Markov risk mappings and risk-averse optimal stopping under ambiguity

Randall Martyr, John Moriarty

Published 2020-01-19Version 1

We aim to analyse a Markovian discrete-time optimal stopping problem for a risk-averse decision maker under model ambiguity. In contrast to the analytic approach based on transition risk mappings, a probabilistic setting is introduced based on novel concepts of regular conditional risk mapping and Markov update rule. To accommodate model ambiguity we introduce appropriate notions of history-consistent updating and of transition consistency for risk mappings on nested probability spaces.