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

53 Pages Posted: 24 Jan 2021 Last revised: 13 Sep 2021

See all articles by Huining Henry Cao

Huining Henry Cao

Cheung Kong Graduate School of Business

Liye Ma

University of Maryland - Department of Marketing

Z. Eddie Ning

Cheung Kong Graduate School of Business

Baohong Sun

Cheung Kong Graduate School of Business (New York); Cheung Kong Graduate School of Business (New York)

Date Written: November 30, 2020

Abstract

Through repeated interactions, firms today refine their understanding of individual users' preferences adaptively for personalization. In this paper, we use a continuous-time multi-agent bandit model to analyze firms that supply content to 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 firms that compete for users' attention focuses more on exploitation than exploration than a monopoly would. When users are impatient, competition decreases firms' incentives to develop forward-looking algorithms. On the other hand, development of the optimal forward-looking algorithm may hurt users under monopoly but always benefits users under competition. We are among the first to examine this multi-agent bandit problem under different competitive scenarios, and our results provide implications for AI adoption as well as for policy makers on the effect of market power on innovation and consumer welfare.

Keywords: AI, multi-agent bandit, recommendation algorithm, innovation, competition, reinforcement learning, experimentation, CLV, value of learning, forward-looking optimization

JEL Classification: C73, D40, D83, L10, M31

Suggested Citation

Cao, Huining Henry and Ma, Liye and Ning, Z. Eddie and Sun, Baohong and Sun, Baohong, How Does Competition Affect Exploration vs. Exploitation? A Tale of Two Recommendation Algorithms (November 30, 2020). Available at SSRN: https://ssrn.com/abstract=3740164 or http://dx.doi.org/10.2139/ssrn.3740164

Huining Henry Cao

Cheung Kong Graduate School of Business ( email )

Oriental Plaza, Tower E3
One East Chang An Avenue
Beijing, 100738
China

Liye Ma

University of Maryland - Department of Marketing ( email )

College Park, MD 20742
United States
(301) 405-8982 (Phone)

Z. Eddie Ning (Contact Author)

Cheung Kong Graduate School of Business ( email )

Oriental Plaza, Tower E3
One East Chang An Avenue
Beijing, 100738
China

HOME PAGE: http://www.eddiening.com

Baohong Sun

Cheung Kong Graduate School of Business (New York) ( email )

230 Park Avenue
Suite 540
New York, NY 10169
United States

HOME PAGE: http://english.ckgsb.edu.cn/faculty/sun-baohong/

Cheung Kong Graduate School of Business (New York) ( email )

Oriental Plaza, Tower E3
One East Chang An Avenue
Beijing, 100738
China

HOME PAGE: http://english.ckgsb.edu.cn/faculty/sun-baohong/

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