Multi-dimensional multi-option opinion dynamics leads to the emergence of clusters in social networks
Bibliographic record
Abstract
In real-world social networks, opinions evolve within a multidimensional space of multiple topics being concurrently discussed, and a multi-option decision-making process, rather than a simple binary choice, takes place. Our work introduces a multi-dimensional multi-option opinion dynamics model capturing the complexity of opinion evolution in social networks. The model exploits the coupling of inner opinion and outward action, emphasizing how similar actions strengthen interactions between agents. Unlike existing research, in which consensus, clustering or polarization result from specific network structures, we find that different attitude patterns towards neighbours lead to the spontaneous emergence of such macroscopic phenomena, which are therefore independent of network structural features. We provide analytical conditions for the transitions to these behaviours, confirming them via simulations on different networks. Thus, our model allows one to explain the emergence of collective phenomena observed in real-world situations, thereby providing insights in areas such as opinion guidance and multi-agent decision-making. • First opinion model with realistic dynamics of beliefs, actions and choices. • Mathematical explanation of how the model reproduces real-world phenomena • Independence of the results from the structure supporting the social interactions.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".