Consider the process of collective decision-making, in which a group of individuals interactively select a preferred outcome from among a universe of alternatives. In this context, "representation" is the activity of making an individual's preferences present in the process via participation by a proxy agent -- i.e. their "representative". To this end, learned models of human behavior have the potential to fill this role, with practical implications for multi-agent scenario studies and mechanism design. In this work, we investigate the possibility of training \textit{language agents} to behave in the capacity of representatives of human agents, appropriately expressing the preferences of those individuals whom they stand for. First, we formalize the setting of \textit{collective decision-making} -- as the episodic process of interaction between a group of agents and a decision mechanism. On this basis, we then formalize the problem of \textit{digital representation} -- as the simulation of an agent's behavior to yield equivalent outcomes from the mechanism. Finally, we conduct an empirical case study in the setting of \textit{consensus-finding} among diverse humans, and demonstrate the feasibility of fine-tuning large language models to act as digital representatives.
View on arXiv@article{jarrett2025_2502.09369,
title={ Language Agents as Digital Representatives in Collective Decision-Making },
author={ Daniel Jarrett and Miruna Pîslar and Michiel A. Bakker and Michael Henry Tessler and Raphael Köster and Jan Balaguer and Romuald Elie and Christopher Summerfield and Andrea Tacchetti },
journal={arXiv preprint arXiv:2502.09369},
year={ 2025 }
}