#### 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

- 2 years 4 months ago

#### 2014 Weather & Climate Extreme Loss of Arable Land and Ocean Fertility - The World3+ Model: Forecaster

##### Guy Lakeman

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 ADDEDUse 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.

Environment Demographics Population Growth Population Weather Climate Failure Death Mortality Science Technology Engineering Strategy Economics Politics Fertility Health Services Resources Land Jobs Labor Urban Industrial Rural Lifetime Pollution Regeneration Yield Ocean Sea Fish Plants Animals

- 1 year 2 months ago

#### Complex Decision Technologies

##### Geoff McDonnell ★

Health Care Methods Hybrid ABM Decision Making EHealth Technology Systems Engineering Complexity Forrester

- 3 months 1 week ago

#### Complex Intervention Modeling Areas

##### Geoff McDonnell ★

Health Care Methods Behavior Hybrid ABM Decision Making Causation Complexity Systems Engineering Multiscale

- 9 months 2 weeks 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

- 1 year 2 months ago

#### Understanding Systems Science

##### Geoff McDonnell ★

- 1 month 3 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

- 4 years 1 month 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

- 4 years 1 month ago

#### People with Healthcare in any population - Disabled Determination

##### Lisa Martinez

Small groups would collectively distribute six sigma projects on the converters to understand the current state in an objective manner.

The intent of the model merely collecting evidence, students from within their communities serve as a new role to bridge the trust and rebuild hope with direct engagement.

Thematic subjects from the SDG's

Economic, Justice, Health, nutrition and the poverty equation are segmented by three income or economic groupings. The grouping in this manner enables us to identify patterns which are true in all parts of the world. (80/20 rule)

- 1 year 4 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

- 1 year 2 months ago

#### Improvement Science

##### Geoff McDonnell ★

Health Care Methods Realist Deming Design Services Engineering Systems Complexity

- 3 months 1 week ago

#### Project management 104

##### Geoff McDonnell ★

Addition of an acceptance test which discovers rework (Cooper et al.) plus introduction of new tasks and tipping point (Taylor and Ford). Here schedule pressure producing overtime is also added

- 2 years 10 months ago

#### Safety and Performance Pressures NASA

##### Geoff McDonnell ★

Columbia loss dynamics from FIg 13.1 Nancy Leveson's Engineering a Safer World Book

- 2 years 10 months ago

#### Project management 101

##### Geoff McDonnell ★

Project management in an ideal world. The project has a defined scope, work rate and runs according to the initial schedule.

Schedule pressure is constant until the project is completed.

- 3 years 11 months ago

#### Project management 102

##### Geoff McDonnell ★

Addition of an acceptance test which discovers rework (Cooper et al.)

- 3 years 11 months ago

#### Project management 103

##### Geoff McDonnell ★

Addition of an acceptance test which discovers rework (Cooper et al.) plus introduction of new tasks and tipping point (Taylor and Ford)

- 3 years 11 months ago

#### Brooks Law

##### Geoff McDonnell ★

What happens when a program chooses to ignore Brooks' Law? This archetype explains the dynamics behind Brooks' Law and why it applies to most programs. From William E. Novak and Linda Levine CMU SEI Sept 2010 Success in Acquisition: Using Archetypes to Beat the Odds paper and see webpage

Archetype Technology EHealth IT Project Software Engineering

- 4 months 2 weeks ago

#### Mass And Heat Balance for POME

##### Victor Gomgom

- 2 years 2 months ago

#### Process Architecture Framework

##### Geoff McDonnell ★

*Process*Architecture Framework for Product Development Systems Engineering: paper

- 1 year 8 months ago

#### Three Tanks

##### Faustino Moreno Gamboa

- 1 year 6 months ago

#### TCA Thermal System

##### Omar

- 2 years 5 months ago

#### Easter Island

##### Isabelle Vernon

- 1 year 9 months ago

#### Public interest in engineering

##### Blake Macnair

- 3 years 6 months ago

#### Clone of Understanding Systems Science

##### Pagandai V Pannirselvam

- 1 month 2 weeks ago