The field of temporal logic and decision making is witnessing significant developments, with a focus on improving the efficiency and scalability of existing methods. Researchers are exploring new techniques to express and reason about temporal objectives, enabling more effective decision making in complex systems. Notably, the use of automata-based methods and compositional approaches is gaining traction, allowing for more efficient and flexible decision making frameworks. Furthermore, there is a growing interest in integrating temporal logic with other areas, such as preference learning and social choice theory, to tackle challenging problems in these domains. Some noteworthy papers in this area include: Solving MDPs with LTLf+ and PPLTL+ Temporal Objectives, which demonstrates the effectiveness of LTLf+ and PPLTL+ logics in the context of MDPs. Automata Learning of Preferences over Temporal Logic Formulas from Pairwise Comparisons, which introduces a novel approach to learning preferences over temporal logic formulas. Lifted Forward Planning in Relational Factored Markov Decision Processes with Concurrent Actions, which presents a first-order representation to efficiently compute policies for numerous indistinguishable objects and actions. Preference Learning with Response Time, which proposes methodologies to incorporate response time information into human preference learning frameworks. Generative Social Choice: The Next Generation, which extends the framework of generative social choice to produce concise slates of statements that proportionally represent user opinions.