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
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
Source: brycewang-stanford/Awesome-Journal-Skills → Marketing-Science-Skills/skills/mksc-theory-development/SKILL.md
1---2name: mksc-theory-development3description: 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).4---567# Model & Mechanism Development (mksc-theory-development)89## When to trigger1011- The phenomenon is interesting but there is no formal model yet12- The "mechanism" is verbal and needs to be written as primitives, payoffs, and equilibrium13- A structural story lacks an identification argument (what variation pins down each parameter)14- An analytical model lacks crisp comparative statics or testable predictions1516## In Marketing Science, "theory" means a model1718Unlike behavior-first venues where theory is a verbal mechanism, here a contribution is carried by a **mathematical model**. Build whichever genre the question demands.1920### Analytical (game-theoretic) models2122- State **primitives**: players (firms, consumers, platform), action spaces, information structure, timing, and payoffs.23- Solve for **equilibrium** (Nash/subgame-perfect/Bayesian) and prove existence/uniqueness where needed.24- Derive **comparative statics** — sign how equilibrium prices, advertising, or profits move with a parameter — and surface the counterintuitive result that is the contribution.25- Keep assumptions transparent and motivated by marketing institutions (double marginalization, competitive response, targeting).2627### Structural econometric models2829- Write a **demand** model (random-coefficients/BLP logit, nested logit, dynamic discrete choice) and, where relevant, a **supply/equilibrium** condition (FOCs, pricing game).30- State **micro-foundations**: utility, state transitions, firm objective.31- 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.32- Define the **counterfactual** the estimated model will simulate; the model must be rich enough to answer it and no richer.3334## Connecting reduced-form or behavioral evidence3536If 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.3738## Checklist3940- [ ] Primitives (players, actions, information, timing, payoffs) fully specified41- [ ] Equilibrium concept stated; existence/uniqueness addressed (analytical)42- [ ] Comparative statics / testable predictions derived (analytical)43- [ ] Demand (and supply) specified with micro-foundations (structural)44- [ ] Identification argument explicit: variation/instruments → each parameter45- [ ] The counterfactual question the model must answer is named up front4647## Anti-patterns4849- A regression relabeled as "a model" with no primitives or equilibrium.50- A structural model with parameters but no identification argument.51- Assumptions chosen for tractability that contradict the marketing institution.52- Comparative statics asserted, not derived.535455## Theory pass for Marketing Science5657Treat 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.5859- **Do the pass:** Name the construct, mechanism, boundary condition, and falsifiable implication separately; do not let a literature summary masquerade as theory.60- **Return a ledger:** give `claim / evidence / risk / manuscript location` rows, so the next agent can edit rather than rediscover the issue.61- **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.62- **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.6364## Output format6566```67【Genre】analytical / structural68【Primitives】players, actions, info, timing, payoffs69【Equilibrium / estimand】concept; existence/uniqueness OR demand+supply70【Identification】variation/instruments → parameters; exogeneity logic71【Predictions / counterfactual】key comparative static OR policy to simulate72【Next step】mksc-literature-positioning then mksc-methods73```7475---7677**Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `Marketing-Science-Skills/skills/mksc-theory-development/SKILL.md`
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