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# **Appendix — Methodological Notes**
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## **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.
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## **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
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## **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.
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## **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.
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## **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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