New ARDL model embeds fairness in retail pricing strategies

Researchers have developed a new methodology for dynamic pricing in retail that incorporates fairness constraints to balance profitability with consumer welfare. The approach uses a log-log Autoregressive Distributed Lag (ARDL) model to forecast retail sales, with pricing decisions optimized to maximize sales while adhering to price bounds linked to the Consumer Price Index (CPI). This framework aims to prevent consumer exploitation by ensuring prices do not exceed a CPI-anchored ceiling, with simulated annealing used to find conservative prices that reduce consumer costs while still meeting sales targets. The study also benchmarks forecast accuracy against traditional models like ARIMA and SARIMA, and notes that positive nominal elasticities observed are largely influenced by inflation. AI

IMPACT Introduces a novel approach to dynamic pricing that integrates consumer fairness, potentially influencing retail strategies and economic models.

RANK_REASON Academic paper detailing a new methodology for retail sales forecasting and pricing. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

• Ardleigh
• Arima
• Canada
• consumer price index
• linear programming
• Sarima
• simulated annealing
• Sujay Uday Rittikar

• paper
• product
• policy

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