Ors tables figures

Tables, Figures & Exhibits (ors-tables-figures)

When to trigger

• The model, proofs, and computational results exist and must be displayed clearly.
• You need a clean comparison table separating your method from prior work.
• Reviewers should be able to read a result table without hunting through the text.

Exhibits that carry an OR paper

Unlike empirical social-science papers built around correlation and regression tables, Operations Research exhibits are about structure, guarantees, and computation:

• Formal-statement layout: number theorems, propositions, lemmas, and assumptions consistently (Theorem 1, Assumption 1) and reference them by number everywhere. Use the INFORMS LaTeX style files / amsthm so environments render uniformly.
• Comparison table (prior work × properties): rows = methods/papers, columns = {assumptions, strongest result, approximation factor, complexity/rate}. This is the single most persuasive OR exhibit for novelty.
• Computational-results table: instances/sizes × {optimality gap, time, iterations, baseline comparison}. Report units, time limits, and which configuration produced the row; bold or mark wins honestly.
• Plots: convergence curves, scaling (size vs. time, ideally log-log), performance profiles across instance sets, and for stochastic output error bars / confidence intervals — never bare means.

INFORMS house-style notes

• Format: prepare for 1.5-spaced, 11-point, 1-inch-margin manuscript layout; submit as PDF (source LaTeX/Word on acceptance). Use the provided LaTeX style files.
• Self-contained exhibits: each table/figure has a complete caption (what, units, instance set, what "best" means) so it stands alone.
• Notation consistency: symbols in exhibits match the text exactly; define them in the caption or a notation table.
• E-companion: large result tables and ablations can go to the e-companion (which must not be longer than the manuscript); keep the headline exhibits in the main text.

Vector tooling

Use TikZ/PGFPlots (or matplotlib exported to vector) for crisp figures; performance profiles and log-log scaling plots reproduce well as vectors. Keep raw data/scripts in the ORJournal repository so every exhibit regenerates.

Exhibit pushback patterns and the INFORMS-style fix

Referee remark on an exhibit	What it flags	Venue-specific fix
"Computational study lacks benchmarks"	no recognized baseline column	add a column for the closest prior method and a strong solver on standard instances
"Cannot tell what 'best' means"	undefined win marking	caption defines the metric and tie-breaking; bold/mark honestly
"Results not reproducible from the table"	missing units/time-limit/hardware	state units, time limit, termination, hardware, and the producing configuration
"Means hide variability"	bare means for stochastic output	error bars / confidence intervals on every random-output figure
"Theorem numbers don't match the text"	label drift	single numbering scheme via amsthm; reference by number everywhere
"Scaling claim unsupported"	linear-axis time plot	log-log size-vs-time plot or a performance profile across the instance set

These are the exhibit-level versions of the two flags that sink OR computational sections: no benchmarks and structure-not-connected-to-application. Because Operations Research is the INFORMS flagship demanding both theorem-grade rigor and a credible computational study, the comparison table and the performance profile carry as much referee weight as the theorem environments.

Worked exhibit pair (illustrative numbers)

A paper proves a 1.5-approximation and validates it. Two exhibits do the persuading:

Comparison table — Method × {assumption, factor, complexity}:

Method	Assumption	Factor	Complexity
Prior (Author year)	bounded ratios	2.0	O(n²)
This paper	general weights	1.5	O(n² log n)

Computational table — Instance set × {mean gap %, time (s), solver baseline}, e.g. TSPLIB-derived instances showing 1.8% mean gap vs. 31% for the heuristic baseline at a 600 s limit on stated hardware, with the proved 50% bound never violated. The figure pairs them: a log-log scaling plot with confidence-interval bands over 30 seeds. The table corroborates the theorem's bound; the plot shows it holds as size grows.

Execution bridge (StatsPAI / Stata MCP)

Generate exhibits from the fitted result, not by retyping numbers (the usual source of body-vs-appendix drift). Full map: execution-with-mcp. Operations Research is predominantly analytical / optimization / stochastic modeling; use the chain below only for its empirical/causal papers — modeling, optimization, and simulation are outside this causal-inference toolchain.

• Tables: etable (multi-model columns) or did_summary_to_latex straight from the result_id.
• Figures: plot_from_result / enhanced_event_study_plot / event_study_table — axis units and the SE/clustering note baked in.
• Every note names the estimator + clustering and states the effect size in interpretable units.

See a full fitted-result → exhibit chain in the JF execution walkthrough.

Anti-patterns

• A results table with no time limits, units, or hardware — irreproducible.
• Bare means for stochastic output with no confidence intervals.
• A comparison table that omits the closest competitor.
• Theorem/assumption numbers that drift between text and exhibits.
• Raster screenshots of plots instead of vector figures.

Output format

【Formal statements】numbered consistently; INFORMS/amsthm style
【Comparison table】prior work × {assumptions, result, complexity}: drafted?
【Computational table】instances × {gap, time, baseline}; units/limits stated
【Plots】convergence / scaling / CIs present?
【House style】1.5-spaced, 11-pt; self-contained captions; e-companion split
【Next step】ors-writing-style