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...

An agent has access to multiple information sources, each of which provides information about a different attribute of an unknown state. Information is acquired continuously---where the agent chooses both which sources to sample from, and also how to allocate attention across them---until an...

We use machine learning to uncover regularities in the initial play of matrix games. We first train a prediction algorithm on data from past experiments. Examining the games where our algorithm predicts correctly, but existing economic models don't, leads us to add a parameter to the best...

We develop a model of social learning from complementary information: Short-lived agents sequentially choose from a large set of flexibly correlated information sources for prediction of an unknown state, and information is passed down across periods. Will the community collectively acquire the...

To evaluate how well economic models predict behavior it is important to have a measure of how well any theory could be expected to perform. We provide a measure of the amount of predictable variation in the data that a theory captures, which we call its "completeness." We evaluate the...