Robust maximum detection: Full information best choice problem under multiple priors - Lazar Obradović (Bielefeld University, Center for Mathematical Economics (IMW), 2018)
We consider a robust version of the full information best choice problem (Gilbert and Mosteller (1966)): there is ambiguity (represented by a set of priors) about the measure driving the observed process. We solve the problem under a very general class of multiple priors in the setting of Riedel (2009). As in the classical case, it is optimal to stop if the current observation is a running maximum that exceeds certain thresholds. We characterize the decreasing sequence of thresholds, as well as the (history dependent) minimizing measure. We introduce locally constant ambiguity neighborhood (LCAn) which has connections to coherent risk measures. Sensitivity analysis is performed using LCAn and exponential neighborhood from Riedel (2009).
urn:nbn:de:0070-pub-29169338
Keywords: 330