arXiv2026
We study the problem of predicting price equilibria on e-commerce platforms where sellers compete across multiple attributes (e.g., price, average rating, delivery speed). In these settings, a platform's design choices --- such as its display ranking, filtering tools, and promotional badges --- critically shape customer search and purchase behavior, which in turn determine sellers' equilibrium pricing strategies. Our goal is to develop a tractable framework that allows a platform to anticipate the market impact of its design interventions. We consider a behavioral model --- Consider-then-Choose with Lexicographic Choice (CLC) --- specifically tailored to platform-mediated search. We establish that any local Nash equilibrium admits a sequential-move characterization; this yields a tractable procedure for computation under an interpretable sufficient condition, which we term gradient dominance. We further prove that under gradient dominance, simple, decentralized gradient-based algorithms converge to an equilibrium, providing platforms with a method for simulating market outcomes. Finally, we use our framework to study how platform design affects market outcomes. Our framework applies to any platform in which sellers compete on multiple attributes and customer choice is guided by the platform's interface. In these environments, sellers' pricing strategies must be understood not in isolation, but as a response to the platform's design. Our work provides platform operators with a rigorous toolbox to efficiently evaluate how changes to interface design, information disclosure, and ranking policies can affect competitive outcomes.