Table 1.

Summary of Main Differences Between Ordinary Differential Equations and Agent-Based Models

Ordinary differential equation	Agent-based Model
Highly aggregate (makes an abstraction from multiple events and individuals)	Highly disaggregate (based on actions of individual agents and their interactions with other agents)
Broad boundary	Narrow boundary
Perfect mixing assumption	Heterogeneity in agent attributes
Small number of parameters	Large number of parameters
Computationally efficient	Computationally intensive
Continuous time	Discrete time
Does not capture spatial dynamics or stochastic effects	Captures spatial dynamics or stochastic effects
Evaluation of what-if scenarios	Observing patterns of emerging behavior