & B2 create a vicious reinforcing loop that describes the escalating and worsening situation created by the dilemma.

Please look at Insight No. 238770 that provides background information and also at the information labels attached to majority of the variables in the model. 

Simple Model of the Food Chain

Simple Model of the Food Chain

Water model for a country

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.Nj+1 = Nj  + mu (1- Nj / Nmax ) Nj

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.

Examining the ecosystem of the sea turtle and how that influences its population as an endangered species.

This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

The time-variable solution to a step-function change in inflow concentration for an ideal, completely mixed lake.

Interplay between wolves eating sheep and farmers killing wolves.

To develop a model and rating system to be able assess how sustainably responsible the Queensland Government, Local Government, Government Agencies, and Industry are. The rating system is based on the key sustainability factors identified by the United Nations: Social, Environment, and Economic.

From Schluter et al 2017 article A framework for mapping and comparing behavioural theories in models of social-ecological systems COMSeS2017 video. See also Balke and Gilbert 2014 JASSS article How do agents make decisions? (recommended by Kurt Kreuger U of S)

Created by Dominic Beer, Christopher Dyer, Arthur Van Lerberghe and Patrick Griffith for CIV172 Coursework

For Sustainability & Eco Innovation class

Very simple model demonstrating growth of phytoplankton using Steele's equation for potential production and Michaelis-Menten equation for nutrient limitation.

Both light and nutrients (e.g. nitrogen) are modelled as forcing functions, and the model is "over-calibrated" for stability.

The phytoplankton model approximately reproduces the spring-summer diatom bloom and the (smaller) late summer dinoflagellate bloom.
 
Oyster growth is modelled only as a throughput from algae. Further developments would include filtration as a function of oyster biomass, oyster mortality, and other adjustments.

Primitives for Watershed modeling project. Click Clone Insight at the top right to make a copy that you can edit.

The converter in this file contains precipitation for Phoenix only.

Eastern oyster growth model calibrated for Long Island Sound
Developed and implemented by Joao G. Ferreira and Camille Saurel; growth data from Eva Galimany, Gary Wickfors, and Julie Rose; driver data from Julie Rose and Suzanne Bricker; Culture practice from the REServ team and Tessa Getchis. This model is a workbench for combining ecological and economic components for REServ. Economic component added by Trina Wellman.

This is a one box model for an idealized farm with one million oysters seeded (one hectare @ a stocking density of 100 oysters per square meter)

1. Run WinShell individual growth model for one year with Long Island Sound growth drivers;

2. Determine the scope for growth (in dry tissue weight per day) for oysters centered on the five weight classes)
 
3. Apply a classic population dynamics equation:

dn(s,t)/dt = -d[n(s,t)g(s,t)]/ds - u(s)n(s,t)

s: Weight (g)
t: Time
n: Number of individuals of weight s
g: Scope for growth (g day-1)
u: Mortality rate (day-1)

4. Set mortality at 30% per year, slider allows scenarios from 30% to 80% per year

5. Determine harvestable biomass, i.e. weight class 5, 40-50 g (roughly three inches length)

A clone of the first model with the addition of a converter to describe the competition between rabbits for available vegetation based on the relationship between rabbit density and rabbit birth rate

From
Mobus, G. E., & Kalton, M. C. (2014). Principles of Systems Science (2015 edition). Springer.

Based on example from pp 682 (13.5.1), the simulation results should be compared to Graph 13.2 on pp 685.

This model describes the flow of energy from generation to consumption for neighborhoods in the metro Atlanta area. It also calculates the cost of energy production and the number of years it will take to recover that cost.

Simple mass balance model for aquaculture area, based on the Vollenweider equation:

dMw/dt = Min - sMw - Mout