This is like putting thousands of tiny robot price managers on Amazon who constantly watch each other and change prices. The study analyzes how those robots behave in the real world and what that does to prices and competition.
It investigates how automated pricing algorithms used by Amazon marketplace sellers actually affect prices, competition, and potential tacit collusion in practice, helping platforms, regulators, and large sellers understand the risks and dynamics of algorithm-driven pricing.
Empirical transaction-level marketplace data and behavioral insights into competing pricing algorithms, which are hard for outsiders to replicate.
Classical-ML (Scikit/XGBoost)
Structured SQL
High (Custom Models/Infra)
Access to granular marketplace transaction and pricing data, and the need to continuously adapt models to changing competitive behavior.
Early Majority
Focuses on empirical, large-scale measurement of real marketplace algorithmic pricing behavior rather than just proposing a pricing algorithm, giving unique insight into competitive and potentially collusive dynamics on a major ecommerce platform.
77 use cases in this application