The field of game theory is witnessing significant advancements, driven by innovative approaches to solving complex problems. A key direction is the development of more efficient algorithms for approximating Nash equilibria in general-sum games, with a focus on minimizing correlations in strategies produced by regret minimizers. Another area of research is the study of performative effects in multi-agent reinforcement learning, where the deployment of policies changes the environment's dynamics. The impact of time preferences on the price of anarchy in congestion games is also being explored, with findings indicating that myopic behavior can lead to extreme inefficiency. Additionally, the institution bootstrapping problem is being addressed through the integration of cognitive biases and perceptual noise into game-theoretic frameworks. Noteworthy papers include:

• Approximating Nash Equilibria in General-Sum Games via Meta-Learning, which uses meta-learning to minimize correlations in strategies produced by a regret minimizer.
• Independent Learning in Performative Markov Potential Games, which introduces the concept of performatively stable equilibria and provides convergence results for state-of-the-art algorithms.
• Uncertainty, bias and the institution bootstrapping problem, which demonstrates how misperception and cognitive biases can mitigate the institution bootstrapping problem.