Systems for algorithmic decision-making are on the rise. Self-driving cars have been a classical example, but such systems are also used to individualize decision rules in many other domains. In particular, the current focus on precision medicine reflects the interest in individualized decision rules, adapted to a patient’s characteristics. In this talk, I will introduce new theory and methods for finding optimal decision rules. In particular, I will discuss an apparent paradox in the optimal regimes literature: in plausible decision settings, there is no formal guarantee that conventional optimal regimes, learned algorithmically from data, will outperform human decision-makers, like medical doctors. Then I will introduce superoptimal decision rules, which resolve this ostensible shortcoming. I further discuss how the superoptimal rules can be identified and estimated in different contexts, using both experimental data and (possibly confounded) observational data. The results will be illustrated by examples from medicine and economics.