Statistical decision theory is concerned with making optimal decisions under statistical uncertainty, often maximizing expected utility. It can be applied to many areas such as economics, medicine, finance, and business, and draws heavily on Bayesian statistics, meta-analysis, optimization, POMDPs, reinforcement learning, causal modeling, game theory, and operations research. Goals include cost-benefit analyses (calculating expected utility of specific choices), defining relevant loss functions, the value of perfect data and the optimal amount of data to gather, balancing taking (estimated) optimal actions with learning about other suboptimal actions, inferring causal mechanisms in an environment, eliciting expert beliefs for priors, and examining sensitivity of conclusions about decisions to the data or modeling choices. Discussion of underlying philosophical issues like Newcomb's dilemma is permitted (but try to not be tedious).