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 the model setup, equilibrium concept, or proof architecture is the bottleneck for an American Economic Journal: Microeconomics (AEJ: Micro) manuscript — the central skill for a theory-first paper. Builds the model to the AEJ: Micro bar (clean, general, well-motivated result); it does not check whether your theorem is correct.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
This is the central skill for AEJ: Micro. The journal is theory-first: the deliverable is a clean, general, well-motivated result — a theorem, characterization, or impossibility — that a broad microeconomics audience finds illuminating. Get the model and the proof right before anything else.
aejmic-identification)AEJ: Micro wants the economics to be the point: the model should be the simplest environment in which the mechanism is visible, and the result should travel beyond the example. It sits between JET/GEB (specialist, often maximal generality) and AEJ: Applied (empirical). Aim for a result that a non-specialist micro theorist can state in one sentence and that a specialist finds non-obvious.
aejmic-robustness.A monopolist screens buyers with private values. A weak setup throws in a continuum of types and three frictions at once; the optimal-contract characterization becomes a thicket. The AEJ: Micro move: start with two types, derive the standard "no distortion at the top, downward distortion below" characterization as a clean Proposition, prove it by the relaxed-program-then-verify-monotonicity architecture, then in aejmic-robustness show the continuum case preserves the qualitative result. The two-type model is the simplest environment where the distortion is visible — and the result travels.
【Model】players / actions / information / timing / payoffs (substantive vs. WLOG flagged)
【Solution concept】[Nash/SPE/PBE/IC/stability/…] + existence/uniqueness
【Main result】Proposition stated in words
【Proof architecture】[construction / revelation+optimize / fixed-point / duality / induction / impossibility]
【Generality call】where proved + cost of any simplification
【Proofs live】main text: [...]; appendix: [...]
【Next step】aejmic-identification (what makes it tight) or aejmic-robustness (extensions)
../../resources/worked-examples/01-introduction.md — a theory-paper intro arc (puzzle → model → proposition → driving assumption)../../resources/exemplars/library.md — real AEJ: Micro theory papers by area还没有评价。欢迎分享你的使用感受。
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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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