Before choosing her action to match the state of the world, an agent observes a stream of messages generated by some unknown binary signal. The agent can either learn the underlying signal for free and update her belief accordingly or ignore the observed message and keep her prior belief. After...

Ride-sharing systems strive to provide affordable on-demand mobility in urban areas by effectively consolidating incoming transportation requests. To ensure that transportation offers meet travelers' individual time requirements and constraints, service operators offer multiple pickup times from...

Before choosing her action to match the state of the world, an agent observes a stream of messages generated by some unknown binary signal. The agent can either learn the underlying signal for free and update her belief accordingly or ignore the observed message and keep her prior belief. After...

We present a problem motivated by discussions with Colombian e-commerce platforms for agri-food products. In regular time intervals (periods), the platforms collect groceries from local farmers and stores them at a warehouse to distribute them to local customers. The supply quantities and prices...

In ride-sharing systems, platform providers aim to distribute the drivers in the city to meet current and potential future demand and to avoid service cancellations. Ensuring such distribution is particularly challenging in the case of a crowdsourced fleet, as drivers are not centrally...

Recent theoretical and empirical work characterizes attention as a limited resource that decision-makers strategically allocate. There has been less research on the dynamic interdependence of attention: how paying attention now may affect performance later. In this paper, we exploit...

Recent theoretical and empirical work characterizes attention as a limited resource that decision-makers strategically allocate. There has been less research on the dynamic interdependence of attention: how paying attention now may affect performance later. In this paper, we exploit...