Search in the Dark: The Case with Recall and Gaussian Learning

Abstract

In “Search in the Dark: The Case with Recall and Gaussian Learning,” Manel Baucells and Saša Zorc address the classic sequential search problem where decision makers sample from a distribution to maximize rewards minus sampling costs. They focus on search with recall, where the reward is the highest sampled value. They solve the long-standing problem of determining optimal stopping rules when the sampling distribution is normal but both its mean and variance are unknown, and hence, they are progressively learned via sampling. The solution reveals that traditional methods, which rely on single reservation prices, are inadequate. The proposed approach offers a practical and efficient way to determine the optimal stopping rule. Relevant applications include job search, the sale of an asset, or technology adoption.