The recent developments in the research area indicate a shift towards more nuanced and sophisticated approaches to decision-making and resource allocation, particularly in the context of hiring strategies and collaborative machine learning. There is a growing emphasis on understanding and mitigating discrimination in automated decision-making tools, with a focus on non-distributional sources of inequality such as relational inequality. This trend is exemplified by studies that explore the dynamics of hiring markets and the potential for asymmetric outcomes even under symmetric conditions. Additionally, the field is witnessing advancements in contract design for collaborative machine learning, where innovative solutions are being proposed to address the challenges of rent-seeking behaviors and the stochastic nature of rewards. These developments highlight the importance of optimizing contracts to ensure fair and efficient resource distribution, particularly when models are used as rewards. Furthermore, there is progress in the study of linear contracts for combinatorial problems in multi-agent settings, with new approaches that offer additive PTAS for scenarios involving different agent costs. Overall, the research is moving towards more inclusive and equitable models of decision-making and collaboration, with a strong focus on theoretical advancements and practical implications.