We introduce an accurate, easily implementable, and fast algorithm to compute optimal decisions in discrete-time long-horizon welfaremaximizing problems. The algorithm is useful when interest is only in the decisions up to period T, where T is small. It relies on a flexible parametrization of...

We study social learning in a continuous action space experiment. Subjects, acting in sequence, state their belief about the value of a good, after observing their predecessors' statements and a private signal. We compare the behavior in the laboratory with the Perfect Bayesian Equilibrium...

We study social learning in a continuous action space experiment. Subjects, acting in sequence, state their belief about the value of a good, after observing their predecessors' statements and a private signal. We compare the behavior in the laboratory with the Perfect Bayesian Equilibrium...

We present a social learning experiment in which subjects predict the value of a good in sequence. We elicit each subject's belief twice: first ("first belief"), after he observes his predecessors' prediction; second, after he also observes a private signal. Our main result is that subjects...

We present a novel experimental design to study social learning in the laboratory. Subjects have to predict the value of a good in a sequential order. We elicit each subject's belief twice: first ("prior belief"), after he observes his predecessors' action; second ("posterior belief"), after he...

We review the literature on robust Bayesian analysis as a tool for global sensitivity analysis and for statistical decision-making under ambiguity. We discuss the methods proposed in the literature, including the different ways of constructing the set of priors that are the key input of the...