Ambiguous partially observable Markov decision processes: Structural results and applications

Importantly, this paper extends various structural results from POMDPs to APOMDPs. These results enable the decision maker to make robust decisions. Robustness is achieved by using α-maximin expected utility (α-MEU), which (a) differentiates between ambiguity and ambiguity attitude, (b) avoids the over conservativeness of traditional maximin approaches, and (c) is found to be suitable in laboratory experiments in various choice behaviors including those in portfolio selection. The structural results provided also help to handle the “curse of dimensionality,” since they significantly simplify the search for an optimal policy. The analysis also identifies a performance guarantee for the proposed approach by developing a bound for its maximum reward loss due to model ambiguity.