Bayesian learning and decision theory (arguably) characterize rationality for idealized agents. However, carrying out Bayesian calculations can often be costly. In particular, the kind of agent one is--whether a human, lizard, or computer--constrains the kind of information processing one can do. This gestures towards a question: what is the relationship between idealized rationality and rationality… Continue reading Paper Review: Bayes, Bounds, and Rational Analysis