A general mathematical method for predicting spatio-temporal correlations emerging from agent-based models.

ABSTRACT: Agent-based models are used to study complex phenomena in many fields of science. While simulating agent-based models is often straightforward, predicting their behaviour mathematically has remained a key challenge. Recently developed mathematical methods allow the prediction of the emerging spatial patterns for a general class of agent-based models, whereas the prediction of spatio-temporal pattern has been thus far achieved only for special cases. We present a general and mathematically rigorous methodology that allows deriving the spatio-temporal correlation structure for a general class of individual-based models. To do so, we define an auxiliary model, in which each agent type of the primary model expands to three types, called the original, the past and the new agents. In this way, t

SUBMITTER: Ovaskainen O 

PROVIDER: S-EPMC7653394 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature