# Language Agents as Digital Representatives in Collective Decision-Making arXiv: https://arxiv.org/abs/2502.09369 ยท HTML: https://mlsanity.com/paper/2502.09369/ ## Key facts - Cited by 16 papers in the map (data through 14 Oct 2026). - Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. - Builds on 17 of its 61 references that are in the map. - First posted on arXiv 13 Feb 2025. ## Placed in - LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/) ## Builds on (most cited first, 17 of 17) - [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023) - [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022) - [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017) - [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023) - [Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation](https://mlsanity.com/paper/2302.09664/) (2023) - [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023) - [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023) - [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017) - [The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation](https://mlsanity.com/paper/2301.01768/) (2023) - [Does Synthetic Data Generation of LLMs Help Clinical Text Mining?](https://mlsanity.com/paper/2303.04360/) (2023) - [Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting](https://mlsanity.com/paper/2309.07034/) (2023) - [Moral Mimicry: Large Language Models Produce Moral Rationalizations Tailored to Political Identity](https://mlsanity.com/paper/2209.12106/) (2022) - [Harnessing large-language models to generate private synthetic text](https://mlsanity.com/paper/2306.01684/) (2023) - [Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science](https://mlsanity.com/paper/2305.15041/) (2023) - [CommunityLM: Probing Partisan Worldviews from Language Models](https://mlsanity.com/paper/2209.07065/) (2022) - [OpinionGPT: Modelling Explicit Biases in Instruction-Tuned LLMs](https://mlsanity.com/paper/2309.03876/) (2023) - [The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation](https://mlsanity.com/paper/2106.04240/) (2021) ## Built on by (most cited first, 16 of 16) - [Do Large Language Models Solve the Problems of Agent-Based Modeling? A Critical Review of Generative Social Simulations](https://mlsanity.com/paper/2504.03274/) (2025) - [An overview of model uncertainty and variability in LLM-based sentiment analysis. Challenges, mitigation strategies and the role of explainability](https://mlsanity.com/paper/2504.04462/) (2025) - [Continuous Latent Diffusion Language Model](https://mlsanity.com/paper/2605.06548/) (2026) - [Simulating Society Requires Simulating Thought](https://mlsanity.com/paper/2506.06958/) (2025) - [Towards Strategic Persuasion with Language Models](https://mlsanity.com/paper/2509.22989/) (2025) - [To Mask or to Mirror: Human-AI Alignment in Collective Reasoning](https://mlsanity.com/paper/2510.01924/) (2025) - [AI-Enhanced Deliberative Democracy and the Future of the Collective Will](https://mlsanity.com/paper/2503.05830/) (2025) - [The Oversight Game: Learning to Cooperatively Balance an AI Agent's Safety and Autonomy](https://mlsanity.com/paper/2510.26752/) (2025) - [Discovering Differences in Strategic Behavior Between Humans and LLMs](https://mlsanity.com/paper/2602.10324/) (2026) - [PoliCon: Evaluating LLMs on Achieving Diverse Political Consensus Objectives](https://mlsanity.com/paper/2505.19558/) (2025) - [Adaptive Pluralistic Alignment: A pipeline for dynamic artificial democracy](https://mlsanity.com/paper/2605.01642/) (2026) - [The Deliberative Deficit: An Empirical Critique of LLMs in Democratic Discourse](https://mlsanity.com/paper/2608.10186/) (2026) - [A Criminology of Machines](https://mlsanity.com/paper/2511.02895/) (2025) - [Can AI mediation improve democratic deliberation?](https://mlsanity.com/paper/2601.05904/) (2026) - [AI and Collective Decisions: Strengthening Legitimacy and Losers' Consent](https://mlsanity.com/paper/2604.05368/) (2026) - [Worse Together: How Performance Breaks Down in Multi-User Multi-Agent Teams](https://mlsanity.com/paper/2610.00583/) (2026) ## Abstract (arXiv) 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 textitlanguage 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 textitcollective 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 textitdigital 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 textitconsensus-finding among diverse humans, and demonstrate the feasibility of fine-tuning large language models to act as digital representatives.