Equilibrium Design

Equilibrium design focuses on modifying existing game structures to achieve desired outcomes, a more constrained approach than mechanism design which creates games from scratch. Current research explores this through various models, including reward machines for dynamic incentive structures and the application of optimal control principles to neural network learning, aiming to understand how to steer systems towards specific equilibrium states. This field is significant for its potential applications in diverse areas such as multi-agent systems, improving the performance of machine learning algorithms, and inferring underlying dynamics from observed behavior in complex systems.