Many settings of interest involving humans and machines -- from virtual
personal assistants to autonomous vehicles -- can naturally be modelled as
principals (humans) delegating to agents (machines), which then interact with
each other on their principals' behalf. We refer to these multi-principal,
multi-agent scenarios as delegation games. In such games, there are two
important failure modes: problems of control (where an agent fails to act in
line their principal's preferences) and problems of cooperation (where the
agents fail to work well together). In this paper we formalise and analyse
these problems, further breaking them down into issues of alignment (do the
players have similar preferences?) and capabilities (how competent are the
players at satisfying those preferences?). We show -- theoretically and
empirically -- how these measures determine the principals' welfare, how they
can be estimated using limited observations, and thus how they might be used to
help us design more aligned and cooperative AI systems.

本文中，我们正式分析了代理人协作中的控制问题、合作问题、对齐问题和能力问题，以及这些问题对委托人利益的影响，并展示了如何通过有限观察来估计这些指标，并为设计更协调和合作的 AI 系统提供帮助。