The field of multi-agent systems is witnessing significant developments in fairness and game theory. Researchers are exploring new solution concepts, such as fairness metrics and no-regret learning algorithms, to improve the efficiency and equity of interactions between self-interested agents. The study of envy-freeness and truthfulness in data valuation and allocation is also gaining traction. Furthermore, the application of game-theoretic approaches to real-world problems, like electric vehicle charging and energy hub networks, is becoming increasingly prominent. Notable papers in this area include:

• The Limits of 'Fairness' of the Variational Generalized Nash Equilibrium, which introduces a new solution concept for fairness in generalized Nash equilibrium problems.
• From Fairness to Truthfulness: Rethinking Data Valuation Design, which adapts payment rules from mechanism design to ensure truthful reporting of costs in data markets.