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simulation models and Insights now. Features
Simulate
Explore powerful simulation algorithms for System Dynamics and Agent
Based Modeling. Use System Dynamics to gain insights into your
system and Agent Based Modeling to dig into the details. Types of Modeling
Collaborate
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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.
WIP Overview model structures of Khalid Saeed's 2014 WPI paper Jay
Forrester’s Disruptive Models of Economic Behavior See also General SD and Macroeconomics CLDs IM-168865
Bienvenue sur le simulateur du modèle à flux constants.
Ce modèle est basé sur notre cas d'étude simplifié : un groupe de guépards dans un parc national géré, où les entrées et sorties sont contrôlés par les gestionnaires (translocation entre parcs).
Fonctionnement :
Le [Variable d'état] qui représente l'effectif de guépards (le nombre d'individus).
Les [Flux] (B, I, D, E) sont le nombre fixe d'individus qui entrent ou sortent à chaque pas de temps.
Variables forçantes : Les décisions des gestionnaires sont les "variables forçantes" qui rendent ces flux constants.
Votre mission :
Utilisez les curseurs pour régler les conditions et lancez la simulation en cliquant sur le bouton "SIMULATE" en haut à droite, pour observer comment l'effectif évolue sur le graphique !
Tento model slouží jakožto simulace práce na projektu. Pomocí proměnných lze simulovat práci týmu, chybovost a hlavně to, jak dlouho bude projekt trvat
This model simulates the tradeoff between AI costs in resources and the benefits of increased efficiency and effectiveness over time, using adaptive learning principles. It demonstrates how AI development evolves through different stages early, growth, and mature -with changing rates of investment, efficiency gains, and resource utilization.
The model tracks how AI systems stabilize over time as efficiency gains become harder to achieve, leading to a more mature and balanced system of investment and performance. It aims to provide insights into real-world AI dynamics, showing how resources translate into efficiency improvements and how diminishing returns, learning saturation, and reinforcement learning affect long-term growth.
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)
