Title: Optimal and Myopic Information Acquisition,
Abstract: We consider the problem of optimal dynamic information acquisition from many
correlated information sources. Each period, the decision-maker jointly takes
an action and allocates a fixed number of observations across the available
sources. His payoff depends on the actions taken and on an unknown state. In
the canonical setting of jointly normal information sources, we show that the
optimal dynamic information acquisition rule proceeds myopically after finitely
many periods. If signals are acquired in large blocks each period, then the
optimal rule turns out to be myopic from period 1. These results demonstrate
the possibility of robust and "simple" optimal information acquisition, and
simplify the analysis of dynamic information acquisition in a widely used
informational environment.