Conceptually, in ABM you give instructions to virtual agents that allow the agents to interact. Agents can be animals, tanks, parcels, delivery trucks, or any discrete object. From the resulting decisions and actions of the agents, patterns are created in space and time. Unlike many other modeling techniques that quantify and then re-create the patterns, agent-based models explore the causes of the patterns; the patterns are emergent properties from the individual decisions of the agents.
Agent-based modeling, combined with spatial data, allows you to address a wide array of problems such as the following:
- Developing corridor connectivity networks for wildlife movement
- Anticipating potential terrorist attacks
- Analyzing traffic congestion or producing evacuation strategies
- Planning for the potential spread of disease such as bird or swine flu
- Understanding land-use change
- Optimizing timber tract cutting
- Exploring energy flow on electrical networks
- Performing crime analysis to deter future impact
Many phenomena or agents exist and make decisions in, and relative to, space. The location of an agent and its surrounding environment will influence the agent’s decision making. The agent can influence or change the landscape it interacts with. A GIS is a spatial modeling tool that stores, displays, and analyzes data on spatial relationships. A natural synergy exists between ABM and a GIS. Agent Analyst is free, open-source software developed to integrate an ABM development platform—the Recursive Porous Agent Simulation Toolkit (Repast)—within a GIS (ArcGIS). Agent Analyst is a mid-level integration that takes advantage of both modeling environments.
Source: Agent Analyst – Agent-Based Modeling in ArcGIS by Kevin M. Johnston, Esri Press (2013)