How to Accelerate Innovation with Open Source Simulation

Insight Maker: Accelerating innovation and bigger thinking using open source simulation

Originally Presented: May 11, 2017 (OSCON, Austin, TX)

Simulation models are great tools for performing experiments and for employing systems-thinking principles to visualize and gain a deeper insight of how one area of a system impacts its other parts. Simulation and modeling tools are especially effective for understanding the root causes of a problem and for aligning stakeholders toward a consensus for action.

Insight Maker, an accessible free and open source simulation and modeling tool, offers a web-based multiuser environment for developing, analyzing, and sharing system insights and provides a graphical interface and includes systems dynamics modeling, agent-based modeling, and advanced features such as model optimization and scripting via its JavaScript API. Designed to be accessible to a broad audience of users, Insight Maker has been in development for many years and has been adopted by well over 20,000 registered users.

Kishau Rogers offers an in-depth review of how open source interactive simulation tools are being used to promote rapid learning, understand the structure and dynamics of complex systems, and solve problems. Using Insight Maker, Kishau highlights real-world simulation projects and their use of qualitative and quantitative data combined with systems thinking principles to model complex systems in industries such as health, technology, and business—scaling complex business operations, developing adaptive and intelligent applications, and evaluating the broader impact of service expansion.

Topics include:

  • Functional specifications: Understand the requirements for getting started
  • Types of models: Systems dynamics modeling versus agent-based modeling
  • Model building: The impact of actors, events, inputs, and outputs
  • Models versus truth: Verification and validation
  • Designing, conducting, and analyzing experiments
  • Reviewing, documenting, and sharing results
  • Customizing and manipulating models with JavaScript
  • Simulation case studies in the areas of healthcare, technology, interactive gaming, and business help desk operations
Total
0
Shares
Related Posts
Read More

Characteristics of Machine Learning solutions

It is estimated that 87% of data science projects never reach production. One of the pitfalls to developing a production-ready machine learning solution is the ability to define if it's an appropriate tool for solving the problem. Not every problem should be solved with machine learning.
Read More

Data Iceberg Model for Machine Learning

One of the pitfalls to developing production-ready machine learning solutions is the failure to identify the appropriate data assets. In evaluating the data assets to be used for your project, use the Data Iceberg Model approach to determine the underlying (i.e. not visible) structures that triggered the creation of the dataset. The following Data Iceberg Model can be used to evaluate the quality & limitations of the data used to train your machine learning models.
Read More

Simulation & Modeling Resources

Simulation and computer modeling tools allow engineers to model and evaluate real world events in a computer generated environment. Here are a few simulation tools and projects to get started: