How Does Competition Affect Exploration vs. Exploitation? A Tale of Two Recommendation Algorithms

H. Henry Cao (), Liye Ma (), Z. Eddie Ning () and Baohong Sun ()
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H. Henry Cao: Department of Finance, Cheung Kong Graduate School of Business, Beijing 100006, China
Liye Ma: Department of Marketing, Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742
Z. Eddie Ning: Department of Marketing and Behavioural Science, Sauder School of Business, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
Baohong Sun: Department of Marketing, Cheung Kong Graduate School of Business, Beijing 100006, China

Management Science, 2024, vol. 70, issue 2, 1029-1051

Abstract: Through repeated interactions, firms today refine their understanding of individual users’ preferences adaptively for personalization. In this paper, we use a continuous-time bandit model to analyze firms that recommend content to multihoming consumers, a representative setting for strategic learning of consumer preferences to maximize lifetime value. In both monopoly and duopoly settings, we compare a forward-looking recommendation algorithm that balances exploration and exploitation to a myopic algorithm that only maximizes the quality of the next recommendation. Our analysis shows that, compared with a monopoly, firms competing for users’ attention focus more on exploitation than exploration. When users are impatient, competition decreases the return from developing a forward-looking algorithm. In contrast, development of a forward-looking algorithm may hurt users under monopoly but always benefits users under competition. Competing firms’ decisions to invest in a forward-looking algorithm can create a prisoner’s dilemma. Our results have implications for artificial intelligence adoption and for policy makers on the effect of market power on innovation and consumer welfare.

Keywords: AI; bandit; multihoming; recommendation algorithm; customization; personalization; content; competition; experimentation; reinforcement learning (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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