PhD Dissertation

Algorithms and Seller Behavior in Online Markets

Digital markets increasingly feature algorithms that perform various functions, from pricing products to issuing movie recommendations. Algorithms may increase the efficiency and effectiveness of markets, but they can also reduce competitiveness and harm consumers. As the use of algorithms by dominant firms such as Amazon, Google and Meta increases, their ability to disrupt market competition also increases. Antitrust authorities have repeatedly taken issue with the dominant firms using consumer data to increase their own sales, create entry barriers, and drive out competition. The regulators are also concerned by the lack of transparency, algorithmic collusion, and the potential for predatory pricing.

My research analyses the interaction of dominant firms and small sellers in the online markets, given the use of algorithms.

Working papers:

Nikandrova, A. and Parekh, A. (2025) ʻAlgorithmic Exclusion by Large Language Models ' SSRN Link (Job Market Paper)

This paper explores whether large language models (LLMs) can learn predatory strategies in dynamic environments in which an incumbent faces repeated entry threat. Using Ope-nAI's GPT-4.1 as decision-making agents, we find that LLMs learn to predate when both predation and accommodation are theoretically viable, and adopt aggressive strategies when only accommodation is theoretically viable. Further, profit optimization is limited, highlighting both strategic learning and its limitations.

Parekh, A. (2024) ʻPersonalized Pricing and Data Advantage: How much is too much? ʼ.

Two competitors located at opposite ends of the Hotelling line compete for the unit mass of consumers. Consumer preferences vary across two dimensions: horizontal (brand preference) and vertical (quality preference). We compare two scenarios : (1) competitors are uninformed about either dimension of consumer preferences and set a uniform price; (2) competitors remain uninformed about the horizontal dimension but possess perfect information about the vertical dimension, which enables them to set prices conditional on this information. The analysis reveals how personalized pricing affects market coverage, firm behavior, and consumer surplus for varying degrees of horizontal product differentiation.

Parekh, Anushree (2022) 'Information Sharing by a Hybrid Platform: The Truth-Telling Problem'.

A dual-role hybrid platform acts both as a competitor and a marketplace provider, charging a seller commission. The platform has access to information about the demand conditions. The paper analyses how the platform’s incentive to share truthful information is influenced by factors such as product differentiation and commission rates. The findings highlight that, even in the case of high commission rates, the platform may still prefer to mislead sellers when products are close substitutes. While prior studies assume that platforms act as pure marketplaces, this study broadens our understanding of the strategic information-sharing behaviors of hybrid platforms and their impact on market outcomes.

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