Modeling Competing Agents in Social Media Networks
Bibliographic record
Abstract
In this paper, we consider a discrete private and expressed (synchronous and asynchronous) opinion dynamics model with competitive relationships. Unlike the usual agent-based opinion dynamics models, competition between individuals is investigated in a social media network. The expressed opinions, or states of the individuals in the network, are represented by asynchronous dynamics, where each individual has a choice to express his/her opinion at each time step. Each agent uses a Q-earning algorithm to decide when to express its opinion with the purpose of swaying the opinions of other connected agents to a desired outcome. The private opinions, or states of the individuals, are derived from a combination of their own private opinions and the expressed opinions of connected agents. The dynamics of the social media environment are modeled by private and expressed, both asynchronous and synchronous, opinion dynamics model. The system is investigated for polarization or consensus under different conditions. To illustrate the results, simulations of the system dynamics are provided.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".