Jasss: The Journal of Artificial Societies and Social Simulation
Metamodeling refers to modeling a model. There are two metamodeling approaches for ABMs: (1) top-down and (2) 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 fromthe runs of an ABM. The bottom-up metamodel makes behavior of the ABM comprehensible and exploratory analyses feasible. Formost users the construction of a bottom-up metamodel entails: (1) creating an experimental design, (2) running the simulation for all cases specified by the design, (3) collecting the inputs and output in a dataset and (4) 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: (1) automatically generates Boolean conditions that highlight when substitutions and tradeoffs among variables are valid and (2) augments the bottom-up metamodel with the conditions to improve validity and accuracy. We evaluate our approach using several established agent-based simulations.
Original Publication Citation
Gore, R., Diallo, S., Lynch, C., & Padilla, J. (2017). Augmenting bottom-up metamodels with predicates. Jasss: The Journal of Artificial Societies and Social Simulation, 20(1), 1-20. doi:10.18564/jasss.3240
0000-0003-4065-6146 (Gore), 0000-0003-2389-2809 (Diallo), 0000-0002-4830-7488 (Lynch), 0000-0002-1262-8861 (Padillo)
Gore, Ross J.; Diallo, Saikou; Lynch, Christopher; and Padilla, Jose, "Augmenting Bottom-Up Metamodels with Predicates" (2017). VMASC Publications. 9.