| name | jru-writing-style |
| description | Use when the prose of a Journal of Risk and Uncertainty (JRU) manuscript must convey a decision model and its measurement with precision. Sharpens abstract, intro, and exposition for a risk/uncertainty audience; it does not invent evidence or citations. |
Writing Style (jru-writing-style)
When to trigger
- The abstract says "we study risk preferences" without naming the primitive, the method, or the headline number
- The introduction motivates with a policy anecdote but never states the decision-theoretic claim
- Notation drifts: the same symbol means utility in one section and value in another, or w(p) is never formally introduced
- The paper reads as either pure theory or pure empirics when JRU readers expect the bridge between them
The JRU voice
JRU prose is precise about the decision model and honest about measurement. The reader is a risk-and-uncertainty specialist who wants, fast: which primitive the paper moves, how it was elicited or estimated, and how big and how precise the effect is. The house register is technical but not ornamental — the model earns its symbols, and every claim is calibrated to what the identification supports.
Abstract (treat each sentence as load-bearing)
- The primitive and the question ("How does ambiguity attitude shape insurance takeup?").
- The method in a phrase (incentive-compatible elicitation; hedonic-wage estimation; an axiomatization).
- The headline result as a number with a direction (a magnitude, not "significant").
- The decision-theoretic interpretation — what it implies for EU vs. its alternatives.
Keep within the journal's abstract limit (待核实 — verify exact word cap on the official guidelines).
Introduction
- Open on the decision-theoretic puzzle, not a news hook. The motivating tension should be a gap in how we represent or measure a risk primitive.
- State the contribution as a property of a primitive in the first page (mirror
jru-literature-positioning).
- Preview the identification in one or two sentences — JRU readers want to know what pins the parameter before the results.
- Tell the reader what the paper does not claim; over-claiming is the fastest route to a skeptical referee.
Exposition
- Introduce the representation formally before interpreting it; define u, w(p), the prior set, the reference point once and use them consistently.
- Report effects as magnitudes with precision, in interpretable units (a VSL in dollars, an elasticity, a λ).
- Keep the experiment's incentive structure and the estimand visible in the main text, not buried in a procedures appendix.
Calibrating claims to the evidence
JRU's referees are quick to flag prose that runs ahead of the identification. A few habits keep the register honest:
- Match the verb to the design. Write "is associated with" for a correlation, "raises" only where the identification supports causation, "is consistent with" when a model fits but is not uniquely identified.
- Quantify, then interpret. Lead with the number and its precision; follow with the decision-theoretic reading. Avoid an interpretation the confidence interval cannot bear.
- Name the scope of the claim. A lab estimate of loss aversion is a statement about the elicited population and stakes, not a universal constant — say so.
- Reserve "robust" for the robustness section. Do not assert robustness in the intro before the checks in
jru-robustness are reported.
Section-by-section register
- Model section: terse and formal — definitions, then results, then one paragraph of interpretation.
- Design / data section: concrete — the mechanism, the stakes, the estimand, the sample, in plain sequence.
- Results section: numbers with precision first, decision-theoretic meaning second.
- Discussion: the boundary of the claim and what it implies for EU vs. its alternatives, without re-litigating the introduction.
Checklist
Anti-patterns
- An abstract that says "significant effects" without a number or a direction
- A policy-anecdote opening that never reaches the decision model
- Symbol drift (u vs. v, w(p) introduced only in a footnote)
- Hiding the incentive mechanism or estimand in an appendix while the intro makes strong claims
- Prose that over-states generality beyond what the elicitation/estimation supports
Worked vignette (illustrative)
A draft abstract reads: "We run an experiment on decision-making and find significant behavioral effects." The JRU rewrite: "We elicit ambiguity attitudes with an incentive-compatible matching-probabilities task (N illustrative) and estimate an α-MEU model. Ambiguity aversion (α illustrative 0.62) raises the willingness to pay for an unambiguous prospect by [magnitude], a wedge expected utility cannot generate. The result implies insurance demand responds to ambiguity, not just to risk." Same study — but the primitive, method, magnitude, and theoretical stakes are all visible.
Terminology a JRU reader expects you to use precisely
Sloppy vocabulary signals a non-specialist. Use these distinctions exactly:
- Risk (known probabilities) vs. uncertainty / ambiguity (unknown probabilities) — never interchangeable.
- Risk aversion (utility curvature) vs. probability weighting (distortion of p) vs. loss aversion (asymmetry around a reference point) — three different things, not synonyms for "cautious."
- Value v(·) (prospect-theory carrier of utility over gains/losses) vs. utility u(·) (EU carrier over final wealth) — keep the symbols distinct.
- Certainty equivalent, risk premium, willingness to pay — defined precisely, not used loosely.
A paper that uses these terms cleanly reads as written by an insider; one that blurs them invites the referee to doubt the rest.
Output format
【Journal】Journal of Risk and Uncertainty
【Skill】jru-writing-style
【Verdict】lands / tighten / rebuild abstract
【Abstract has】primitive + method + signed magnitude + EU-vs-alt interpretation [Y/N]
【Intro】decision-theoretic puzzle + contribution on page 1 [Y/N]
【Notation】consistent u / w(p) / priors / reference point [Y/N]
【Limits】abstract/length within journal caps (待核实) [Y/N]
【Next skill】jru-replication-package
