Identification and Welfare Evaluation in Sequential Sampling Models

Consider an agent who, before making a choice privately learns information about an uncertain, objective state of the world through a technology of sequential experiments. We consider two cases of learning costs. In the first, the agent discounts future payoffs geometrically. In the second, she incurs a constant flow cost of time. We show that if the observable data consist only of the joint distributions over chosen actions and decision times, an analyst can uniquely identify the discount factor in the first case and the flow cost of time in the second case, besides identifying the agent's prior belief and taste. Moreover, we show how an analyst can recover the agent's ex-ante welfare in both cases. Our approach does not rely on any knowledge about the underlying sequential experiment