Skill UI

A guide for designing rigorous numerical experiments and simulations for academic manuscripts in management science. This methodology focuses on validating proven theoretical properties, assessing algorithm performance, exploring parameter sensitivity, and extracting actionable managerial insights. It emphasizes that simulations must complement, but never replace, formal mathematical proofs.

A comprehensive skill for authors targeting top development economics journals (like JDE). It guides the framing and methodological rigor of micro-development work, covering requirements for credible identification (RCTs, natural experiments), structuring the 'external lesson,' and adhering to modern academic publishing standards. Use this when refining a field experiment or policy paper for publication.

This skill provides a comprehensive guide for authors targeting the Journal of Marketing Research (JMR). It analyzes the journal's demanding standards for methodological rigor, covering quantitative modeling, behavioral experiments, and design validity. Use this tool to assess if your manuscript's contribution, structure, and evidence base meet JMR's high academic bar, or to suggest more appropriate alternative venues.

Provides comprehensive methodological guidelines for designing rigorous studies in public policy and marketing. The framework covers both prospective (e.g., realistic experiments) and retrospective (e.g., DiD, RDD) evaluation designs, emphasizing the necessity of establishing a strong counterfactual basis and ensuring policy relevance in data collection and analysis.

A rigorous methodological framework for identifying core risk and uncertainty parameters in academic research. This guide details how to design experiments or structural models to explicitly separate confounds (e.g., utility curvature vs. probability weighting, preferences vs. beliefs) and ensure that the measured parameter is genuinely identified, thereby enhancing the scientific rigor of behavioral finance and econometrics studies.

A comprehensive guide for structuring the empirical section of high-level machine learning papers (e.g., KDD). It details the 'four-axis evidence plan'—Quality, Scale, Efficiency, and Mechanism—and emphasizes critical data hygiene practices. Learn how to prevent temporal leakage, structure ablations in a matrix format, and design post-launch measurements to ensure academic rigor and reproducibility.

This guide details how to conduct rigorous, reproducible numerical simulations for mathematical finance research. It emphasizes that numerical experiments must strictly support a theoretical theorem (e.g., convergence rates, error bounds) rather than performing empirical data fitting. Learn how to properly specify schemes, report theoretical vs. empirical error rates, and maintain full reproducibility to meet high academic standards.

This guide assists researchers in selecting and rigorously defending the appropriate methodology for Management Science (INFORMS) manuscripts. It covers both analytical modeling approaches (e.g., optimization, stochastic processes, game theory) and empirical designs (e.g., quasi-experiments, field trials). It ensures the chosen design not only matches the research question but also meets strict departmental standards and generates decision-relevant managerial insights.

This comprehensive guide assists authors in developing rigorous methodology sections for academic papers in Marketing Science. It covers advanced techniques such as structural econometrics, causal ML, dynamic games, and quasi-experiments. Key focus areas include ensuring model identifiability, specifying estimators (GMM, MLE), and rigorously defending exogeneity assumptions.

This guide provides an expert methodology for designing and auditing Natural Language Processing (NLP) experiments for top-tier academic submissions. It instructs researchers on how to map meta-review claims to measurable evidence, ensure budget-fair comparisons, and systematically address potential reviewer probes (scope, fairness, contamination, etc.) to maximize publication rigor.

A comprehensive guide for stress-testing the evidence supporting ML claims before submitting to top-tier academic conferences. Covers rigorous auditing of baselines, ablation studies, robustness checks, data leakage, and calibrating claims against evidence strength, ensuring scientific reproducibility.

Analyze PIV image pairs with OpenPIV: preprocessing, cross-correlation, vector validation, outlier replacement, and computing vorticity, strain rate, and turbulence statistics for fluid flow experiments.