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.
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).| 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.
# 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.
【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