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
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.

Like previous models, this model shows the operation of a simple economy, the influence of changes in the consumption rate, and the effect of government intervention. In addition, this model shows changes in the hypothetical general price level. It gives an idea of changes in price trends based on c
Like previous models, this model shows the operation of a simple economy, the influence of changes in the consumption rate, and the effect of government intervention. In addition, this model shows changes in the hypothetical general price level. It gives an idea of changes in price trends based on changes in the quantity of money. NOTE: No general price level exists. Prices provide information for the exchange of individual economic goods.
Calculating EOQ using classical inventory model
Calculating EOQ using classical inventory model
The housing market is heavily dependent on two main factors; supply and demand. Both play a major role in determining an equilibrium price for both sellers and buyers in the real estate market.     Residents, or the general population of individuals, place significant reliance on financial instituti
The housing market is heavily dependent on two main factors; supply and demand. Both play a major role in determining an equilibrium price for both sellers and buyers in the real estate market. 

Residents, or the general population of individuals, place significant reliance on financial institutions to provide sources of capital i.e mortgages, to fund their purchases of homes. The rate of interest charged by these organisations in turn gives buyers (consumers) purchasing power, creating demand. 

Supply is made up of the number of houses in the market, and consequently, of these, the number of houses which are up for sale. As the prices of houses for sale increases, the demand for purchase of these properties decreases. Conversely, the lower price, the higher the demand. Once the market reaches an equilibrium point, to which buyers and sellers form an agreement, houses are sold accordingly. An underlying factor to consider is the cost of construction, which impacts producers, or suppliers in this instance, and thus the number of homes for sale, and the expected profit sellers hope to achieve. 

The simulated graph highlights the common scenario within the housing market, to which we see that as price increases, the total number for houses for sale decreases, generating an opposite slope to the price. As the price for houses increases, the demand for the houses decreases and vice versa. The equilibrium is evident at time 14 whereby the price of houses and the number of houses for sale overlaps which in turn creates a market to which both buyers and sellers are happy.








 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 wi

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.

​Summary of Hermans Scale dynamics of grassroots innovations through parallel pathways of  transformative change Ecological Economics 2016  article (paywalled)  This is applied to  health in a subsequent insight
​Summary of Hermans Scale dynamics of grassroots innovations through parallel pathways of  transformative change Ecological Economics 2016 article (paywalled) This is applied to health in a subsequent insight
A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy.  Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover    Assumptions  The government has reduced both the e
A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy.  Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover

Assumptions
The government has reduced both the epidemic and economic development by controlling immigration.




 Adam Smith's The Invisible Hand: The Feedback Structure of Markets. From Sterman JD Business Dynamics p170 Fig 5-26. A price-mediated resource allocation system..

Adam Smith's The Invisible Hand: The Feedback Structure of Markets. From Sterman JD Business Dynamics p170 Fig 5-26. A price-mediated resource allocation system..

Circular equations WIP for Runy.    Added several versions of the model. Added a flow to make C increase. Added a factor to be able to change the value 0.5. Older version cloned at  IM-46280
Circular equations WIP for Runy.

Added several versions of the model. Added a flow to make C increase. Added a factor to be able to change the value 0.5. Older version cloned at IM-46280
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,
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.