The field of strategic decision making and game theory is witnessing significant advancements, driven by the development of innovative algorithms and models. Researchers are exploring new approaches to address complex challenges, such as information asymmetry, knowledge transportability, and the learnability of mixed-strategy Nash Equilibrium. A key direction of research is the design of sample-efficient algorithms that can learn optimal policies in the presence of private types and strategic agents. Another area of focus is the application of mean field games theory to forecast public sentiments and model complex systems. The development of new numerical methods, such as semi-Lagrangian schemes and learning value algorithms, is also enabling the solution of previously intractable problems. Noteworthy papers in this area include:

• Learning to Lead: Incentivizing Strategic Agents in the Dark, which presents a novel pipeline for learning an optimal coordination mechanism in a generalized principal-agent model.
• The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability, which explores the fundamental question of whether non-i.i.d. actions can be employed to learn about confounders in online learning.