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Online Price Competition Under Ranking Algorithms and Consumer Search Frictions
Thesis   Open access

Online Price Competition Under Ranking Algorithms and Consumer Search Frictions

Zarina Lin
Bachelor of Science (BS), Brandeis University
05/10/2026
DOI:
https://doi.org/10.48617/etd.1508

Abstract

Online Price Competition Dynamic pricing Platform Design

This paper develops a dynamic model of price competition in online marketplaces

with technological asymmetry and platform-controlled product visibility. The key fea-

ture is a platform-designed ranking algorithm that determines which product appears

at the top of the page. I show that when ranking depends partly on past sales, which

the platform views as a signal of strong past demand and continued consumer appeal,

and when there exists a large enough share of attention-limited consumers, the strategic

seller adopts an invest–then–harvest strategy: it cuts price initially to build sales and

secure a past-sales-dependent ranking advantage, then raises price in the subsequent

period to extract higher margins from attention-limited consumers. As a result, equi-

librium prices exceed the benchmark Bertrand level on average. An infinite-horizon

extension shows that the same mechanism can recur over time, generating intertempo-

ral price fluctuations. These findings imply that platform design has important welfare

consequences: interface designs that make cross-product comparison easier, together

with ranking rules that place greater weight on price, can reduce supracompetitive

pricing and price fluctuations, thereby improving consumer welfare.

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