Boundedly Rational Voters in Large(r) Networks
Bibliographic record
Abstract
In Iterative Voting, voters first cast their ballots but may change their minds upon observing the ballots of others. Previous models have extended Iterative Voting to the incomplete information domain of social networks, where voters only observe the ballots of their friends. However, these models are based on computationally-intensive calculations of expected utilities. We propose a framework of bounded rationality for voters situated in social networks. Using this framework, we propose and test a number of heuristics that reduce the computation required for optimal strategic reasoning by several orders of magnitude compared to previous work, while retaining similar qualitative behaviors. These heuristics enable us to conduct simulations on how the size of the voting population affects strategic behavior. To illustrate the effectiveness of our approach, we apply our heuristics to explore the Micromega rule --- an observation in political science that large political parties favor small assemblies. We find that the size of electoral districts is a contributing factor to the Micromega rule in some networks. Fringe candidates retain more support in smaller districts, while larger parties dominate in larger districts.
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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 | 0.000 | 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".