Agent-Based Models (ABMs) are arguably the most generalized framework for modeling and simulation of complex systems, which actually include both cellular automata and dynamical networks as special cases. ABMs are widely used in a variety of disciplines to simulate dynamical behaviors of systems made of a large number of entities, such as traders’ behaviors in a market (in economics), migration of people (in social sciences), interaction among employees and their performance improvement (in organizational science), ﬂocking/schooling behavior of birds/ﬁsh (in behavioral ecology), cell growth and morphogenesis (in developmental biology), and collective behavior of granular materials (in physics).
- 19.1: What Are Agent-Based Models?
- At last, we have reached the very ﬁnal chapter, on agent-based models (ABMs). ABMs are arguably the most generalized framework for modeling and simulation of complex systems, which actually include both cellular automata and dynamical networks as special cases.
- 19.2: Building an Agent-Based Model
- Let’s get started with agent-based modeling. In fact, there are many great tutorials already out there about how to build an ABM, especially those by Charles Macal and Michael North, renowned agent-based modelers at Argonne National Laboratory .
- 19.3: Agent-Environment Interaction
- One important component you should consider adding to your ABM is the interaction between agents and their environment. The environmental state is still part of the system’s overall state, but it is deﬁned over space, and not associated with speciﬁc agents.
- 19.4: Ecological and Evolutionary Models
- In this very ﬁnal section of this textbook, we will discuss ABMs of ecological and evolutionary dynamics. Such ABMs are different from the other examples discussed so far in this chapter regarding one important aspect: Agents can be born and can die during a simulation.