Insight maker tutorial

Insight Maker Guide Welcome to the Insight Maker Guide. This guide is organized as a hierarchical book with chapters and sections describing different portions of Insight Maker.

You can navigate sequentially through the guide using the links at the bottom of this page or jump directly to a section of interest using the Table of Contents on the right. You may also create a print-friendly version of this entire guide.

Getting Help Insight Maker is a complex application and there is a lot to learn about it. Fortunately, we've put together a number of resources to help you. These include:

This Manual: Use the links on the right side of this page to learn more about Insight Maker. Community Forum: Post feedback and ask questions. Beyond Connecting the Dots: Read a book on Systems Thinking and Modeling using Insight Maker. Systems Thinking World: Insight Maker: Take a network perspective to learning Insight Maker and watch dozens of detailed videos describing Insight Maker's features.

Features Insight Maker is a powerful simulation tool that runs right in your web browser. Best of all, it's completely free! Insight Maker supports the following features and more:

Building Models Use Insight Maker to start with a conceptual map of your Insight and then convert it into a complete simulation model. Insight Maker supports extensive diagramming and modeling features that enable you to easily create representations of your system.

System Dynamics Modeling

Causal Loop Diagrams

Stock and Flow Models

Graphical Inputs

Ghosting Primitives

Vectorizing Primitives

Extensive Units Support

Agent Based Modeling

States and Transitions Diagrams

Custom Actions

https://groups.google.com/forum/#!forum/insightmaker

http://beyondconnectingthedots.com/

https://kumu.io/stw/insight-maker

https://insightmaker.com/converters

https://insightmaker.com/ghosting

https://insightmaker.com/vectors

https://insightmaker.com/units

https://insightmaker.com/actions

Spatial Relationships

Network Relationships

Diagraming and Rich Pictures

Extensive Styling Features

Custom and Built-in Library of Pictures

Folding and Unfolding of Portions of Diagram

Storytelling

Loop Identification Run Models Insight Maker supports powerful simulation methods that rival many commercial programs. With Insight Maker you can use System Dynamics modeling, Agent Based Modeling or integrate the two methods seamlessly.

Results

Times Series and Scatterplots/Phase-Planes

Maps and Network Diagrams

Tables Data Export to CSV

Time Machine Analysis

Functions and Programming

Large Library of Built-In Functions

User Created Macros and Functions

Procedural Programming

Functional Programming

Object-Oriented Programming

Simulation Algorithms

Euler's Method

4th Order Runge-Kutte Method

Advanced

Sensitivity Testing

Model Scripting

Built-In Optimizer Sharing Models Insight Maker has extensive capabilities for sharing your models with others. Just send them a link or embed your model in your website or blog. Also, you can give others access to your models so they can work on them collaboratively with you right in their own browsers.

Sharing

https://insightmaker.com/spatialgeography

https://insightmaker.com/networkgeography

https://insightmaker.com/storytelling

https://insightmaker.com/functions

https://insightmaker.com/macros

https://insightmaker.com/advancedequations#procedural

https://insightmaker.com/advancedequations#functional

https://insightmaker.com/advancedequations#objectoriented

https://insightmaker.com/sensitivitytesting

https://insightmaker.com/scripting

https://insightmaker.com/optimization

Send Model Link

Embed Model in a Web Page

Publish Model as Web Page

Access Control

Enable Shared Editing

Make Insights Private or Public Cost Free!

Types of Modeling Insight Maker is a multi-method modeling solution packaged within a fluid and cohesive software environment.

At one level, you can use Insight Maker purely to map out conceptual models: using casual loop diagrams or rich pictures to describe a system. In this mode, Insight Maker functions as a powerful diagraming tool that lets you illustrate a model and then easily share it with others.

Once you have a model diagram created, you can start to add behavior to the different components using Insight Maker's simulation engine. Insight Maker supports two different modeling paradigms that together can describe most of the models you could imagine:

System Dynamics: System Dynamics (sometimes called differential equation modeling or dynamical systems modeling) concerns itself with the high-level behavior of a system. It helps you understand the aggregate operations of system on a macro-scale. It is great for cutting away unnecessary detail and focusing on what is truly important in a model. Agent Base Modeling: Agent Based models allow you to model individual agents within a system. Where in System Dynamics you might only look at the population as a whole, in Agent Based Modeling you can model each individual in the population and explore the differences and interactions between these individuals.

