The field of collective decision-making is witnessing a significant shift towards more nuanced and adaptive approaches. Researchers are exploring new methods for voting, consensus-building, and probability aggregation that can better capture the complexities of real-world decision-making scenarios. One notable trend is the development of voting systems that can accommodate fractional votes, allowing voters to express more subtle preferences. Another area of focus is the creation of Condorcet-consistent voting methods that are resistant to manipulation and can produce easy-to-interpret results. In the realm of probability aggregation, there is a growing interest in frameworks that can handle dynamic rationality and sequential learning. These approaches enable collective beliefs to update consistently with new information, ensuring that decision-making processes remain fair and adaptive. Noteworthy papers include:

• The River Method, which introduces a novel Condorcet-consistent voting method that is simple to compute and resistant to agenda-manipulation.
• Consensus in Motion, which proposes a framework for probability aggregation based on propositional probability logic and addresses dynamic rationality.
• Doubly Adaptive Social Learning, which presents a strategy for social learning that can adapt to changing hypotheses and likelihood models over time.