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podc-experiments

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Use when building the evidence for an ACM PODC paper — where "evidence" is a proof, not a benchmark. Covers matching upper and lower bounds, tightness arguments, model and assumption stress-tests, adversary-strength calibration, and the honest, clearly-optional role of any simulation in a distributed-computing-theory paper.

PODC-Skills/skills/podc-experiments/SKILL.md

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# PODC Experiments

At PODC the "experiment" is a **proof**, and the strongest result is a **matching bound**. This
skill is the theory analogue of an empirical-evaluation section: it is about making the *evidence for
correctness and optimality* as strong as the venue expects. There is no benchmark leaderboard, no
artifact track, and a simulation never establishes the result — it can only illustrate it.

## Match the evidence to the claim shape

| Claim | What counts as evidence at PODC | Common failure caught |
|---|---|---|
| "Algorithm A solves problem P in model M" | A proof of the required properties (e.g., agreement, validity, termination) in exactly M | Property proved in a stronger model than claimed |
| "A costs O(f(n))" | A proof of the upper bound over all executions/adversary choices | Bound holds only in the best case, not worst/expected as stated |
| "A is optimal" | A **matching Ω(f(n)) lower bound** in the same model | "Optimal" asserted with no lower bound |
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