Regulation of resource allocation to service in response to service quality. A non-price-mediated resource allocation system. From Sterman JD Business Dynamics p172 Fig 5-27

Regulation of resource allocation to service in response to service quality. A non-price-mediated resource allocation system. From Sterman JD Business Dynamics p172 Fig 5-27

Ocean/atmosphere/biosphere model tuned for interactive economics-based simulations from Y2k on.
Ocean/atmosphere/biosphere model tuned for interactive economics-based simulations from Y2k on.
A system diagram for the Mojave Desert including example socio-economic factors for an assignment at OSU- RNG 341.
A system diagram for the Mojave Desert including example socio-economic factors for an assignment at OSU- RNG 341.
Study of the self-and all the rest society
Study of the self-and all the rest society
 This model is designed for the local government of Burnie, Tasmania, aiming to help with balancing COIVD-19 and economic impacts during a possible outbreak.   The model has been developed based upon the SIR model (Susceptible, Infected, Recovered) model used in epidemiology.   It lists several poss

This model is designed for the local government of Burnie, Tasmania, aiming to help with balancing COIVD-19 and economic impacts during a possible outbreak. 

The model has been developed based upon the SIR model (Susceptible, Infected, Recovered) model used in epidemiology. 

It lists several possible actions that can be taken by the government during a COVID-19 outbreak and provide the economic impact simulation. 

The model allow users to Change the government policies factors (Strength of Policies) and simulate the total economic impact.

Interestingly, the government plicies largely help with controlling the COVID outbreak. However, the stronger the policies are, the larger impact on local economy

Simple mock-up model of how prioritizing various push-pull factors impacts the size of the immigrant population over time as well as economic benefits to the U.S. economy.
Simple mock-up model of how prioritizing various push-pull factors impacts the size of the immigrant population over time as well as economic benefits to the U.S. economy.
Circular equations WIP for Runy.    Added several versions of the model. Added a flow to make C increase. Added a factor to be able to change the value 0.5. Older version cloned at  IM-46280
Circular equations WIP for Runy.

Added several versions of the model. Added a flow to make C increase. Added a factor to be able to change the value 0.5. Older version cloned at IM-46280
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. 
 Nobody seems to notice bubbles until they burst. One possible reason is that those caught up in a bubble are completely blinded by the grip, the overpowering logic and force  excerted by the positive feedback loop that drives it. Financial bubbles occur time and time again - and nobody seems to lea

Nobody seems to notice bubbles until they burst. One possible reason is that those caught up in a bubble are completely blinded by the grip, the overpowering logic and force excerted by the positive feedback loop that drives it. Financial bubbles occur time and time again - and nobody seems to learn. Another example on a different time scale is an argument that spins out of control and ends in violence. The participants seem to be blind to the consequences; the immediate and imperative logic of the feedback loop imposes itself. The vortex created by the feedback loop even seems to draw in outsiders, such as new investors. Is this the reason why we don't notice bubbles? This explanation is meant to stimulate discussion!

 This model simulates a COVID outbreak occurring at Burnie, Tasmania.
It links the extent to the pandemic with governments intervention policies
aiming to limit the spread of the virus. The other part of the model illustrates
how will the COVID statistics and the government enforcement jointly influ

This model simulates a COVID outbreak occurring at Burnie, Tasmania. It links the extent to the pandemic with governments intervention policies aiming to limit the spread of the virus. The other part of the model illustrates how will the COVID statistics and the government enforcement jointly influence the economic environment in the community. A number of variables are taken into account, indicating positive or negative relationship in the infection and the economy model respectively.

 

Assumptions

·         Government takes responsive actions when the number of acquired cases exceeds 10.

·         Government’s prompt actions, involving closure of the state border, lockdown within the city, plans on mandatory vaccination and testing, effectively control the infection status.

·         Economic activities are reduced due to stagnation in statewide tourism, closure of brick-and-mortar businesses, and increased unemployment rate, as results of government restrictions.

 

Insights

Government’s rapid intervention can effectively reduce the infected cases. The national vaccination rollout campaign raises vaccination rate in Australians, and particularly influence the death rate in the infection model. Please drag the slider of vaccination to a higher rate and run the model to compare the outcomes.

Although local economy is negatively affected by government restriction policies, consumer demand in online shopping and government support payments neutralize the negative impact on economy and maintain the level of economic activities when infections get controlled. 

Jay Forrester's "Market Growth as Influenced by Capital Investment" model as rebuilt by Eric Stiens
Jay Forrester's "Market Growth as Influenced by Capital Investment" model as rebuilt by Eric Stiens