On partial information retrieval: the unconstrained 100 prisoner problem

We consider the classical 100 Prisoner problem and its variant, involving empty boxes, introduced by Gal and Miltersen. Unlike previous studies, here we focus on the winning probabilities for an arbitrary number of winners and attempts, which we call the unconstrained problem. We introduce general classes of strategies, applicable to different settings and quantify how efficient they are. We make use of Monte Carlo simulations, whenever analytic results are not available, to estimate with high accuracy the probabilities of winning. Finally, we also comment on the possible applications of our results in understanding processes of information retrieval, such as "memory" in living organisms.