Can digital twins improve democratic deliberation?
A Replica for our Democracies? On Using Digital Twins to Enhance Deliberative Democracy
This paper explores using Digital Twin (DT) technology to improve deliberative democracy. It proposes using DTs as virtual "regulatory sandboxes" to simulate different ways of structuring public discussions and decision-making, allowing researchers to test which methods work best for achieving specific goals like inclusivity and reducing polarization.
Key points relevant to LLM-based multi-agent systems include: the potential of using LLMs within Agent-Based Models (ABMs) to simulate realistic human behavior in deliberative settings; using Natural Language Processing (NLP) techniques like Structured Topic Modeling (STM) and sentiment analysis to analyze discussions and calibrate the DT; and the importance of incorporating cognitive architectures within AI agents to better align with human reasoning and mitigate the "black box" nature of some AI models. The paper also highlights the potential of using DTs to experiment with newer deliberation formats enabled by AI, such as “re-mixing” which allows dynamic idea combination and refinement.