An agent has access to multiple information sources, each modeled as a Brownian motion whose drift provides information about a different component of an unknown Gaussian 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 endogenously chosen time, at which point a decision is taken. We demonstrate conditions on the agent's prior belief under which it is possible to exactly characterize the optimal information acquisition strategy. We then apply this characterization to derive new results regarding: (1) endogenous information acquisition for binary choice, (2) the dynamic consequences of attention manipulation, and (3) strategic information provision by biased news sources.

Economic models are evaluated by testing the correctness of their predictions. We suggest an additional measure, “completeness”: the fraction of the predictable variation in the data that the model captures. We calculate the completeness of prominent models in three problems from experimental economics: assigning certainty equivalents to lotteries, predicting initial play in games, and predicting human generation of random sequences. The completeness measure reveals new insights about these models, including how much room there is for improving their predictions.