Metamodeling: a useful tool for applying innovative simulation techniques in agricultural economics

Computational simulation models are widely used in the field of agricultural economics for a variety of tasks, particularly for evidence-based policy analysis. Despite the substantial and continuing growth of computing power and speed, the growing complexity together with the implicit nature of the simulation models, on the one hand, still lead to high computational costs in applying the models along with great difficulties in the parameter specification where data availability and parametrization constraints for empirical calibration problems are notably challenging. On the other hand, they also limit the use of simulation models in many other aspects such as integration into other research frameworks like policy optimization coupled with uncertainty analysis. In this paper, we attempt to systematically and comprehensively introduce the metamodeling technique and investigate several metamodel types in terms of accuracy, computational time, variable importance, and potential practical applications.