#### Private Utility's Objective: Provision of a Public Good or Profit?

##### Hanns-Jürgen Hodann

Public utilities do not need to make a profit. They can concentrate on providing a quality service, a public good. By contrast, the primary objective of private utilities is not to provide a public good, but profit and profitability. This simple CLD tries to show the conflict that can arise from this and a hidden dynamic, a reinforcing feedback loop, that can lead to disaster. Unfortunately, there are examples where failure in infrastructure maintenance has led to disaster. On the 9th of July 2009, the German newspaper 'Welt Online' reported that the authorities in Berlin had to intervene and force the company that was running Berlin's S-Bahn (suburban rail service) to withdraw half of all the city's trains from service because they were considered unsafe! Something similar happened in the UK where failure to maintain rail tracks led to serious accidents.

- 3 years 10 months ago

#### Clone of Coffee Pods ISD Humanities v 1.02

##### Malena Dolff Gonzalez

- 3 years 2 months ago

#### Keynes theory of employment and inflation

##### Geoff McDonnell ★

Macroeconomics Economics Employment Inflation Labour Dynamics Keynes

- 1 year 7 months ago

#### Clone of Z602 Population with four age groups

##### Tatiana Costache

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.

- 5 years 12 months ago

#### Yuzuki Aizawa 10.3 Coffee Pods ISD Humanities v 1.02

##### Yuzuki Aizawa

- 3 years 2 months ago

#### Miles Tomlinson version 1 (Coffee Pods ISD Humanities v 1.02)

##### Miles Xavier Tomlinson

- 3 years 2 months ago

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

##### Ivan Stamenkovic

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 11 months ago

#### NATIONAL DEBT MODEL

##### David Percy

- 3 years 2 weeks ago

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

##### atif

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

- 7 years 10 months ago

#### K Collins Coffee Pods ISD Humanities v 1.02

##### Kevin Collins

- 3 years 2 months ago

#### Labour and Capital

##### Gerald Thomas

Interactions between labour and capital

- 8 years 2 months ago

#### Clone of Human and Nature Dynamics of Societal Inequality

##### Holger Arndt

- 5 years 5 months ago

#### Clone of Clone of Z602 Population with four age groups

##### Bechara Assouad

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.

- 4 years 3 months ago

#### Food Industry Regulation - Health & Social Spending - Budget Implications

##### Joachim P Sturmberg ★

- 4 years 12 months ago

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

##### Shrishail

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

- 7 years 9 months ago

#### Clone of FORCED GROWTH INTO TURBULENCE

##### Sayantan Das

**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

- 7 years 7 months ago

#### Clone of Clone of Z602 Population with four age groups

##### Anca Badea

- 5 years 11 months ago

#### ISD Savings Plan - Science Intro

##### Kevin Collins

- 3 years 7 months ago

#### Clone of The Logistic Map

##### Nicole M Radziwill

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 Robert May, in part as a discrete-time demographic model analogous to the logistic equation first created by Pierre François Verhulst.

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 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

- 4 years 11 months ago

#### Clone of Goodwin Model

##### Harshit Jayaswal

**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

- 5 years 2 weeks ago

#### Clone of MMT Fiscal position

##### emilio piccoli

- 5 years 1 week ago

#### Helene D. Coffee Pods ISD Humanities

##### Helene D.

- 3 years 2 months ago

#### Business Cycle

##### Sid

- 6 years 3 months ago

#### Clone of Goodwin Business Cycle

##### Razvan Nan

Goodwin business cycle model, modified from Keen and Blatt

- 4 years 10 months ago