| # **Appendix — Methodological Notes** | |
| --- | |
| ## **Why Elasticity Is Observational** | |
| Retail prices are not randomized. | |
| Observed price–quantity relationships reflect correlation, not causal response. | |
| This system does not attempt causal identification. | |
| It focuses on robust decision-making given observed behavior. | |
| --- | |
| ## **Why Bootstrap Is Used** | |
| Closed-form uncertainty assumptions are fragile in pricing contexts. | |
| Bootstrap resampling: | |
| * captures parameter uncertainty | |
| * avoids distributional assumptions | |
| * supports downside-aware evaluation | |
| --- | |
| ## **Why No Machine Learning Models Are Used** | |
| The pricing decision is low-dimensional. | |
| Additional model complexity: | |
| * increases opacity | |
| * complicates governance | |
| * does not improve decision quality at this stage | |
| ML pricing belongs to later integration phases. | |
| --- | |
| ## **Out-of-Scope Extensions** | |
| The following are intentionally excluded: | |
| * causal pricing experiments | |
| * promotion-response modeling | |
| * multi-SKU or portfolio pricing | |
| * inventory-constrained pricing | |
| * dynamic or reinforcement learning pricing | |
| These extensions require additional data and governance structures. | |
| --- | |
| ## **Closing Note** | |
| The system is designed to answer one question well: | |
| > **What price can be deployed with confidence under uncertainty?** | |
| Everything else is deliberately deferred. | |
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