Inference of Preference Heterogeneity from Choice Data

A decision-maker (DM) faces an intertemporal decision problem, where his payoff depends on actions taken across time as well as on an unknown Gaussian state. The DM can learn about the state from different (correlated) information sources, and allocates a budget of samples across these sources each period. A simple information acquisition strategy for the DM is to neglect dynamic considerations and allocate samples
myopically. How inefficient is this strategy relative to the optimal information acquisition strategy? We show that if the budget of samples is sufficiently large then there is no inefficiency: myopic information acquisition is exactly optimal.

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