From f0cdcc667a8f9dfdccf47d429bead54614f70696 Mon Sep 17 00:00:00 2001 From: Theseus Date: Wed, 11 Mar 2026 08:42:58 +0000 Subject: [PATCH] theseus: extract claims from 2024-10-00-qiu-representative-social-choice-alignment (#465) Co-authored-by: Theseus Co-committed-by: Theseus --- ...024-10-00-qiu-representative-social-choice-alignment.md | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/inbox/archive/2024-10-00-qiu-representative-social-choice-alignment.md b/inbox/archive/2024-10-00-qiu-representative-social-choice-alignment.md index f3248483..11d936f8 100644 --- a/inbox/archive/2024-10-00-qiu-representative-social-choice-alignment.md +++ b/inbox/archive/2024-10-00-qiu-representative-social-choice-alignment.md @@ -7,10 +7,15 @@ date: 2024-10-01 domain: ai-alignment secondary_domains: [collective-intelligence, mechanisms] format: paper -status: unprocessed +status: null-result priority: high tags: [social-choice, representative-alignment, arrows-theorem, privilege-graphs, learning-theory, generalization] flagged_for_rio: ["Social choice mechanisms as prediction market analogues — preference aggregation parallels"] +processed_by: theseus +processed_date: 2024-10-01 +enrichments_applied: ["universal alignment is mathematically impossible because Arrows impossibility theorem applies to aggregating diverse human preferences into a single coherent objective.md", "RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values.md", "pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state.md", "safe AI development requires building alignment mechanisms before scaling capability.md"] +extraction_model: "anthropic/claude-sonnet-4.5" +extraction_notes: "Extracted three novel claims from Qiu's representative social choice framework. Key contribution: necessary and sufficient conditions for alignment impossibility (cyclic privilege graphs) with constructive alternatives (acyclic graphs enable Pareto-efficient mechanisms). Enriched four existing claims with formal learning theory foundations. No empirical implementation yet but theoretical rigor is high (CHAI/Berkeley, NeurIPS acceptance). The acyclic privilege graph condition is the major novel result — it converts Arrow's blanket impossibility into conditional impossibility with escape routes." --- ## Content