Agent Based

Agent-based modeling (ABM) is a computational approach used to simulate complex systems by modeling the interactions of autonomous agents, offering insights into emergent behavior not readily apparent from individual agent characteristics. Current research focuses on integrating ABM with large language models (LLMs) to enhance agent decision-making and realism, particularly in social simulations and economic modeling, often employing architectures that incorporate machine learning techniques like reinforcement learning and neural networks. This approach is proving valuable in diverse fields, from epidemiology and transportation planning to business process simulation and the study of social dynamics, providing a powerful tool for understanding and potentially mitigating complex real-world challenges.