Rules Before Oracles: Auditable, User-Configurable Argument Selection for Deliberative Polling
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Abstract (arXiv)
In a deliberative poll, once submissions outnumber what anyone will read, some mechanism chooses which arguments each voter sees, acquiring much of the decision; practice delegates it to opaque learned rankers, so a voter cannot recompute or contest the exposure that shaped their vote. We ask whether it can be a published rule over publicly recomputable evidence with parameters held by the voter, treating legibility as an admissibility condition on usable mechanisms, not an objective traded against accuracy. We formalise a poll over bipolar justification sets, judging a slate by reason coverage, the order it arrives in, and captured endorsement mass; we give seven checkable criteria for a civic recommender and a rule meeting them: a one-hop reversed endorsement flow parameterised by a relation-weight function. An agentic simulator records every slate at every vote, over about 17,000 seed-paired runs. Served slates fall 0.035 short of a label-reading ceiling upper-bounding every selection procedure, opaque ones included: any unconstrained ranker's advantage is bounded and small. On coverage alone, with non-degenerate authoring, the rule is indistinguishable from a random slate, a null due to an order-blind, charity-blind instrument; on the other two it leads at every prefix by a margin widening with adversarial pressure and dominates on mass by a factor of 3.3. Once a realistic fraction of submissions carries no reasons, the coverage margin returns and grows. Label-homogeneous flooding collapses completeness from 0.81 to 0.34 under a flat weight policy, only to 0.44 under author-count normalisation, making the weight function a security control worth 10% of completeness. The choice between ranking arms is a position on a coverage-versus-mass frontier, not a fact, the kind of choice only a legible rule can hand to the person it affects. It maps onto an open-source peer-to-peer platform.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 98 references that are in the map. First posted on arXiv 25 Aug 2026.
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References · 12
- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
- 2023Towards Understanding Sycophancy in Language Modelscited by 988
- 2016The Mythos of Model Interpretabilitycited by 716
- 2020A Survey on the Explainability of Supervised Machine Learningcited by 225
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- 2021Socially Responsible AI Algorithms: Issues, Purposes, and Challengescited by 56
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- 2023Proportional Aggregation of Preferences for Sequential Decision Makingcited by 17
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- 2014Recommending the Most Encompassing Opposing and Endorsing Arguments in Debatescited by 1
- 2020Representative Committees of Peerscited by 1
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- 2026Minority Sentinel: When to Overturn Majority Voting in Multi-Agent LLM Debates
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