Language Agents as Digital Representatives in Collective Decision-Making

Paper proposes training LLMs as “digital representatives” that can stand in for individual humans in collective decision-making processes, expressing their preferences in group interactions like consensus-finding. It explains the concept of digital representation, defines metrics for evaluating how well an agent represents a human, and demonstrates empirically that fine-tuning LLMs on individual behavioral data makes this feasible.