Learning What Others Like: Preference Learning as a Mixed Multinomial Logit Model
نویسنده
چکیده
People flexibly draw generalizations from others’ choices. For example, when one learns that a friend likes a set of movies within a particular genre, one might also infer that said friend would also like novel movies with similar features. The present project uses a Church implementation of mixed multinomial logit (MML) models to capture how people learn and generalize from other people’s preferences. Experiment 1 demonstrates that the model can accurately recover a dummy target’s preferences from a sequence of observations. Experiment 2 compares model performance to trial-by-trial human performance in a game where participants had to learn someone else’s preferences from a sequence of pairwise choices. Here, the model outperformed humans. Finally, we will discuss how to refine the model by including an extra inferential step: preference learning might not only involve learning someone’s preferences along a certain set of dimensions, but also discovering the dimensions themselves.
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