WIP Summary of Davies 2017 article from special Theory Culture and Society issue on Elites and Power after Financialization

Simpler view IM-70351 combined with Economic ViewIM-69774 in preparation for integrating with Prevention Investment Framework (private) IM

This model is an attempt to simulate what is commonly referred to as the “pesticide treadmill” in agriculture and how it played out in the cotton industry in Central America after the Second World War until around the 1990s.

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.

ECONOMIC GROWTH feeds on itself, provided the growth engine is fed with materials and finance. In this highly simplified representation  some of the factors that influence economic growth are show in the incircled green fields. Governments can influence economic growth positively via investments  and payouts. The most obvious tool which governments can use to slow an overheated economy is taxation.

Simple model of the global economy, the global carbon cycle, and planetary energy balance.

Estruturas dev  miniempresa  e  balanco de massa de calculadora de consumo paaso1 um de  projeto de sintese   de  fluxogramas  visando sintese  Gestao de viabilidade  tecnologicas via  diversos fluxogramasde blocos  ,procesos , Analise  de  fluxo materials  de  sistemas  miniempresa  industrial

Circular equations WIP for Runy.

How education causes the gap between socio-economic status?

WIP Exttension of IM-172005 Simulation of Goodwin01 Minsky Model. Compare with Part3 slide 5 of presentation in patreon

Simple model of the global economy, the global carbon cycle, and planetary energy balance.

Challenge p.212 Business Dynamics, Sterman

Calculating EOQ using classical inventory model

Causal loop diagram capturing the interactions, trade-offs, and synergies between agriculture (SDG 2), water availability (SDG 6), economic growth (SDG 8), and life on land (SDG 15). Positive feedback linkages are shown as a positive sign (+), whereas negative feedback linkages are shown with a negative sign (−). The purple arrows indicate the enviro-biophysical linkages. The green arrows indicate the socio-economic linkages. The SDG icons are courtesy of the UN SDG communications material. 

Reference - Bandari, Reihaneh, et al. "Participatory Modeling for Analyzing Interactions Between High‐Priority Sustainable Development Goals to Promote Local Sustainability." Earth's Future 11.12 (2023): e2023EF003948.

Starts from the bathtub model of economics developed by TSSEF.se (see the explanation here). It adds rich and poor and you can change the constraints on the system by moving the sliders (taxes, wages, rates, dividends etc) to see how the economic system functions at national level.

Solow model without external factors.

An initial study of the economics of single use coffee pods.

An initial study of the economics of single use coffee pods.

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.