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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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 area아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.