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.

本文提出和开发了一个针对带赌徒反馈的人机协作问题的解决方案，旨在利用人机互补性最大化决策收益，在多个人类决策者的环境中展开并表现出效用，并呈现出当机器和人类各自单独做出决策时，我们的方法胜过两者的能力，以及如何在多个人类决策者的情况下实现个性化路由以进一步提高人机团队的表现。