A Bandit Model for Human-Machine Decision Making with Private Information and Opacity. (arXiv:2007.04800v3 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2007.04800

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 …

arxiv decision decision making human information machine making model