Agent-Based Modeling Using GAMA Platform for Beginners

Navigate the GAMA interface and understand the workspace, editor, console, and simulation views.

Create your first GAMA project and organize the project files properly.

Run your first simulation model and observe how simulations execute inside GAMA.

Learn the basics of GAML syntax and understand the structure of a GAMA simulation model.

Learn how to create variables and parameters that control simulation behavior.

Create simulation agents and define their properties and characteristics.

Implement movement and behavior rules that control how agents interact inside simulations.

Build autonomous moving agents and observe movement behavior inside the simulation environment.

Create a classroom simulation using multiple student agents inside a virtual environment. This lecture demonstrates how Agent-Based Modeling can represent real-world indoor movement and interactions.

Enhance the classroom simulation by adding randomness to agent movement and behavior. This lecture demonstrates how random behavior creates more dynamic and realistic simulations.

Incorporate unpredictability into agent behavior to improve the simulation. Students will see how more dynamic and lifelike simulations are produced through random movement and decision-making.

Improve simulation understanding using visualization techniques such as colors, movement trails, and direction indicators. This lecture demonstrates how visualization helps analyze agent behavior more effectively inside simulations.

Understand the fundamentals of Geographic Information Systems (GIS) and learn how spatial data can be integrated into Agent-Based Modeling simulations using the GAMA Platform.

Learn how to import GIS shapefiles into the GAMA Platform and use real-world spatial data inside simulations. This lecture demonstrates how GIS environments improve the realism of Agent-Based Modeling systems.

Create a GIS-based simulation environment using real-world spatial data in GAMA. In this lecture, students will learn how to spawn agents inside GIS environments and simulate movement within mapped spaces.

Enhance GIS simulations using visualization techniques such as movement trails, direction vectors, dynamic colors, and analytics displays. This lecture demonstrates how visualization improves understanding of spatial behavior inside simulations.

Transform the GIS simulation into a disaster scenario by simulating emergency movement behavior inside the environment. This lecture introduces disaster modeling concepts and demonstrates how Agent-Based Modeling can represent emergency situations and evacuation behavior.

Create evacuation agents that move toward emergency exits during disaster situations. Students will implement goal-oriented movement and evacuation behavior.

Improve evacuation realism using pathfinding concepts and intelligent navigation. Agents will move more efficiently through the environment toward exits.

Analyze evacuation simulations using real-time analytics, charts, and monitoring tools. This lecture focuses on understanding simulation behavior through data analysis.

Prepare for the final simulation project by exploring capstone expectations, simulation ideas, workflows, and project planning strategies.

Design the structure of the final simulation project by defining the environment, agents, behaviors, outputs, and analytics required for implementation.

Build the final capstone simulation system using GIS environments, evacuation agents, analytics tools, and visualization techniques learned throughout the course.

Present and evaluate the completed simulation project. In this final lecture, students will demonstrate their Agent-Based Modeling systems, explain simulation behavior, analyze results, and reflect on the overall project implementation using the GAMA Platform.