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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

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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.

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The Sensitivity Analysis option allows you to develop a sense of the extent to which some part of the model is affected by changes in some other part of the model. I think you'll find the approach to implementing Sensitivity Analysis rather straightforward and powerful at the same time.
@LinkedIn, Twitter, YouTube
Insight Maker/Sensitivity Analysis
Insight diagram
Il modello dinamico di Baranyi e Roberts per la curva di crescita di microrganismi (Baranyi, J., Roberts, T. (1994). A dynamic approach to predicting bacterial growth in food International journal of food microbiology  23(), 1 - 18).

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E' un modello dinamico che assume che la fase lag sia dovuto all'accumulo di un composto essenziale (la cui quantità iniziale riflette lo stato iniziale delle cellule nell'ambiente E1, da cui provengono), secondo una cinetica di primo ordine, ad una velocità che dipende dall'ambiente E2. Il modello è lievemente modificato rispetto all'originale, per evitare che la quantità del prodotto essenziale tenda all'infinito. 
D-model (curve di Richards)
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La situación modelada expresa el crecimiento de las ventas impulsadas por la motivación y productividad, pero es frenada por el tamaño del nicho de mercado.
Límite de Crecimiento
Insight diagram
Modèle de base de la mécanique de Newton appliquée à la chute d'un ballon dans l'air soumise à la force de friction 

Traduction du modèle Insight Maker conçu par Hans Niedderer
Chute de ballon
2 weeks ago
Insight diagram
Some key nested diagrams from George Mobus' Sapience Book Draft esp Components of Sapience article
Wisdom, Sapience, Judgment and Decisions
3 last week
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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.

AI Costs vs. Efficiency Tradeoff Model