Simulating Hyperinflation for 3650 days.  If private bond holdings are going down and the government is running a big deficit then the central bank has to monetize bonds equal to the deficit plus the decrease in private bond holdings.  We don't show the details of the central bank buying bonds here,
Simulating Hyperinflation for 3650 days.

If private bond holdings are going down and the government is running a big deficit then the central bank has to monetize bonds equal to the deficit plus the decrease in private bond holdings.  We don't show the details of the central bank buying bonds here, just the net results.

See blog at http://howfiatdies.blogspot.com for more on hyperinflation, including a hyperinflation FAQ.
 The World3 model is a detailed simulation of human population growth from 1900 into the future. It includes many environmental and demographic factors. THIS MODEL BY GUY LAKEMAN, FROM METRICS OBTAINED USING A MORE COMPREHENSIVE VENSIM SOFTWARE MODEL, SHOWS CURRENT CONDITIONS CREATED BY THE LATEST W

The World3 model is a detailed simulation of human population growth from 1900 into the future. It includes many environmental and demographic factors.

THIS MODEL BY GUY LAKEMAN, FROM METRICS OBTAINED USING A MORE COMPREHENSIVE VENSIM SOFTWARE MODEL, SHOWS CURRENT CONDITIONS CREATED BY THE LATEST WEATHER EXTREMES AND LOSS OF ARABLE LAND BY THE  ALBEDO EFECT MELTING THE POLAR CAPS TOGETHER WITH NORTHERN JETSTREAM SHIFT NORTHWARDS, AND A NECESSITY TO ACT BEFORE THERE IS HUGE SUFFERING.
BY SETTING THE NEW ECOLOGICAL POLICIES TO 2015 WE CAN SEE THAT SOME POPULATIONS CAN BE SAVED BUT CITIES WILL SUFFER MOST. 
CURRENT MARKET SATURATION PLATEAU OF SOLID PRODUCTS AND BEHAVIORAL SINK FACTORS ARE ALSO ADDED

Use the sliders to experiment with the initial amount of non-renewable resources to see how these affect the simulation. Does increasing the amount of non-renewable resources (which could occur through the development of better exploration technologies) improve our future? Also, experiment with the start date of a low birth-rate, environmentally focused policy.

Simulating Hyperinflation for 3650 days.  If private bond holdings are going down and the government is running a big deficit then the central bank has to monetize bonds equal to the deficit plus the decrease in private bond holdings.  We don't show the details of the central bank buying bonds here,
Simulating Hyperinflation for 3650 days.

If private bond holdings are going down and the government is running a big deficit then the central bank has to monetize bonds equal to the deficit plus the decrease in private bond holdings.  We don't show the details of the central bank buying bonds here, just the net results.

See blog at http://howfiatdies.blogspot.com for more on hyperinflation, including a hyperinflation FAQ.
Extremely basic stock-flow diagram of compound interest with table and graph output in interest and savings development per year. Initial deposit, interest rate, yearly deposit and withdrawal can all be modified in Dutch.
Extremely basic stock-flow diagram of compound interest with table and graph output in interest and savings development per year. Initial deposit, interest rate, yearly deposit and withdrawal can all be modified in Dutch.
  Goodwin Model:   This is a basic version of the Goodwin Model based on Kaoru Yamagushi (2013),  Money and Macroeconomic Dynamics , Chapter 4.5 ( link )     Equilibrium conditions:   Labor Supply  = 100  Devation from the equilibrium conditions generates growth cycles.
Goodwin Model:
This is a basic version of the Goodwin Model based on Kaoru Yamagushi (2013), Money and Macroeconomic Dynamics, Chapter 4.5 (link)

