Evaluation of Alternative-Based Information Systems for Deliberative Polling using an Agentic Simulator
arXiv:2606.11692v1 Announce Type: cross Abstract: Deliberative polling promises to improve collective decision-making by exposing shareholders to a broad range of arguments before they vote. Yet ensuring that every voter encounters a representative sample of the reason space, the coverage problem, remains an open challenge, particularly at scale and in adversarial or strategically motivated electorates. This paper introduces a way of evaluating solutions using the LLM-based Agentic Bipolar Argumentation Simulator, grounded in a framework which formalises a poll as a six-tuple of endorsing and
The increasing sophistication of LLMs and agentic systems makes it possible to simulate complex social and political dynamics with greater fidelity, addressing long-standing challenges in collective decision-making research.
This development offers a novel, scalable method to test and improve democratic processes like deliberative polling, potentially leading to more informed and resilient governance in a complex world.
The ability to evaluate deliberative polling mechanisms using agentic simulators provides a new tool for designing robust decision-making systems, moving beyond theoretical models or small-scale human trials.
- · Political scientists
- · AI ethicists
- · Democratic institutions
- · Simulation platform developers
- · Traditional polling methods
- · Unsophisticated political analysis
- · Manipulation campaigns
The adoption of agent-based simulations facilitates the design of more effective and representative deliberative processes in various domains.
This could lead to widespread experimentation with digitally-augmented deliberative democracy platforms, enhancing the quality of public discourse and policy formation.
These tools might eventually become integrated into foundational societal decision-making infrastructure, fundamentally altering how collective choices are made and legitimacy is conferred.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI