  The notion of bounded rationality originated from the insight that perfectly
rational behavior cannot be realized by agents with limited cognitive or
computational resources. Research on bounded rationality, mainly initiated by
Herbert Simon, has a longstanding tradition in economics and the social
sciences, but also plays a major role in modern AI and intelligent agent
design. Taking actions under bounded resources requires an agent to reflect on
how to use these resources in an optimal way - hence, to reason and make
decisions on a meta-level. In this paper, we will look at automated machine
learning (AutoML) and related problems from the perspective of bounded
rationality, essentially viewing an AutoML tool as an agent that has to train a
model on a given set of data, and the search for a good way of doing so (a
suitable "ML pipeline") as deliberation on a meta-level.
