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Data-driven dynamic decision models
2015
2015 Winter Simulation Conference (WSC)
This article outlines a method for automatically generating models of dynamic decision-making that both have strong predictive power and are interpretable in human terms. This is useful for designing empirically grounded agent-based simulations and for gaining direct insight into observed dynamic processes. We use an efficient model representation and a genetic algorithm-based estimation process to generate simple approximations that explain most of the structure of complex stochastic
doi:10.1109/wsc.2015.7408381
dblp:conf/wsc/NayG15
fatcat:vvkfas6yxzgt7eanxv6vn72tsq