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. 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). Current state-of-the-art approaches to solve CDM are limited by the quality of the best expert in the group, and perform poorly if experts are not qualified or if they are overly biased, thus 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 expertises. We explore homogeneous, heterogeneous and polarised expert groups and show that this approach is able to effectively exploit the collective expertise, irrespective of whether the provided advice is directly conducive to good performance, 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, especially when heterogeneous expertise is readily available.

翻译：专家判断,无论是人还是人工专家的判断,都可有助于作出正确的决定,特别是在探索替代解决办法代价高昂的情况下。专家意见可能会有偏差,找到正确的替代办法的问题可以作为一个集体决策问题来处理。目前解决清洁发展机制的最先进办法受到最佳专家质量的限制,如果专家不合格或过于偏颇,则表现不佳,从而有可能破坏决策进程。在本文件中,我们提出基于背景性多武装匪帮问题的新算法方法,以确定和抵制这种偏颇的专门知识。我们探讨的是同质、混杂和对立的专家组,并表明这一方法能够有效地利用集体专门知识,而不论所提供的咨询意见是否直接有利于良好业绩,优于最佳专家,优于先进方法,特别是在所提供的专门知识质量下降时。我们新的CMAB启发性方法取得了更高的最后业绩,在比以往的适应性更迅速变异的情况下,特别在容易获得的变异性分析时这样做。