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

##### Guy Lakeman

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)

Environment Economics Finance Mathematics Physics Biology Health Fractals Chaos TURBULENCE Engineering Navier Stokes Supply Demand Strategy

- 7 years 2 weeks ago

#### Vermögensentwicklung nominal und real

##### Holger Arndt

- 6 months 6 days ago

#### Schuldenentwicklung

##### Holger Arndt

- 9 months 4 weeks ago

#### POPULATION LOGISTIC MAP (WITH FEEDBACK)

##### Guy Lakeman

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.

Environment MATHS Mathematics Chaos Fractals BIFURCATION Model Economics Finance TURBULENCE Population Growth DECAY STABILITY SUSTAINABLE Engineering Science Demographics Strategy

- 8 years 8 months ago

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

##### Guy Lakeman

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.

Environment Economics Finance Mathematics Physics Biology Health Fractals Chaos TURBULENCE Engineering Navier Stokes Science Demographics Population Growth BIFURCATIONS MTBF Risk Failure Strategy

- 5 years 10 months ago

#### Hyperinflation Simulation

##### Vincent Cate

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.

- 1 year 10 months ago

#### FORCED GROWTH INTO TURBULENCE

##### Guy Lakeman

**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)

Environment Economics Finance Mathematics Physics Biology Health Fractals Chaos TURBULENCE Engineering Navier Stokes Science Demographics Population Growth BIFURCATIONS MTBF Strategy Weather

- 8 years 8 months ago

#### THE ILLUSION OF A U.S. PUBLIC DEBT MOUNTAIN.

##### Hanns-Jürgen Hodann

- 1 year 10 months ago

#### OVERSHOOT GROWTH INTO TURBULENCE

##### Guy Lakeman

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)

Environment Economics Finance Mathematics Physics Biology Health Fractals Chaos TURBULENCE Engineering Navier Stokes Science Demographics Population Growth Strategy Weather

- 5 years 10 months ago

#### ISD Savings Plan

##### Kevin Collins

- 4 years 4 months ago

#### FIRE_simulation

##### Phillip Balding

FIRE_simulationv1.020200618

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.

Finance Fire Assets Income Savings Rate Retirement Stock Market

- 1 year 3 months ago

#### A Framework to Evaluate the Sustainability of Debt

##### Jide Lewis

- 2 years 5 months ago

#### Project Management: Human Resources

##### Karan Khosla

- 6 years 10 months ago

#### Oil Price Influencers (3-Loop)

##### Franz Weismann

- 6 years 6 months ago

#### Factors Affecting the Real Estate Market by 42151619

##### Ying Chen

**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.

- 6 years 6 days ago

#### Variables Influencing Business Problems Related to SAMS

##### Franz Weismann

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.

Finance Government Budget COTS IT Applications Shifting The Burden Limits To Success

- 6 years 4 months ago

#### Killing the Host

##### Geoff McDonnell ★

Finance Neoliberal Economics Politics Macroeconomics Monetary

- 2 months 2 weeks ago

#### Disease Dynamics (Agent Based Modeling) Guy Lakeman

##### Guy Lakeman

- 7 years 1 week ago

#### First Basic Inflow -Outflow Model

##### Sascha Kress ★

- 6 years 10 months ago

#### Time Value of Money - Simple

##### Jeffrey Connor

- 5 years 5 months ago

#### factoring platform on blockchain

##### Olga Konoval

- 4 years 3 weeks ago

#### Personal Financial Plan

##### Ziqiu Kang

- 3 years 1 month ago

#### IS GOLD A SAFE INVESTMENT

##### TBS GROUP

Many articles say that the gold price is manipulated and some analysts predict that the bubble will burst. (1)

We think that understanding how gold can be influenced by different factors is an interesting research topic. The variation of the gold price is a real-world problem which evaluates through the interaction of a group of different elements.

It seems that the gold price is a very complex problem understanding. Of course everybody has his own thinking about the problem according to his own filter.

But this approach is most of the time not valuable because there is not a full view of all the variables and their link. In a context of a growing demand and a constant supply, be able to determine if gold price will continue to increase and if this asset will represent a safe investment for the new decade.

In September 2011, gold price surged a record, $1,274,75 an ounce. According to the Commodities guru George Soros “gold was the ultimate bubble" and was no longer a safe investment.

On the other hand, the research conducts by metal consultant GFMS predicted that gold will hit a new record of $1,300 an ounce. (2)

Who was right? Both of them.

This example illustrates how complex is the problem.

At the time of this research the price of gold is $1,316,79 an ounce.

Wealthy persons are concerned by preserving their fortune, they also look to maximise their wealth and to keep it safe. Many options are available to investors, despite buillion is a popular asset on a long-term portfolio, nowadays is it gold a safe investment? That is a good question. Also understanding the impact of gold on the economy and how it is link to poverty might be interesting. To analyze an issue, one must first define it.

In order to get a better understanding of the gold price we will model this complex problem. Our goal is to visualize the interconnection of elements and be able to identify feedback loops with the aim to understand the complexity of the problem.

We will analyse different documents from various sources, underline variables and identify their relationships over time.

(1) https://www.moneymetals.com/news/2017/04/28/who-controls-gold-price-001058

(2) https://www.bullionbypost.co.uk/index/gold-investment/is-gold-a-safe-investment/

- 3 years 6 months ago

#### HODL vs. cloud mining

##### Andrej Soucek

- 3 years 8 months ago