1. Agent-Based Modeling
ANB 218a
Jeff Schank

2. What is ABM?
ABM is a bottom up strategy for modeling complex systems, i.e., systems consisting of many similar parts following simple rules
ABM focuses on individuals and their interactions
Agents can represent people, animals, or entities at different levels of organization
Agents use “simple rules” to interact with their environment and others
ABM involves both experimental and mathematical styles of thinking
Experimental
Specify behaviors and properties of agents and environment
Design experiments
Analyze data
Mathematical
Investigate the entire parameter space of the model
Formulate general principles

3. What are the uses of ABM?
To model complex systems in which individual behavior and properties are better understood than the behavior and properties of the system
Molecular and cellular biology
Ecology
Anthropology and other social sciences
Animal behavior
Exploratory modeling
Artificial life
Evolutionary game theory
Investigating the robustness of analytical results
Evolutionary Biology

4. What are they good for? Complex systems Emergent phenomena
When we understand the parts better than the whole
When we seek mechanistic explanations
When we are faced with multiple levels of organization

5. Phenomena

6. Emergence and Complexity

7. Emergence and Simple Rules

8. A Simple Model
Rule 1: Particles (agents) move randomly in a 2-dimensional toroidal space.
Rule 2: If a moving agent contacts an agent that is not moving, it stops at that location permanently.
Rule 3: A single non-moving agent is placed in the middle of the space at the start of a simulation.

9. Particles in Space at the Start

10. What Shape will Form?

11. Rat Pups

12. Rat Pups

13. Simulated Rat Pups

14. Robotic Rat Pups

15. Analysis of Models Parameter sweeps
Systematically vary one or move parameters of a model
There are limitations are on the number of parameters
If there are two parameters and you want to look at 5 values for each parameter, then you must conduct 5 × 5 = 25 sets of simulations
As you can see, the number of sets of simulations to be conducted increases exponentially with the number of parameters to be swept
Another approach is to use various genetic algorithms to evolve models that either fit some set of goals or data of interest