Augmenting Bottom-up Metamodels with Predicates

Metamodeling refers to modeling a model. There are two metamodeling approaches for ABMs: ( ) top-down and ( ) bottom-up. The top down approach enables users to decompose high-level mental models into behaviors and interactions of agents. In contrast, the bottom-up approach constructs a relatively small, simple model that approximates the structure and outcomes of a dataset gathered from the runs of an ABM. The bottom-up metamodel makes behavior of the ABM comprehensible and exploratory analyses feasible. For most users the construction of a bottom-up metamodel entails: ( ) creating an experimental design, ( ) running the simulation for all cases specified by the design, ( ) collecting the inputs and output in a dataset and ( ) applying first-order regression analysis to find a model that e ectively estimates the output. Unfortunately, the sums of input variables employed by first-order regression analysis give the impression that one can compensate for one component of the system by improving some other component even if such substitution is inadequate or invalid. As a result the metamodel can be misleading. We address these deficiencies with an approach that: ( ) automatically generates Boolean conditions that highlight when substitutions and tradeo s among variables are valid and ( ) augments the bottom-up metamodel with the conditions to improve validity and accuracy. We evaluate our approach using several established agent-based simulations.