Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.
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Use when presenting results for an American Economic Journal: Microeconomics (AEJ: Micro) manuscript — propositions, numerical examples, schematic theory figures, and empirical/experimental tables. Builds exhibits to AEA house norms; it does not derive the results (see aejmic-theory-model).
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
AEJ: Micro results are mostly propositions, characterizations, and numerical examples, not regression tables. The exhibit job is to make a formal result legible and concrete.
| What you want to convey | Best exhibit |
|---|---|
| A characterization holds | A numbered Proposition + an intuition sentence; rarely a table |
| How an optimum moves with a primitive | A numerical-example plot (object vs. primitive) |
| The structure of an equilibrium | A schematic (timeline, partition, region plot) |
| A magnitude from estimation/experiment | A three-line table with SEs / coverage sets |
| Model fit (structural) | Data vs. model overlay, not a fit statistic alone |
Pick one exhibit per idea. If a sentence does the job, do not add a figure; an AEJ: Micro theory paper can be exhibit-light without penalty.
Generate exhibits from the fitted result, not by retyping numbers (the usual source of
body-vs-appendix drift). Full map: execution-with-mcp.
etable (multi-model columns) or did_summary_to_latex straight from the
result_id — one variable definition, one set of numbers, body and appendix in sync.plot_from_result / enhanced_event_study_plot / event_study_table —
axis units and the SE/clustering note baked in.See a full fitted-result → exhibit chain in the JF execution walkthrough.
An information-design paper proves the sender-optimal signal is a two-message partition. The presentation: state Proposition 2 cleanly ("The sender-optimal signal partitions the state into {low, high} with cutoff x*"), precede it with the intuition (pooling above x* maximizes the receiver's action while preserving credibility), then a schematic figure of the state line with the cutoff and a small numerical example showing how x* moves with the sender's bias. No regression table, no asterisks — the exhibits make the mechanism visible.
【Formal results】numbered propositions/theorems, each self-contained? [Y/N]
【Economic-content-first prose】present for each? [Y/N]
【Numerical example】illustrates which proposition; reproducible? [Y/N]
【Schematic figures】one idea each; vector; clean labels? [Y/N]
【Applied tables】SEs/coverage, no asterisks? [Y/N or N/A]
【Next step】aejmic-writing-style
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Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.
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Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round before SPC/AC discussion.
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Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.
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Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.
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Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a related-work section legible to non-specialist reviewers.
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Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.
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