Equilibrium conditions:
  • Labor Supply = 100
Devation from the equilibrium conditions generates growth cycles.
 Acest model este adaptat după reprezentarea lui Harmut Bossel, în lucrarea  "System Zoo 3 Simulation Models, Economy, Society, Development."  Utilizarea modelului ne poate ajuta pentru a vizualiza evolutia populatiei pe grupe de varsta sau pentru a gestiona probleme cum ar fi ocuparea forței de mun
Acest model este adaptat după reprezentarea lui Harmut Bossel, în lucrarea  "System Zoo 3 Simulation Models, Economy, Society, Development."
Utilizarea modelului ne poate ajuta pentru a vizualiza evolutia populatiei pe grupe de varsta sau pentru a gestiona probleme cum ar fi ocuparea forței de muncă.
From Bill Mitchell and Warren Mosler December2018 billy  blog entry   and mosler's MMT  white paper  (google docs) 2019. Some highly aggregated stocks and flows and boundaries introduced.
From Bill Mitchell and Warren Mosler December2018 billy blog entry  and mosler's MMT white paper (google docs) 2019. Some highly aggregated stocks and flows and boundaries introduced.
This model shows the structure and operation of a simple economy. It can represent economic systems at different levels of abstraction (e.g. a single good, a group of goods, multiple groups, & an "economy.")  In summary, lower rates of consumption (based on production) result in higher rates of
This model shows the structure and operation of a simple economy. It can represent economic systems at different levels of abstraction (e.g. a single good, a group of goods, multiple groups, & an "economy.")

In summary, lower rates of consumption (based on production) result in higher rates of production and consumption in the long-run. Rates of consumption over 100% of production will diminish the savings stock and eventually cause rates of production and consumption to fall.
 The L ogistic 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  Rob

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

 The World3 model is a detailed simulation of human population growth from 1900 into the future. It includes many environmental and demographic factors. THIS MODEL BY GUY LAKEMAN, FROM METRICS OBTAINED USING A MORE COMPREHENSIVE VENSIM SOFTWARE MODEL, SHOWS CURRENT CONDITIONS CREATED BY THE LATEST W

The World3 model is a detailed simulation of human population growth from 1900 into the future. It includes many environmental and demographic factors.

THIS MODEL BY GUY LAKEMAN, FROM METRICS OBTAINED USING A MORE COMPREHENSIVE VENSIM SOFTWARE MODEL, SHOWS CURRENT CONDITIONS CREATED BY THE LATEST WEATHER EXTREMES AND LOSS OF ARABLE LAND BY THE  ALBEDO EFECT MELTING THE POLAR CAPS TOGETHER WITH NORTHERN JETSTREAM SHIFT NORTHWARDS, AND A NECESSITY TO ACT BEFORE THERE IS HUGE SUFFERING.
BY SETTING THE NEW ECOLOGICAL POLICIES TO 2015 WE CAN SEE THAT SOME POPULATIONS CAN BE SAVED BUT CITIES WILL SUFFER MOST. 
CURRENT MARKET SATURATION PLATEAU OF SOLID PRODUCTS AND BEHAVIORAL SINK FACTORS ARE ALSO ADDED

Use the sliders to experiment with the initial amount of non-renewable resources to see how these affect the simulation. Does increasing the amount of non-renewable resources (which could occur through the development of better exploration technologies) improve our future? Also, experiment with the start date of a low birth-rate, environmentally focused policy.

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
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.
This model shows the structure and operation of a simple economy. It can represent economic systems at different levels of abstraction (e.g. a single good, a group of goods, multiple groups, & an "economy.")  In summary, lower rates of consumption (based on production) result in higher rates of
This model shows the structure and operation of a simple economy. It can represent economic systems at different levels of abstraction (e.g. a single good, a group of goods, multiple groups, & an "economy.")

In summary, lower rates of consumption (based on production) result in higher rates of production and consumption in the long-run. Rates of consumption over 100% of production will diminish the savings stock and eventually cause rates of production and consumption to fall.
 Goodwin cycle  IM-2010  with debt and taxes added, modified from Steve Keen's illustration of Hyman Minsky's Financial Instability Hypothesis "stability begets instability". This can be extended by adding the Ponzi effect of borrowing for speculative investment.

Goodwin cycle IM-2010 with debt and taxes added, modified from Steve Keen's illustration of Hyman Minsky's Financial Instability Hypothesis "stability begets instability". This can be extended by adding the Ponzi effect of borrowing for speculative investment.

Investigations into the relationships responsible for the success and failure of nations. This investigation was prompted after reading numerous references on the subject and perceiving that *Why Nations Fail: The Origins of Power, Prosperity, and Poverty* by Acemoglu and Robinson seem to make a gre
Investigations into the relationships responsible for the success and failure of nations. This investigation was prompted after reading numerous references on the subject and perceiving that *Why Nations Fail: The Origins of Power, Prosperity, and Poverty* by Acemoglu and Robinson seem to make a great deal of sense.
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
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.
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
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.