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# Finance

#### Clone of BATHTUB MEAN TIME BETWEEN FAILURE (MTBF) RISK

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 LifeIf 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.
• 4 years 5 months ago

#### Clone of Bob - Expanded

This is what I would imagine how most of the US's personal finances look: The individual has a retirement account set up or will be getting pensions upon retiring and has replaced his or her rent payment with a mortgage, which will go away after 15-30 years.
• 6 years 5 months ago

#### Clone of Jamie- Exact

This is an exact diagram of Jamie's personal finance which includes accounts for her Medicare, Social Security, and 401k withholdings.
• 3 years 9 months ago

#### Clone of OVERSHOOT GROWTH INTO TURBULENCE

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)

• 5 years 3 months ago

#### Clone of POPULATION LOGISTIC MAP (WITH FEEDBACK)

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.
• 4 years 4 months ago

#### Clone of ISD Savings Plan

Um simples sistema de planejamento
O sistema inicialmente possui uma entrada
simples na conta(Economias em conta).
ativos. As despesas são fluxos de saídas
que contém gastos médios do mês(variáveis)
como gastos com alimentação, aluguel e roupas.
Há ainda um outro fluxo de saída para investimentos.
Esse fluxo vai para um stock de economias que
além de receber esse fluxo constante ainda
recebe um acrecimo dado pela taxa de juros.

• 3 years 11 months ago

#### Clone of THE BROKEN LINK BETWEEN SUPPLY AND DEMAND CREATES CHAOTIC TURBULENCE (+controls)

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)

• 4 years 10 months ago

#### Model-SIM-GD

Model-SIM-GD is model-SIM from chapter 3 of Wynn Godley and Marc Lavoie's Monetary Economics, but modified. Simplest model with government money that is also stock-flow consistent, but with government debt (GD) added to the system.
Households consume out of both current income (wages + interest income from government bonds) and prior stock of wealth. Model assumes households only own a portion of existing government debt (equity position of government sector), so interest payment flows on government debt are defined as only those going to the households sector, the remaining proportion is assumed to be owned by the government itself and interest is paid to itself (think of a consolidated government Treasury and Central Bank as CB remits interest income, minus operational expenses, back to Treasury). The production sector is a pass through of income back to households. The production sector does not save and does not invest (i.e., buy "capital" goods from itself).
The model is stock-flow consistent as all sectoral expenditure flows are monitored to confirm balances balance as an accounting identity, as does equity.
• 3 months 3 weeks ago

#### Voorraad-stroom diagram - sparen - enkelvoudige interest

Very basic stock-flow diagram of simple interest with table and graph output in interest, bank account and savings development per year. Initial deposit, interest rate, yearly deposit and withdrawal, and initial balance bank account can all be modified in Dutch.
• 5 months 1 week ago

#### Clone of Contract Tracking

In an environment where there is a probability of contracts won and lost each month track the projected monthly revenue and number of active contracts.
• 8 years 1 month ago

#### Clone of FORCED GROWTH INTO TURBULENCE

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)

• 4 years 2 months ago