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Source note2 min read

Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents

Source

arXiv

arxiv.org/abs/2607.15095v1

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Plain-Language Brief

Researchers have developed a multi-agent framework using Large Language Models (LLMs) to simulate coalition formation in political elections. The framework combines Supervised Fine-Tuning, Direct Preference Optimization, and Retrieval-Augmented Generation to create party-specific personas that adhere to their official manifestos. The framework was tested on the 2019 Flemish election, and it successfully predicted the stable winner and ranking of parties. The researchers also introduced a Multi-Layered Information Lineage Topology (MILT) to make the negotiation interpretable, and they found that manifesto-anchored lineage accurately predicts real-world outcomes.

What the Document Says

  • The research presents a multi-agent framework for coalition formation simulation using LLMs.
  • The framework combines Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Retrieval-Augmented Generation (RAG) to create party-specific personas and keep each agent bound to its official manifesto.
  • The framework was tested on the 2019 Flemish election, and it successfully predicted the stable winner and ranking of parties.
  • A Multi-Layered Information Lineage Topology (MILT) was introduced to make the negotiation interpretable.
  • Manifesto-anchored lineage in the MILT accurately predicts real-world outcomes.

Key Concepts Mentioned

  • coalition formation
  • political elections
  • Large Language Models (LLMs)
  • Supervised Fine-Tuning (SFT)
  • Direct Preference Optimization (DPO)
  • Retrieval-Augmented Generation (RAG)
  • Multi-Layered Information Lineage Topology (MILT)
  • Coalition Influence Score (CIS)

Claims Made by the Document

  • The presented framework can successfully predict the stable winner and ranking of parties in political elections.

Evidence Used

  • Across three independent simulations, the framework yielded a stable winner and ranking that matched the real-world outcomes.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

The formation of political coalitions is a complex negotiation driven by both concrete policy objectives and deep-seated ideological convictions. While Large Language Models (LLMs) open new avenues for computational political science, the neutrality and helpfulness biases instilled by Reinforcement Learning from Human Feedback (RLHF) prevent them from sustaining steadfast partisan behaviour. We present a multi-agent framework that reconciles factual grounding with ideological alignment by combining Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Retrieval-Augmented Generation (RAG): DPO instils aggressive party-specific personas, while a per-party RAG pipeline keeps each agent bounded to its official manifesto. We operationalize the framework on the 2019 Flemish election, deploying the partisan agents in a hub-and-spoke negotiation arbitrated by a formateur. To make the emergent negotiation interpretable, we introduce a Multi-Layered Information Lineage Topology (MILT) that traces every clause in the final agreement back to its manifesto origin and classifies it into five provenance states, a Coalition Influence Score (CIS) that aggregates these traceable contributions to identify which party shaped the agreement, and a real-world grounding pass that benchmarks each simulated provision against the historically adopted coalition agreement. Across three independent simulations the framework yields a stable winner and ranking (N-VA ahead of CD&V and Open Vld), and manifesto-anchored lineage reliably predicts real-world materialization whereas hallucinated content does not. The result is a transparent, scalable testbed for the ex-ante exploration of party compatibility and formateur-mediated compromise.