In this accessible yet rigorous column, Christel Baier and Clemens Dubslaff provide a timely and valuable overview of the vibrant area of algorithmic synthesis problems based on Markov decision processes. The latter are one of the most prominent stochastic models, both in theory and in applications. The overview is extensive, covering synthesis with non-standard objectives as well as family-based analysis, and indicating several emerging directions.