Optimal and Myopic Information Acquisition
This simplifies analysis for decision-makers in economics or AI dealing with correlated information, though it is incremental as it builds on known normal models.
The paper tackles the problem of optimal dynamic information acquisition from correlated sources, showing that in jointly normal settings, the optimal rule becomes myopic after finitely many periods or from the start if signals are acquired in large blocks.
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.