On Stopping Sets and Continuous-Time Decision Trees

Abstract: We study stopping sets and continuous-time decision trees (CTDTs) for Poisson point processes. We first give a proof of the spatial Markov property without assuming that the stopping set contains finitely many points or admits a finite approximation. The proof uses the multivariate Mecke formula on the complement of the stopping set. We then prove that every $μ$-continuous CTDT has an explicit exploration representation: between successive discoveries, the explored set grows according to a deterministic rule depending on the points already observed. If $μ$ is diffuse, the points are revealed one by one almost surely; this need not be imposed as a separate assumption. We also allow atoms of $μ$, in which case all points at one location are revealed together. Finally, for diffuse $μ$, we prove that a locally bounded stopping set revealing at most one point is attainable by a $μ$-continuous exploration. More generally, an increasing sequence of locally bounded stopping sets, each revealing at most one additional point, can be attained by a single exploration at successive stopping times.