Das Modell sensibilisiert für die langfristigen Folgen von Inflation und Besteuerung bei Kapitalanlagen
Das Modell sensibilisiert für die langfristigen Folgen von Inflation und Besteuerung bei Kapitalanlagen
25 5 months ago
THE BROKEN LINK BETWEEN SUPPLY AND DEMAND CREATES TURBULENT CHAOTIC DESTRUCTION  The existing global capitalistic growth paradigm is totally flawed  Growth in supply and productivity is a summation of variables as is demand ... when the link between them is broken by catastrophic failure in a compon
THE BROKEN LINK BETWEEN SUPPLY AND DEMAND CREATES TURBULENT CHAOTIC DESTRUCTION

The existing global capitalistic growth paradigm is totally flawed

Growth in supply and productivity is a summation of variables as is demand ... when the link between them is broken by catastrophic failure in a component the creation of unpredictable chaotic turbulence puts the controls ito a situation that will never return the system to its initial conditions as it is STIC system (Lorenz)

The chaotic turbulence is the result of the concept 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 working containers (villages communities)

Das Modell zeigt die Entwicklung der Verschuldung auf.
Das Modell zeigt die Entwicklung der Verschuldung auf.
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 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.
Simulation of MTBF with controls   F(t) = 1 - e ^ -λt   Where    • F(t) is the probability of failure    • λ is the failure rate in 1/time unit (1/h, for example)   • t is the observed service life (h, for example)  The inverse curve is the trust time On the right the increase in failures brings its
Simulation of MTBF with controls

F(t) = 1 - e ^ -λt 
Where  
• F(t) is the probability of failure  
• λ is the failure rate in 1/time unit (1/h, for example) 
• t is the observed service life (h, for example)

The inverse curve is the trust time
On the right the increase in failures brings its inverse which is loss of trust and move into suspicion and lack of confidence.
This can be seen in strategic social applications with those who put economy before providing the priorities of the basic living infrastructures for all.

This applies to policies and strategic decisions as well as physical equipment.
A) Equipment wears out through friction and preventive maintenance can increase the useful lifetime, 
B) Policies/working practices/guidelines have to be updated to reflect changes in the external environment and eventually be replaced when for instance a population rises too large (constitutional changes are required to keep pace with evolution, e.g. the concepts of the ancient Greeks, 3000 years ago, who based their thoughts on a small population cannot be applied in 2013 except where populations can be contained into productive working communities with balanced profit and loss centers to ensure sustainability)

Early Life
If we follow the slope from the leftmost start to where it begins to flatten out this can be considered the first period. The first period is characterized by a decreasing failure rate. It is what occurs during the “early life” of a population of units. The weaker units fail leaving a population that is more rigorous.

Useful Life
The next period is the flat bottom portion of the graph. It is called the “useful life” period. Failures occur more in a random sequence during this time. It is difficult to predict which failure mode will occur, but the rate of failures is predictable. Notice the constant slope.  

Wearout
The third period begins at the point where the slope begins to increase and extends to the rightmost end of the graph. This is what happens when units become old and begin to fail at an increasing rate. It is called the “wearout” period. 
 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 th
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)

 *scroll to bottom for user inputs*     FIRE_simulation  v1.0  20200618     A personal finance simulation to predict retirement date.       with some adjustable variables, and some probabilistic variables, you can run a simulation of 500 clones of yourself pre->post FIRE and see how many clones r
*scroll to bottom for user inputs*

FIRE_simulation
v1.0
20200618

A personal finance simulation to predict retirement date. 

with some adjustable variables, and some probabilistic variables, you can run a simulation of 500 clones of yourself pre->post FIRE and see how many clones retire at what years.

Some clones get lucky with the market and eg low child costs -> retire early.
Some clones get bad luck and take a few more years to retire!

can also track a clones assets, income, savings rate over time.

Also can use to stress-test (eg poor market returns), and goal seek (assets go to zero when i die. to retire earlier)

Top right are variables about me.
Top left are market variables.
bottom right are simulant/clone (output) info.

