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Explore powerful simulation algorithms for System Dynamics and Agent
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Explore What Others Are Building
Here is a sample of public Insights made by Insight Maker users. This list is auto-generated and updated daily.
Example from book "Complex System Research in Psychology" by Han van der Maas (https://santafeinstitute.github.io/ComplexPsych/)
This simulation allows you to compare different approaches to influence flow, the Flow Times and the throughput of a work process.
By adjusting the sliders below you can
observe the work process without any work in process limitations (WIP Limits),
with process step specific WIP Limits* (work state WIP limits),
or you may want to see the impact of the Tameflow approach with Kanban Token and Replenishment Token
or see the impact of the Drum-Buffer-Rope** method.
* Well know in (agile) Kanban
** Known in the physical world of factory production
The "Tameflow approach" using Kanban Token and Replenishment Token as well as the Drum-Buffer-Rope method take oth the Constraint (the weakest link of the work process) into consideration when pulling in new work items into the delivery "system".
You can also simulate the effects of PUSH instead of PULL.
Feel free to play around and recognize the different effects of work scheduling methods.
If you have questions or feedback get in touch via twitter @swilluda
The work flow itself
Look at the simulation as if you would look on a kanban board.
The simulation mimics a "typical" software delivery process.
From left to right you find the following ten process steps.
Input Queue (Backlog)
Selected for work (waiting for analysis or work break down)
Attempt to clarify the differences in the energy balance and carbohydrate insulin models described in Speakman and Hall's 2021 science article and insight and Nature Metabolism Obesity Causal Model Differences 2024 article and insight
Obesity Energy Balance and Carbohydrate Insulin Model Differences
This model shows the growth of the human population of Edmonton, Alberta over a 100 year period. The model starts with an estimated population of 1.3 million and uses birth/death rates to determine the population's natural growth.
As the population increases, the per-capita growth rate decreases. This causes population growth to slow down as Edmonton approaches its carrying capacity of 2 million people. A 10 year delay represents the time for population density to affect growth.
Ver. 2.2.1 - Single glacial dust pulse, dynamic total Sr inventory, fixed VIBD, first-order Sr co-deposition, delayed recycled 87Sr/86Sr and δ88/86Sr compositions, single glacial dust pulse as climatic forcing
Model (RK4)_Ver 2.2.1_With delayed CaCO3 dissolutiont_dynamic gross Sr removal_single glacial dust pulse_260825
This model represents the population of ancient Rome in 200 BC with a density-dependent birth rate...the birth rate does not equal the death rate when the population is at carrying capacity because immigration and emigration are also factors. All values are based on historical estimates, as precise figures are not available.
Carrying capacity becomes less effective at maintaining a population when outsiders can migrate in and out. Even though the birth rate may be slightly negative, immigration can lead to a booming population increase
The Model is for an increase in population growth and can not represent a decrease. The time frame is 300 years rather than 100, as it provides a clearer picture of where the long-term growth is heading
