# Optimal and Myopic Information Acquisition

## TL;DR

This paper demonstrates that in a dynamic setting with correlated normal information sources, the optimal information acquisition strategy becomes myopic after some periods, simplifying the analysis of such problems.

## Contribution

It shows that optimal dynamic information acquisition rules are often myopic after finitely many periods in correlated normal environments, even with complex source interactions.

## Key findings

- Optimal rules become myopic after finitely many periods.
- Large block acquisitions lead to immediate myopic optimality.
- Simplifies analysis of dynamic information acquisition in normal environments.

## 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.