Middle 'folder' represents a clone of me.

some vars arent fixed, rather probabilities eg child costs being unknown, i have normally distributed it (my half of costs) around $12k pa and each clone of me gets a random cost on the dist for the simulation. I will add and update in next version

Sign up to insightmaker, click "clone insight" and build/adjust your own modelling. Or send feedback to phillip.balding@gmail.com


programming notes:
-market return years running consecutively not random.
-future years return FIRE rule
-cap_gains and pay_super flows can now be neg
-intro of super still seems too high, grows too much after 60
-rearrange user input variables

To do:
-get actual historical dividends
-goalseek to die with 0 assets -> minimise retirement age.
-year begin not integer?
-auto interpolation seems good.
-tidy the fucking model map mess
-fix child costs at initial random dist.
In this model, we look at "human resource" supply chains and how quickly and unpredictably an organization can enter long periods of being either overstaffed or understaffed.
In this model, we look at "human resource" supply chains and how quickly and unpredictably an organization can enter long periods of being either overstaffed or understaffed.
Neoliberalism
uses a deceptive narrative to declare that money the government spends into the economy in excesses of the taxes it collects creates a ‘government debt’.
In fact, the money the government spends into the economy in excess of the
taxes is an income, a benefit for the private sector. Whe
Neoliberalism uses a deceptive narrative to declare that money the government spends into the economy in excesses of the taxes it collects creates a ‘government debt’. In fact, the money the government spends into the economy in excess of the taxes is an income, a benefit for the private sector. When the government issues bonds, the money the private sector uses to buy them via banks comes from a residual cushion of dollars that the government already spent into the economy but has not yet taxed back.  If this were not the case, if the government had taxed back all the money it spent into the economy, then the economy could not function. There would be no dollars in the economy, since the government is the sole supplier of U.S. dollars! In the doted rectangle in the graph you can see that the dollars paid to the government for bonds sits in a dollar asset account. When the government issues bonds it simply provides the public and institutions with a desirable money substitute that pays interest i.e. Treasury bonds. It is a swap of one kind of financial asset for another. To register this swap the government debits the dollar asset account and credits the bond account.  When the time comes to redeem (take back) the bonds, all the government does is revers the swap, and that’s all!  When you look at the total amount of finacial assets in the private sector,  these remain constant at $ 25 BN  after the payment of $ 5 BN taxes. This implies that  no lending of financial assets of the private sector to the government has taken place during the swap operation. The money was always there. The debt mountain is an illusion!
OVERSHOOT GROWTH GOES INTO TURBULENT CHAOTIC DESTRUCTION  The existing global capitalistic growth paradigm is totally flawed  The chaotic turbulence is the result of the concept of infinite bigness this has been the destructive influence on all empires and now shown up by Feigenbaum numbers and Dunb
OVERSHOOT GROWTH GOES INTO TURBULENT CHAOTIC DESTRUCTION

The existing global capitalistic growth paradigm is totally flawed

The chaotic turbulence is the result of the concept 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)

A simple budget planning system.  What additional complexities can you add?
A simple budget planning system.  What additional complexities can you add?
A causal loop diagram illustrating a subset of variables influencing business problems related to the transitioning of the Social Assistance Management System (SAMS) from initial production deployment to steady state.    Inherent in the diagram is a representation of two well-known system dynamics a
A causal loop diagram illustrating a subset of variables influencing business problems related to the transitioning of the Social Assistance Management System (SAMS) from initial production deployment to steady state.

Inherent in the diagram is a representation of two well-known system dynamics archetypes:

  • Shifting the Burden, represented in the interplay between the B1, B2, and R3 loops, and
  • Limits to Success, represented in the interplay between the B1 and R5 loops.
This framework can be used to evaluate the sustainability of a country's debt profile. The dynamics generated are based on the interaction and feedback between a government agent, a rating agency and the financial market in a stock-flow consistent manner.
This framework can be used to evaluate the sustainability of a country's debt profile. The dynamics generated are based on the interaction and feedback between a government agent, a rating agency and the financial market in a stock-flow consistent manner.
Causal loop diagram illustrating a variety of feedback loops influencing the price of oil.
Causal loop diagram illustrating a variety of feedback loops influencing the price of oil.
This is a first basic inflow - outflow model / linear
This is a first basic inflow - outflow model / linear
 Macquarie University | MGMT220: Fundamentals of Business Analytics |  Assignment Task #3: Complex Systems by Ying Chen (42151619)  This simple model uses the following key factors to demostrate the behaviour within the real estate market, bank's interest rates, median sale price, and listed sale pr
Macquarie University | MGMT220: Fundamentals of Business Analytics | Assignment Task #3: Complex Systems by Ying Chen (42151619)

This simple model uses the following key factors to demostrate the behaviour within the real estate market, bank's interest rates, median sale price, and listed sale price.

