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The notion of bounded rationality originated from the insight that perfectlyrational behavior cannot be realized by agents with limited cognitive orcomputational resources. Research on bounded rationality, mainly initiated byHerbert Simon, has a longstanding tradition in economics and the socialsciences, but also plays a major role in modern AI and intelligent agentdesign. Taking actions under bounded resources requires an agent to reflect onhow to use these resources in an optimal way - hence, to reason and makedecisions on a meta-level. In this paper, we will look at automated machinelearning (AutoML) and related problems from the perspective of boundedrationality, essentially viewing an AutoML tool as an agent that has to train amodel on a given set of data, and the search for a good way of doing so (asuitable "ML pipeline") as deliberation on a meta-level.

Automated Machine Learning, Bounded Rationality, and Rational Metareasoning