# A Replica for our Democracies? On Using Digital Twins to Enhance Deliberative Democracy

arXiv: https://arxiv.org/abs/2504.07138 · HTML: https://mlsanity.com/paper/2504.07138/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2025.

## Built on by (most cited first, 4 of 4)

- [Prompt Injection Vulnerability of Consensus Generating Applications in Digital Democracy](https://mlsanity.com/paper/2508.04281/) (2025)
- [Aligning Trustworthy AI with Democracy: A Dual Taxonomy of Opportunities and Risks](https://mlsanity.com/paper/2505.13565/) (2025)
- [Beyond the Townhall: Spatial Anchoring and LLM Agents for Scalable Participatory Urban Planning](https://mlsanity.com/paper/2604.16348/) (2026)
- [What is Left for Us? Second Scholarship Against the Degradation of Research by AI](https://mlsanity.com/paper/2607.04049/) (2026)

## Abstract (arXiv)

Deliberative democracy depends on carefully designed institutional frameworks, such as participant selection, facilitation methods, and decision-making mechanisms, that shape how deliberation performs. However, identifying optimal institutional designs for specific contexts remains challenging when relying solely on real-world observations or laboratory experiments: they can be expensive, ethically and methodologically tricky, or too limited in scale to give us clear answers. Computational experiments offer a complementary approach, enabling researchers to conduct large-scale investigations while systematically analyzing complex dynamics, emergent and unexpected collective behavior, and risks or opportunities associated with novel democratic designs. Therefore, this paper explores Digital Twin (DT) technology as a computational testing ground for deliberative systems (with potential applicability to broader institutional analysis). By constructing dynamic models that simulate real-world deliberation, DTs allow researchers and policymakers to rigorously test "what-if" scenarios across diverse institutional configurations in a controlled virtual environment. This approach facilitates evidence-based assessment of novel designs using synthetically generated data, bypassing the constraints of real-world or lab-based experimentation, and without societal disruption. The paper also discusses the limitations of this new methodological approach and suggests where future research should focus.