Human-machine complementarity is important when neither the algorithm nor the human yield dominant performance across all instances in a given domain. Most research on algorithmic decision-making solely centers on the algorithm's performance, while recent work that explores human-machine collaboration has framed the decision-making problems as classification tasks. In this paper, we first propose and then develop a solution for a novel human-machine collaboration problem in a bandit feedback setting. Our solution aims to exploit the human-machine complementarity to maximize decision rewards. We then extend our approach to settings with multiple human decision makers. We demonstrate the effectiveness of our proposed methods using both synthetic and real human responses, and find that our methods outperform both the algorithm and the human when they each make decisions on their own. We also show how personalized routing in the presence of multiple human decision-makers can further improve the human-machine team performance.

翻译：当算法和人类在特定领域的所有情况下都无法产生主导性业绩时,人类机器的互补性是重要的。关于算法和人类产生的主要性能,大多数关于算法决策的研究都仅仅以算法的性能为中心,而最近探索人体机器合作的工作则将决策问题定为分类任务。在本文中,我们首先提出,然后为在强盗反馈环境中出现新的人体机器合作问题制定解决办法。我们的解决方案旨在利用人体机器的互补性,以尽量扩大决策奖赏。我们然后将我们的方法扩大到多重人类决策者的场合。我们用合成和真实的人类反应来展示我们拟议方法的有效性,我们发现我们的方法在每个人自己做决定时都超越了算法和人。我们还展示了在多个人类决策者在场的情况下个人化模式如何进一步提高人类机器团队的性能。