Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
› smr-simulation-studies
smr-simulation-studies
GitHub用于设计社会学方法研究论文的蒙特卡洛模拟，涵盖数据生成过程、竞争对手基准及性能指标。旨在构建严谨实验以验证方法在有限样本下的性质及优势，确保结果可信。
Trigger Scenarios
设计SMR论文的数据生成过程
构建蒙特卡洛模拟实验
选择模拟中的竞争对手基准
确定模拟性能评估指标
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill smr-simulation-studies -g -y
SKILL.md
Frontmatter
{
"name": "smr-simulation-studies",
"description": "Use when designing the Monte Carlo simulation study for a Sociological Methods & Research (SMR) paper — data-generating processes, competing methods, performance metrics, and the regimes where the method wins or breaks. Designs the simulation; does not derive properties or run the real-data illustration."
}
SMR Simulation Studies
Use this to build the Monte Carlo that an SMR reviewer will trust. At a methods journal the simulation is not a formality — it is the primary evidence that the analytical properties hold in finite samples and that the method beats real competitors. A weak or self-serving simulation sinks otherwise sound papers.
Design the DGP space deliberately
Reviewers attack the data-generating process first. Specify it as a designed experiment, not a convenient example:
- Factors and levels: sample size (and, for panels/networks, the relevant dimensions), the parameter that controls the difficulty (effect size, dependence, missingness rate, sparsity), and any nuisance complications. State why each level is realistic for sociological data.
- Coverage of the assumption boundary: include cells where your own assumptions fail, so the paper shows the method's limits, not just its triumphs. SMR rewards honesty about breakdown.
- Calibration to the application: at least one DGP should be calibrated to the real dataset in
smr-empirical-illustration, so the simulation speaks to a setting readers care about. - Replications and seeds: enough Monte Carlo replications for stable estimates of the metrics, with seeds fixed and reported for reproducibility.
The competitor set (non-negotiable)
A simulation that compares the new method only to a naive baseline is the classic reject. Include:
- The current default practitioners actually use.
- The strongest existing alternative for the same problem (often from a neighboring discipline —
see
smr-literature-positioning). - Where relevant, an oracle / infeasible benchmark to show the gap your method closes.
If your method loses to a competitor in some cell, report it and explain when each method is preferable — conditional recommendations are more credible than universal victory.
Metrics that match the claim
| Claim type | Report | Common SMR pitfall |
|---|---|---|
| Point estimation | bias, RMSE, relative efficiency | reporting bias but hiding variance |
| Inference / testing | empirical size, power, CI coverage and width | "performs well" with no coverage number |
| Selection / classification | accuracy + the costs of each error | accuracy only, ignoring imbalance |
| Computation | runtime, scaling, convergence rate | feasibility claim with no timing |
Coverage and size near the nominal level are the metrics SMR reviewers scrutinize most for inference methods — report the actual numbers, not adjectives.
Presenting the study compactly
- Summarize the full grid in a table or a small-multiples figure; do not narrate every cell.
- Lead with the cell that makes the contribution's point (where the incumbent breaks and the method holds), then show the boundary where the method itself degrades.
- Hand the exhibit design to
smr-tables-figuresso the grid is self-contained and readable in print.
Checklist
- The DGP is specified as a factorial design with realistic levels, each justified.
- Cells where the method's own assumptions fail are included.
- At least one DGP is calibrated to the empirical illustration's data.
- The competitor set includes the current default and the strongest alternative.
- Metrics match each claim (coverage/size for inference, bias+variance for estimation).
- Replication count and seeds are reported.
- Cells where the method loses are reported with a conditional recommendation.
Anti-patterns
- Strawman comparison: only a naive baseline, never the real competitor.
- Sunny-cell selection: showing only regimes that favor the method.
- Adjective metrics: "good size control" with no rejection rates.
- Cherry-picked n: one favorable sample size with no scaling pattern.
- Uncalibrated fantasy DGP: a design unrelated to any sociological data.
- Hidden seeds / replication count: results that cannot be reproduced.
Output format
[Simulation status] convincing / needs repair / not ready
[DGP factors] <factor : levels, with realism note>
[Competitor set] <default + strongest alternative (+ oracle)>
[Metrics] <bias/RMSE/coverage/size/power/runtime as claimed>
[Boundary cell] <where the method degrades and why that is honest>
[Next SMR skill] smr-empirical-illustration
Version History
- 1839142 Current 2026-07-05 14:26
