mksc-theory-development

GitHub

用于构建营销科学论文的形式化模型,将现象转化为博弈论或结构计量模型。明确原始设定、均衡、比较静态及识别论证,为实证分析提供理论机制支撑。

Marketing-Science-Skills/skills/mksc-theory-development/SKILL.md brycewang-stanford/Awesome-Journal-Skills

触发场景

需要建立正式数学模型描述营销现象 机制仅停留在文字描述需形式化 结构模型缺乏明确的识别论证

安装

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mksc-theory-development -g -y
更多选项

非标准路径

npx skills add https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Marketing-Science-Skills/skills/mksc-theory-development -g -y

不安装直接使用

npx skills use brycewang-stanford/Awesome-Journal-Skills@mksc-theory-development

指定 Agent (Claude Code)

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mksc-theory-development -a claude-code -g -y

安装 repo 全部 skill

npx skills add brycewang-stanford/Awesome-Journal-Skills --all -g -y

预览 repo 内 skill

npx skills add brycewang-stanford/Awesome-Journal-Skills --list

SKILL.md

Frontmatter
{
    "name": "mksc-theory-development",
    "description": "Use when building the formal model for a Marketing Science manuscript — turning a marketing phenomenon into an analytical (game-theoretic) model or a structural econometric model with a clear identification argument. Develops the model and mechanism; it does not run the estimation (mksc-data-analysis) or pick the empirical genre at a high level (mksc-methods)."
}

Model & Mechanism Development (mksc-theory-development)

When to trigger

  • The phenomenon is interesting but there is no formal model yet
  • The "mechanism" is verbal and needs to be written as primitives, payoffs, and equilibrium
  • A structural story lacks an identification argument (what variation pins down each parameter)
  • An analytical model lacks crisp comparative statics or testable predictions

In Marketing Science, "theory" means a model

Unlike behavior-first venues where theory is a verbal mechanism, here a contribution is carried by a mathematical model. Build whichever genre the question demands.

Analytical (game-theoretic) models

  • State primitives: players (firms, consumers, platform), action spaces, information structure, timing, and payoffs.
  • Solve for equilibrium (Nash/subgame-perfect/Bayesian) and prove existence/uniqueness where needed.
  • Derive comparative statics — sign how equilibrium prices, advertising, or profits move with a parameter — and surface the counterintuitive result that is the contribution.
  • Keep assumptions transparent and motivated by marketing institutions (double marginalization, competitive response, targeting).

Structural econometric models

  • Write a demand model (random-coefficients/BLP logit, nested logit, dynamic discrete choice) and, where relevant, a supply/equilibrium condition (FOCs, pricing game).
  • State micro-foundations: utility, state transitions, firm objective.
  • Make the identification argument explicit before estimation: which moments/instruments (cost shifters, BLP instruments, exclusion restrictions, panel variation, experiments/discontinuities) identify preferences, dynamics, and supply parameters — and why they are exogenous.
  • Define the counterfactual the estimated model will simulate; the model must be rich enough to answer it and no richer.

Connecting reduced-form or behavioral evidence

If you include experiments, surveys, or reduced-form results, tie them to the model: as model-free evidence motivating an assumption, as a source of identifying variation, or as validation of a mechanism the model formalizes.

Checklist

  • Primitives (players, actions, information, timing, payoffs) fully specified
  • Equilibrium concept stated; existence/uniqueness addressed (analytical)
  • Comparative statics / testable predictions derived (analytical)
  • Demand (and supply) specified with micro-foundations (structural)
  • Identification argument explicit: variation/instruments → each parameter
  • The counterfactual question the model must answer is named up front

Anti-patterns

  • A regression relabeled as "a model" with no primitives or equilibrium.
  • A structural model with parameters but no identification argument.
  • Assumptions chosen for tractability that contradict the marketing institution.
  • Comparative statics asserted, not derived.

Theory pass for Marketing Science

Treat this skill as an executable review pass, not a prose hint. First lock the demand/supply mechanism, fit evidence, and counterfactual decision margin; then judge whether the current manuscript answers the venue's real reader: quantitative marketing reviewers who read the model through the managerial counterfactual it makes possible.

  • Do the pass: Name the construct, mechanism, boundary condition, and falsifiable implication separately; do not let a literature summary masquerade as theory.
  • 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 Journal of Marketing Research for empirical marketing breadth, Management Science for wider OR/MS reach, Quantitative Marketing and Economics for specialist modeling; 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

【Genre】analytical / structural
【Primitives】players, actions, info, timing, payoffs
【Equilibrium / estimand】concept; existence/uniqueness OR demand+supply
【Identification】variation/instruments → parameters; exogeneity logic
【Predictions / counterfactual】key comparative static OR policy to simulate
【Next step】mksc-literature-positioning then mksc-methods

版本历史

  • 1839142 当前 2026-07-05 14:04

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元信息

文件数
0
版本
5717eab
Hash
721cfd6d
收录时间
2026-07-05 14:04

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