Year: 2019
Type: article
Abstract: Agent-based (AB) models are rule based and are a relatively simple discrete method that can be used to simulate complex interactions of many agents. The relative ease of implementing the computational model is often counterbalanced by the difficulty of performing rigorous analysis to determine emergent behaviors. In addition, without precise definitions of agent interactions, calculating existence of fixed points and their stability is not tractable from an analytical perspective and can become computationally expensive, involving potentially thousands of simulations. Through developing a precise definition of an off-lattice AB model with a specified interaction neighborhood, we develop a general method to determine a global recurrence rule (GRR). This allows estimates of the state densities in time, which can be easily calculated for a range of parameters in the model. The utility of this framework is tested on an epidemiological agent-based model where agents correspond to people that are in the susceptible, infected, or recovered states. The interaction neighborhoods of agents are determined in a mathematical formulation that allows the GRR to accurately predict the long term behavior and steady states. The modeling framework outlined will be generally applicable to many areas and can be easily extended.
DOI: 10.1137/18m1186939
Authors: Michael A. Yereniuk, Sarah D. Olson
Institution: Worcester Polytechnic Institute
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2972186461