  Quite some real-world problems can be formulated as decision-making problems
wherein one must repeatedly make an appropriate choice from a set of
alternatives. Multiple expert judgements, whether human or artificial, can help
in taking correct decisions, especially when exploration of alternative
solutions is costly. As expert opinions might deviate, the problem of finding
the right alternative can be approached as a collective decision making problem
(CDM) via aggregation of independent judgements. Current state-of-the-art
approaches focus on efficiently finding the optimal expert, and thus perform
poorly if all experts are not qualified or if they are overly biased, thereby
potentially derailing the decision-making process. In this paper, we propose a
new algorithmic approach based on contextual multi-armed bandit problems (CMAB)
to identify and counteract such biased expertise. We explore homogeneous,
heterogeneous and polarised expert groups and show that this approach is able
to effectively exploit the collective expertise, outperforming state-of-the-art
methods, especially when the quality of the provided expertise degrades. Our
novel CMAB-inspired approach achieves a higher final performance and does so
while converging more rapidly than previous adaptive algorithms.
