agentsclimarketplace

Geb data analysis

Skill brycewang-stanford/Awesome-Journal-Skills/Games-and-Economic-Behavior-Skills/skills/geb-data-analysis

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 geb-data-analysis

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 a Games and Economic Behavior (GEB) manuscript involves experimental data or numerical illustration — analyzing strategic-game experiments and building verified worked examples. Adapts analysis to GEB's game-theory nature, where data supports the theory rather than carrying a causal claim.

SKILL.md

4.6 KB, as published. Nobody here has run it

Data & Numerical Analysis (geb-data-analysis)

When to trigger

  • You ran a lab/online game experiment and need to analyze choices and play paths
  • You want numerical examples or simulations to illustrate a theorem
  • A referee may question your experimental inference or example construction
  • You are fitting a behavioral model (QRE, level-k, social preferences) to choices

How analysis works at GEB

GEB is primarily a theory journal that also publishes experimental and computational work advancing game theory. So data analysis here is usually in service of a strategic claim — does observed play match an equilibrium prediction, distinguish solution concepts, or illustrate a mechanism — rather than estimating a treatment effect for its own sake. Keep it lighter and tightly tied to the model.

A. Experimental game data

  • Unit of observation = the session. Subjects within a session interact and are not independent; cluster standard errors at the independent-session level, or use session-level summaries for nonparametric tests.
  • Describe play, then test. Report distributions of actions, convergence over rounds, and deviations from the predicted equilibrium before running tests.
  • Match tests to the design. Wilcoxon/Mann–Whitney or permutation tests across sessions for treatment comparisons; mixed/random-effects models for repeated play.
  • Behavioral structural fits. Quantal response equilibrium, level-k / cognitive hierarchy, or social-preference models — report fit and identification, and compare to the equilibrium benchmark.
  • Power and pre-registration. Justify cells' sample sizes; reference any pre-analysis plan and report deviations. Under-powered interactive experiments are a standard referee objection.

B. Numerical examples & simulation (for theory papers)

  • Examples illustrate, never substitute for, proofs. Use a solver (e.g., Gambit, nashpy) to exhibit the equilibria your theorem describes and to make an abstract construction concrete.
  • Boundary / counterexamples. A clean numerical counterexample showing an assumption is necessary is high-value.
  • Reproducible computation. Set and report seeds; pin solver and library versions; ship a script that regenerates every example and figure (see geb-replication-and-data-policy — sharing is encouraged but not required at GEB).

Anti-patterns

  • Treating individual subjects as independent observations (ignoring session clustering)
  • Presenting a few simulations as evidence a theorem is "probably true"
  • Running treatment comparisons with no power justification
  • A behavioral structural fit with no comparison to the equilibrium prediction
  • Over-interpreting an experiment as a general causal claim — GEB rewards the strategic insight

Evidence pass for Games and Economic Behavior

Treat this skill as an executable review pass, not a prose hint. First lock the primitives, equilibrium concept, comparative statics, and proof or experiment boundary; then judge whether the current manuscript answers the venue's real reader: game theorists who ask what the model teaches beyond a clever example.

  • Do the pass: Audit the research design before polishing prose: unit of analysis, comparison set, uncertainty, sensitivity, missingness, and reproducibility must be visible.
  • Return a ledger: give claim / evidence / risk / manuscript location rows, so the next agent can edit rather than rediscover the issue.
  • Sibling guard: compare against JET for theory abstraction, Theoretical Economics for compact theory contribution, Experimental Economics for experiment-first designs; if a sibling owns the contribution, recommend re-routing before polishing format.
  • Stop condition: do not give submission-ready advice until the pack's resources/official-source-map.md has been checked for volatile rules and the manuscript has one concrete fix for the largest venue-specific risk.

Output format

【Mode】experimental data / numerical examples / both
【(Exp) clustering】session-level? [Y/N] — tests used
【(Exp) power & pre-reg】justified / referenced? [Y/N]
【(Exp) structural fit】model + comparison to equilibrium? [Y/N / NA]
【(Num) role】illustrates which result; counterexample? 
【Reproducibility】seeds + pinned versions + run_all? [Y/N]
【Next step】geb-tables-figures

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.