jet-data-analysis

Numerical & Computational Content (jet-data-analysis)

When to trigger

• Your theory paper includes a worked numerical example, a simulation, a computed equilibrium, or

(rarely) empirical/experimental evidence

• You want to know how much computational content JET will accept and how to present it
• You need to keep a computation from overshadowing the theorem

The JET rule: theory-first, computation subordinate

JET publishes rigorous, original theoretical results. Empirical, experimental, quantitative, and

computational work is welcome only when firmly grounded in theory — i.e., as the **illustration or

test of a theoretical contribution that is itself the paper's point**, never as a stand-alone empirical

or computational paper. This skill is deliberately light: most JET papers are pure theory, so the

default is minimal numerical content.

How to present numerical content

• Make it serve the theorem. A numerical example should make an assumption bite, exhibit the

characterized object, or show tightness of a bound — not stand alone as a finding.

• Keep examples small and transparent. A 2x2 game, a two-type screening problem, or a three-agent

matching market usually communicates more than a large simulation.

• Use computation to probe necessity. A computed counterexample is the cleanest way to show an

assumption cannot be dropped (feeds jet-identification-strategy and jet-rebuttal).

• Reproducibility. Provide a small self-contained script (SymPy/numpy/scipy, Julia, MATLAB/Octave)

that regenerates every reported number and figure; pin versions and set/report seeds for anything

stochastic. If the paper uses research data, Elsevier Option C requires a repository citation/link

or a cannot-share explanation; if it only has computation, share enough code for the referee to

reproduce the numerical claim (see jet-replication-and-data-policy).

• If genuinely empirical/experimental: state the theoretical prediction first, then test it; the

prediction is the contribution.

Picking the smallest environment that makes the point

| Theoretical claim | Smallest honest illustration | Why it convinces a JET referee |

|---|---|---|

| An assumption cannot be dropped | a 2x2 game or two-type screening problem violating only that assumption | the failure is checkable by hand in minutes |

| A bound is tight | an environment attaining the bound exactly | tightness becomes a verifiable statement, not a plot |

| A characterized mechanism is implementable | computed transfers/allocations for two or three types | the numbers confirm the closed form line by line |

| The equilibrium set has the claimed shape | a three-agent matching market or a two-state ambiguity example | the entire set can be enumerated and inspected |

| A dynamic characterization is operational | one computed path of the recursive contract | the recursion is seen to close |

If the smallest environment that exhibits the phenomenon needs more than a page to describe,

reconsider whether the example belongs in the body or in an appendix.

Minimal verification script (template)

# verify_example_1.py — regenerates every number in Example 1
# (tightness of the bound in Theorem 2 for the two-type screening problem)
import sympy as sp

v_H, v_L, p = sp.symbols("v_H v_L p", positive=True)
rent = (v_H - v_L) * p                      # information rent at the optimum, matches eq. (7)
bound = sp.Rational(1, 2) * (v_H - v_L)     # the Theorem 2 bound
print(sp.simplify(rent.subs(p, sp.Rational(1, 2)) - bound))  # 0 → bound attained at p = 1/2
# Nothing here is stochastic; if an example is FOUND by random search,
# fix the seed, report it, and ship the search script too.

One short script per numbered Example, named after the theorem it serves, beats one monolithic

notebook — referees check examples against statements, not pipelines.

Where computation sits in an accepted JET paper

• As a numbered Example placed immediately after the theorem it illustrates, or as a short

"Numerical illustration" subsection — almost never as a stand-alone section competing with the

results. Conventions drift across subfields; check recent JET papers in yours.

• Figures generated from computation follow jet-tables-figures: vector output, notation identical

to the body, the generating script named in the note.

Anti-patterns

• A large simulation presented as the result, with theory as decoration (off-fit for JET)
• A numerical figure whose underlying values cannot be reproduced
• Calibration/estimation with no theorem behind it (send elsewhere)
• Stochastic illustration with no seed reported

Output format

【Content type】worked example | simulation | computed equilibrium | empirical test | none
【Role】illustrates / tests / counterexample to <theorem/assumption>
【Subordinate to theory?】[Y/N]  ← must be Y for JET
【Reproducible】script + pinned env + seed? [Y/N]
【Next】jet-tables-figures / jet-replication-and-data-policy