Augmenting Bottom-up Metamodels with Predicates
Ross Joseph Gore, Saikou Y. Diallo, Christopher J. Lynch, José J. Padilla · Journal of Artificial Societies and Social Simulation · 2017
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 effectively 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 tradeoffs 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.