How can agent hierarchy improve LLM opinion consensus?
An Investigation into the Causal Mechanism of Political Opinion Dynamics: A Model of Hierarchical Coarse-Graining with Community-Bounded Social Influence
April 2, 2025
https://arxiv.org/pdf/2504.00877This paper investigates how individual opinions combine to form collective consensus, particularly in political discourse. It uses a multi-agent model simulating Bayesian opinion updating within social groups and migration between groups to explore how consensus emerges. The key finding is that inter-group connectivity strongly influences information integration, resulting in three distinct regimes: independent (isolated groups), parallel (fast global consensus), and iterative (slow consensus with transient diversity). The "iterative" regime most closely resembles real-world political discourse.
Key points for LLM-based multi-agent systems:
- Hierarchical coarse-graining: Agents simplify information locally, then this simplified information is aggregated at higher levels, leading to downward causation where collective beliefs influence individual opinions. This concept could be applied to LLM agents summarizing and aggregating information from multiple sources.
- Community-bounded social influence: Agents within the same group (sharing a label) influence each other more strongly. This can be implemented in LLM agent systems by weighting interactions based on shared attributes or goals.
- Impact of connectivity: The degree of interaction between groups critically determines how consensus emerges. This could be a tunable parameter in LLM multi-agent systems, allowing for different modes of consensus formation.
- Transient diversity: The iterative regime, with lower inter-group connectivity, leads to a temporary increase in opinion diversity before consensus, suggesting a more informed outcome. This could be a desirable characteristic for LLM agents exploring diverse solutions before converging on a final decision.
- Downward causation: Emergent collective beliefs influencing individual agent behavior. This is highly relevant to LLM-based multi-agent apps, where emergent group behavior could be used to steer individual agent actions.
- Noise as enabling communication: Randomness in agent behavior can facilitate information flow between otherwise isolated groups, a principle applicable to LLM agents by introducing controlled stochasticity in their actions or communication.