Paper # Author Title
We study a model of sequential learning, where agents choose what kind of information to acquire from a large, fixed set of Gaussian signals with arbitrary correlation. In each period, a short-lived agent acquires a signal from this set of sources to maximize an individual objective. All signal realizations are public. We study the community's asymptotic speed of learning, and characterize the set of sources observed in the long run. A simple property of the correlation structure guarantees that the community learns as fast as possible, and moreover that a \best" set of sources is eventually observed. When the property fails, the community may get stuck in an inefficient set of sources and learn (arbitrarily) slowly. There is a specific, diverse set of possible final outcomes, which we characterize. Download Paper
Consider a decision-maker who dynamically acquires Gaussian signals that are related by a completely flexible correlation structure. Such a setting describes information acquisition from news sources with correlated biases, as well as aggregation of complementary information from specialized sources. We study the optimal sequence of information acquisitions. Generically, myopic signal acquisitions turn out to be optimal at sufficiently late periods, and in classes of informational environments that we describe, they are optimal from period 1. These results hold independently of the decision problem and its (endogenous or exogenous) timing. We apply these results to characterize dynamic information acquisition in games. Download Paper