Individual-based models, also called agent-based models, are a population and community modeling approach that allows for a high degree of complexity of individuals and of interactions among individuals. Individual-based models simulate populations or systems of populations as being composed of discrete individual organisms. Each individual has a set of state variables or attributes and behaviors. State variables can include spatial location, physiological traits and behavioral traits. Ref: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4047944/#:~:text=Individual-based%20models%20simulate%20populations%20or%20systems%20of%20populations%20as,physiological%20traits%20and%20behavioral%20traits.
Individual-based models (IBMs) simulate the fate of each individual in a population, rather than cohorts, and like matrix-models, IBMs usually incorporate demographic and environmental stochasticity, habitat quality, and density dependence. The family of models IBMs entail has been extensively reviewed by Grimm (1999). The major advantage of IBMs is that individual heterogeneity is modeled explicitly. For instance, individuals may differ in the vital rates because of body condition, breeding status, the habitat quality within their exclusive home range, or because of their pedigree of inbreeding. These attributes reflect the relative contribution that each individual makes toward maintaining population viability, and can therefore be important to represent (White 2000). The caveats to developing IBMs are in the detailed data they demand, and the computational constraints of simulating large population sizes.
Ref: https://www.sciencedirect.com/topics/earth-and-planetary-sciences/individual-based-model