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 (ABAS), grounded in a framework which formalizes a poll as a six-tuple of endorsing and opposing justifications, attack and enhance relations, and shareholder- and relation-weights.
ABAS simulates N autonomous shareholder agents, each assigned a latent opinion according to desired distributions in [-1, 1], who sequentially vote, choose or author justifications, and optionally submit argumentation-graph links. The simulator implements recommendations that rank existing justifications by their observable endorsement mass. It evaluates the mechanism's success by coverage, namely the fraction of the corpus reason-tag set represented in the K recommendations presented to each shareholder, as a solution to the NP-hard Subsuming Justification Problem.
Reported experiments characterize how creativity rate (pown), recommendation size (K), argumentation density (plinks), and population size (N) affect coverage and corpus diversity. In an authenticated electorate where Sybil attacks are impossible and only the relation graph is gameable, we stress-test the scoring with coordinated strategic voting attacks: a tag-flood attack collapses coverage, while author-count relation weighting through a reversed-PageRank rule resists the flood markedly better than uniform weights.
Blogger's Review: This paper introduces an innovative approach to addressing the coverage problem in traditional deliberative polling through the Agentic Bipolar Argumentation Simulator. The ability to effectively simulate voter behavior and assess decision quality, particularly in adversarial environments, highlights the complexities and strategic challenges in complex systems. Future research directions may include optimizing the simulator for broader application scenarios.