Agent-based models (ABMs) simulate complex systems by modeling individual actors and their interactions. Instead of relying on averages, ABMs reveal how real-world patterns—such as viral growth, market shifts, or congestion—emerge from simple rules, making them a powerful tool for scenario testing and decision support in business, policy, and research.