Language Agents as Digital Representatives in Collective Decision-Making

• URL: http://arxiv.org/abs/2502.09369v1
• Date: Thu, 13 Feb 2025 14:35:40 GMT
• Title: Language Agents as Digital Representatives in Collective Decision-Making
• Authors: Daniel Jarrett, Miruna Pîslar, Michiel A. Bakker, Michael Henry Tessler, Raphael Köster, Jan Balaguer, Romuald Elie, Christopher Summerfield, Andrea Tacchetti,
• Abstract summary: "representation" is the activity of making an individual's preferences present in the process via participation by a proxy agent. We investigate the possibility of training textitlanguage agents to behave in the capacity of representatives of human agents.
• Score: 22.656601943922066
• License:
• Abstract: 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.

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