Agent-Based Modeling (ABM)
Agent-Based Modeling (ABM) offers a modern approach for simulating heterogeneous populations of individuals, or agents, that can interact with each other and their environment. These agents, which are modeled based on real-world data, can change behaviors over time in response to various factors such as environmental shifts or social network dynamics.
Key Components:
- Synthetic populations: virtual representations of real populations based on data from sources like the U.S. Census and geographic information system (GIS) data. These synthetic populations enable the study of group dynamics on populations that are similar to real-world populations, without putting those populations at risk.
- Lifelike Simulations: We have integrated reinforcement learning (RL) into ABM simulations, creating what we refer to as ABM with Reinforcement Learning (ABMRL). This approach not only allows agents to adapt to their environment but also enables the environment itself to evolve in response to the actions of the agents. Including reinforcement lets agents adjust their behaviors based on the outcomes of their actions and changes to the environment and each other.
Mobius Logic has successfully applied ABMRL in a variety of projects, including studies on the spread of violence within communities and automated satellite inspections. These applications demonstrate the power of ABMRL to model complex, adaptive systems and provide insights that would be difficult to achieve with traditional methods.
