Research Paper

Core Innovation

This paper develops optimal purchase policies for autonomous buying agents under three distinct information regimes: stationary, Bayesian, and robust. It advances prior work by providing dynamic threshold-based rules with theoretical guarantees and validating them on real-world Amazon price data, integrating with language models for practical deployment.

Why It Matters

Consumers face uncertainty and complexity in timing online purchases to get the best prices. Strategic buying agents automate this decision, improving savings and convenience while adapting to market dynamics. This approach can scale across e-commerce platforms, transforming how consumers shop and save.

Potential Customers & Pain Points

• Online shoppers – Difficulty timing purchases for best prices
• E-commerce platforms – Need to enhance user engagement and satisfaction
• Retail analytics firms – Require advanced pricing and consumer behavior models.

Business Model

Subscription or commission-based model targeting consumers and e-commerce platforms; licensing AI policies and APIs to retail analytics and shopping assistant apps.

Competitive Landscape

• Honey
• Capital One Shopping
• Octane AI
• Shopify AI tools

Implementation Challenges

• Integration complexity with diverse e-commerce platforms
• Consumer trust and adoption of autonomous purchasing
• Accurate modeling of dynamic and uncertain price changes

Validation Strategy

• Pilot deployment with select online retailers to measure consumer surplus improvements
• A/B testing of agent policies versus baseline buying behaviors
• User studies on language model integration for regime selection and decision support