The field of game theory and multi-agent decision-making is witnessing significant developments, with a focus on addressing uncertainty, misaligned perceptions, and complex strategic interactions. Researchers are exploring innovative approaches to model and analyze imperfect-information games, hypergames, and non-coercive extortion mechanisms. The integration of causal learning, graph reinforcement learning, and hierarchical game-based decision-making frameworks is also gaining traction, particularly in applications such as autonomous vehicles and multi-agent systems. Noteworthy papers in this area include:

• Modeling Uncertainty: Constraint-Based Belief States in Imperfect-Information Games, which investigates the use of constraint-based models for state estimation in imperfect-information games.
• Hypergames: Modeling Misaligned Perceptions and Nested Beliefs for Multi-agent Systems, which presents a systematic review of hypergame theory and its applications in dynamic multi-agent environments.
• Non-coercive extortion in game theory, which introduces a novel mechanism for extracting profit through outcome-contingent payments.
• Hierarchical Game-Based Multi-Agent Decision-Making for Autonomous Vehicles, which develops a game-theoretic decision-making framework for autonomous driving in multi-agent scenarios.
• Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning, which integrates causal learning with reinforcement learning-based methods for autonomous vehicles.