Mgsci Theory Development

---
name: mgsci-theory-development
description: Use when building the theoretical core of a Management Science (INFORMS) paper — either a formal analytical model (assumptions, equilibrium, propositions/theorems, comparative statics) or empirically testable hypotheses derived from a clear mechanism. Adapts to the paper's lane; it does not run the analysis (mgsci-data-analysis) or frame the contribution (mgsci-contribution-framing).
---

# Theory Development (mgsci-theory-development)

## When to trigger

- You have an intuition but no model or no mechanism
- Hypotheses are descriptive ("X relates to Y") with no stated mechanism
- An analytical model has results but unclear/unstated assumptions
- A Department Editor or reviewer asked "what is the theory here?" or "why is this the right model?"

Because Management Science is **bimethodological**, "theory development" means one of two disciplined things depending on your Department lane.

## Lane A — Analytical / formal model

For Optimization and Decision Analytics, Stochastic Models and Simulation, Operations Management, Finance (theory), Business Strategy (theory), and economic-theory submissions:

- **State assumptions explicitly and defend them.** Each assumption should be necessary, behaviorally or economically reasonable, and you should know which results break without it.
- **Define the decision problem.** Players/decision-maker, actions, timing, information, objective. Make the optimization/equilibrium concept precise.
- **Derive results as propositions/theorems** with proofs (in text or appendix). Reviewers check that proofs are correct and that results are not artifacts of a knife-edge assumption.
- **Comparative statics and intuition.** The managerial payoff lives here: *how* the optimal policy / equilibrium moves with primitives, and *why* — stated in plain words, not only symbols.
- **Robustness of the model.** Show which extensions (heterogeneity, alternative timing, relaxed assumption) preserve the qualitative insight.

## Lane B — Empirical hypotheses from a mechanism

For Accounting, Finance (empirical), Information Systems, Behavioral Economics and Decision Analysis, Marketing, Data Science:

- **Name the mechanism** (incentive, information, frictional, behavioral) that produces the predicted effect; the hypothesis is the mechanism's observable implication.
- **Derive predictions a priori**, before looking at outcomes — HARKing (hypothesizing after results) is a credibility failure.
- **Moderation = boundary condition of the mechanism**; **mediation = the mechanism's intermediate step.** Predict the sign and the conditions, not just "an effect exists."
- Where a behavioral prediction comes from a model or from decision theory, state the formal source.

## The unifying bar

Whichever lane, the theory must yield a **decision-relevant, cross-department insight** — Management Science rewards a mechanism whose managerial implication a reader in another Department still finds informative. Formal elegance without managerial reading, or hypotheses without a mechanism, will not clear the bar.

## Anti-patterns

- A model whose results depend on an undefended knife-edge assumption.
- Propositions with no comparative statics and no plain-language intuition.
- Empirical hypotheses with no named mechanism (pure correlation dressed as theory).
- Post hoc hypotheses fitted to the data (HARKing).

## Output format

```
【Lane】analytical model / empirical-mechanism
【Core mechanism】...
【Assumptions or constructs】necessary? defended?
【Results】propositions/theorems OR a priori hypotheses (sign + conditions)
【Comparative statics / boundary】managerial intuition ...
【Next step】mgsci-methods
```