Spiral of Silence in Large Language Model Agents
Bibliographic record
Abstract
The Spiral of Silence (SoS) theory posits that, in human societies, fear of social isolation drives individuals holding a minority opinion to quieten down, allowing the majority opinion to dominate public discourse. When agents are large language models (LLMs) rather than humans, the classic affective explanation no longer applies because language models do not have emotions or social anxiety. Therefore, a fundamental question appears: Can purely statistical language generation mechanisms give rise to SOS dynamics in collectives of LLM agents? We introduce an evaluation framework based on rating sequences and design four controlled experimental conditions by varying the presence of persona configurations and historical interaction signals. To measure opinion dynamics, we employ concentration metrics, including Interquartile Range and Kurtosis, along with trend analysis methods such as the Mann-Kendall test and Spearman rank correlation coefficient. We experiment on six widely used open source models: DeepSeek-V2-Lite-Chat, Llama-3.1-8B-Instruct, Mistral-8B-Instruct-2410, and Qwen-2.5-Instruct series (1.5 B, 3 B, 7 B), covering cross-family comparisons on a similar scale and within-family scaling analyses for Qwen, and a close source model GPT-4o-mini. The results of the experiment indicate that \text{(i)} most of the models show a strong default bias in the absence of social signals; \text{(ii)} persona introduces opinion heterogeneity, while history exerts an anchoring force; and \text{(iii)} combining both signals self-reinforcing the majority opinion dominance appears much more frequent in the test cases than others, despite the lack of affect of the agents. These findings challenge traditional affect-based explanations of SoS and provide empirical evidence to understand and mitigate opinion convergence in LLM-based agent systems and offer a conceptual link between computational sociology and the design of responsible artificial intelligence systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".