Modelling and Simulation: An Overview of Code and No-Code Tools

Modelling and Simulation: An Overview of Code and No-Code Tools
Photo by Roman Synkevych / Unsplash

The evolution of technology has provided us with a plethora of modelling and simulation tools. These tools, falling into two primary categories: no-code and code-based, each offer distinct advantages and challenges. This blog post will present an overview of popular no-code and code modelling tools, detailing their features and discussing their pros and cons.

Unpacking No-Code Modelling and Simulation Tools

No-code tools provide a user-friendly interface for developing and executing models, eliminating the need for programming skills.


Primarily used for game design, Machinations offers a real-time, interactive environment. It employs a node-based system, allowing you to visualize and balance game mechanics with ease.


Vensim's strength lies in its application to complex dynamic systems. It employs a visual interface that supports drag-and-drop functionality, simplifying the modelling process. Vensim excels at system dynamics modelling, particularly useful for business strategies, environmental issues, and public policy.


Similar to Vensim, Stella is a tool designed for system dynamics modelling. It features a user-friendly interface, including a model "flight simulator" that allows users to manipulate inputs over time and examine the model's responses.

Part of MATLAB's software suite, Simulink offers a graphical editor for model-based design. It is ideal for engineering applications, offering functionalities for multi-domain simulation, system-level design, and embedded system engineering.

Exploring Code Modelling and Simulation Frameworks

Code-based tools require programming knowledge, but they provide a high degree of flexibility, customization, and complexity in models.

Python-Based Libraries

Python offers numerous libraries for simulation and modelling:

  • SimPy is ideal for process-based discrete-event simulation.
  • PyDSTool is designed for dynamical systems.
  • Pyomo is a robust tool for mathematical optimization.
  • PyCX is used for simulating complex systems.


Beyond its graphical environment Simulink, MATLAB is a powerful environment for numerical computation, allowing for detailed simulations and models using code.

R-Based Libraries

R, a programming language primarily used for statistical computation, provides several packages for modelling and simulation:

  • deSolve is effective for solving differential equations.
  • simecol is useful for simulating ecological (and other) dynamic systems.

Pros and Cons: Making the Right Choice

No-Code Tools


  • User-Friendly: No-code tools are accessible to users without programming skills.
  • Quick Setup: Pre-built functionalities and drag-and-drop interfaces ensure rapid setup.
  • Collaborative: These tools often support team-based work, allowing simultaneous input from multiple users.


  • Limited Customization: While user-friendly, no-code tools may not support complex or custom models.
  • Performance: These tools might not be as efficient when dealing with large-scale simulations.

Code-Based Tools


  • Customization: Code-based tools provide significant flexibility and customization for your models.
  • Scalability: These models can handle larger, more complex systems and are often optimized for performance.
  • Integration: Coded models can be integrated with other software systems and utilize the full power of the host language.


  • Learning Curve: Code-based tools require programming skills, and complex models may be challenging to master.
  • Time-Consuming: Developing custom models can be a time-intensive process, especially for complex systems or models that need extensive debugging.

Final Thoughts

The choice between no-code and code-based tools depends on your specific needs, the complexity of your model, and your technical expertise. No-code tools like Machinations, Vensim, Stella, and Simulink offer accessibility and are ideal for simpler models. On the other hand, code-based frameworks offer flexibility and depth, making them more suitable for complex simulations and for users comfortable with programming. Understanding the features, benefits, and limitations of each will help you make an informed decision for your modelling and simulation needs.

This content was generated using OpenAI's GPT Large Language Model (with some human curation!). Check out the post "Explain it like I'm 5: What is ChatGPT?" to learn more.