agentsclimarketplace

Aamas review process

Skill brycewang-stanford/Awesome-Journal-Skills/AAMAS-Skills/skills/aamas-review-process

Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. | 覆盖主流期刊的 Claude Code/Codex 期刊技能包,从选题、识别策略到表格规范与审稿回复全流程,助你快速发论文。

Install
npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill aamas-review-process

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

Use when explaining or planning around AAMAS peer review, covering OpenReview review release, the double-blind rebuttal on preliminary reviews, area-chair discussion, the mixed game-theory, MARL, and systems reviewer pool, the public posting of reviews and decisions, and how acceptance criteria weigh the interaction contribution.

SKILL.md

3.5 KB, as published. Nobody here has run it

AAMAS Review Process

Use this to reason about review-stage strategy. Reopen the current CFP, OpenReview group, author and reviewer instructions, and the code of conduct before making process claims; AAMAS review specifics move between editions.

Process model

  • AAMAS runs submission and review on OpenReview under the IFAAMAS namespace in recent cycles.
  • Reviewers evaluate technical correctness, the significance of the interaction contribution, the reality and rigor of the multiagent evaluation, clarity, reproducibility, and fit with agents-and-multiagent-systems scope.
  • A rebuttal lets authors respond to preliminary reviews before the final decision; area chairs then synthesize.
  • Accepted papers and their reviews are published, so the review record is durable and public.
  • The most useful rebuttal gives the area chair a clean rationale for acceptance, not a point-by-point defense of every comment.

Who reviews here

  • The pool mixes game theorists, multiagent-RL researchers, mechanism-design and social-choice specialists, and systems-minded reviewers; expect at least one to read the game definition and solution concept line by line.
  • Because AAMAS is specialized, a paper is likely to meet a reviewer who works on exactly its subarea, so a vague equilibrium claim or an under-specified opponent set gets caught rather than skimmed.
  • Borderline interaction papers usually fail on one of three edges: the result turns out to be single-agent in disguise, the solution concept is never pinned down, or the multiagent evaluation is thin (self-play only, no seeds, no held-out opponents).

Scoring leverage table

Review dimensionWhat raises itWhat sinks it
CorrectnessA stated game, a named solution concept, and a body-level proof sketchHidden information structure; an equilibrium asserted but never defined
SignificanceA finding that only exists because agents interactAn incremental single-agent gain wearing a multiagent label
Empirical supportExperiments that probe strategy: held-out opponents, deviation testsSelf-play-only curves disconnected from the claim
ClarityOne notation source and a legible game descriptionNotation and payoff conventions that shift between sections

Stage-by-stage realism

  • Initial reviews: triage by what the area chair would weigh, not by reviewer tone.
  • Rebuttal: windows are short; an early, precise reply anchored in submitted evidence beats a late exhaustive one.
  • Decision: the area chair synthesizes, and one unanswered correctness or interaction-reality objection outweighs several resolved clarity complaints.
  • Public record: assume the reviews and your rebuttal will be visible with the paper, and keep the exchange professional and concrete.

Output format

[Current stage] submitted / reviews / rebuttal / decision / camera-ready
[Decision actors] <reviewers / area chair / program chairs>
[Likely leverage] <correctness / interaction-reality / significance / experiments / clarity>
[Forbidden moves] <identity leak / new results / revised-paper upload if disallowed>
[Next response move] <one action>

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.