论文标题
私人信息和不透明度的人机决策的强盗模型
A Bandit Model for Human-Machine Decision Making with Private Information and Opacity
论文作者
论文摘要
机器学习的应用为人类决策者提供了广泛的任务。最终的问题通常是根据单个决策者提出的。我们认为，应该将其描述为一个两人学习问题，其中一个玩家是机器，另一个人是人类。尽管两位玩家都试图优化最终决定，但设置通常以（1）私人信息和（2）不透明度的存在为特征，这是决策者之间不了解的理解。我们证明，这两种属性都可以使决策变得非常复杂。下限量量化了最糟糕的硬度，即建议不透明或可以访问私人信息的决策者建议。上限表明，简单的协调策略几乎是最小的最佳选择。在对问题的某些假设下，可以进行更有效的学习，例如，两个参与者都学会独立采取行动。这种假设在现有文献中是隐含的，例如在机器学习的医学应用中，但理论上没有被描述或辩解。
Applications of machine learning inform human decision makers in a broad range of tasks. The resulting problem is usually formulated in terms of a single decision maker. We argue that it should rather be described as a two-player learning problem where one player is the machine and the other the human. While both players try to optimize the final decision, the setup is often characterized by (1) the presence of private information and (2) opacity, that is imperfect understanding between the decision makers. We prove that both properties can complicate decision making considerably. A lower bound quantifies the worst-case hardness of optimally advising a decision maker who is opaque or has access to private information. An upper bound shows that a simple coordination strategy is nearly minimax optimal. More efficient learning is possible under certain assumptions on the problem, for example that both players learn to take actions independently. Such assumptions are implicit in existing literature, for example in medical applications of machine learning, but have not been described or justified theoretically.
