Abstract: We study the impact of uncertainty, learning, and competition on Bayesian decisions/strategies. In the first part of the talk, we present the impact of learning on the optimal policy and the time-to-decision in an infinite-horizon Bayesian sequential decision model with two irreversible alternatives: exit and expansion. In the second part of the talk, we discuss the interplay between learning effects and externalities in the problem of competitive investments with uncertain returns. In particular, we focus on regime of a war of attrition between two firms; in this regime, because of the opportunity to learn from the competitor's performance, each firm would prefer to wait until its rival invests first. Contrary to the conventional war of attrition where an increase in benefits for the follower generally delays the first move, an increase in the rate of learning--- which tends to benefit the follower---can hasten the first investment.