Influencing Opinions of Heterogeneous Populations over Finite Time Horizons
Bibliographic record
Abstract
We propose a variant of the popular voter model. This variant models different types of individuals in a social network, for e.g., conformist/rebel individuals. In addition, our model allow individuals to change their “type” with time. Motivated by prevalence of online social networks, we consider a well-connected society where all individuals in society can communicate with each other. Moreover, we allow external influence to influence the opinion of individuals. The motivation of this work comes from advertising where it is key to use the limited advertising budget efficiently for opinion shaping. We focus on a finite-time horizon problem and analyse the effect of the nature of individuals on the nature of optimal opinion-shaping strategies. In one of the key results in this work, we show that the conventional wisdom of ramping up advertising towards the end of the time-horizon, as is typically done in election campaigns, is not always optimal.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".