「theory」の検索結果

Use when running controlled perf experiments to validate hypotheses.

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Use when generating performance hypotheses backed by git history and code evidence.

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Quantum mechanics simulations and analysis using QuTiP (Quantum Toolbox in Python). Use when working with quantum systems including: (1) quantum states (kets, bras, density matrices), (2) quantum operators and gates, (3) time evolution and dynamics (Schrödinger, master equations, Monte Carlo), (4) open quantum systems with dissipation, (5) quantum measurements and entanglement, (6) visualization (Bloch sphere, Wigner functions), (7) steady states and correlation functions, or (8) advanced methods (Floquet theory, HEOM, stochastic solvers). Handles both closed and open quantum systems across various domains including quantum optics, quantum computing, and condensed matter physics.

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Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.

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Comprehensive adversarial audit of a theory, proof, math/econ paper, codebase, or set of claims — decompose into components, fan out independent skeptics that must return CONCRETE defects, adjudicate every finding with a separate judge, fix all confirmed defects, then re-verify. Use when correctness must be bulletproof and single-pass or round-by-round review is too slow and too shallow. Invoke for "audit this rigorously", "find ALL the bugs/gaps", "make this rock solid", "converge faster on correctness".

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When throughput or latency is pipeline-limited, identify the single binding constraint and exploit, subordinate, elevate, then recheck—ignore non-constraints.

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Use when drafting AISTATS author responses or author-reviewer discussion replies under OpenReview, covering text-only discussion, no-link guidance, no revised-paper upload, anonymity requirements, statistician-reviewer pushback patterns, and decision-focused clarification strategy for theory-plus-experiments papers.

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Use when planning an AISTATS project timeline from venue fit through abstract submission, full-paper upload, supplementary material, review release, author-reviewer discussion, decision, camera-ready, PMLR publication, registration, presentation, and artifact release, with backward-planning offsets for theory-plus-experiments papers.

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Use when preparing AISTATS supplementary material, appendices, proof details, code/data archives, simulation scripts, additional tables, and anonymized artifacts under deadline, size, anonymity, and reviewer-discretion constraints, including how to split a theory-plus-experiments paper between body and supplement.

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Use when building the conceptual/theoretical argument of an American Anthropologist (AA) manuscript into a four-field contribution — whether the work is ethnographic, archaeological, biological, or linguistic. AA rewards a portable concept and reflexive argument over a bare finding or thick description alone. Structures the argument; it does not run analyses or write fieldnotes.

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Use when anticipating referee objections or calibrating review expectations for an American Economic Journal: Microeconomics (AEJ: Micro) manuscript before submission. Pre-empts the objections theory referees raise and reads the editorial process; it does not draft the response letter (see aejmic-rebuttal).

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Use when assembling the proof appendix and any code/data deposit for an American Economic Journal: Microeconomics (AEJ: Micro) manuscript under the AEA Data and Code Availability Policy. Covers proof appendices for pure theory plus numerical/structural/experimental code; it does not run your estimation.

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Use when the quantitative model is the bottleneck for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript — DSGE, New Keynesian, heterogeneous-agent (HANK / Aiyagari-Bewley), or structural estimation — and calibration, parameter identification, solution accuracy, or counterfactual validity need discipline. For empirical shock identification see aejmac-identification.

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Use when deciding whether a microeconomics project fits the American Economic Journal: Microeconomics (AEJ: Micro) rather than JET, GEB, Theoretical Economics, Econometrica, or AEJ: Applied. Tests scope and broad-interest fit; it does not develop the model (see aejmic-theory-model).

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Use when building and refining exhibits for an Administrative Science Quarterly (ASQ) manuscript — qualitative data structures and data-to-theory tables, process models, and quantitative tables. Designs exhibits; it does not run the analysis behind them.

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Use when an AEJ: Economic Policy manuscript needs a framework that maps reduced-form estimates into a welfare, cost-benefit, or distributional policy object — sufficient statistics, MVPF, optimal-policy, or a small applied model. Builds the estimate-to-welfare bridge and states its assumptions; it does not run the empirical estimation or write the prose.

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Use when building the theoretical engine for an Administrative Science Quarterly (ASQ) manuscript — mechanisms, process vs. variance logic, constructs, and boundary conditions. Builds the argument; it does not select methods or run analysis.

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Use when the empirical identification of a macro shock or dynamic causal effect is the bottleneck for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript — SVAR, local projections, narrative, high-frequency/proxy-VAR, or micro-data macro designs. Stress-tests the identification to the AEJ: Macro broad-interest quantitative bar; for model-parameter identification see aejmac-theory-model.

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Use when the contribution of an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript relative to the macro frontier is fuzzy, contested, or undersold. Frames the marginal contribution against quantitative-theory and identified-empirical literatures; it does not fix the identification or the model itself.

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Use when the headline result of an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript must be shown stable across specification, sample, identification, and tuning choices. Builds the robustness program a macro referee will demand; it does not establish the primary identification or model (use aejmac-identification / aejmac-theory-model first).

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Use for full-manuscript prose and narrative craft on an Administrative Science Quarterly (ASQ) manuscript — voice, structure, and the storytelling ASQ is known for. Polishes the writing; it does not change the theory or analysis.

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Use when judging whether a research idea fits Administrative Science Quarterly (ASQ) and has the *surprising* theoretical insight ASQ demands. Sharpens the puzzle and the contribution claim; it does not design the study or write theory sections.

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Use when choosing and justifying the research design for an Administrative Science Quarterly (ASQ) manuscript — qualitative (grounded-theory, ethnographic, historical) or quantitative — and setting the rigor bar. Designs the study; it does not run the analysis (see asq-data-analysis).

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Use when deciding whether a security project fits ACM CCS versus IEEE S&P, USENIX Security, NDSS, PETS, or a crypto/theory venue, identifying the security contribution type, and sharpening the threat model and attacker capability before writing begins.

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