Collective Decision Making

Collective decision-making studies how groups of agents, whether robots, humans, or even biological systems, reach consensus on a shared goal. Current research focuses on developing and analyzing algorithms for decentralized decision-making, often employing models like voter models, majority rule, Bayesian approaches, and increasingly, machine learning techniques including neural networks and large language models, to optimize for speed, accuracy, and fairness. These advancements are crucial for improving the efficiency and robustness of multi-agent systems in diverse applications, ranging from swarm robotics and urban planning to online community management and public policy. Furthermore, understanding biases and mitigating their influence on collective outcomes is a significant area of investigation.