Optimal stopping for measure-valued piecewise deterministic Markov processes

Bertrand Cloez (1) , Benoîte de Saporta (2) , Maud Joubaud (2)

1 MISTEA - Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie
2 IMAG - Institut Montpelliérain Alexander Grothendieck

This paper investigates the random horizon optimal stopping problem for measure-valued piecewise deterministic Markov processes (PDMPs). This is motivated by population dynamics applications, when one wants to monitor some characteristics of the individuals in a small population. The population and its individual characteristics can be represented by a point measure. We first define a PDMP on a space of locally finite measures. Then we define a sequence of random horizon optimal stopping problems for such processes. We prove that the value function of the problems can be obtained by iterating some dynamic programming operator. Finally we prove on a simple counter-example that controlling the whole population is not equivalent to controlling a random lineage.

Mots clés: Markov processes; measure space; optimal stopping; dynamic programming; population dynamics

Domaines: Probabilités [math.PR]

Bertrand Cloez, Benoîte de Saporta, Maud Joubaud. Optimal stopping for measure-valued piecewise deterministic Markov processes. Journal of Applied Probability, 2020, 57 (2), pp.497-512. ⟨10.1017/jpr.2020.18⟩. ⟨hal-02922688⟩