Agent-Based Modeling ANB 218a Jeff Schank

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

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

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

Phenomena

Emergence and Complexity

Emergence and Simple Rules

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.

Particles in Space at the Start

What Shape will Form?

Rat Pups

Rat Pups

Simulated Rat Pups

Robotic Rat Pups

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