This paper studies sequential information acquisition by an ambiguity-averse decision-maker (DM), who decides how long to collect information before taking an irreversible action. The agent optimizes against the worst-case belief and updates prior by prior. We show that the consideration of ambiguity gives rise to rich dynamics: compared to the Bayesian DM, the DM here tends to experiment excessively when facing modest uncertainty and, to counteract it, may stop experimenting prematurely when facing high uncertainty. In the latter case, the DM’s stopping rule is nonmonotonic in beliefs and features randomized stopping.

We develop a model of bailout stigma in which accepting a bailout signals a firm's balance‐sheet weakness and reduces its funding prospects. To avoid stigma, high‐quality firms withdraw from subsequent financing after receiving bailouts or refuse bailouts altogether to send a favorable signal. The former leads to a short‐lived stimulation followed by a market freeze even worse than if there were no bailout. The latter revives the funding market, albeit with delay, to the level achievable without any stigma and implements a constrained optimal outcome. A menu of multiple bailout programs compounds bailout stigma and exacerbates the market freeze.

We give two characterizations of Pareto optimality via “near” weighted utilitarian welfare maximization. One characterization sequentially maximizes utilitarian welfare functions using a finite sequence of nonnegative and eventually positive welfare weights. The other maximizes a utilitarian welfare function with a certain class of positive hyperreal weights. The social welfare ordering represented by these “near” weighted utilitarian welfare criteria is characterized by the standard axioms for weighted utilitarianism under a suitable weakening of the continuity axiom.