Enhancing Agent-Based Model Comprehension through Virtual Reality

Abstract

Agent-based models (ABMs) are a valuable tool for simulating complex systems as they represent the interactions of autonomous individual components. However, their interpretability is often limited by the two-dimensional (2D) visualizations of spatially located static images on a grid. This makes it challenging for inexperienced users to understand how emergent behavior arises and how agents function. To address this challenge, we developed two agent-based models in the game engine Roblox Studio enhanced with virtual reality (VR). Using Roblox’s toolkit, we created a wolf-sheep predation model adapted from NetLogo, and a Kuramoto-FBM firefly model simulating blinking and movement. We designed these models interactive with support for both VR and keyboard and mouse, each with unique features including (1) spectating the agents from their point of view, (2) possessing the agents with tailored controls for each implementation, (3) capturing the agents, and (4) user-tunable parameters. In addition, custom three-dimensional (3D) models for the agents and scenery were made in Maya and Blender, along with animations, sound design, and visual effects. This allows the user to experience the simulation environment in an engaging and immersive way, while also producing accurate behavioral, population, and environmental data. Overall, these VR implementations leverage the users' understanding beyond other 2D visualizations by enabling users to spatially and temporally experience agents' decisions, bridging the gap between agent rules and emergent behavior. We hope this new implementation of ABMs in Roblox and VR serves as a reference for future research focused on expanding the accessibility, intuition, and interactivity in the modeling and simulation field. Future work comprises investigating other domains in this field, increasing the immersion with improved multiplayer support and visuals, and bettering accessibility to such platforms.