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Title: Learning Rule-based Explanatory Models From Exploratory Multi-simulation For Decision-support Under Uncertainty
Exploratory modeling and simulation is an effective strategy when there are substantial contextual uncertainty and representational ambiguity in problem formulation. However, two significant challenges impede the use of an ensemble of models in exploratory simulation. The first challenge involves streamlining the maintenance and synthesis of multiple models from plausible features that are identified from and subject to the constraints of the research hypothesis. The second challenge is making sense of the data generated by multi-simulation over a model ensemble. To address both challenges, we introduce a computational framework that integrates feature-driven variability management with an anticipatory learning classifier system to generate explanatory rules from multi-simulation data.  more » « less
Award ID(s):
1910794
PAR ID:
10169475
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Proceedings of the 2020 Winter Simulation Conference
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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