Active Machine Learning for Consideration Heuristics

@article{Dzyabura2011ActiveML,
  title={Active Machine Learning for Consideration Heuristics},
  author={Daria Dzyabura and John R. Hauser},
  journal={Mark. Sci.},
  year={2011},
  volume={30},
  pages={801-819}
}
We develop and test an active-machine-learning method to select questions adaptively when consumers use heuristic decision rules. The method tailors priors to each consumer based on a “configurator.” Subsequent questions maximize information about the decision heuristics (minimize expected posterior entropy). To update posteriors after each question, we approximate the posterior with a variational distribution and use belief propagation (iterative loops of Bayes updating). The method runs… 

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