In applied AI we often face the problems of understanding how incentives of participants (human or artificial agents) can be used to improve the behavior of the overall system. This relates to a field in economic theory called Mechanism Design, which is “reverse game theory”: instead of starting with a game and solving for the outcome, we start from a desired outcome (for example, social welfare maximization) and design an institution that would accomplish it. The mechanism then takes into account strategic participants in allocating resources or pricing goods and services. Advances in mechanism design have implications for many areas in computing, including search and recommendation. Similarly, progress can be made on many fundamental questions in Mechanism Design by adopting computational approaches. For example, the focus on complexity and approximation and the use of simulations provide a necessary bridge between mechanism design in theory and its implementation in practice.