This three loop goal-seeking structure identifies the three key influences on managing blood glucose for people with diabetes - insulin injections reduce blood glucose levels, exercise reduces blood glucose levels, and food increases blood glucose levels.  The balance of all three is necessary to ma
This three loop goal-seeking structure identifies the three key influences on managing blood glucose for people with diabetes - insulin injections reduce blood glucose levels, exercise reduces blood glucose levels, and food increases blood glucose levels.  The balance of all three is necessary to manage diabetes.
 A Susceptible-Infected-Recovered (SIR) disease model with waning immunity

A Susceptible-Infected-Recovered (SIR) disease model with waning immunity

   Model description:   This model is designed to simulate the outbreak of Covid-19 in Burnie in Tasmania, death cases, the governmental responses and Burnie local economy.     More importantly, the impact of governmental responses to both Covid-19 infection and to local economy, the impact of death
Model description:
This model is designed to simulate the outbreak of Covid-19 in Burnie in Tasmania, death cases, the governmental responses and Burnie local economy. 

More importantly, the impact of governmental responses to both Covid-19 infection and to local economy, the impact of death cases to local economy are illustrated. 

The model is based on SIR (Susceptible, Infected and recovered) model. 

Variables:
The simulation takes into account the following variables: 

Variables related to Covid-19: (1): Infection rate. (2): Recovery rate. (3): Death rate. (4): Immunity loss rate. 

Variables related to Governmental policies: (1): Vaccination mandate. (2): Travel restriction to Burnie. (3): Economic support. (4): Gathering restriction.

Variables related to economic growth: Economic growth rate. 

Adjustable variables are listed in the part below, together with the adjusting range.

Assumptions:
(1): Governmental policies are aimed to control(reduce) Covid-19 infections and affect (both reduce and increase) economic growth accordingly.

(2) Governmental policy will only be applied when reported cases are 10 or more. 

(3) The increasing cases will negatively influence Burnie economic growth.

Enlightening insights:
(1) Vaccination mandate, when changing from 80% to 100%, doesn't seem to affect the number of death cases.

(2) Governmental policies are effectively control the growing death cases and limit it to 195. 

 A Susceptible-Infected-Recovered (SIR) disease model with waning immunity

A Susceptible-Infected-Recovered (SIR) disease model with waning immunity

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)

​This model has been constructed from the model published in the following article:  Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control".    System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
​This model has been constructed from the model published in the following article:
Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control". 
System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
​This model has been constructed from the model published in the following article:  Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control".    System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
​This model has been constructed from the model published in the following article:
Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control". 
System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
 This model was developed as part of the curriculum development for a short introductory course on systems dynamics modelling for health system analysts. This is the fourth and final developmental component of Module 2. The population progression through health states is complete
This model was developed as part of the curriculum development for a short introductory course on systems dynamics modelling for health system analysts.
This is the fourth and final developmental component of Module 2. The population progression through health states is complete

This is reproduction of the tutorial exercise 1, Disease Dynamics.
This is reproduction of the tutorial exercise 1, Disease Dynamics.
​This model has been constructed from the model published in the following article:  Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control".    System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
​This model has been constructed from the model published in the following article:
Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control". 
System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
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. 
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. 
​This model has been constructed from the model published in the following article:  Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control".    System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
​This model has been constructed from the model published in the following article:
Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control". 
System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
​This model has been constructed from the model published in the following article:  Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control".    System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
​This model has been constructed from the model published in the following article:
Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control". 
System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
​This model has been constructed from the model published in the following article:  Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control".    System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
​This model has been constructed from the model published in the following article:
Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control". 
System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
​This model has been constructed from the model published in the following article:  Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control".    System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
​This model has been constructed from the model published in the following article:
Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control". 
System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
 A Susceptible-Infected-Recovered (SIR) disease model with waning immunity

A Susceptible-Infected-Recovered (SIR) disease model with waning immunity

Example of Configurable Conveyor Pattern (vectorized conveyor with multiple 'conveyor speeds')    See Taking The Pill   https://getsatisfaction.com/insightmaker/topics/delay-in-taking-the-pill for the problem statement
Example of Configurable Conveyor Pattern (vectorized conveyor with multiple 'conveyor speeds')

See Taking The Pill 
https://getsatisfaction.com/insightmaker/topics/delay-in-taking-the-pill for the problem statement
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. 
 A Susceptible-Infected-Recovered (SIR) disease model with waning immunity

A Susceptible-Infected-Recovered (SIR) disease model with waning immunity