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Large Language Models and Augmented Democracy — arXiv:2610.04412

Source: arXiv
Original: https://arxiv.org/abs/2610.04412
License (as archived): CC0-1.0
Archived: 2026-10-06T05:28:45.294Z
SHA-256 of the archived text: 207a644fb4d290b610f7fe1e15ce17b1008bae029a635de217e765f2c4824def
Stable URL of this page: https://metraipolis.com/documento/dc_dbfc9c034409

How to cite

Large Language Models and Augmented Democracy — arXiv:2610.04412 — arXiv, https://arxiv.org/abs/2610.04412 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dbfc9c034409 (Metraipolis archive dc_dbfc9c034409, sha256 207a644fb4d290b610f7fe1e15ce17b1008bae029a635de217e765f2c4824def)

BibTeX

@misc{dcdbfc9c034409,
  title = {Large Language Models and Augmented Democracy — arXiv:2610.04412},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.04412},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dbfc9c034409; sha256 207a644fb4d290b610f7fe1e15ce17b1008bae029a635de217e765f2c4824def},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.04412. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Artificial intelligence enables computational agents to represent political preferences and take part in collective decision-making. In this thesis, I investigate the opportunities and challenges of digital twins (DTs) based on Large Language Models (LLMs) as intermediaries in augmented democracy, focusing on individual preference representation, collective representation of political organizations, and the vulnerability of those representations to attackers. First, using data from an online experiment in Brazil, I examine whether personalized DTs can predict citizens' preferences for unseen policy proposals. Second, I extend the DT framework from individuals to political organizations. Using Swiss parliamentary data, I build topic-specific knowledge graphs from lawmakers' legislative records and connect them to LLM-based lawmaker agents, which are organized into party-level DTs representing collective positions. Agentic deliberation among these agents tests whether aggregated party representations capture a broader range of intra-party perspectives than official party communications. Finally, I study the vulnerability and robustness of LLM-mediated deliberation against prompt-injection attacks that amplify viewpoints, suppress opinions, or redirect consensus. Using data from a 2023 deliberative experiment in the United Kingdom, I analyze how attack effectiveness varies with the distribution of opinions and rhetorical strategies, and evaluate a pipeline combining injection detection, structured opinion representations, and reinforcement learning to improve resistance. These findings characterize the opportunities and challenges of LLM-based digital twins in augmented democracy, stressing accurate preference representation, faithful aggregation, and robustness to strategic interaction.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computers and Society (cs.CY) ; Computation and Language (cs.CL); Cryptography and Security (cs.CR).
Pagina del lavoro: https://arxiv.org/abs/2610.04412 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T05:24:20.414Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).