When to Consider Agent-Based ModelingAs a follow-on to yesterday's post about using agent-based simulation to model the economy, I'd like to suggest that the technically minded have a look at a paper (pdf) Charles M. Macal and Michael J. North, both at Argonne National Laboratory, presented at the 2009 Winter Simulation Conference.
The paper discusses both how to think about agent-based modeling and simulation (ABMS), and how to actually do ABMS. The latter portion of the paper includes guidance on software and toolkits specially designed for ABMS.
Even the non-technically minded can benefit from reading through Macal and North's list of criteria for considering an agent-based approach to simulating a dynamic system. The eleven criteria any one of which is sufficient to suggest an agent-based approach are:
- The problem has a natural representation as being comprised of agents
- There are decisions and behaviors that can be well-defined.
- It is important that agents have behaviors that reflect how individuals actually behave (if known).
- It is important that agents adapt and change their behaviors.
- It is important that agents learn and engage in dynamic strategic interactions.
- It is important that agents have a dynamic relationship with other agents, and agent relationships form, change, and decay.
- It is important to model the processes by which agents form organizations, and adaptation and learning are important at the organization level.
- It is important that agents have a spatial component to their behaviors and interactions.
- The past is no predictor of the future because the processes of growth and change are dynamic.
- Scaling-up to arbitrary levels is important in terms of the number of agents, agent interactions and agent states.
- Process structural change needs to be an endogenous result of the model, rather than an input to the model.