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System Dynamics Models

System Dynamics is a powerful approach for modeling change within systems and Insight Maker is perfect for building System Dynamics models.

Agent Based Models

Explore the interactions between dynamic agents. Use Insight Maker to define agent behavior and simulate a population of agents interacting.

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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.

Epidemiological Model

Prof. Dr. Paulo Villela

This model is based on the paper by Crokidakis, Nuno. (2020). *Data analysis and modeling of the evolution of COVID-19 in Brazil*. For further details, see the full paper here.

In addition to modeling a COVID-19 transmission system in Brazil, it shows the conversion of the system of differential equations into a stock-and-flow diagram.

Jay Forrester's major contribution was to propose a new language—System Dynamics—to represent systems of differential equations, which constitutes its solid mathematical foundation, developed by Newton and Leibniz in the 17th century.

The beauty of System Dynamics lies in unifying mathematical rigor with visual clarity. It is a tool that empowers our ability to understand, communicate, and simulate the complexity of the world.

SIQR Epidemiological Model

Paulo Villela

2 weeks ago

Clusters of interacting methods for improving health services network design and delivery. Includes Forrester quotes on statistical vs SD methods and the Modeller's dilemma. Simplified version of IM-14982 combined with IM-17598 and IM-9773

Complex Decision Technologies

Geoff McDonnell

34 10 months ago

Spring, 2020: in the midst of on-line courses, due to the pandemic of Covid-19.

With the onset of the Covid-19 coronavirus crisis, we focus on SIRD models, which might realistically model the course of the disease.

We start with an SIR model, such as that featured in the MAA model featured in

https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

Without mortality, with time measured in days, with infection rate 1/2, recovery rate 1/3, and initial infectious population I_0=1.27x10-4, we reproduce their figure

With a death rate of .005 (one two-hundredth of the infected per day), an infectivity rate of 0.5, and a recovery rate of .145 or so (takes about a week to recover), we get some pretty significant losses -- about 3.2% of the total population.

Resources:

http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

Coronavirus: A Simple SIR (Susceptible, Infected, Recovered) with death