This thesis consists of three chapters that investigate how economic agents acquire information and learn from the information. I use theoretical models to understand information acquisition and learning. Chapter 1 studies how decision-makers acquire information when they compete for a first-mover advantage. I show that the first-mover advantage gives the decision-makers incentives to preempt. In equilibrium, decision-makers create strategic uncertainties on when they stop acquiring information and taking an action. Chapter 2 studies a single decision-maker’s search and learning problem in which the signals that can be obtained are binary. I show that the decision maker’s optimal strategy depends on her time risk attitude and her patience level. Chapter 3 investigates the question of whether a designer can learn how a decision-maker learns from information by observing the signals she receives and the actions she takes. It studies the probability of the designer learning the decision-maker’s prior and characterises the optimal payoff structure that maximises this probability.