Skill UI

A comprehensive guide for academic authors on how to strategically position their research contribution within specialized fields like risk, uncertainty, and decision theory. It details how to engage with canonical academic debates (e.g., Expected Utility critiques, Prospect Theory, Ambiguity) by citing primary literature and clearly defining the novelty relative to the closest prior work, enhancing the paper's academic rigor.

A comprehensive guide for crafting scientific response letters after receiving peer review feedback from specialized journals. It outlines how to triage technical objections, structure point-by-point rebuttals, reconcile conflicting referee demands, and maintain a professional, calibrated tone, ensuring the manuscript addresses all major concerns effectively.

This guide helps authors prepare manuscripts for submission to specialized academic journals by simulating the critiques from various expert subcultures (e.g., decision theorists, experimentalists). It teaches users how to anticipate potential objections, strengthen core arguments, and structure their findings to maximize the likelihood of acceptance.

A comprehensive guide for assembling a fully transparent and reproducible research package required for academic submissions (e.g., JRU/Springer). It structures the necessary components—including full experimental instructions, source code (z-Tree/Qualtrics), estimation scripts, and de-identified data—to allow any external party to replicate the study's findings end-to-end. Focuses on both procedural reproducibility and structural econometrics re-runnability.

A comprehensive framework for conducting and structuring robustness checks in academic papers, particularly in econometrics and behavioral economics. It guides researchers on identifying specific threats to the core parameter (e.g., functional form dependence, incentive bias, sample selection) and organizing tests by threat, ensuring the findings are statistically reliable and withstand expert scrutiny.

A comprehensive preflight guide designed for authors submitting manuscripts to highly specialized academic journals. It covers essential checks beyond standard drafting, including stylistic conformity, mandatory disclosures (Ethics, Data Availability, AI usage), and ensuring the paper's theoretical fit within the journal's specific scope. Essential for maximizing submission success.

This guide provides rigorous standards for transforming complex behavioral and risk data into clear, statistically sound, and academically rigorous tables and figures. It ensures that key decision-theoretic content, such as choice patterns, parameter estimates, and uncertainty, are visibly and interpretably presented for academic publication.

A comprehensive guide for researchers developing models in decision theory under risk and uncertainty. It outlines how to strengthen theoretical representations by ensuring the model is anchored by clear axiomatic foundations, a mathematically tractable functional form, and crucially, verifiable behavioral content (i.e., what the model predicts or forbids). This helps bridge the gap between abstract theory and empirical data.

This guide helps researchers determine if their core contribution is focused on measuring or refining a specific risk or uncertainty primitive (e.g., ambiguity attitudes, VSL, probability weighting). It provides a framework to assess scope fit and distinguish between behavioral anomalies, methodological findings, and foundational risk theory, ensuring the paper is positioned correctly for high-tier academic journals like JRU.

This specialized router guides authors through the complex, multi-stage process of writing a manuscript for the Journal of Risk and Uncertainty (JRU). It diagnoses the current bottleneck—be it theoretical representation, empirical elicitation, structural estimation, or writing style—and directs the user to the most appropriate specialized skill needed to advance the research, ensuring the core focus remains on decision-making under risk and uncertainty primitives.

This guide provides rigorous structural and stylistic guidelines for academic manuscripts in the field of risk and uncertainty. It instructs authors on how to precisely articulate decision models, quantify results with appropriate magnitude and precision, and structure arguments (Abstract, Introduction, Exposition) to meet the high standards of specialized journals, ensuring technical honesty and clarity.

A rigorous guide for advanced spatial econometrics, designed to stress-test causal claims in urban and spatial economics papers. It provides a framework to defend against the three critical spatial confounds: selection/sorting, spatial spillovers (SUTVA failure), and spatial autocorrelation. Essential for publishing high-impact work in journals like JUE.