  Computational modeling of a complex system is limited by the parts of the
system with the least information. While detailed models and high-resolution
data may be available for parts of a system, abstract relationships are often
necessary to connect the parts and model the full system. For example, modeling
food security necessitates the interaction of climate and socioeconomic
factors, with models of system components existing at different levels of
information in terms of granularity and resolution. Connecting these models is
an ongoing challenge. In this work, we demonstrate methodology to quantize and
integrate information from data and detailed component models alongside
abstract relationships in a hybrid element-based modeling and simulation
framework. In a case study of modeling food security, we apply quantization
methods to generate (1) time-series model input from climate data and (2) a
discrete representation of a component model (a statistical emulator of crop
yield), which we then incorporate as an update rule in the hybrid element-based
model, bridging differences in model granularity and resolution. Simulation of
the hybrid element-based model recapitulated the trends of the original
emulator, supporting the use of this methodology to integrate data and
information from component models to simulate complex systems.