Sliders located below can be used to set values to simulate the affects over time.
We are modeling future cash flows in the system consisting of three interacting parties, one of which secures deals between the two others which do not trust each other.
We are modeling future cash flows in the system consisting of three interacting parties, one of which secures deals between the two others which do not trust each other.
Simulation compares Bitcoin cloud mining opportunity (hashflare.io) to HODL. The model does not calculate with mining difficulty, pool's efficiency and changes in fees. Using monthly cloud fees as of the end of November 2017.  Used https://www.coinwarz.com/calculators/bitcoin-mining-calculator for m
Simulation compares Bitcoin cloud mining opportunity (hashflare.io) to HODL.
The model does not calculate with mining difficulty, pool's efficiency and changes in fees. Using monthly cloud fees as of the end of November 2017.
Used https://www.coinwarz.com/calculators/bitcoin-mining-calculator for mining calculations.

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.
This model gives an insight in the Study Book sales of Bol.com. Bol.com provides, besides of the B2C online business model, selling new books from suppliers to their customers, also a C2C online business model. This gives a customer the opportunity to sell their own books to other customers of Bol.c
This model gives an insight in the Study Book sales of Bol.com. Bol.com provides, besides of the B2C online business model, selling new books from suppliers to their customers, also a C2C online business model. This gives a customer the opportunity to sell their own books to other customers of Bol.com via Bol.com's website, by simply filling in the ISBN of the book on their second hand book website. The consumer pays a (relatively small) fee to bol.com for each book sold successfully. The payment part is handled by Bol.com, but the shipment has to be done by the customer (seller) itself. This model gives an insight of this process.
Simulation of MTBF with controls   F(t) = 1 - e ^ -λt   Where    • F(t) is the probability of failure    • λ is the failure rate in 1/time unit (1/h, for example)   • t is the observed service life (h, for example)  The inverse curve is the trust time On the right the increase in failures brings its
Simulation of MTBF with controls

F(t) = 1 - e ^ -λt 
Where  
• F(t) is the probability of failure  
• λ is the failure rate in 1/time unit (1/h, for example) 
• t is the observed service life (h, for example)

The inverse curve is the trust time
On the right the increase in failures brings its inverse which is loss of trust and move into suspicion and lack of confidence.
This can be seen in strategic social applications with those who put economy before providing the priorities of the basic living infrastructures for all.

This applies to policies and strategic decisions as well as physical equipment.
A) Equipment wears out through friction and preventive maintenance can increase the useful lifetime, 
B) Policies/working practices/guidelines have to be updated to reflect changes in the external environment and eventually be replaced when for instance a population rises too large (constitutional changes are required to keep pace with evolution, e.g. the concepts of the ancient Greeks, 3000 years ago, who based their thoughts on a small population cannot be applied in 2013 except where populations can be contained into productive working communities with balanced profit and loss centers to ensure sustainability)

Early Life
If we follow the slope from the leftmost start to where it begins to flatten out this can be considered the first period. The first period is characterized by a decreasing failure rate. It is what occurs during the “early life” of a population of units. The weaker units fail leaving a population that is more rigorous.

Useful Life
The next period is the flat bottom portion of the graph. It is called the “useful life” period. Failures occur more in a random sequence during this time. It is difficult to predict which failure mode will occur, but the rate of failures is predictable. Notice the constant slope.  

Wearout
The third period begins at the point where the slope begins to increase and extends to the rightmost end of the graph. This is what happens when units become old and begin to fail at an increasing rate. It is called the “wearout” period.