Better and Faster Decisions with Recommendation Algorithms

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Yiting Chen

Lingnan University - Department of Economics

Ziye Wu

National University of Singapore (NUS), Department of Economics

Songfa Zhong

National University of Singapore (NUS) - Department of Economics

Date Written: December 09, 2024

Abstract

While recommendation algorithms are increasingly powerful and prevalent, their influences on individual decision-making remain largely unexplored. To address this question, we conduct a randomized controlled experiment where subjects of a US representative sample make risky decisions. Subjects receive no recommendations in one baseline condition and random recommendations in another baseline condition. In three treatment conditions, subjects receive recommendations based on decisions of the majority, their own past decisions, or decisions of similar subjects. Compared with baseline conditions, subjects tend to follow recommendations and they exhibit less stochastic choices, behave more consistently with expected utility, and make faster decisions. Moreover, subjects are willing to pay to receive recommendations for subsequent decisions. This study helps understand behavioral mechanisms underlying recommendation algorithms and sheds light on the design of choice architecture with the assistance of artificial intelligence.

Keywords: preference, noise, risk, recommendation algorithm, experiment

Suggested Citation

Chen, Yiting and Wu, Ziye and Zhong, Songfa, Better and Faster Decisions with Recommendation Algorithms (December 09, 2024). Available at SSRN: https://ssrn.com/abstract=

Yiting Chen

Lingnan University - Department of Economics ( email )

8 Castle Peak Road
Lingnan University
Hong Kong, New Territories
China

Ziye Wu (Contact Author)

National University of Singapore (NUS), Department of Economics ( email )

1 Arts Link AS2
Singapore, 117570
Singapore

Songfa Zhong

National University of Singapore (NUS) - Department of Economics ( email )

1 Arts Link, AS2 #06-02
Singapore 117570, Singapore 119077
Singapore

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