System Dynamics and Agent Based Modeling complement each other. In Insight Maker you can use either approach or integrate both of them together into one seamless model. To understand the pros and cons of an Agent Based Model versus a System Dynamics model, we can explore how these two techniques might approach the same problem: modeling the spread of an infectious disease in a population.

An Example: SIR Disease Model

For this example, let us model the spread of a disease such as the flu. We can classify people in this model as being in one of three states:

Susceptible: Healthy and susceptible to catching the disease Infected: Infected with the disease and able to spread it to susceptible individuals Recovered: No longer infected with the disease and temporarily immune to the disease (for diseases like the flu, a temporary immunity will be conferred after infection which will fade with time)

The commonly used acronym to describe this type of model -- SIR -- comes from the initials of these three states.

Individuals will move between the three states: moving from susceptible to infected to recovered and back to susceptible. The movement from susceptible to infected will be governed by some infection rate equation that takes into account the status of currently infected individuals. The movement from infected to recovered and back to susceptible will be governed by the average duration of the disease and the average duration of the immunity conferred by it.

System Dynamics Implementation

Using the System Dynamics methodology, we model each of the three states using a Stock primitive that stores the number of individuals currently in that state. So, for instance, we have a Susceptible Stock storing the portion of the population that is currently in the susceptible state. We then use Flows to move individuals between the Stocks based on different factors. For instance, for the flow moving individuals between the Infected and Recovered Stocks, we would use an equation such as [Infected]*1/[Average Infection Duration]. If the average infection duration was ten days, this would move roughly 10% of the infected population every day.

The following embedded model illustrates the full System Dynamics implementation of this model. Please note the smooth aggregate curves in the resulting simulations.

Agent Based Implementation

To create the Agent Based Modeling implementation of this disease model, we first create an agent definition that defines the behavior of a single individual in our model. We use three State primitives in this model, one to represent each of the three disease states a person can be in. We connect these states with Transition primitives that instruct how a single individual moves between the states. Where in the System Dynamics models we had flows with rates, in the Agent Based models there are transitions that are given probabilities. These probabilities determine when the transition will be activated and the agent will switch states.

Susceptible

Infected

Recovered

Infection

Recovery

Immunity Loss

Immunity Loss Rate

Recovery Rate

Infection Factor

SIR Model

A simple Susceptible -

Infected - Recovered

disease model.

Tags: Disease, KeLE ABM

Insight Author: Scott Fortmann-Roe

Susceptible Initial number of susceptible individuals.

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Infected Initial number of infected

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The Agent Based approach allows us to implement features in this model that would simply be impossible using System Dynamics. For instance we can look at the geographic proximity of agents and use this to affect our transmission probability. Susceptible agents that are closer to infected agents are more likely to become sick than those that are farther away. Similarly, we could look at social structure: how the connections between agents will influence their probability of coming into contact with the infection and falling ill. All this would simply not be possible to look at using System Dynamics.

The following embedded model illustrates the full Agent Based Modeling implementation of this model. An added twist included here is that the susceptible agents will actually try to run away from the infected agents!

System Dynamics Insight Maker supports System Dynamics modeling: a powerful method for exploring systems on an aggregate level. By "aggregate", it is meant that System Dynamics models look at collections of objects, not the objects themselves. For instance, if you created a model of a water leakage from a bucket, a System Dynamics model would concern itself with the quantity of water as a whole, not with individual droplets or even molecules. Similarly, if you were modeling a population of rabbits, the System Dynamics model would look at the population as a whole, not at the individual rabbits.

System Dynamics models are constructed from a set basic building blocks also known as "primitives". The key primitives are Stocks, Flows, Variables and Links.

Stock Stocks store a material. For instance a bank account is a Stock that stores money. A bucket is aStock that stores water. A population is a Stock that stores people.

Flow A Flow moves material between stocks. For instance, in the case of a bank account you couldhave an inflow of deposits and an outflow of withdrawals. Variables Variables are dynamically calculated values or constants. In the bank account model you could