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This paper proposes a fairness-aware pricing framework for retail food products using Autoregressive Distributed Lag (ARDL) models for sales forecasting and optimizes prices with Linear Programming and Simulated Annealing under CPI-based bounds to prevent consumer exploitation.
The article critiques the common practice of splitting large changes into many small pull requests (like simulated annealing), arguing it can hinder necessary large-scale changes. It also discusses how AI-driven coding tools enable rapid exploration but introduce risks of incoherence and failure.