Seminar Series: Nora Bello

Title: Hierarchical Modeling of Heterogeneous Networks for Animal Production Systems

Abstract: 

Understanding the interconnections between performance outcomes in a system is increasingly important for integrated management. Structural equation models (SEMs) are a type of multiple-variable modeling strategy that allows investigation of directionality in the interconnections between outcome variables, thereby providing insight into links defining a functional network. A key assumption underlying SEMs is that of a homogeneous network structure, whereby the structural coefficients defining functional links are assumed homogeneous and impervious to environmental conditions or management factors. This assumption seems questionable as systems are regularly subjected to explicit interventions to optimize the necessary trade-offs between outcomes. Using a Bayesian approach, we propose methodological extensions to hierarchical SEMs that accommodate structural heterogeneity of the network by explicitly specifying structural coefficients as functions of systematic and non-systematic sources of variation. We validate the proposed approach using simulation and apply it to a dataset from commercial swine production. Results indicate that explicit modeling of network heterogeneity enhances understanding of complex systems in animal production agriculture.