This paper extends the classical search framework and allows the decision-maker to choose information endogenously and flexibly. We consider a problem in which the manager chooses the best among two ex ante identical candidates. The manager learns the qualities of candidates sequentially and can...

We study a variation of the classical Pandora's problem in which a decision-maker (DM) sequentially explores alternatives from a given set and learns their values while trying to acquire the best alternative. The variations in the model we study are (i) alternatives randomly become unavailable...

We study the problem of optimal search (with costly information acquisition) for the best alternative among a pool of candidates that belong to different societal groups. Due to baked-in biases in the prior data, group-unaware comparisons of candidates may lead to unfair treatment of certain...

We propose a general protocol for calibration and validation of complex simulation models by an approach based on discovery and comparison of causal structures. The key idea is that configurations of parameters of a given theoretical model are selected by minimizing a distance index between two...

I propose a model in which agents decide on job search intensity for each possible wage, unlike the usual setup of constant search intensity over wage draws. The proposed framework entails efficiency gains in that agents do not waste effort to searching for low paying unacceptable jobs or less...