Abstract
The process of aggregation of mathematical models and simulation is qualitatively described. Models are conceptually viewed as hierarchical structures. Aggregation results when state-variables and parameters of a model at one level are grouped and replaced by indices at a higher level as the domain of the model becomes broader. Conceptual clarity and computational feasibility of large and complex models stem from judicious use of aggregation. Properties of a system may emerge as a result of moving from lower to higher levels of organization. Quantitative genetic theory and methods often stem from an aggregation process. Several potential applications of models that are aggregated more highly than the level of individual animals and that require expertise in quantitative genetics and livestock production are identified and justified.
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